David Larson

All Publications

• Ranking the Relative Importance of Image Quality Features in CT by Consensus Survey. Journal of the American College of Radiology : JACR Gress, D. A., Samei, E., Frush, D. P., Pelzl, C. E., Fletcher, J. G., Mahesh, M., Larson, D. B., Bhargavan-Chatfield, M. 2024

  Abstract

  This study sought to determine consensus opinions from subspecialty radiologists and imaging physicists on the relative importance of image quality features in CT.A prospective survey of subspecialty radiologists and medical physicists was conducted to collect consensus opinions on the relative importance of ten image quality features: axial sharpness, blooming, contrast, longitudinal sharpness, low contrast axial sharpness, metal artifact, motion, noise magnitude, noise texture, and streaking. The survey was first sent to subspecialty radiologists in volunteer leadership roles in the American College of Radiology and Radiological Society of North America, thereafter relying on snowball sampling. Surveyed subspecialties were abdominal, cardiac, emergency, musculoskeletal, neuro, pediatric, and thoracic radiology, and medical physics. Individual respondents' ratings were normalized for calculation of mean normalized ratings and priority rankings for each feature within subspecialties. Also calculated were intraclass correlation coefficients across image quality features within subspecialties, and analysis of variance across subspecialties within each feature.Most subspecialties had moderate to excellent intraclass agreement. For every radiology subspecialty except musculoskeletal, motion was the most important image quality feature. There was agreement across subspecialties that axial sharpness and contrast are only moderately important. There was disagreement across subspecialties on the relative importance of noise magnitude. Blooming was highly important to cardiac radiologists, and noise texture was highly important to musculoskeletal radiologists.Image quality preferences differ based on clinical tasks and challenges in each anatomical radiology subspecialty. CT image analysis and development of quantitative measures of quality and protocol optimization-and related policy initiatives-should be specific to radiology subspecialty.

  View details for DOI 10.1016/j.jacr.2024.10.006

  View details for PubMedID 39427722

• Ethical Considerations in the Design and Conduct of Clinical Trials of Artificial Intelligence. JAMA network open Youssef, A., Nichol, A. A., Martinez-Martin, N., Larson, D. B., Abramoff, M., Wolf, R. M., Char, D. 2024; 7 (9): e2432482

  Abstract

  Importance: Safe integration of artificial intelligence (AI) into clinical settings often requires randomized clinical trials (RCT) to compare AI efficacy with conventional care. Diabetic retinopathy (DR) screening is at the forefront of clinical AI applications, marked by the first US Food and Drug Administration (FDA) De Novo authorization for an autonomous AI for such use.Objective: To determine the generalizability of the 7 ethical research principles for clinical trials endorsed by the National Institute of Health (NIH), and identify ethical concerns unique to clinical trials of AI.Design, Setting, and Participants: This qualitative study included semistructured interviews conducted with 11 investigators engaged in the design and implementation of clinical trials of AI for DR screening from November 11, 2022, to February 20, 2023. The study was a collaboration with the ACCESS (AI for Children's Diabetic Eye Exams) trial, the first clinical trial of autonomous AI in pediatrics. Participant recruitment initially utilized purposeful sampling, and later expanded with snowball sampling. Study methodology for analysis combined a deductive approach to explore investigators' perspectives of the 7 ethical principles for clinical research endorsed by the NIH and an inductive approach to uncover the broader ethical considerations implementing clinical trials of AI within care delivery.Results: A total of 11 participants (mean [SD] age, 47.5 [12.0] years; 7 male [64%], 4 female [36%]; 3 Asian [27%], 8 White [73%]) were included, with diverse expertise in ethics, ophthalmology, translational medicine, biostatistics, and AI development. Key themes revealed several ethical challenges unique to clinical trials of AI. These themes included difficulties in measuring social value, establishing scientific validity, ensuring fair participant selection, evaluating risk-benefit ratios across various patient subgroups, and addressing the complexities inherent in the data use terms of informed consent.Conclusions and Relevance: This qualitative study identified practical ethical challenges that investigators need to consider and negotiate when conducting AI clinical trials, exemplified by the DR screening use-case. These considerations call for further guidance on where to focus empirical and normative ethical efforts to best support conduct clinical trials of AI and minimize unintended harm to trial participants.

  View details for DOI 10.1001/jamanetworkopen.2024.32482

  View details for PubMedID 39240560

• Invited Commentary: Understanding and Addressing Cognitive Biases in Radiology. Radiographics : a review publication of the Radiological Society of North America, Inc Larson, D. B. 2024; 44 (7): e230244

  View details for DOI 10.1148/rg.230244

  View details for PubMedID 38843096

• The ACR Mammography Positioning Improvement Collaborative: A Multi-Center Improvement Program within a Learning Network Framework. Journal of the American College of Radiology : JACR Pittman, S. M., Zacharias-Andrews, K., Tomkins, K. G., Bhargavan-Chatfield, M., Larson, D. B. 2024

  Abstract

  To share the experience and results of the first cohort of the ACR Mammography Positioning Improvement Collaborative, in which participating sites aimed to increase the mean percentage of screening mammograms meeting the established positioning criteria to 85% or greater and show at least modest evidence of improvement at each site by the end of the improvement program.The sites comprising the first cohort of the Collaborative were selected on the basis of strength of local leadership support, intra-organizational relationships, access to data and analytic support, and experience with quality improvement (QI) initiatives. During the improvement program, participating sites organized their teams, developed goals, gathered data, evaluated their current state, identified key drivers and root causes of their problems, and developed and tested interventions. A standardized image quality scoring system was also established. The impact of the interventions implemented at each site was assessed by tracking the percentage of screening mammograms meeting overall passing criteria over time.Six organizations were selected to participate as the first cohort, beginning with participation in the improvement program. Interventions developed and implemented at each site during the program resulted in improvement in the average percentage of screening mammograms meeting overall passing criteria per week from a collaborative mean of 51% to 86%, with four of six sites meeting or exceeding the target mean performance of 85% by the end of the improvement program. Afterwards, all respondents to the post-program survey indicated that the program was a positive experience.Using a structured improvement program within a learning network framework, the first cohort of the Collaborative demonstrated that improvement in mammography positioning performance can be achieved at multiple sites simultaneously, and validated the hypothesis that local sites' shared experiences, insights, and learnings would not only improve performance but would also build a community of improvers collaborating to create the best experience for technologists, staff, and patients.

  View details for DOI 10.1016/j.jacr.2024.06.013

  View details for PubMedID 38950833

• Improving Prostate MR Image Quality in Practice - Initial results from the ACR Prostate MR Image Quality Improvement Collaborative. Journal of the American College of Radiology : JACR Purysko, A. S., Zacharias-Andrews, K., Tomkins, K. G., Turkbey, I. B., Giganti, F., Bhargavan-Chatfield, M., Larson, D. B., ACR Prostate MR Image Quality Improvement Collaborative, Weinreb, J., Tempany, C., Smith, C., Hester, A., Chang, K., Martin, S., Gupta, R., Owenby, E., McLean, L., Campbell, L., Furlan, A., Grills, A. 2024

  Abstract

  OBJECTIVE: Variability in prostate MRI quality is an increasingly recognized problem that negatively affects patient care. This report aims to describe the results and key learnings of the first cohort of the ACR Learning Network Prostate MR Image Quality Improvement Collaborative.METHODS: Teams from five organizations in the U.S. were trained on a structured improvement method. After reaching a consensus on image quality and auditing their images using the Prostate Imaging Quality (PI-QUAL) system, teams conducted a current state analysis to identify barriers to obtaining high-quality images. Through plan-do-study-act cycles involving frontline staff, each site designed and tested interventions targeting image quality key drivers. The percentage of exams meeting quality criteria (i.e., PI-QUAL score ≥ 4) was plotted on a run chart, and project progress was reviewed in weekly meetings. At the collaborative level, the goal was to increase the percentage of exams with PI-QUAL ≥ 4 to at least 85%.RESULTS: Across 2380 exams audited, the mean weekly rates of prostate MR exams meeting image quality criteria increased from 67% (range: 60-74%) at baseline to 87% (range: 80-97%) upon program completion. The most commonly employed interventions were MR protocol adjustments, development and implementation of patient preparation instructions, personell training and development of an auditing process mechanism.CONCLUSION: A Learning Network model, where organizations share knowledge and work together toward a common goal, can improve prostate MR image quality at multiple sites simultaneously. The inaugural cohort's key learnings provide a roadmap for improvement on a broader scale.

  View details for DOI 10.1016/j.jacr.2024.04.008

  View details for PubMedID 38729590

• Proceedings from the 2022 ACR-RSNA Workshop on Safety, Effectiveness, Reliability, and Transparency in AI. Journal of the American College of Radiology : JACR Larson, D. B., Doo, F. X., Allen, B., Mongan, J., Flanders, A. E., Wald, C. 2024

  Abstract

  Despite the surge in AI development for healthcare applications, particularly for medical imaging applications, there has been limited adoption of such AI tools into clinical practice. During a one-day workshop in November, 2022, co-organized by the American College of Radiology (ACR) and the Radiological Society of North America (RSNA), participants outlined experiences and problems with implementing AI in clinical practice, defined the needs of various stakeholders in the AI ecosystem, and elicited potential solutions and strategies related to the safety, effectiveness, reliability, and transparency of AI algorithms. Participants included radiologists from academic and community radiology practices, informatics leaders responsible for AI implementation, regulatory agency employees, and specialty society representatives. The major themes that emerged fell into two categories: 1) AI product development and 2) implementation of AI-based applications in clinical practice. In particular, participants highlighted key aspects of AI product development to include clear clinical task definitions; well-curated data from diverse geographic, economical, and healthcare settings; standards and mechanisms to monitor model reliability; and transparency regarding model performance, both in controlled and real-world settings. For implementation, participants emphasized the need for strong institutional governance; systematic evaluation, selection, and validation methods carried out by local teams; seamless integration into the clinical workflow; performance monitoring and support by local teams; performance monitoring by external entities; and alignment of incentives through credentialing and reimbursement. Participants predicted that clinical implementation of AI in radiology will continue to be limited until the safety, effectiveness, reliability, and transparency of such tools are more fully addressed.

  View details for DOI 10.1016/j.jacr.2024.01.024

  View details for PubMedID 38354844

• A Vision for Global CT Radiation Dose Optimization. Journal of the American College of Radiology : JACR Larson, D. B. 2024

  Abstract

  The topic of CT radiation dose management is receiving renewed attention following the recent approval by CMS for new CT dose measures. Widespread variation in CT dose persists in practices across the world, suggesting that current dose optimization techniques are lacking. This article outlines a proposed strategy for facilitating global CT radiation dose optimization. CT radiation dose optimization can be defined as the routine use of CT scan parameters that consistently produce images just above the minimum threshold of acceptable image quality for a given clinical indication and accounting for relevant patient characteristics using the most dose-efficient techniques available on the scanner. To accomplish this, an image quality-based target dose must be established for every protocol; for non-head CT applications, these target dose values must be expressed as a function of patient size. As variation in outcomes is reduced, the dose targets can be decreased to more closely approximate the minimum image quality threshold. Maintaining CT radiation dose optimization requires a process control program, including measurement, evaluation, feedback, and control. This is best accomplished by local teams, made up of radiologists, medical physicists, and technologists, supported with protected time and needed tools, including analytics and protocol management applications. Other stakeholders critical to facilitating CT radiation dose management include researchers, funding agencies, industry, regulators, accreditors, payers, and the American College of Radiology. Analogous coordinated approaches have transformed quality in other industries and can be the mechanism for achieving the universal goal of CT radiation dose optimization.

  View details for DOI 10.1016/j.jacr.2024.01.014

  View details for PubMedID 38302037

• Perceptions of Data Set Experts on Important Characteristics of Health Data Sets Ready for Machine Learning: A Qualitative Study. JAMA network open Ng, M. Y., Youssef, A., Miner, A. S., Sarellano, D., Long, J., Larson, D. B., Hernandez-Boussard, T., Langlotz, C. P. 2023; 6 (12): e2345892

  Abstract

  The lack of data quality frameworks to guide the development of artificial intelligence (AI)-ready data sets limits their usefulness for machine learning (ML) research in health care and hinders the diagnostic excellence of developed clinical AI applications for patient care.To discern what constitutes high-quality and useful data sets for health and biomedical ML research purposes according to subject matter experts.This qualitative study interviewed data set experts, particularly those who are creators and ML researchers. Semistructured interviews were conducted in English and remotely through a secure video conferencing platform between August 23, 2022, and January 5, 2023. A total of 93 experts were invited to participate. Twenty experts were enrolled and interviewed. Using purposive sampling, experts were affiliated with a diverse representation of 16 health data sets/databases across organizational sectors. Content analysis was used to evaluate survey information and thematic analysis was used to analyze interview data.Data set experts' perceptions on what makes data sets AI ready.Participants included 20 data set experts (11 [55%] men; mean [SD] age, 42 [11] years), of whom all were health data set creators, and 18 of the 20 were also ML researchers. Themes (3 main and 11 subthemes) were identified and integrated into an AI-readiness framework to show their association within the health data ecosystem. Participants partially determined the AI readiness of data sets using priority appraisal elements of accuracy, completeness, consistency, and fitness. Ethical acquisition and societal impact emerged as appraisal considerations in that participant samples have not been described to date in prior data quality frameworks. Factors that drive creation of high-quality health data sets and mitigate risks associated with data reuse in ML research were also relevant to AI readiness. The state of data availability, data quality standards, documentation, team science, and incentivization were associated with elements of AI readiness and the overall perception of data set usefulness.In this qualitative study of data set experts, participants contributed to the development of a grounded framework for AI data set quality. Data set AI readiness required the concerted appraisal of many elements and the balancing of transparency and ethical reflection against pragmatic constraints. The movement toward more reliable, relevant, and ethical AI and ML applications for patient care will inevitably require strategic updates to data set creation practices.

  View details for DOI 10.1001/jamanetworkopen.2023.45892

  View details for PubMedID 38039004

• Organizational Factors in Clinical Data Sharing for Artificial Intelligence in Health Care. JAMA network open Youssef, A., Ng, M. Y., Long, J., Hernandez-Boussard, T., Shah, N., Miner, A., Larson, D., Langlotz, C. P. 2023; 6 (12): e2348422

  Abstract

  Limited sharing of data sets that accurately represent disease and patient diversity limits the generalizability of artificial intelligence (AI) algorithms in health care.To explore the factors associated with organizational motivation to share health data for AI development.This qualitative study investigated organizational readiness for sharing health data across the academic, governmental, nonprofit, and private sectors. Using a multiple case studies approach, 27 semistructured interviews were conducted with leaders in data-sharing roles from August 29, 2022, to January 9, 2023. The interviews were conducted in the English language using a video conferencing platform. Using a purposive and nonprobabilistic sampling strategy, 78 individuals across 52 unique organizations were identified. Of these, 35 participants were enrolled. Participant recruitment concluded after 27 interviews, as theoretical saturation was reached and no additional themes emerged.Concepts defining organizational readiness for data sharing and the association between data-sharing factors and organizational behavior were mapped through iterative qualitative analysis to establish a framework defining organizational readiness for sharing clinical data for AI development.Interviews included 27 leaders from 18 organizations (academia: 10, government: 7, nonprofit: 8, and private: 2). Organizational readiness for data sharing centered around 2 main constructs: motivation and capabilities. Motivation related to the alignment of an organization's values with data-sharing priorities and was associated with its engagement in data-sharing efforts. However, organizational motivation could be modulated by extrinsic incentives for financial or reputational gains. Organizational capabilities comprised infrastructure, people, expertise, and access to data. Cross-sector collaboration was a key strategy to mitigate barriers to access health data.This qualitative study identified sector-specific factors that may affect the data-sharing behaviors of health organizations. External incentives may bolster cross-sector collaborations by helping overcome barriers to accessing health data for AI development. The findings suggest that tailored incentives may boost organizational motivation and facilitate sustainable flow of health data for AI development.

  View details for DOI 10.1001/jamanetworkopen.2023.48422

  View details for PubMedID 38113040

• Sustaining Mammography Image Quality With a Technologist Coaching Program in the Era of the Enhancing Quality Using the Inspection Program (EQUIP). Journal of breast imaging Kozlov, A., Larson, D., DeMartini, W. B., Pal, S., Cowart, P., Strain, A., Ikeda, D. M. 2023; 5 (6): 675-684

  Abstract

  To evaluate the ability of a long-term technologist coaching program to sustain gains in mammography quality made by a previously implemented quality improvement (QI) initiative.Mammography quality metrics from July 2014 to June 2020 were reviewed. Numbers of screening mammograms performed/audited, monthly average mammogram overall quality pass rates, changes in facilities/staffing, and technical recall rates were evaluated. Performance metrics at baseline (July 2013), during the improvement (July 2014 to January 2015), postimprovement (February 2015 to August 2015), and sustained coaching periods (after initiation of the technologist coaching model, from September 2015 to June 2020) were compared.During the postimprovement and sustained coaching periods, 93% (501/541) and 90% (8902/9929) of audited mammograms, respectively, met overall passing criteria, achieving or exceeding the QI goal of 90%, and results for both periods were significantly higher than that during the improvement period (74%, 1098/1489), at P < 0.0001 and P < 0.0001, respectively. The technical recall rates during the improvement and postimprovement periods were 2.6% (85/3321) and 1.7% (54/3236), respectively; the rate during the sustained coaching period was significantly lower than these, at 1.2% (489/40 440) (P < 0.0001 and P = 0.0232, respectively). Sustained quality passing rates and lower technical recall rates were observed despite statistically significantly increases in screening volumes.A technologist coaching program resulted in sustained high mammographic quality for almost 5 years.

  View details for DOI 10.1093/jbi/wbad075

  View details for PubMedID 38141238

• Establishing a Point-of-Care Ultrasound (POCUS) Program: An Institutional Approach for Developing a POCUS Program Infrastructure. Journal of the American College of Radiology : JACR Shen, L., Lobo, V. E., Cordova, D., Larson, D. B., Kamaya, A. 2023

  Abstract

  Point of care ultrasound (POCUS) is rapidly accelerating in adoption and applications outside of the traditional realm of diagnostic radiology departments. While utilization of this imaging technology in a distributed fashion has great potential, there are many associated challenges. To address these challenges, we developed an enterprise-wide POCUS program at our institution (Stanford Health Care). Here, we share our experience, the governance organization, our approach to device and information security, training, and quality assurance. We also share our basic principles we use to guide our approach to manage these challenges. Through our work, we have learned that a foundational framework of defining POCUS, the different levels of POCUS use, and delineating program management elements are critical. We hope our experience may be helpful to others who are also interested in POCUS or in the process of creating a POCUS program in their institution. With a clearly established framework, patient safety and quality of care are improved for everyone.

  View details for DOI 10.1016/j.jacr.2023.10.026

  View details for PubMedID 37984768

• Leveraging Quality Improvement Principles for Radiology Sustainability: Bridging Advocacy and Action. Journal of the American College of Radiology : JACR Woolen, S. A., Larson, D. B., Lewis, G. M., Malik, K., Foster, C. A., Martin, M. F., Maturen, K. E. 2023

  Abstract

  The healthcare industry is a top polluter, and the United States healthcare is among the worst offenders worldwide. Radiology contributes disproportionately to the carbon footprint of healthcare and may contribute up to 1% of global greenhouse gas emissions. This commentary underscores the untapped potential within radiology departments to integrate quality improvement principles with sustainability. By embracing these principles and setting environmental quality standards, radiologists can transition from sustainability advocacy to improvements in their radiology practices.

  View details for DOI 10.1016/j.jacr.2023.11.008

  View details for PubMedID 37956883

• Sustaining Mammography Image Quality With a Technologist Coaching Program in the Era of the Enhancing Quality Using the Inspection Program (EQUIP) JOURNAL OF BREAST IMAGING Kozlov, A., Larson, D., Demartini, W. B., Pal, S., Cowart, P., Strain, A., Ikeda, D. M. 2023

  View details for DOI 10.1093/jbi/wbad075

  View details for Web of Science ID 001099597700001

• Response to: American College of Radiology Appropriateness Criteria®: A bibliometric analysis of panel members. Insights into imaging Lockhart, M. E., Larson, D. B., Thorwarth, W. T. 2023; 14 (1): 131

  View details for DOI 10.1186/s13244-023-01457-y

  View details for PubMedID 37466743

  View details for PubMedCentralID PMC10356712

• American College of Radiology initiatives on prostate magnetic resonance imaging quality. European journal of radiology Purysko, A. S., Tempany, C., Macura, K. J., Turkbey, B., Rosenkrantz, A. B., Gupta, R. T., Attridge, L., Hernandez, D., Garcia-Tomkins, K., Bhargavan-Chatfield, M., Weinreb, J., Larson, D. B. 2023; 165: 110937

  Abstract

  Magnetic resonance imaging (MRI) has become integral to diagnosing and managing patients with suspected or confirmed prostate cancer. However, the benefits of utilizing MRI can be hindered by quality issues during imaging acquisition, interpretation, and reporting. As the utilization of prostate MRI continues to increase in clinical practice, the variability in MRI quality and how it can negatively impact patient care have become apparent. The American College of Radiology (ACR) has recognized this challenge and developed several initiatives to address the issue of inconsistent MRI quality and ensure that imaging centers deliver high-quality patient care. These initiatives include the Prostate Imaging Reporting and Data System (PI-RADS), developed in collaboration with an international panel of experts and members of the European Society of Urogenital Radiology (ESUR), the Prostate MR Image Quality Improvement Collaborative, which is part of the ACR Learning Network, the ACR Prostate Cancer MRI Center Designation, and the ACR Appropriateness Criteria. In this article, we will discuss the importance of these initiatives in establishing quality assurance and quality control programs for prostate MRI and how they can improve patient outcomes.

  View details for DOI 10.1016/j.ejrad.2023.110937

  View details for PubMedID 37352683

• Redesign of the American Board of Radiology Diagnostic Radiology Certifying Examination. AJR. American journal of roentgenology Larson, D. B., Flemming, D. J., Barr, R. M., Canon, C. L., Morgan, D. E. 2023

  Abstract

  On April 13, 2023, the American Board of Radiology (ABR) announced plans to replace the current computer-based diagnostic radiology (DR) certifying examination with a new oral examination, to be administered remotely, beginning in 2028. This article describes the planned changes and the process that led to those changes. In keeping with its commitment to continuous improvement, the ABR gathered stakeholder input regarding the DR initial certification process. Respondents generally agreed that the qualifying (core) examination was satisfactory but expressed concerns regarding the current computer-based certifying examination's effectiveness and impact on training. Examination redesign was conducted using input from key stakeholders with a goal of effectively evaluating competence and incentivizing study behaviors that best prepare candidates for radiology practice. Major design elements included: examination structure, breadth and depth of content, and timing. The new oral examination will focus on critical findings as well as common and important diagnoses routinely encountered in all diagnostic specialties, including radiology procedures. Candidates will first be eligible for the examination in the calendar year after residency graduation. Additional details will be finalized and announced in coming years. The ABR will continue to engage with stakeholders throughout the implementation process.

  View details for DOI 10.2214/AJR.23.29585

  View details for PubMedID 37315014

• From Acceptable to Superlative: Scaling a Technologist Coaching Intervention to Improve Image Quality. Journal of the American College of Radiology : JACR Hwang, G. L., Vilendrer, S., Amano, A., Brown-Johnson, C., Kling, S. M., Faust, A., Willis, M. H., Larson, D. B. 2023; 20 (6): 570-584

  Abstract

  To explore factors influencing the expansion of the peer-based technologist Coaching Model Program (CMP) from its origins in mammography and ultrasound to all imaging modalities at a single tertiary academic medical center.After success in mammography and ultrasound, efforts to expand the CMP across all Stanford Radiology modalities commenced in September 2020. From February to April 2021 as lead coaches piloted the program in these novel modalities, an implementation science team designed and conducted semistructured stakeholder interviews and took observational notes at learning collaborative meetings. Data were analyzed using inductive-deductive approaches informed by two implementation science frameworks.Twenty-seven interviews were collected across modalities with radiologists (n = 5), managers (n = 6), coaches (n = 11), and technologists (n = 5) and analyzed with observational notes from six learning meetings with 25 to 40 recurrent participants. The number of technologists, the complexity of examinations, or the existence of standardized auditing criteria for each modality influenced CMP adaptations. Facilitators underlying program expansion included cross-modality learning collaborative, thoughtful pairing of coach and technologist, flexibility in feedback frequency and format, radiologist engagement, and staged rollout. Barriers included lack of protected coaching time, lack of pre-existing audit criteria for some modalities, and the need for privacy of auditing and feedback data.Adaptations to each radiology modality and communication of these learnings were key to disseminating the existing CMP to new modalities across the entire department. An intermodality learning collaborative can facilitate the dissemination of evidence-based practices across modalities.

  View details for DOI 10.1016/j.jacr.2022.10.007

  View details for PubMedID 37302811

• The ACR Learning Network: Facilitating Local Performance Improvement Through Shared Learning. Journal of the American College of Radiology : JACR Larson, D. B., Tomkins, K. G., Zacharias-Andrews, K., Irani, N., Pittman, S. M., Purysko, A. S., Wandtke, B., Bhargavan-Chatfield, M. 2023; 20 (3): 369-376

  Abstract

  The ACR Learning Network was established to test the viability of the learning network model in radiology. In this report, the authors review the learning network concept, introduce the ACR Learning Network and its components, and report progress to date and plans for the future.Patterned after institutional programs developed by the principal investigator, the ACR Learning Network was composed of four distinct improvement collaboratives. Initial participating sites were solicited through broad program advertisement. Candidate programs were selected on the basis of assessments of local leadership support, experience with quality improvement initiatives, intraorganizational relationships, and access to data and analytic support. Participation began with completing a 27-week formal quality improvement training and project support program, with local teams reporting weekly progress on a common performance measure.Four improvement collaborative topics were chosen for the initial cohort with the following numbers of participating sites: mammography positioning (6), prostate MR image quality (6), lung cancer screening (6), and follow-up on recommendations for management of incidental findings (4). To date, all sites have remained actively engaged and have progressed in an expected fashion. A detailed report of the results of the improvement phase will be provided in a future publication.To date, the ACR Learning Network has successfully achieved planned milestones outlined in the program's plan, with preparation under way for the second and third cohorts. By providing a shared platform for improvement training and knowledge sharing, the authors are optimistic that the network may facilitate widespread performance improvement in radiology on a number of topics for years to come.

  View details for DOI 10.1016/j.jacr.2023.01.004

  View details for PubMedID 36922112

• Organization-Wide Approaches to Foster Effective Unit-Level Efforts to Improve Clinician Well-Being. Mayo Clinic proceedings Shanafelt, T. D., Larson, D., Bohman, B., Roberts, R., Trockel, M., Weinlander, E., Springer, J., Wang, H., Stolz, S., Murphy, D. 2023; 98 (1): 163-180

  Abstract

  Health care delivery organizations are positioned to have a tremendous impact on addressing the variables in the practice environment that contribute to occupational distress and that, when optimized, can promote clinician well-being. Many organizations are committed to this work and have clarity on how to address general, system-wide issues and provide resources for individual clinicians. While such top of the organization elements are essential for success, many of the specific improvement efforts that are necessary must address local challenges at the work unit level (department, division, hospital ward, clinic). Uncertainty of how to address variability and the unique needs of different work units is a barrier to effective action for many health care delivery systems. Overcoming this challenge requires organizations to recognize that unit-specific improvement efforts require a system-level approach. In this manuscript, we outline 7 steps for organizations to consider as they establish the infrastructure to improve professional well-being and provide a description of application and evidence of efficacy from a large academic medical center. Such unit-level efforts to address the unique needs of each specialty and occupation at the work unit level have the ability to address many of the day-to-day issues that drive clinician well-being. An enterprise approach is necessary to systematically advance such unit-level action.

  View details for DOI 10.1016/j.mayocp.2022.10.031

  View details for PubMedID 36603944

• Results of a virtual multi-institutional program for quality improvement training and project facilitation. Journal of the American College of Radiology : JACR Miranda-Schaeubinger, M., Garcia Tomkins, K., Larsen, E., Rigby, V., White, A. M., Sze, R. W., Larson, D. B. 2022

  Abstract

  OBJECTIVE: The purpose of this project was to describe the results of this multi-institutional quality improvement (QI) program conducted in a virtual format.METHODS: Developed at Stanford in 2016, the Realizing Improvement through Team Empowerment (RITE) program utilizes a team-based, project-based improvement approach to QI. The program was planned to be replicated at two other institutions through respective onsite programs, but was converted to a multi-institutional virtual format in 2020 in response to the COVID-19 pandemic. The virtual program began in July 2020 and ended in December 2020. The two institutions participated jointly in the cohort, with ten 2-hour training sessions every two weeks for a total of 18 weeks. Project progress was monitored using a predetermined project progress scale by the program manager, who provided more direct project support as needed.RESULTS: The cohort consisted of six teams (37 participants) from two institutions. Each team completed a QI project in subjects including MRI, US, CT, DR and ACR certification. All projects reached levels of between 3.0 (initial test cycles begun with evidence of modest improvement) and 4.0 (performance data meeting goal and statistical process control criteria for improvement), and met graduation criteria for program completion.DISCUSSION: We found the structured problem-solving method, along with timely focused QI education materials via a virtual platform, to be effective in simultaneously facilitating improvement projects from multiple institutions. The combination of two institutions fostered encouragement and shared learning across institutions.

  View details for DOI 10.1016/j.jacr.2022.08.014

  View details for PubMedID 36272524

• Evaluating clinician-led quality improvement initiatives: A system-wide embedded research partnership at Stanford Medicine. Learning health systems Vilendrer, S., Saliba-Gustafsson, E. A., Asch, S. M., Brown-Johnson, C. G., Kling, S. M., Shaw, J. G., Winget, M., Larson, D. B. 2022; 6 (4): e10335

  Abstract

  Many healthcare delivery systems have developed clinician-led quality improvement (QI) initiatives but fewer have also developed in-house evaluation units. Engagement between the two entities creates unique opportunities. Stanford Medicine funded a collaboration between their Improvement Capability Development Program (ICDP), which coordinates and incentivizes clinician-led QI efforts, and the Evaluation Sciences Unit (ESU), a multidisciplinary group of embedded researchers with expertise in implementation and evaluation sciences.To describe the ICDP-ESU partnership and report key learnings from the first 2 y of operation September 2019 to August 2021.Department-level physician and operational QI leaders were offered an ESU consultation to workshop design, methods, and overall scope of their annual QI projects. A steering committee of high-level stakeholders from operational, clinical, and research perspectives subsequently selected three projects for in-depth partnered evaluation with the ESU based on evaluability, importance to the health system, and broader relevance. Selected project teams met regularly with the ESU to develop mixed methods evaluations informed by relevant implementation science frameworks, while aligning the evaluation approach with the clinical teams' QI goals.Sixty and 62 ICDP projects were initiated during the 2 cycles, respectively, across 18 departments, of which ESU consulted with 15 (83%). Within each annual cycle, evaluators made actionable, summative findings rapidly available to partners to inform ongoing improvement. Other reported benefits of the partnership included rapid adaptation to COVID-19 needs, expanded clinician evaluation skills, external knowledge dissemination through scholarship, and health system-wide knowledge exchange. Ongoing considerations for improving the collaboration included the need for multi-year support to enable nimble response to dynamic health system needs and timely data access.Presence of embedded evaluation partners in the enterprise-wide QI program supported identification of analogous endeavors (eg, telemedicine adoption) and cross-cutting lessons across QI efforts, clinician capacity building, and knowledge dissemination through scholarship.

  View details for DOI 10.1002/lrh2.10335

  View details for PubMedID 36263267

  View details for PubMedCentralID PMC9576232

• Openness and Transparency in the Evaluation of Bias in Artificial Intelligence. Radiology Larson, D. B. 2022: 222263

  View details for DOI 10.1148/radiol.222263

  View details for PubMedID 36165797

• Evaluating clinician-led quality improvement initiatives: A system-wide embedded research partnership at Stanford Medicine LEARNING HEALTH SYSTEMS Vilendrer, S., Saliba-Gustafsson, E. A., Asch, S. M., Brown-Johnson, C. G., Kling, S. R., Shaw, J. G., Winget, M., Larson, D. B. 2022

  View details for DOI 10.1002/lrh2.10335

  View details for Web of Science ID 000843397900001

• Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How? Radiology Daye, D., Wiggins, W. F., Lungren, M. P., Alkasab, T., Kottler, N., Allen, B., Roth, C. J., Bizzo, B. C., Durniak, K., Brink, J. A., Larson, D. B., Dreyer, K. J., Langlotz, C. P. 2022: 212151

  Abstract

  As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, manage resources, and ensure patient safety. In this article, a framework is established for the infrastructure required for clinical AI implementation and presents a road map for governance. The road map answers four key questions: Who decides which tools to implement? What factors should be considered when assessing an application for implementation? How should applications be implemented in clinical practice? Finally, how should tools be monitored and maintained after clinical implementation? Among the many challenges for the implementation of AI in clinical practice, devising flexible governance structures that can quickly adapt to a changing environment will be essential to ensure quality patient care and practice improvement objectives.

  View details for DOI 10.1148/radiol.212151

  View details for PubMedID 35916673

• ACR-RADS Programs Current State and Future Opportunities: Defining a Governance Structure to Enable Sustained Success. Journal of the American College of Radiology : JACR Davenport, M. S., Chatfield, M., Hoang, J., Maturen, K. E., Obuchowski, N., Tse, J., Weinreb, J., Kaur, D., Attridge, L., Kurth, D., Larson, D. 2022

  Abstract

  In the spring of 2021, the ACR approved a proposal to improve the consistency, transparency, and administrative oversight of the ACR Reporting and Data Systems (RADS). A working group of experts and stakeholders was convened to draft this governance document. Major advances include (1) forming a RADS Steering Committee, (2) establishing minimum requirements and evidence standards for new and existing RADS, and (3) outlining a governance structure and communication strategy for RADS.

  View details for DOI 10.1016/j.jacr.2022.03.012

  View details for PubMedID 35487247

• Moving Toward Seamless Interinstitutional Electronic Image Transfer. Journal of the American College of Radiology : JACR Larson, D. B., Krishnaraj, A., Mendelson, D. S., Langlotz, C. P., Wald, C. 1800

  Abstract

  The fact that medical images are still predominately exchanged between institutions via physical media is unacceptable in the era of value-driven health care. Although better solutions are technically possible, problems of coordination and market dynamics may be inhibiting progress more than technical factors. We provide a macrosystem analysis of the problem of interinstitutional medical image exchange and propose a strategy for nudging the market toward a patient-friendly solution. The system can be viewed as a network, with autonomous nodes interconnected by links through which information is exchanged. A variety of potential network configurations include those that depend on individual carriers, peer-to-peer links, one or multiple hubs, or a hybrid of models. We find the linked multihub model, in which individual institutions are connected to other institutions via image exchange companies, to be the configuration most likely to create a patient-friendly electronic image exchange system. To achieve this configuration, image exchange companies, which operate in a competitive marketplace, must exchange images with each other. We call on these vendors to immediately commit to coordinating in this manner. We call on all other stakeholders, including medical societies, payers, and regulators, to actively encourage and facilitate this behavior. Specifically, we call on institutions to create appropriate market incentives by only contracting with image exchange vendors who are committed to begin vendor-to-vendor image exchange by no later than2024.

  View details for DOI 10.1016/j.jacr.2021.11.017

  View details for PubMedID 35114138

• Increasing the Utilization of Moderate Sedation Services for Pediatric Imaging. Radiographics : a review publication of the Radiological Society of North America, Inc Zucker, E. J., Wintch, S., Chang, Y., Commerford, L., Diaz, R., Redfern, T. H., Wang, T. N., Lam, L., Frush, D. P., Larson, D. B. 2021; 41 (7): 2127-2135

  Abstract

  Performing motion-free imaging is frequently challenging in children. To bridge the gap between examinations performed in children who are awake and in those under general anesthesia, a moderate sedation program was implemented at our institution but was seldom used despite substantial eligibility. In conjunction with a 5-month quality improvement (QI) course, a multidisciplinary team was assembled and, by using an A3 approach, sought to address the most important key drivers of low utilization, namely the need for clear moderate sedation eligibility criteria, reliable protocol routing order, consistent moderate sedation screening performed by registered nurses (RNs), and enhanced visibility of moderate sedation services to ordering providers. Initial steps focused on developing better-defined criteria and protocoling standard work for technologists and RNs, with coaching and audits. Modality-specific forecasting was then implemented to reroute profiles of patients who were awaiting scheduling or already scheduled for an examination with general anesthesia to the moderate sedation queue to identify more eligible patients. These manual efforts were coupled with higher reliability but more protracted electronic health record changes, facilitating automated protocol routing on the basis of moderate sedation eligibility and order entry constraints. As a result, scheduled imaging examinations requiring moderate sedation increased from a mean of 1.2 examinations per week to a sustained 6.1 examinations per week (range, 4-8) over the 5-month period, exceeding the team SMART (specific, measurable, achievable, relevant, and time bound) goal to achieve an average of five examinations per week by the QI course end. By targeting the most high-impact yet modifiable process deficiencies through a multifaceted team approach and initially investing in manual efforts to gain cultural buy-in while awaiting higher-reliability interventions, the project achieved success and may serve as a more general model for workflow change when there is organizational resistance. ©RSNA, 2021.

  View details for DOI 10.1148/rg.2021210061

  View details for PubMedID 34723694

• Regulatory Frameworks for Development and Evaluation of Artificial Intelligence-Based Diagnostic Imaging Algorithms: Summary and Recommendations JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY Larson, D. B., Harvey, H., Rubin, D. L., Irani, N., Tse, J. R., Langlotz, C. P. 2021; 18 (3): 413–24

  View details for DOI 10.1016/j.jacr.2020.09.060413

  View details for Web of Science ID 000631977100012

• Optimizing Professional Practice Evaluation to Enable a Nonpunitive Learning Health System Approach to Peer Review. Pediatric quality & safety Sandborg, C. I., Hartman, G. E., Su, F., Williams, G., Teufe, B., Wixson, N., Larson, D. B., Donnelly, L. F. 2021; 6 (1): e375

  Abstract

  Healthcare organizations are focused on 2 different and sometimes conflicting tasks; (1) accelerate the improvement of clinical care delivery and (2) collect provider-specific data to determine the competency of providers. We describe creating a process to meet both of these aims while maintaining a culture that fosters improvement and teamwork.Methods: We created a new process to sequester activities related to learning and improvement from those focused on individual provider performance. We describe this process, including data on the number and type of cases reviewed and survey results of the participant's perception of the new process.Results: In the new model, professional practice evaluation committees evaluate events purely to identify system issues and human factors related to medical decision-making, resulting in actional improvements. There are separate and sequestered processes that evaluate concerns around an individual provider's clinical competence or behavior. During the first 5 years of this process, 207 of 217 activities (99.5%) related to system issues rather than issues concerning individual provider competence or behavior. Participants perceived the new process as focused on identifying system errors (4.3/5), nonpunitive (4.2/5), an improvement (4.0/5), and helped with engagement in our system and contributed to wellness (4.0/5).Conclusion: We believe this sequestered approach has enabled us to achieve both the oversight mandates to ensure provider competence while enabling a learning health systems approach to build the cultural aspects of trust and teamwork that are essential to driving continuous improvement in our system of care.

  View details for DOI 10.1097/pq9.0000000000000375

  View details for PubMedID 33409427

• Artificial Intelligence Algorithm Improves Radiologist Performance in Skeletal Age Assessment: A Prospective Multicenter Randomized Controlled Trial. Radiology Eng, D. K., Khandwala, N. B., Long, J., Fefferman, N. R., Lala, S. V., Strubel, N. A., Milla, S. S., Filice, R. W., Sharp, S. E., Towbin, A. J., Francavilla, M. L., Kaplan, S. L., Ecklund, K., Prabhu, S. P., Dillon, B. J., Everist, B. M., Anton, C. G., Bittman, M. E., Dennis, R., Larson, D. B., Seekins, J. M., Silva, C. T., Zandieh, A. R., Langlotz, C. P., Lungren, M. P., Halabi, S. S. 2021: 204021

  Abstract

  Background Previous studies suggest that use of artificial intelligence (AI) algorithms as diagnostic aids may improve the quality of skeletal age assessment, though these studies lack evidence from clinical practice. Purpose To compare the accuracy and interpretation time of skeletal age assessment on hand radiograph examinations with and without the use of an AI algorithm as a diagnostic aid. Materials and Methods In this prospective randomized controlled trial, the accuracy of skeletal age assessment on hand radiograph examinations was performed with (n = 792) and without (n = 739) the AI algorithm as a diagnostic aid. For examinations with the AI algorithm, the radiologist was shown the AI interpretation as part of their routine clinical work and was permitted to accept or modify it. Hand radiographs were interpreted by 93 radiologists from six centers. The primary efficacy outcome was the mean absolute difference between the skeletal age dictated into the radiologists' signed report and the average interpretation of a panel of four radiologists not using a diagnostic aid. The secondary outcome was the interpretation time. A linear mixed-effects regression model with random center- and radiologist-level effects was used to compare the two experimental groups. Results Overall mean absolute difference was lower when radiologists used the AI algorithm compared with when they did not (5.36 months vs 5.95 months; P = .04). The proportions at which the absolute difference exceeded 12 months (9.3% vs 13.0%, P = .02) and 24 months (0.5% vs 1.8%, P = .02) were lower with the AI algorithm than without it. Median radiologist interpretation time was lower with the AI algorithm than without it (102 seconds vs 142 seconds, P = .001). Conclusion Use of an artificial intelligence algorithm improved skeletal age assessment accuracy and reduced interpretation times for radiologists, although differences were observed between centers. Clinical trial registration no. NCT03530098 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Rubin in this issue.

  View details for DOI 10.1148/radiol.2021204021

  View details for PubMedID 34581608

• Program for Supporting Frontline Improvement Projects in an Academic Radiology Department. AJR. American journal of roentgenology Larson, D. B., Kumar, S. n., Mickelsen, L. J., Cordova, D. A., Faust, A. N. 2021: 1–10

  Abstract

  OBJECTIVE. The purpose of this study was to describe the results of an ongoing program implemented in an academic radiology department to support the execution of small- to medium-size improvement projects led by frontline staff and leaders. MATERIALS AND METHODS. Staff members were assigned a coach, were instructed in improvement methods, were given time to work on the project, and presented progress to department leaders in weekly 30-minute reports. Estimated costs and outcomes were calculated for each project and aggregated. An anonymous survey was administered to participants at the end of the first year. RESULTS. A total of 73 participants completed 102 projects in the first 2 years of the program. The project type mix included 25 quality improvement projects, 22 patient satisfaction projects, 14 staff engagement projects, 27 efficiency improvement projects, and 14 regulatory compliance and readiness projects. Estimated annualized outcomes included approximately 4500 labor hours saved, $315K in supply cost savings, $42.2M in potential increased revenues, 8- and 2-point increase in top-box patient experience scores at two clinics, and a 60-incident reduction in near-miss safety events. Participant time equated to approximately 0.35 full-time equivalent positions per year. Approximately 0.4 full-time equivalent was required to support the program. Survey results indicated that the participants generally viewed the program favorably. CONCLUSION. The program was successful in providing a platform for simultaneously solving a large number of organizational problems while also providing a positive experience to frontline personnel.

  View details for DOI 10.2214/AJR.20.23421

  View details for PubMedID 33909468

• CT Volumes from 2,398 Radiology Practices in the United States: A Real-Time Indicator of the Effect of COVID-19 on Routine Care, January to September 2020. Journal of the American College of Radiology : JACR Davenport, M. S., Fruscello, T., Chatfield, M., Weinstein, S., Sensakovic, W. F., Larson, D. B. 2020

  Abstract

  PURPOSE: To determine the effect of coronavirus disease 2019 (COVID-19) on CT volumes in the United States during and after the first wave of the pandemic.METHODS: CT volumes from 2,398 United States radiology practices participating in the ACR Dose Index Registry from January 1, 2020, to September 30, 2020, were analyzed. Data were compared to projected CT volumes using 2019 normative data and analyzed with respect to time since government orders, population-normalized positive COVID-19 tests, and attributed deaths. Data were stratified by state population density, unemployment status, and race.RESULTS: There were 16,198,830 CT examinations (2,398 practices). Volume nadir occurred an average of 32 days after each state-of-emergency declaration and 12 days after each stay-at-home order. At nadir, the projected volume loss was 38,043 CTs per day (of 71,626 CTs per day; 53% reduction). Over the entire study period, there were 3,689,874 fewer CT examinations performed than predicted (of 18,947,969; 19% reduction). There was less reduction in states with smaller population density (15% [169,378 of 1,142,247; quartile 1] versus 21% [1,894,152 of 9,140,689; quartile 4]) and less reduction in states with a lower insured unemployed proportion (13% [279,331 of 2,071,251; quartile 1] versus 23% [1,753,521 of 7,496,443; quartile 4]). By September 30, CT volume had returned to 84% (59,856 of 71,321) of predicted; recovery of CT volume occurred as positive COVID-19 tests rose and deaths were in decline.CONCLUSION: COVID-19 substantially reduced US CT volume, reflecting delayed and deferred care, especially in states with greater unemployment. Partial volume recovery occurred despite rising positive COVID-19 tests.

  View details for DOI 10.1016/j.jacr.2020.10.010

  View details for PubMedID 33129768

• Recognizing and Avoiding the Most Common Mistakes in Quality Improvement. Journal of the American College of Radiology : JACR Larson, D. B., Willis, M. H., Hwang, G. L. 2020

  View details for DOI 10.1016/j.jacr.2020.09.053

  View details for PubMedID 33069677

• Critical Results in Radiology: Defined by Clinical Judgment or by a List? Journal of the American College of Radiology : JACR Kuhn, K., Larson, D. B., 2020 Radiology Improvement Summit Critical Results Workgroup, Becker, C., Bierhals, A., Broder, J., City, R., Cooke, E., Cordova, D., Curci, N. E., Davenport, M. S., Dinan, D., Duncan, J. R., Dungan, D., Facchini, D., Heller, R. E., Hwang, G., Irani, N., Joshi, A., Kadom, N., Kaplan, S. L., Kolli, K. P., Krishnaraj, A., Marsh, D., Miller, A., Mintz, A., Pahade, J., Policeni, B., Rubio, E. I., Towbin, A. J., Wald, C., Wandtke, B., Willis, M. 2020

  View details for DOI 10.1016/j.jacr.2020.07.009

  View details for PubMedID 32783896

• Transitioning From Peer Review to Peer Learning: Report of the 2020 Peer Learning Summit. Journal of the American College of Radiology : JACR Larson, D. B., Broder, J. C., Bhargavan-Chatfield, M., Donnelly, L. F., Kadom, N., Khorasani, R., Sharpe, R. E., Pahade, J. K., Moriarity, A. K., Tan, N., Siewert, B., Kruskal, J. B. 2020

  Abstract

  Since its introduction nearly 20 years ago, score-based peer review has not been shown to have meaningful impact on improving radiologist performance or to be a valid measurement instrument of radiologist performance. A new paradigm has emerged, peer learning, which is a group activity in which expert professionals review one another's work, actively give and receive feedback in a constructive manner, teach and learn from one another, and mutually commit to improving performance as individuals, as a group, and as a system. Many radiology practices are beginning to transition from score-based peer review to peer learning. To address challenges faced by these practices, a 1-day summit was convened at Harvard Medical School in January 2020, sponsored by the ACR. Several key themes emerged. Elements considered key to a peer-learning program include broad group participation, active identification of learning opportunities, individual feedback, peer-learning conferences, link with process and system improvement activities, preservation of organizational culture, sequestration of peer-learning activities, and program management. Radiologists and practice leaders are encouraged to develop peer-learning programs tailored to their local practice environment and foster a positive organizational culture. Health system administrators should support active peer-learning programs in the place of score-based peer review. Accrediting organizations should formally recognize it as an acceptable form of peer review and specify minimum criteria for peer-learning programs. IT system vendors should actively collaborate with radiology organizations to develop solutions that support the efficient and effective management of local peer-learning programs.

  View details for DOI 10.1016/j.jacr.2020.07.016

  View details for PubMedID 32771491

• Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework. Radiology Larson, D. B., Magnus, D. C., Lungren, M. P., Shah, N. H., Langlotz, C. P. 2020: 192536

  Abstract

  In this article, the authors propose an ethical framework for using and sharing clinical data for the development of artificial intelligence (AI) applications. The philosophical premise is as follows: when clinical data are used to provide care, the primary purpose for acquiring the data is fulfilled. At that point, clinical data should be treated as a form of public good, to be used for the benefit of future patients. In their 2013 article, Faden et al argued that all who participate in the health care system, including patients, have a moral obligation to contribute to improving that system. The authors extend that framework to questions surrounding the secondary use of clinical data for AI applications. Specifically, the authors propose that all individuals and entities with access to clinical data become data stewards, with fiduciary (or trust) responsibilities to patients to carefully safeguard patient privacy, and to the public to ensure that the data are made widely available for the development of knowledge and tools to benefit future patients. According to this framework, the authors maintain that it is unethical for providers to "sell" clinical data to other parties by granting access to clinical data, especially under exclusive arrangements, in exchange for monetary or in-kind payments that exceed costs. The authors also propose that patient consent is not required before the data are used for secondary purposes when obtaining such consent is prohibitively costly or burdensome, as long as mechanisms are in place to ensure that ethical standards are strictly followed. Rather than debate whether patients or provider organizations "own" the data, the authors propose that clinical data are not owned at all in the traditional sense, but rather that all who interact with or control the data have an obligation to ensure that the data are used for the benefit of future patients and society.

  View details for DOI 10.1148/radiol.2020192536

  View details for PubMedID 32208097

• Automatic Extraction of Skeletal Maturity from Whole Body Pediatric Scoliosis X-rays Using Regional Proposal and Compound Scaling Convolutional Neural Networks Ha, A., Vorhies, J., Campion, A., Fang, C., Fadell, M., Dou, S., Halabi, S., Larson, D., Wang, E., Lee, Y., Langner, J., Kaur, J., Do, B., Park, T., Cho, Y. R., Hu, Yoo, Woo, H. G., Wang, J., Facelli, J., Nam, S., Kang, M. IEEE COMPUTER SOC. 2020: 996-1000

  View details for DOI 10.1109/BIBM49941.2020.9313251

  View details for Web of Science ID 000659487101011

• Needs of Referring Providers by Practice Type: Results of a Survey at an Academic Medical Center. AJR. American journal of roentgenology Larson, D. B., Hwang, G. L. 2020

  Abstract

  OBJECTIVE. The purpose of this study was to test a previously-published hypothetical framework of different referring provider needs for primary care, specialty care, and urgent/emergent care practitioners through questions asked in an annual survey at an academic medical center. MATERIALS AND METHODS. Seven questions regarding provider needs were included in an annual online anonymous survey of referring providers. Multiple choice responses were provided. Differences in responses between provider types were assessed using the Mann-Whitney U test RESULTS. The survey was sent to 3,325 providers. 514 responses were received (response rate = 15.5%). 349 responses were included in the analysis, including 81 responses from primary care, 205 responses from specialty care, and 54 responses from urgent or emergency care. Results indicated that 1) urgent care providers need examinations to be performed and interpreted more quickly, 2) specialist providers prefer greater radiologist specialization, 3) urgent care providers order imaging with greater frequency 4) primary care and urgent care providers order a greater breadth of imaging, 5) primary care providers report greater reliance on radiologist interpretations, and 6) all provider types highly value direct interactions with radiologists. All results were statistically significant and matched the previously-established hypotheses. CONCLUSION. These results support the concept that referring providers tend to value different aspects of radiology services differently, according to predictable characteristics. The findings suggest that the concept of value in radiology is highly context-specific and can potentially be evaluated, at least in part, using practice-specific referring provider assessments.

  View details for DOI 10.2214/AJR.19.22738

  View details for PubMedID 32603226

• Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis JOURNAL OF CYSTIC FIBROSIS Zucker, E. J., Barnes, Z. A., Lungren, M. P., Shpanskaya, Y., Seekins, J. M., Halabi, S. S., Larson, D. B. 2020; 19 (1): 131–38

  View details for DOI 10.1016/j.jcf.2019.04.016

  View details for Web of Science ID 000514757100021

• Variables Influencing Radiology Volume Recovery During the Next Phase of the Coronavirus Disease 2019 (COVID-19) Pandemic. Journal of the American College of Radiology : JACR Madhuripan, N. n., Man-Ching Cheung, H. n., Alicia Cheong, L. H., Jawahar, A. n., Willis, M. n., Larson, D. B. 2020

  Abstract

  The coronavirus disease 2019 (COVID-19) pandemic has reduced radiology volumes across the country as providers have decreased elective care to minimize the spread of infection and free up health care delivery system capacity. After the stay-at-home order was issued in our county, imaging volumes at our institution decreased to approximately 46% of baseline volumes, similar to the experience of other radiology practices. Given the substantial differences in severity and timing of the disease in different geographic regions, estimating resumption of radiology volumes will be one of the next major challenges for radiology practices. We hypothesize that there are six major variables that will likely predict radiology volumes: (1) severity of disease in the local region, including potential subsequent "waves" of infection; (2) lifting of government social distancing restrictions; (3) patient concern regarding risk of leaving home and entering imaging facilities; (4) management of pent-up demand for imaging delayed during the acute phase of the pandemic, including institutional capacity; (5) impact of the economic downturn on health insurance and ability to pay for imaging; and (6) radiology practice profile reflecting amount of elective imaging performed, including type of patients seen by the radiology practice such as emergency, inpatient, outpatient mix and subspecialty types. We encourage radiology practice leaders to use these and other relevant variables to plan for the coming weeks and to work collaboratively with local health system and governmental leaders to help ensure that needed patient care is restored as quickly as the environment will safely permit.

  View details for DOI 10.1016/j.jacr.2020.05.026

  View details for PubMedID 32505562

• Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices. Journal of magnetic resonance imaging : JMRI Chaudhari, A. S., Sandino, C. M., Cole, E. K., Larson, D. B., Gold, G. E., Vasanawala, S. S., Lungren, M. P., Hargreaves, B. A., Langlotz, C. P. 2020

  Abstract

  Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the large number of retrospective studies using DL, there are fewer applications of DL in the clinic on a routine basis. To address this large translational gap, we review the recent publications to determine three major use cases that DL can have in MRI, namely, that of model-free image synthesis, model-based image reconstruction, and image or pixel-level classification. For each of these three areas, we provide a framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices. We draw inspiration for this framework from advances in computer vision in natural imaging as well as additional healthcare fields. We further emphasize the need for reproducibility of research studies through the sharing of datasets and software. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.

  View details for DOI 10.1002/jmri.27331

  View details for PubMedID 32830874

• Regulatory Frameworks for Development and Evaluation of Artificial Intelligence-Based Diagnostic Imaging Algorithms: Summary and Recommendations. Journal of the American College of Radiology : JACR Larson, D. B., Harvey, H. n., Rubin, D. L., Irani, N. n., Tse, J. R., Langlotz, C. P. 2020

  Abstract

  Although artificial intelligence (AI)-based algorithms for diagnosis hold promise for improving care, their safety and effectiveness must be ensured to facilitate wide adoption. Several recently proposed regulatory frameworks provide a solid foundation but do not address a number of issues that may prevent algorithms from being fully trusted. In this article, we review the major regulatory frameworks for software as a medical device applications, identify major gaps, and propose additional strategies to improve the development and evaluation of diagnostic AI algorithms. We identify the following major shortcomings of the current regulatory frameworks: (1) conflation of the diagnostic task with the diagnostic algorithm, (2) superficial treatment of the diagnostic task definition, (3) no mechanism to directly compare similar algorithms, (4) insufficient characterization of safety and performance elements, (5) lack of resources to assess performance at each installed site, and (6) inherent conflicts of interest. We recommend the following additional measures: (1) separate the diagnostic task from the algorithm, (2) define performance elements beyond accuracy, (3) divide the evaluation process into discrete steps, (4) encourage assessment by a third-party evaluator, (5) incorporate these elements into the manufacturers' development process. Specifically, we recommend four phases of development and evaluation, analogous to those that have been applied to pharmaceuticals and proposed for software applications, to help ensure world-class performance of all algorithms at all installed sites. In the coming years, we anticipate the emergence of a substantial body of research dedicated to ensuring the accuracy, reliability, and safety of the algorithms.

  View details for DOI 10.1016/j.jacr.2020.09.060

  View details for PubMedID 33096088

  View details for PubMedCentralID PMC7574690

• Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis. Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society Zucker, E. J., Barnes, Z. A., Lungren, M. P., Shpanskaya, Y., Seekins, J. M., Halabi, S. S., Larson, D. B. 2019

  Abstract

  BACKGROUND: The aim of this study was to evaluate the hypothesis that a deep convolutional neural network (DCNN) model could facilitate automated Brasfield scoring of chest radiographs (CXRs) for patients with cystic fibrosis (CF), performing similarly to a pediatric radiologist.METHODS: All frontal/lateral chest radiographs (2058 exams) performed in CF patients at a single institution from January 2008-2018 were retrospectively identified, and ground-truth Brasfield scoring performed by a board-certified pediatric radiologist. 1858 exams (90.3%) were used to train and validate the DCNN model, while 200 exams (9.7%) were reserved for a test set. Five board-certified pediatric radiologists independently scored the test set according to the Brasfield method. DCNN model vs. radiologist performance was compared using Spearman correlation (rho) as well as mean difference (MD), mean absolute difference (MAD), and root mean squared error (RMSE) estimation.RESULTS: For the total Brasfield score, rho for the model-derived results computed pairwise with each radiologist's scores ranged from 0.79-0.83, compared to 0.85-0.90 for radiologist vs. radiologist scores. The MD between model estimates of the total Brasfield score and the average score of radiologists was -0.09. Based on MD, MAD, and RMSE, the model matched or exceeded radiologist performance for all subfeatures except air-trapping and large lesions.CONCLUSIONS: A DCNN model is promising for predicting CF Brasfield scores with accuracy similar to that of a pediatric radiologist.

  View details for PubMedID 31056440

• Quality and safety in pediatric radiology. Pediatric radiology Dillman, J. R., Larson, D. B. 2019; 49 (4): 431–32

  View details for PubMedID 30923874

• Quality and safety in pediatric radiology PEDIATRIC RADIOLOGY Dillman, J. R., Larson, D. B. 2019; 49 (4): 431-432

  View details for DOI 10.1007/s00247-019-04353-0

  View details for Web of Science ID 000462754100001

• Measuring Diagnostic Radiologists: What Measurements Should We Use? JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY Davenport, M. S., Larson, D. B. 2019; 16 (3): 333-335

  View details for DOI 10.1016/j.jacr.2018.12.011

  View details for Web of Science ID 000460849700016

• Measuring Diagnostic Radiologists: What Measurements Should We Use? Journal of the American College of Radiology : JACR Davenport, M. S., Larson, D. B. 2019

  View details for PubMedID 30718210

• Imaging Quality Control in the Era of Artificial Intelligence. Journal of the American College of Radiology : JACR Larson, D. B., Boland, G. W. 2019

  Abstract

  The advent of artificial intelligence (AI) promises to have a transformational impact on quality in medicine, including in radiology. However, experience has shown that quality tools alone are often not sufficient to bring about consistent excellent performance. Specifically, rather than assuming outcome targets are consistently met, in quality control, managers assume that wide variation is likely present unless proven otherwise with objective performance data. In this article, we discuss what we consider to be the eight essential elements required to achieve comprehensive process control, necessary to deliver consistent quality in radiology: a process control framework, performance measures, performance standards and targets, monitoring applications, prediction models, optimization models, feedback mechanisms, and accountability mechanisms. We consider these elements to be universally applicable, including in the application of AI-based models. We also discuss how the lack of specific elements of a quality control program can hinder widespread quality control efforts. We illustrate the concept using the example of a CT radiation dose optimization and process control program previously developed by one of the authors and provide several examples of how AI-based tools might be used for quality control in radiology.

  View details for DOI 10.1016/j.jacr.2019.05.048

  View details for PubMedID 31254491

• CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., Seekins, J., Mong, D. A., Halabi, S. S., Sandberg, J. K., Jones, R., Larson, D. B., Langlotz, C. P., Patel, B. N., Lungren, M. P., Ng, A. Y., AAAI ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2019: 590–97

  View details for Web of Science ID 000485292600073

• Improving Automated Pediatric Bone Age Estimation Using Ensembles of Models from the 2017 RSNA Machine Learning Challenge. Radiology. Artificial intelligence Pan, I. n., Thodberg, H. H., Halabi, S. S., Kalpathy-Cramer, J. n., Larson, D. B. 2019; 1 (6): e190053

  Abstract

  To investigate improvements in performance for automatic bone age estimation that can be gained through model ensembling.A total of 48 submissions from the 2017 RSNA Pediatric Bone Age Machine Learning Challenge were used. Participants were provided with 12 611 pediatric hand radiographs with bone ages determined by a pediatric radiologist to develop models for bone age determination. The final results were determined using a test set of 200 radiographs labeled with the weighted average of six ratings. The mean pairwise model correlation and performance of all possible model combinations for ensembles of up to 10 models using the mean absolute deviation (MAD) were evaluated. A bootstrap analysis using the 200 test radiographs was conducted to estimate the true generalization MAD.The estimated generalization MAD of a single model was 4.55 months. The best-performing ensemble consisted of four models with an MAD of 3.79 months. The mean pairwise correlation of models within this ensemble was 0.47. In comparison, the lowest achievable MAD by combining the highest-ranking models based on individual scores was 3.93 months using eight models with a mean pairwise model correlation of 0.67.Combining less-correlated, high-performing models resulted in better performance than naively combining the top-performing models. Machine learning competitions within radiology should be encouraged to spur development of heterogeneous models whose predictions can be combined to achieve optimal performance.© RSNA, 2019 Supplemental material is available for this article. See also the commentary by Siegel in this issue.

  View details for DOI 10.1148/ryai.2019190053

  View details for PubMedID 32090207

  View details for PubMedCentralID PMC6884060

• Re: "Reducing Variability of Radiation Dose in CT". Journal of the American College of Radiology : JACR Larson, D. B. 2018; 15 (12): 1669–70

  View details for PubMedID 30522642

• Re: "Reducing Variability of Radiation Dose in CT" JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY Larson, D. B. 2018; 15 (12): 1669-1670

  View details for DOI 10.1016/j.jacr.2018.07.008

  View details for Web of Science ID 000452939000003

• Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLoS medicine Bien, N., Rajpurkar, P., Ball, R. L., Irvin, J., Park, A., Jones, E., Bereket, M., Patel, B. N., Yeom, K. W., Shpanskaya, K., Halabi, S., Zucker, E., Fanton, G., Amanatullah, D. F., Beaulieu, C. F., Riley, G. M., Stewart, R. J., Blankenberg, F. G., Larson, D. B., Jones, R. H., Langlotz, C. P., Ng, A. Y., Lungren, M. P. 2018; 15 (11): e1002699

  Abstract

  BACKGROUND: Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model's predictions to clinical experts during interpretation.METHODS AND FINDINGS: Our dataset consisted of 1,370 knee MRI exams performed at Stanford University Medical Center between January 1, 2001, and December 31, 2012 (mean age 38.0 years; 569 [41.5%] female patients). The majority vote of 3 musculoskeletal radiologists established reference standard labels on an internal validation set of 120 exams. We developed MRNet, a convolutional neural network for classifying MRI series and combined predictions from 3 series per exam using logistic regression. In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the receiver operating characteristic curve (AUC) values of 0.937 (95% CI 0.895, 0.980), 0.965 (95% CI 0.938, 0.993), and 0.847 (95% CI 0.780, 0.914), respectively, on the internal validation set. We also obtained a public dataset of 917 exams with sagittal T1-weighted series and labels for ACL injury from Clinical Hospital Centre Rijeka, Croatia. On the external validation set of 183 exams, the MRNet trained on Stanford sagittal T2-weighted series achieved an AUC of 0.824 (95% CI 0.757, 0.892) in the detection of ACL injuries with no additional training, while an MRNet trained on the rest of the external data achieved an AUC of 0.911 (95% CI 0.864, 0.958). We additionally measured the specificity, sensitivity, and accuracy of 9 clinical experts (7 board-certified general radiologists and 2 orthopedic surgeons) on the internal validation set both with and without model assistance. Using a 2-sided Pearson's chi-squared test with adjustment for multiple comparisons, we found no significant differences between the performance of the model and that of unassisted general radiologists in detecting abnormalities. General radiologists achieved significantly higher sensitivity in detecting ACL tears (p-value = 0.002; q-value = 0.019) and significantly higher specificity in detecting meniscal tears (p-value = 0.003; q-value = 0.019). Using a 1-tailed t test on the change in performance metrics, we found that providing model predictions significantly increased clinical experts' specificity in identifying ACL tears (p-value < 0.001; q-value = 0.006). The primary limitations of our study include lack of surgical ground truth and the small size of the panel of clinical experts.CONCLUSIONS: Our deep learning model can rapidly generate accurate clinical pathology classifications of knee MRI exams from both internal and external datasets. Moreover, our results support the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation. Further research is needed to validate the model prospectively and to determine its utility in the clinical setting.

  View details for PubMedID 30481176

• Improving and Maintaining Radiologic Technologist Skill Using a Medical Director Partnership and Technologist Coaching Model AMERICAN JOURNAL OF ROENTGENOLOGY Kagoma, Y. K., Netz, R. J., Strain, A., Larson, D. B. 2018; 211 (5): 986-992

  View details for DOI 10.2214/AJR.18.19970

  View details for Web of Science ID 000450915200017

• Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet PLOS MEDICINE Bien, N., Rajpurkar, P., Ball, R. L., Irvin, J., Park, A., Jones, E., Bereket, M., Patel, B. N., Yeom, K. W., Shpanskaya, K., Halabi, S., Zucker, E., Fanton, G., Amanatullah, D. F., Beaulieu, C. F., Riley, G. M., Stewart, R. J., Blankenberg, F. G., Larson, D. B., Jones, R. H., Langlotz, C. P., Ng, A. Y., Lungren, M. P. 2018; 15 (11)

  View details for DOI 10.1371/journal.pmed.1002699

  View details for Web of Science ID 000451827800015

• Strategies for Implementing a Standardized Structured Radiology Reporting Program. Radiographics : a review publication of the Radiological Society of North America, Inc Larson, D. B. 2018; 38 (6): 1705–16

  Abstract

  Radiology practices are increasingly implementing standardized report templates to overcome the drawbacks of individual templates. However, implementing a standardized structured reporting program is not necessarily straightforward. This article provides practical guidance for radiologists who wish to implement standardized structured reporting in their practice. Challenges that radiology groups encounter tend to fall into two categories: technical and organizational. Defining and carrying out technical work can be tedious but tends to be relatively straightforward, whereas overcoming organizational challenges often requires changes in individuals' strongly held values, beliefs, roles, and relationships. Established organizational change models can help frame the organizational strategy to implement a standardized structured reporting program. Once leadership support is secured, a standardized structured reporting committee can be convened to establish report priorities, standards, design principles, and guidelines. Report standards help to establish the common framework upon which all report templates are constructed, helping to ensure report consistency. By using these standards, committee members can create reports relevant to their subspecialties, which can then be edited for formatting and content. Once report templates have been developed, edited, and published, an abbreviated form of the same process can be used to maintain the reports, which can be accomplished with much less effort than that initially required to create the templates. After standardized structured report templates are implemented and become embedded in practice, most radiologists eventually appreciate the merits of the program. ©RSNA, 2018.

  View details for PubMedID 30303804

• Strategies for Radiology to Thrive in the Value Era RADIOLOGY Kruskal, J. B., Larson, D. B. 2018; 289 (1): 3-7

  View details for DOI 10.1148/radiol.2018180190

  View details for Web of Science ID 000444990900001

• Strategies for Implementing a Standardized Structured Radiology Reporting Program RADIOGRAPHICS Larson, D. B. 2018; 38 (6): 1705-1716

  View details for DOI 10.1148/rg.2018180040

  View details for Web of Science ID 000446868100015

• Strategies for Radiology to Thrive in the Value Era. Radiology Kruskal, J. B., Larson, D. B. 2018: 180190

  View details for PubMedID 30179112

• Improving and Maintaining Radiologic Technologist Skill Using a Medical Director Partnership and Technologist Coaching Model. AJR. American journal of roentgenology Kagoma, Y. K., Netz, R. J., Strain, A., Larson, D. B. 2018: 1–7

  Abstract

  OBJECTIVE: Consistent excellence in radiologic technologist performance, including ensuring high technical image quality, patient safety and comfort, and efficient workflow, largely depends on individual technologist skill. However, sustained growth in the size and complexity of health care organizations has increased the difficulty in developing and maintaining technologist expertise. In this article, we explore underlying organizational structures that contribute to this problem and propose organizational models to promote continued excellence in technologist skill.CONCLUSION: We have found that a relatively modest investment in medical directorship combined with a coaching model can bring about a significant level of improvement in skilled clinical performance. We believe that widespread implementation of similar programs could contribute to substantial improvements in quality in radiology and other health care settings.

  View details for PubMedID 30063376

• Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs RADIOLOGY Larson, D. B., Chen, M. C., Lungren, M. P., Halabi, S. S., Stence, N. V., Langlotz, C. P. 2018; 287 (1): 313–22

  Abstract

  Purpose To compare the performance of a deep-learning bone age assessment model based on hand radiographs with that of expert radiologists and that of existing automated models. Materials and Methods The institutional review board approved the study. A total of 14 036 clinical hand radiographs and corresponding reports were obtained from two children's hospitals to train and validate the model. For the first test set, composed of 200 examinations, the mean of bone age estimates from the clinical report and three additional human reviewers was used as the reference standard. Overall model performance was assessed by comparing the root mean square (RMS) and mean absolute difference (MAD) between the model estimates and the reference standard bone ages. Ninety-five percent limits of agreement were calculated in a pairwise fashion for all reviewers and the model. The RMS of a second test set composed of 913 examinations from the publicly available Digital Hand Atlas was compared with published reports of an existing automated model. Results The mean difference between bone age estimates of the model and of the reviewers was 0 years, with a mean RMS and MAD of 0.63 and 0.50 years, respectively. The estimates of the model, the clinical report, and the three reviewers were within the 95% limits of agreement. RMS for the Digital Hand Atlas data set was 0.73 years, compared with 0.61 years of a previously reported model. Conclusion A deep-learning convolutional neural network model can estimate skeletal maturity with accuracy similar to that of an expert radiologist and to that of existing automated models. © RSNA, 2017 An earlier incorrect version of this article appeared online. This article was corrected on January 19, 2018.

  View details for PubMedID 29095675

• Improving Performance of Mammographic Breast Positioning in an Academic Radiology Practice AMERICAN JOURNAL OF ROENTGENOLOGY Pal, S., Ikeda, D. M., Jesinger, R. A., Mickelsen, L., Chen, C. A., Larson, D. B. 2018; 210 (4): 807–15

  Abstract

  The purpose of this project was to achieve sustained improvement in mammographic breast positioning in our department.Between June 2013 and December 2016, we conducted a team-based performance improvement initiative with the goal of improving mammographic positioning. The team of technologists and radiologists established quantitative measures of positioning performance based on American College of Radiology (ACR) criteria, audited at least 35 mammograms per week for positioning quality, displayed performance in dashboards, provided technologists with positioning training, developed a supportive environment fostering technologist and radiologist communication surrounding mammographic positioning, and employed a mammography positioning coach to develop, improve, and maintain technologist positioning performance. Statistical significance in changes in the percentage of mammograms passing the ACR criteria were evaluated using a two-proportion z test.A baseline mammogram audit performed in June 2013 showed that 67% (82/122) met ACR passing criteria for positioning. Performance improved to 80% (588/739; p < 0.01) after positioning training and technologist and radiologist agreement on positioning criteria. With individual technologist feedback, positioning further improved, with 91% of mammograms passing ACR criteria (p < 0.01). Seven months later, performance temporarily decreased to 80% but improved to 89% with implementation of a positioning coach. The overall mean performance of 91% has been sustained for 23 months. The program cost approximately $30,000 to develop, $42,000 to launch, and $25,000 per year to maintain. Almost all costs were related to personnel time.Dedicated performance improvement methods may achieve significant and sustained improvement in mammographic breast positioning, which may better enable facilities to pass the recently instated Enhancing Quality Using the Inspection Program portion of a practice's annual Mammography Quality Standards Act inspections.

  View details for PubMedID 29412019

• Deep Learning to Classify Radiology Free-Text Reports RADIOLOGY Chen, M. C., Ball, R. L., Yang, L., Moradzadeh, N., Chapman, B. E., Larson, D. B., Langlotz, C. P., Amrhein, T. J., Lungren, M. P. 2018; 286 (3): 845–52

  Abstract

  Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) reports from two institutions. Materials and Methods Contrast material-enhanced CT examinations of the chest performed between January 1, 1998, and January 1, 2016, were selected. Annotations by two human radiologists were made for three categories: the presence, chronicity, and location of PE. Classification of performance of a CNN model with an unsupervised learning algorithm for obtaining vector representations of words was compared with the open-source application PeFinder. Sensitivity, specificity, accuracy, and F1 scores for both the CNN model and PeFinder in the internal and external validation sets were determined. Results The CNN model demonstrated an accuracy of 99% and an area under the curve value of 0.97. For internal validation report data, the CNN model had a statistically significant larger F1 score (0.938) than did PeFinder (0.867) when classifying findings as either PE positive or PE negative, but no significant difference in sensitivity, specificity, or accuracy was found. For external validation report data, no statistical difference between the performance of the CNN model and PeFinder was found. Conclusion A deep learning CNN model can classify radiology free-text reports with accuracy equivalent to or beyond that of an existing traditional NLP model. © RSNA, 2017 Online supplemental material is available for this article.

  View details for PubMedID 29135365

• Practical Suggestions on How to Move From Peer Review to Peer Learning AMERICAN JOURNAL OF ROENTGENOLOGY Donnelly, L. F., Larson, D. B., Heller, R. E., Kruskal, J. B. 2018; 210 (3): 578–82

  Abstract

  The purpose of this article is to outline practical steps that a department can take to transition to a peer learning model.The 2015 Institute of Medicine report on improving diagnosis emphasized that organizations and industries that embrace error as an opportunity to learn tend to outperform those that do not. To meet this charge, radiology must transition from a peer review to a peer learning approach.

  View details for PubMedID 29323555

• The Role of Radiology in the Diagnostic Process: Information, Communication, and Teamwork AMERICAN JOURNAL OF ROENTGENOLOGY Larson, D. B., Langlotz, C. P. 2017; 209 (5): 992–1000

  Abstract

  The diagnostic radiology process represents a partnership between clinical and radiology teams. As such, breakdowns in interpersonal interactions and communication can result in patient harm.We explore the role of radiology in the diagnostic process, focusing on key concepts of information and communication, as well as key interpersonal interactions of teamwork, collaboration, and collegiality, all based on trust. We propose 10 principles to facilitate effective information flow in the diagnostic process.

  View details for PubMedID 28742380

• Improving efficiency in the radiology department. Pediatric radiology Towbin, A. J., Perry, L. A., Larson, D. B. 2017; 47 (7): 783-792

  Abstract

  The modern radiology department is built around the flow of information. Ordering providers request imaging studies to be performed, technologists complete the work required to perform the imaging studies, and radiologists interpret and report on the imaging findings. As each of these steps is performed, data flow between multiple information systems, most notably the radiology information system (RIS), the picture archiving and communication system (PACS) and the voice dictation system. Even though data flow relatively seamlessly, the majority of our systems and processes are inefficient. The purpose of this article is to describe the radiology value stream and describe how radiology informaticists in one department have worked to improve the efficiency of the value stream at each step. Through these examples, we identify and describe several themes that we believe have been crucial to our success.

  View details for DOI 10.1007/s00247-017-3828-7

  View details for PubMedID 28536767

• Understanding and Applying the Concept of Value Creation in Radiology. Journal of the American College of Radiology Larson, D. B., Durand, D. J., Siegal, D. S. 2017

  Abstract

  The concept of value in radiology has been strongly advocated in recent years as a means of advancing patient care and decreasing waste. This article explores the concept of value creation in radiology and offers a framework for how radiology practices can create value according to the needs of their referring clinicians. Value only exists in the eyes of a customer. We propose that the primary purpose of diagnostic radiology is to answer clinical questions using medical imaging to help guide management of patient care. Because they are the direct recipient of this service, we propose that referring clinicians are the direct customers of a radiology practice and patients are indirect customers. Radiology practices create value as they understand and fulfill their referring clinicians' needs. To narrow those needs to actionable categories, we propose a framework consisting of four major dimensions: (1) how quickly the clinical question needs to be answered, (2) the degree of specialization required to answer the question, (3) how often the referring clinician uses imaging, and (4) the breadth of imaging that the referring clinician uses. We further identify three major settings in which referring clinicians utilize radiological services: (1) emergent or urgent care, (2) primary care, and (3) specialty care. Practices best meet these needs as they engage with their referring clinicians, create a shared vision, work together as a cohesive team, structure the organization to meet referring clinicians' needs, build the tools, and continually improve in ways that help referring clinicians care for patients.

  View details for DOI 10.1016/j.jacr.2016.12.023

  View details for PubMedID 28223112

• Reducing Functional MR Imaging Acquisition Times by Optimizing Workflow. Radiographics Chwang, W. B., Iv, M., Smith, J., Kalnins, A., Mickelsen, J., Bammer, R., Fleischmann, D., Larson, D. B., Wintermark, M., Zeineh, M. 2017; 37 (1): 316-322

  Abstract

  Functional magnetic resonance (MR) imaging is a complex, specialized examination that is able to noninvasively measure information critical to patient care such as hemispheric language lateralization ( 1 ). Diagnostic functional MR imaging requires extensive patient interaction as well as the coordinated efforts of the entire health care team. We observed in our practice at an academic center that the times to perform functional MR imaging examinations were excessively lengthy, making scheduling of the examination difficult. The purpose of our project was to reduce functional MR imaging acquisition times by increasing the efficiency of our workflow, using specific quality tools to drive improvement of functional MR imaging. We assembled a multidisciplinary team and retrospectively reviewed all functional MR imaging examinations performed at our institution from January 2013 to August 2015. We identified five key drivers: (a) streamlined protocols, (b) consistent patient monitoring, (c) clear visual slides and audio, (d) improved patient understanding, and (e) minimized patient motion. We then implemented four specific interventions over a period of 10 months: (a) eliminating intravenous contrast medium, (b) reducing repeated language paradigms, (c) updating technologist and physician checklists, and (d) updating visual slides and audio. Our mean functional MR imaging acquisition time was reduced from 76.3 to 53.2 minutes, while our functional MR imaging examinations remained of diagnostic quality. As a result, we reduced our routine scheduling time for functional MR imaging from 2 hours to 1 hour, improving patient comfort and satisfaction as well as saving time for additional potential MR imaging acquisitions. Our efforts to optimize functional MR imaging workflow constitute a practice quality improvement project that is beneficial for patient care and can be applied broadly to other functional MR imaging practices. (©)RSNA, 2017.

  View details for DOI 10.1148/rg.2017160035

  View details for PubMedID 28076003

• Reducing Functional MR Imaging Acquisition Times by Optimizing Workflow RADIOGRAPHICS Chwang, W. B., Iv, M., Smith, J., Kalnins, A., Mickelsen, J., Bammer, R., Fleischmann, D., Larson, D. B., Wintermark, M., Zeineh, M. 2017; 37 (1): 315-321

  Abstract

  Functional magnetic resonance (MR) imaging is a complex, specialized examination that is able to noninvasively measure information critical to patient care such as hemispheric language lateralization ( 1 ). Diagnostic functional MR imaging requires extensive patient interaction as well as the coordinated efforts of the entire health care team. We observed in our practice at an academic center that the times to perform functional MR imaging examinations were excessively lengthy, making scheduling of the examination difficult. The purpose of our project was to reduce functional MR imaging acquisition times by increasing the efficiency of our workflow, using specific quality tools to drive improvement of functional MR imaging. We assembled a multidisciplinary team and retrospectively reviewed all functional MR imaging examinations performed at our institution from January 2013 to August 2015. We identified five key drivers: (a) streamlined protocols, (b) consistent patient monitoring, (c) clear visual slides and audio, (d) improved patient understanding, and (e) minimized patient motion. We then implemented four specific interventions over a period of 10 months: (a) eliminating intravenous contrast medium, (b) reducing repeated language paradigms, (c) updating technologist and physician checklists, and (d) updating visual slides and audio. Our mean functional MR imaging acquisition time was reduced from 76.3 to 53.2 minutes, while our functional MR imaging examinations remained of diagnostic quality. As a result, we reduced our routine scheduling time for functional MR imaging from 2 hours to 1 hour, improving patient comfort and satisfaction as well as saving time for additional potential MR imaging acquisitions. Our efforts to optimize functional MR imaging workflow constitute a practice quality improvement project that is beneficial for patient care and can be applied broadly to other functional MR imaging practices. (©)RSNA, 2017.

  View details for DOI 10.1148/rg.2017160035

  View details for Web of Science ID 000397205200021

• The Use of Patient and Family Advisory Councils to Improve Patient Experience in Radiology. AJR. American journal of roentgenology Kuhn, K. J., Mickelsen, L. J., Morimoto, L. N., Larson, D. B. 2016; 207 (5): 965-970

  Abstract

  Rising costs and widespread inefficiencies in current practices have prompted a paradigm shift in American health care from volume- to value-based care with patients and families assuming a central role. Patient and family advisory councils (PFACs) are particularly compelling as a strategy for using patient and family engagement for process improvement. Although relatively new in the radiologic community, PFACs can be a powerful tool in improving patient experience.PFACs are a particularly powerful method of patient and family engagement that can be used in effecting meaningful change in practice. This valuable resource resides within most hospitals and is generally readily accessible. In the era of value-based care, it is essential that radiologists actively engage with patients to improve efficiency, reduce expenditures, and maximize patient satisfaction.

  View details for PubMedID 27440525

• Realizing Improvement through Team Empowerment (RITE): A Team-based, Project-based Multidisciplinary Improvement Program RADIOGRAPHICS Larson, D. B., Mickelsen, L. J., Garcia, K. 2016; 36 (7): 2169-2182

  Abstract

  Performance improvement in a complex health care environment depends on the cooperation of diverse individuals and groups, allocation of time and resources, and use of effective improvement methods. To address this challenge, we developed an 18-week multidisciplinary training program that would also provide a vehicle for effecting needed improvements, by using a team- and project-based model. The program began in the radiology department and subsequently expanded to include projects from throughout the medical center. Participants were taught a specific method for team-based problem solving, which included (a) articulating the problem, (b) observing the process, (c) analyzing possible causes of problems, (d) identifying key drivers, (e) testing and refining interventions, and (f) providing for sustainment of results. Progress was formally reviewed on a weekly basis. A total of 14 teams consisting of 78 participants completed the course in two cohorts; one project was discontinued. All completed projects resulted in at least modest improvement. Mean skill scores increased from 2.5/6 to 4.5/6 (P < .01), and the mean satisfaction score was 4.7/5. Identified keys to success include (a) engagement of frontline staff, (b) teams given authority to make process changes, (c) capable improvement coaches, (d) a physician-director with improvement expertise and organizational authority, (e) capable administrative direction, (f) supportive organizational leaders, (g) weekly progress reviews, (h) timely educational material, (i) structured problem-solving methods, and ( j ) multiple projects working simultaneously. The purpose of this article is to review the program, including the methods and results, and discuss perceived keys to program success. (©) RSNA, 2016.

  View details for DOI 10.1148/rg.2016160136

  View details for Web of Science ID 000391424800015

• Realizing Improvement through Team Empowerment (RITE): A Team-based, Project-based Multidisciplinary Improvement Program. Radiographics Larson, D. B., Mickelsen, L. J., Garcia, K. 2016; 36 (7): 2170-2183

  Abstract

  Performance improvement in a complex health care environment depends on the cooperation of diverse individuals and groups, allocation of time and resources, and use of effective improvement methods. To address this challenge, we developed an 18-week multidisciplinary training program that would also provide a vehicle for effecting needed improvements, by using a team- and project-based model. The program began in the radiology department and subsequently expanded to include projects from throughout the medical center. Participants were taught a specific method for team-based problem solving, which included (a) articulating the problem, (b) observing the process, (c) analyzing possible causes of problems, (d) identifying key drivers, (e) testing and refining interventions, and (f) providing for sustainment of results. Progress was formally reviewed on a weekly basis. A total of 14 teams consisting of 78 participants completed the course in two cohorts; one project was discontinued. All completed projects resulted in at least modest improvement. Mean skill scores increased from 2.5/6 to 4.5/6 (P < .01), and the mean satisfaction score was 4.7/5. Identified keys to success include (a) engagement of frontline staff, (b) teams given authority to make process changes, (c) capable improvement coaches, (d) a physician-director with improvement expertise and organizational authority, (e) capable administrative direction, (f) supportive organizational leaders, (g) weekly progress reviews, (h) timely educational material, (i) structured problem-solving methods, and ( j ) multiple projects working simultaneously. The purpose of this article is to review the program, including the methods and results, and discuss perceived keys to program success. (©) RSNA, 2016.

  View details for PubMedID 27831843

• Peer Feedback, Learning, and Improvement: Answering the Call of the Institute of Medicine Report on Diagnostic Error. Radiology Larson, D. B., Donnelly, L. F., Podberesky, D. J., Merrow, A. C., Sharpe, R. E., Kruskal, J. B. 2016: 161254-?

  Abstract

  In September 2015, the Institute of Medicine (IOM) published a report titled "Improving Diagnosis in Health Care," in which it was recommended that "health care organizations should adopt policies and practices that promote a nonpunitive culture that values open discussion and feedback on diagnostic performance." It may seem counterintuitive that a report addressing a highly technical skill such as medical diagnosis would be focused on organizational culture. The wisdom becomes clearer, however, when examined in the light of recent advances in the understanding of human error and individual and organizational performance. The current dominant model for radiologist performance improvement is scoring-based peer review, which reflects a traditional quality assurance approach, derived from manufacturing in the mid-1900s. Far from achieving the goals of the IOM, which are celebrating success, recognizing mistakes as an opportunity to learn, and fostering openness and trust, we have found that scoring-based peer review tends to drive radiologists inward, against each other, and against practice leaders. Modern approaches to quality improvement focus on using and enhancing interpersonal professional relationships to achieve and maintain high levels of individual and organizational performance. In this article, the authors review the recommendations set forth by the recent IOM report, discuss the science and theory that underlie several of those recommendations, and assess how well they fit with the current dominant approach to radiology peer review. The authors also offer an alternative approach to peer review: peer feedback, learning, and improvement (or more succinctly, "peer learning"), which they believe is better aligned with the principles promoted by the IOM. (©) RSNA, 2016.

  View details for DOI 10.1148/radiol.2016161254

  View details for PubMedID 27673509

• Appendiceal ultrasound: the importance of conveying probability of disease PEDIATRIC RADIOLOGY Trout, A. T., Larson, D. B. 2015; 45 (13): 1930–31

  View details for PubMedID 26280635

• Communicating Potential Radiation-Induced Cancer Risks From Medical Imaging Directly to Patients AMERICAN JOURNAL OF ROENTGENOLOGY Lam, D. L., Larson, D. B., Eisenberg, J. D., Forman, H. P., Lee, C. I. 2015; 205 (5): 962-970

  Abstract

  Over the past decade, efforts have increasingly been made to decrease radiation dose from medical imaging. However, there remain varied opinions about whether, for whom, by whom, and how these potential risks should be discussed with patients. We aimed to provide a review of the literature regarding awareness and communication of potential radiation-induced cancer risks from medical imaging procedures in hopes of providing guidance for communicating these potential risks with patients.We performed a systematic literature review on the topics of radiation dose and radiation-induced cancer risk awareness, informed consent regarding radiation dose, and communication of radiation-induced cancer risks with patients undergoing medical imaging. We included original research articles from North America and Europe published between 1995 and 2014.From more than 1200 identified references, a total of 22 original research articles met our inclusion criteria. Overall, we found that there is insufficient knowledge regarding radiation-induced cancer risks and the magnitude of radiation dose associated with CT examinations among patients and physicians. Moreover, there is minimal sharing of information before nonacute imaging studies between patients and physicians about potential long-term radiation risks.Despite growing concerns regarding medical radiation exposure, there is still limited awareness of radiation-induced cancer risks among patients and physicians. There is also no consensus regarding who should provide patients with relevant information, as well as in what specific situations and exactly what information should be communicated. Radiologists should prioritize development of consensus statements and novel educational initiatives with regard to radiation-induced cancer risk awareness and communication.

  View details for DOI 10.2214/AJR.15.15057

  View details for Web of Science ID 000363814900023

  View details for PubMedID 26295534

• Project Management for Quality Improvement in Radiology. AJR. American journal of roentgenology Larson, D. B., Mickelsen, L. J. 2015; 205 (5): W470-7

  Abstract

  This article outlines a structured approach for applying project management principles to quality improvement in radiology. We highlight the framework we use for managing improvement projects in our department and review basic project management principles.Project management involves techniques for executing projects effectively and efficiently. We recognize the following phases for managing improvement projects: idea, project evaluation and selection, role assignment, planning, improvement, and sustaining improvement.

  View details for DOI 10.2214/AJR.15.14807

  View details for PubMedID 26496568

• Added Value of Radiologist Consultation for Pediatric Ultrasound: Implementation and Survey Assessment. AJR. American journal of roentgenology Zucker, E. J., Newman, B., Larson, D. B., Rubesova, E., Barth, R. A. 2015; 205 (4): 822-826

  Abstract

  The purpose of this study was to determine whether radiologist-parent (guardian) consultation sessions for pediatric ultrasound with immediate disclosure of examination results if desired increases visit satisfaction, decreases anxiety, and increases understanding of the radiologist's role.Parents chaperoning any outpatient pediatric ultrasound were eligible and completed surveys before and after ultrasound examinations. Before the second survey, parents met with a pediatric radiologist on a randomized basis but could opt out and request or decline the consultation. Differences in anxiety and understanding of the radiologist's role before and after the examination were compared, and overall visit satisfaction measures were tabulated.Seventy-seven subjects participated, 71 (92%) of whom spoke to a radiologist, mostly on request. In the consultation group, the mean score (1, lowest; 4, highest) for overall experience was 3.8 ± 0.4 (SD), consultation benefit was 3.7 ± 0.6, and radiologist interaction was 3.7 ± 0.6. Demographics were not predictive of satisfaction with statistical significance in a multivariate model. Forty-six of 68 (68%) respondents correctly described the radiologist's role before consultation. The number increased to 60 (88%) after consultation, and the difference was statistically significant (p < 0.001). There was also a statistically significant decrease in mean anxiety score from 2.0 ± 1.0 to 1.5 ± 0.8 after consultation (p < 0.001). Sixty-four of 70 (91%) respondents indicated that they would prefer to speak with a radiologist during every visit.Radiologist consultation is well received among parents and associated with decreased anxiety and increased understanding of the radiologist's role. The results of this study support the value of routine radiologist-parent interaction for pediatric ultrasound.

  View details for DOI 10.2214/AJR.15.14542

  View details for PubMedID 26397331

• Key Concepts of Patient Safety in Radiology RADIOGRAPHICS Larson, D. B., Kruskal, J. B., Krecke, K. N., Donnelly, L. F. 2015; 35 (6): 1677-1693

  Abstract

  Harm from medical error is a difficult challenge in health care, including radiology. Modern approaches to patient safety have shifted from a focus on individual performance and reaction to errors to development of robust systems and processes that create safety in organizations. Organizations that operate safely in high-risk environments have been termed high-reliability organizations. Such organizations tend to see themselves as being constantly bombarded by errors. Thus, the goal is not to eliminate human error but to develop strategies to prevent, identify, and mitigate errors and their effects before they result in harm. High-level reliability strategies focus on systems and organizational culture; intermediate-level reliability strategies focus on establishment of effective processes; low-level reliability strategies focus on individual performance. Although several classification schemes for human error exist, modern safety researchers caution against overreliance on error investigations to improve safety. Blaming individuals involved in adverse events when they had no intent to cause harm has been shown to undermine organizational safety. Safety researchers have coined the term just culture for the successful balance of individual accountability with accommodation for human fallibility and system deficiencies. Safety is inextricably intertwined with an organization's quality efforts. A quality management system that focuses on standardization, making errors visible, building in quality, and constantly stopping to fix problems results in a safer environment and engages personnel in a way that contributes to a culture of safety. (©)RSNA, 2015.

  View details for DOI 10.1148/rg.2015140277

  View details for Web of Science ID 000364361800007

  View details for PubMedID 26334571

• Root Cause Analysis: Learning from Adverse Safety Events RADIOGRAPHICS Brook, O. R., Kruskal, J. B., Eisenberg, R. L., Larson, D. B. 2015; 35 (6): 1655-1667

  Abstract

  Serious adverse events continue to occur in clinical practice, despite our best preventive efforts. It is essential that radiologists, both as individuals and as a part of organizations, learn from such events and make appropriate changes to decrease the likelihood that such events will recur. Root cause analysis (RCA) is a process to (a) identify factors that underlie variation in performance or that predispose an event toward undesired outcomes and (b) allow for development of effective strategies to decrease the likelihood of similar adverse events occurring in the future. An RCA process should be performed within the environment of a culture of safety, focusing on underlying system contributors and, in a confidential manner, taking into account the emotional effects on the staff involved. The Joint Commission now requires that a credible RCA be performed within 45 days for all sentinel or major adverse events, emphasizing the need for all radiologists to understand the processes with which an effective RCA can be performed. Several RCA-related tools that have been found to be useful in the radiology setting include the "five whys" approach to determine causation; cause-and-effect, or Ishikawa, diagrams; causal tree mapping; affinity diagrams; and Pareto charts. (©)RSNA, 2015.

  View details for DOI 10.1148/rg.2015150067

  View details for Web of Science ID 000364361800005

  View details for PubMedID 26466177

• Conducting a Successful Practice Quality Improvement Project for American Board of Radiology Certification RADIOGRAPHICS Lee, C. S., Wadhwa, V., Kruskal, J. B., Larson, D. B. 2015; 35 (6): 1643-1651

  Abstract

  Practice quality improvement (PQI) is a required component of the American Board of Radiology (ABR) Maintenance of Certification (MOC) cycle, with the goal to "improve the quality of health care through diplomate-initiated learning and quality improvement." The essential requirements of PQI projects include relevance to one's practice, achievability in one's clinical setting, results suited for repeat measurements during an ABR MOC cycle, and reasonable expectation to result in quality improvement (QI). PQI projects can be performed by a group or an individual or as part of a participating institution. Given the interdisciplinary nature of radiology, teamwork is critical to ensure patient safety and the success of PQI projects. Additionally, successful QI requires considerable investment of time and resources, coordination, organizational support, and individual engagement. Group PQI projects offer many advantages, especially in larger practices and for processes that cross organizational boundaries, whereas individual projects may be preferred in small practices or for focused projects. In addition to the three-phase "plan, do, study, act" model advocated by the ABR, there are several other improvement models, which are based on continuous data collection and rapid simultaneous testing of multiple interventions. When properly planned, supported, and executed, group PQI projects can improve the value and viability of a radiology practice. (©)RSNA, 2015.

  View details for DOI 10.1148/rg.2015150024

  View details for Web of Science ID 000364361800003

  View details for PubMedID 26334572

• Appendiceal diameter as a predictor of appendicitis in children: improved diagnosis with three diagnostic categories derived from a logistic predictive model EUROPEAN RADIOLOGY Trout, A. T., Towbin, A. J., Fierke, S. R., Zhang, B., Larson, D. B. 2015; 25 (8): 2231-2238

  Abstract

  To develop and assess the performance of a diameter-based logistic predictive model and a derived 3-category interpretive scheme for the sonographic diagnosis of paediatric appendicitis.Appendiceal diameters were extracted from reports of ultrasound examinations in children and young adults. Data were used to generate a logistic predictive model which was used to define negative, equivocal and positive interpretive categories. Diagnostic performance of the derived 3-category interpretive scheme was compared with simulated binary interpretive schemes.Six hundred forty-one appendix ultrasound reports were reviewed with appendicitis present in 181 (28.2 %). Cut-off diameters based on the logistic predictive model were ≤6 mm = normal, >6 mm-8 mm = equivocal and >8 mm = positive with appendicitis present in 2.6 % (11/428), 64.9 % (72/111) and 96.1 % (98/102) of cases in each group. These cut-offs conferred 97.2 % accuracy with 17.3 % (111/641) of cases considered equivocal. Of the binary cut-offs, a 6 mm cut-off performed best with 91.6 % accuracy. AIC analysis favoured the logistic model over the binary model for prediction of appendicitis.A 3-category interpretive scheme based on a logistic predictive model provides higher accuracy in the diagnosis of appendicitis than traditional binary diameter cut-offs. Inclusion of an equivocal interpretive category more accurately reflects the probability distribution of prediction of appendicitis by ultrasound.• Three diameter categories outperform a 6-mm cut-off to diagnose appendicitis • Three categories allow more confident exclusion of appendicitis • Three categories allow more confident diagnosis of appendicitis • Three categories more accurately reflect the probability of appendicitis by ultrasound.

  View details for DOI 10.1007/s00330-015-3639-x

  View details for Web of Science ID 000357660100005

  View details for PubMedID 25916384

• Tackling the problem of error in diagnostic radiology PEDIATRIC RADIOLOGY Larson, D. B. 2015; 45 (6): 790-792

  View details for DOI 10.1007/s00247-014-3199-2

  View details for Web of Science ID 000355345800002

  View details for PubMedID 25520015

• Toward Large-Scale Process Control to Enable Consistent CT Radiation Dose Optimization AMERICAN JOURNAL OF ROENTGENOLOGY Larson, D. B., Strauss, K. J., Podberesky, D. J. 2015; 204 (5): 959-966

  Abstract

  This article reviews the concepts of CT radiation dose optimization and process control, discusses how to achieve optimization and how to verify that it is consistently accomplished, and proposes strategies to move toward large-scale application.CT dose optimization is achieved when the least amount of radiation necessary is used to achieve adequate image quality. The key to consistent optimization is minimization of unnecessary variation. This minimization is accomplished through local process control mechanisms.

  View details for DOI 10.2214/AJR.14.13918

  View details for Web of Science ID 000356776900028

  View details for PubMedID 25730157

• Radiologist Compliance With California CT Dose Reporting Requirements: A Single-Center Review of Pediatric Chest CT AMERICAN JOURNAL OF ROENTGENOLOGY Zucker, E. J., Larson, D. B., Newman, B., Barth, R. A. 2015; 204 (4): 810-816

  Abstract

  Effective July 1, 2012, CT dose reporting became mandatory in California. We sought to assess radiologist compliance with this legislation and to determine areas for improvement.We retrospectively reviewed reports from all chest CT examinations performed at our institution from July 1, 2012, through June 30, 2013, for errors in documentation of volume CT dose index (CTDIvol), dose-length product (DLP), and phantom size. Reports were considered as legally compliant if both CTDIvol and DLP were documented accurately and as institutionally compliant if phantom size was also documented accurately. Additionally, we tracked reports that did not document dose in our standard format (phantom size, CTDIvol for each series, and total DLP).Radiologists omitted CTDIvol, DLP, or both in nine of 664 examinations (1.4%) and inaccurately reported one or both of them in 56 of the remaining 655 examinations (8.5%). Radiologists omitted phantom size in 11 of 664 examinations (1.7%) and inaccurately documented it in 20 of the remaining 653 examinations (3.1%). Of 664 examinations, 599 (90.2%) met legal reporting requirements, and 583 (87.8%) met institutional requirements. In reporting dose, radiologists variably used less decimal precision than available, summed CTDIvol, included only series-level DLP, and specified dose information from the scout topogram or a nonchest series for combination examinations.Our institutional processes, which primarily rely on correct human performance, do not ensure accurate dose reporting and are prone to variation in dose reporting format. In view of this finding, we are exploring higher-reliability processes, including better-defined standards and automated dose reporting systems, to improve compliance.

  View details for DOI 10.2214/AJR.14.13693

  View details for Web of Science ID 000351614700037

  View details for PubMedID 25794071

• Improvement in Diagnostic Accuracy of Ultrasound of the Pediatric Appendix Through the Use of Equivocal Interpretive Categories AMERICAN JOURNAL OF ROENTGENOLOGY Larson, D. B., Trout, A. T., Fierke, S. R., Towbin, A. J. 2015; 204 (4): 849-855

  Abstract

  The purpose of this article is to evaluate the diagnostic performance of ultrasound of the pediatric appendix using standardized structured reports that incorporate equivocal interpretive categories.Standardized structured appendix ultrasound reports using a five-category interpretive scheme were reviewed. Interpretive categories were positive, intermediate likelihood, or negative when the appendix was visualized, and secondary signs or no secondary signs when the appendix was not visualized. Interpretations were compared with clinical and pathologic follow-up. Diagnostic accuracy was compared with the accuracy of a simulated binary interpretive scheme based on the same data.One thousand three hundred fifty-seven examinations were included, with appendicitis present in 16.9% (230/1357) of cases. The appendix was visualized in 47.2% (641/1357) of cases, with interpretations as follows: positive, 27.5% (176/641); intermediate likelihood, 9.7% (62/641); and normal, 62.9% (403/641). The appendicitis rate in each group was 92.6% (163/176), 25.8% (16/62), and 0.5% (2/403), respectively. The appendix was not visualized in 52.8% (716/1357) of cases, with secondary findings identified in 8.5% (61/716) and no secondary findings in 91.5% (655/716) of cases. The appendicitis rate was 39.3% (24/61) and 3.8% (25/655) in these groups, respectively. Appendicitis was present in 32.5% of equivocal (intermediate likelihood and not visualized, secondary findings) cases and 2.6% of negative (normal and not visualized, no secondary findings) cases. Diagnostic accuracy of a five-category scheme was 96.8% versus 94.1% for a binary scheme.Appendix ultrasound examinations interpreted according to a scheme that incorporates equivocal categories better convey diagnostic certainty and increase diagnostic accuracy compared with a binary interpretive scheme.

  View details for DOI 10.2214/AJR.14.13026

  View details for Web of Science ID 000351614700042

  View details for PubMedID 25794076

• A Framework for Describing Health Care Delivery Organizations and Systems AMERICAN JOURNAL OF PUBLIC HEALTH Pina, I. L., Cohen, P. D., Larson, D. B., Marion, L. N., Sills, M. R., Solberg, L. I., Zerzan, J. 2015; 105 (4): 670-679

  Abstract

  Describing, evaluating, and conducting research on the questions raised by comparative effectiveness research and characterizing care delivery organizations of all kinds, from independent individual provider units to large integrated health systems, has become imperative. Recognizing this challenge, the Delivery Systems Committee, a subgroup of the Agency for Healthcare Research and Quality's Effective Health Care Stakeholders Group, which represents a wide diversity of perspectives on health care, created a draft framework with domains and elements that may be useful in characterizing various sizes and types of care delivery organizations and may contribute to key outcomes of interest. The framework may serve as the door to further studies in areas in which clear definitions and descriptions are lacking.

  View details for DOI 10.2105/AJPH.2014.301926

  View details for Web of Science ID 000357387800034

  View details for PubMedID 24922130

• Beginner's Guide to Practice Quality Improvement Using the Model for improvement JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY Lee, C. S., Larson, D. B. 2014; 11 (12): 1131-1136

  Abstract

  Radiologists in the United States are required to complete the Practice Quality Improvement (PQI) program as part of their Maintenance of Certification by the ABR. The Institute for Healthcare Improvement's (IHI) Model for Improvement (MFI) offers an alternative to the 3-phase approach currently advocated by the ABR. The MFI implicitly assumes that many interventions will need to be tested and refined for any meaningful project, and provides a project management approach that enables rapid assessment and improvement of performance. By collecting data continuously, rather than simply before and after interventions, more interventions can be tested simultaneously and projects can progress more rapidly. In this article, we describe the ABR's 3-phase approach, and introduce the MFI and how it can be employed to affect positive changes. Using a radiology case study, we demonstrate how one can utilize the MFI to enable rapid quality improvement.

  View details for DOI 10.1016/j.jacr.2014.08.033

  View details for Web of Science ID 000345953400012

  View details for PubMedID 25467725

• Communication in diagnostic radiology: meeting the challenges of complexity. AJR. American journal of roentgenology Larson, D. B., Froehle, C. M., Johnson, N. D., Towbin, A. J. 2014; 203 (5): 957-964

  Abstract

  As patients and information flow through the imaging process, value is added step-by-step when information is acquired, interpreted, and communicated back to the referring clinician. However, radiology information systems are often plagued with communication errors and delays. This article presents theories and recommends strategies to continuously improve communication in the complex environment of modern radiology.Communication theories, methods, and systems that have proven their effectiveness in other environments can serve as models for radiology.

  View details for DOI 10.2214/AJR.14.12949

  View details for PubMedID 25341133

• Pediatric CT quality management and improvement program PEDIATRIC RADIOLOGY Larson, D. B., Molvin, L. Z., Wang, J., Chan, F. P., Newman, B., Fleischmann, D. 2014; 44: 519-524

  View details for DOI 10.1007/s00247-014-3039-4

  View details for Web of Science ID 000343721300023

• Optimizing CT radiation dose based on patient size and image quality: the size-specific dose estimate method. Pediatric radiology Larson, D. B. 2014; 44 Suppl 3: 501-5

  Abstract

  The principle of ALARA (dose as low as reasonably achievable) calls for dose optimization rather than dose reduction, per se. Optimization of CT radiation dose is accomplished by producing images of acceptable diagnostic image quality using the lowest dose method available. Because it is image quality that constrains the dose, CT dose optimization is primarily a problem of image quality rather than radiation dose. Therefore, the primary focus in CT radiation dose optimization should be on image quality. However, no reliable direct measure of image quality has been developed for routine clinical practice. Until such measures become available, size-specific dose estimates (SSDE) can be used as a reasonable image-quality estimate. The SSDE method of radiation dose optimization for CT abdomen and pelvis consists of plotting SSDE for a sample of examinations as a function of patient size, establishing an SSDE threshold curve based on radiologists' assessment of image quality, and modifying protocols to consistently produce doses that are slightly above the threshold SSDE curve. Challenges in operationalizing CT radiation dose optimization include data gathering and monitoring, managing the complexities of the numerous protocols, scanners and operators, and understanding the relationship of the automated tube current modulation (ATCM) parameters to image quality. Because CT manufacturers currently maintain their ATCM algorithms as secret for proprietary reasons, prospective modeling of SSDE for patient populations is not possible without reverse engineering the ATCM algorithm and, hence, optimization by this method requires a trial-and-error approach.

  View details for DOI 10.1007/s00247-014-3077-y

  View details for PubMedID 25304711

• Pediatric CT quality management and improvement program. Pediatric radiology Larson, D. B., Molvin, L. Z., Wang, J., Chan, F. P., Newman, B., Fleischmann, D. 2014; 44: 519-524

  Abstract

  Modern CT is a powerful yet increasingly complex technology that continues to rapidly evolve; optimal clinical implementation as well as appropriate quality management and improvement in CT are challenging but attainable. This article outlines the organizational structure on which a CT quality management and improvement program can be built, followed by a discussion of common as well as pediatric-specific challenges. Organizational elements of a CT quality management and improvement program include the formulation of clear objectives; definition of the roles and responsibilities of key personnel; implementation of a technologist training, coaching and feedback program; and use of an efficient and accurate monitoring system. Key personnel and roles include a radiologist as the CT director, a qualified CT medical physicist, as well as technologists with specific responsibilities and adequate time dedicated to operation management, CT protocol management and CT technologist education. Common challenges in managing a clinical CT operation are related to the complexity of newly introduced technology, of training and communication and of performance monitoring. Challenges specific to pediatric patients include the importance of including patient size in protocol and dose considerations, a lower tolerance for error in these patients, and a smaller sample size from which to learn and improve.

  View details for DOI 10.1007/s00247-014-3039-4

  View details for PubMedID 25304715

• Optimizing CT radiation dose based on patient size and image quality: the size-specific dose estimate method PEDIATRIC RADIOLOGY Larson, D. B. 2014; 44: 501-505

  Abstract

  The principle of ALARA (dose as low as reasonably achievable) calls for dose optimization rather than dose reduction, per se. Optimization of CT radiation dose is accomplished by producing images of acceptable diagnostic image quality using the lowest dose method available. Because it is image quality that constrains the dose, CT dose optimization is primarily a problem of image quality rather than radiation dose. Therefore, the primary focus in CT radiation dose optimization should be on image quality. However, no reliable direct measure of image quality has been developed for routine clinical practice. Until such measures become available, size-specific dose estimates (SSDE) can be used as a reasonable image-quality estimate. The SSDE method of radiation dose optimization for CT abdomen and pelvis consists of plotting SSDE for a sample of examinations as a function of patient size, establishing an SSDE threshold curve based on radiologists' assessment of image quality, and modifying protocols to consistently produce doses that are slightly above the threshold SSDE curve. Challenges in operationalizing CT radiation dose optimization include data gathering and monitoring, managing the complexities of the numerous protocols, scanners and operators, and understanding the relationship of the automated tube current modulation (ATCM) parameters to image quality. Because CT manufacturers currently maintain their ATCM algorithms as secret for proprietary reasons, prospective modeling of SSDE for patient populations is not possible without reverse engineering the ATCM algorithm and, hence, optimization by this method requires a trial-and-error approach.

  View details for DOI 10.1007/s00247-014-3077-y

  View details for Web of Science ID 000343721300019

• Guide to Effective Quality Improvement Reporting in Radiology RADIOLOGY Larson, D. B., Duncan, J. R., Nagy, P. G., Kruskal, J. B. 2014; 271 (2): 561-573

  Abstract

  Substantial societal investments in biomedical research are contributing to an explosion in knowledge that the health delivery system is struggling to effectively implement. Managing this complexity requires ingenuity, research and development, and dedicated resources. Many innovative solutions can be found in quality improvement (QI) activities, defined as the "systematic, data-guided activities designed to bring about immediate, positive changes in the delivery of healthcare in particular settings." QI shares many similarities with biomedical research, but also differs in several important ways. Inclusion of QI in the peer-reviewed literature is needed to foster its advancement through the dissemination, testing, and refinement of theories, methods, and applications. QI methods and reporting standards are less mature in health care than those of biomedical research. A lack of widespread understanding and consensus regarding the purpose of publishing QI-related material also exists. In this document, guidance is provided in evaluating quality of QI-related material and in determining priority of submitted material for publication. © RSNA, 2014.

  View details for DOI 10.1148/radiol.14131930

  View details for Web of Science ID 000335153000028

  View details for PubMedID 24555635

• Improving the Availability of Clinical History Accompanying Radiographic Examinations in a Large Pediatric Radiology Department AMERICAN JOURNAL OF ROENTGENOLOGY Hawkins, C. M., Anton, C. G., Bankes, W. M., Leach, A. D., Zeno, M. J., Pryor, R. M., Larson, D. B. 2014; 202 (4): 790-796

  Abstract

  The purpose of this quality improvement initiative was to improve the consistency with which radiologists are provided a complete clinical history when interpreting radiography examinations performed in the outpatient and emergency department settings.The clinical history was considered complete if it contained three elements: nature of the symptoms, description of injury, or cause for clinical concern; duration of symptoms or time of injury; and focal site of pain or abnormality, if applicable. This was reduced to three elements: "what-when-where." A goal was established that 95% of the clinical histories should contain all three elements. To achieve this goal, technologists supplemented referring clinicians' history. The project was divided into four phases: launch, support, transition to sustainability, and maintenance. During the support phase, results of automated weekly audits automatically populated group-level performance reports. During the transition to the sustainability phase, audit results populated individual-level performance reports. During the maintenance phase, quarterly audit results were incorporated into technologists' employee performance goals.Before initiation of the project, 38% (76/200) of radiography examinations were accompanied by a complete clinical history. This increased to 92% (928/1006) by the end of the 15-week improvement phase. Performance was sustained at 96% (1168/1213) 7 months later [corrected].By clearly defining expectations for an appropriate clinical history and establishing system and organizational mechanisms to facilitate verifiable compliance, we were able to successfully and sustainably improve the consistency with which radiography examinations were accompanied by a complete clinical history.

  View details for DOI 10.2214/AJR.13.11273

  View details for Web of Science ID 000333454300033

  View details for PubMedID 24660708

• Twiddler syndrome with a twist: a cause of vagal nerve stimulator lead fracture PEDIATRIC RADIOLOGY Trout, A. T., Larson, D. B., Mangano, F. T., Gonsalves, C. H. 2013; 43 (12): 1647-1651

  Abstract

  Twiddler syndrome is uncommon in children and most commonly described as causing lead retraction with implanted cardiac pacemakers and defibrillators. We report an uncommon case of a child repeatedly "twiddling" a vagal nerve stimulator to the point of lead fracture. The findings of Twiddler syndrome illustrated here apply to all implanted devices and show the complication of lead fracture in addition to the more commonly reported complication of lead retraction. This case highlights the need to be aware of the radiographic findings of this phenomenon in children with implanted vagal nerve stimulators due to the perceived increased risk of "twiddling" in pediatric and developmentally delayed patients.

  View details for DOI 10.1007/s00247-013-2736-8

  View details for Web of Science ID 000327425400014

  View details for PubMedID 23832019

• Practice Policy and Quality Initiatives Quality Improvement and Confirmation Projects: Facilitating Rapid, Measurable Performance Improvement RADIOGRAPHICS Hawkins, C. M., Alsip, C. N., Pryor, R. M., Leach, A. D., Larson, D. B. 2013; 33 (7): E225-E235

  Abstract

  As radiology departments continue to increase in size and complexity, the process of improving and maintaining excellent performance is becoming increasingly challenging. In response, a systematic process for efficiently implementing and sustaining measurable improvement in our radiology department has been developed, which targets focused aspects of individual performance that contribute to overall departmental quality. Projects designed to achieve such improvements have been called quality improvement and confirmation (QuIC) projects. The QuIC project process involves a project champion, medical expert, technical expert, quality improvement technologist specialist, and appropriate leaders, managers, and support personnel. The project champion conducts a preliminary investigation and organizes team members, who define the desired performance through consensus, establish data collection and analysis procedures, and prepare to launch the project. Once launched, the QuIC project process follows an execution period that is divided into four phases: (a) project launch phase, (b) support phase, (c) transition phase, and (d) maintenance phase. The first three phases focus on education, group-level feedback, and individual feedback, respectively. Weekly audits are performed to track performance improvement. Data collection, analysis, and dissemination processes are automated to the extent possible. To date, four such projects have been successfully conducted. The QuIC project concept is an attempt to apply the principles of process improvement to the process of process improvement by enabling any member of a radiology department to efficiently and reliably spearhead a quality improvement project. We consider this to be a work in progress and continue to refine the process with the goal of eventually being able to conduct many projects simultaneously.

  View details for DOI 10.1148/rg.337135058

  View details for Web of Science ID 000327759900003

  View details for PubMedID 23988633

• Emergency Department Computed Tomography Utilization in the United States and Canada ANNALS OF EMERGENCY MEDICINE Berdahl, C. T., Vermeulen, M. J., Larson, D. B., Schull, M. J. 2013; 62 (5): 486-494

  Abstract

  We compare secular trends in computed tomography (CT) utilization in emergency departments (EDs) in the United States and Ontario, Canada.Using a systematic survey in the US (The National Hospital Ambulatory Medical Care Survey) and administrative databases in Ontario, we performed a retrospective study of ED visits from 2003 to 2008. We calculated utilization overall, by visit characteristics, and for 5 clinical conditions in which CT is commonly indicated: abdominal pain, complex abdominal pain (abdominal pain, age ≥65 years, urgent to most urgent triage), admitted complex abdominal pain (abdominal pain, age ≥65 years, urgent to most urgent triage, and admitted to hospital), headache, and chest pain/shortness of breath. US data were weighted to produce national estimates.On-site CT was available for 97% (95% confidence interval [CI] 95% to 99%) of visits in the United States compared with 80% (95% CI 80% to 80%) in Ontario. Visits were more frequently triaged as higher acuity in the United States than in Ontario, with 15.1% (95% CI 13.9% to 16.4%) of US visits categorized as most urgent versus 11.8% (95% CI 11.8% to 11.8%) in Ontario. The proportion of all ED visits in which CT was performed was 11.4% (95% CI 10.8% to 12.0%) in the United States versus 5.9% (95% CI 5.9% to 5.9%) in Ontario. The proportion for children was 4.7% (95% CI 4.3% to 5.1%) in the United States versus 1.4% (95% CI 1.4% to 1.4%) in Ontario. The rate of visits involving CT per year increased faster from 2003 to 2008 in the United States (odds ratio 2.00; 95% CI 1.81 to 2.21) than Ontario (odds ratio 1.69; 95% CI 1.68 to 1.70). Over time, all subgroups experienced increases in CT rate except Ontario children younger than 10 years, who experienced a significant decrease. United States-Ontario differences in CT proportions were significant among patients presenting with headache, abdominal pain, chest pain/shortness of breath, and complex abdominal pain. Proportions for visits involving admitted complex abdominal pain in the two jurisdictions were indistinguishable: 45.8% in the United States (95% CI 39.9% to 51.7%) versus 44.7% (95% CI 44.4% to 45.0%) in Ontario.CT was more readily available in US EDs, and US clinicians used the technology more frequently than their colleagues in Ontario for nearly every category of patients, including children. CT utilization increased over time in both jurisdictions, but faster in the United States. Different demographic features between the two jurisdictions, including triage severity, frequency of hospitalization, and availability of CT scanners, likely account for at least some of the differences in CT utilization. Investigation of both clinical and nonclinical reasons for the differences in CT utilization between the United States and Canada would be a fruitful area for further research.

  View details for DOI 10.1016/j.annemergmed.2013.02.018

  View details for Web of Science ID 000326906200008

  View details for PubMedID 23683773

• System for Verifiable CT Radiation Dose Optimization Based on Image Quality. Part II. Process Control System RADIOLOGY Larson, D. B., Malarik, R. J., Hall, S. M., Podberesky, D. J. 2013; 269 (1): 177-185

  Abstract

  To evaluate the effect of an automated computed tomography (CT) radiation dose optimization and process control system on the consistency of estimated image noise and size-specific dose estimates (SSDEs) of radiation in CT examinations of the chest, abdomen, and pelvis.This quality improvement project was determined not to constitute human subject research. An automated system was developed to analyze each examination immediately after completion, and to report individual axial-image-level and study-level summary data for patient size, image noise, and SSDE. The system acquired data for 4 months beginning October 1, 2011. Protocol changes were made by using parameters recommended by the prediction application, and 3 months of additional data were acquired. Preimplementation and postimplementation mean image noise and SSDE were compared by using unpaired t tests and F tests. Common-cause variation was differentiated from special-cause variation by using a statistical process control individual chart.A total of 817 CT examinations, 490 acquired before and 327 acquired after the initial protocol changes, were included in the study. Mean patient age and water-equivalent diameter were 12.0 years and 23.0 cm, respectively. The difference between actual and target noise increased from -1.4 to 0.3 HU (P < .01) and the standard deviation decreased from 3.9 to 1.6 HU (P < .01). Mean SSDE decreased from 11.9 to 7.5 mGy, a 37% reduction (P < .01). The process control chart identified several special causes of variation.Implementation of an automated CT radiation dose optimization system led to verifiable simultaneous decrease in image noise variation and SSDE. The automated nature of the system provides the opportunity for consistent CT radiation dose optimization on a broad scale.

  View details for DOI 10.1148/radiol.13122321

  View details for Web of Science ID 000325000700020

  View details for PubMedID 23784877

• System for Verifiable CT Radiation Dose Optimization Based on Image Quality. Part I. Optimization Model RADIOLOGY Larson, D. B., Wang, L. L., Podberesky, D. J., Goske, M. J. 2013; 269 (1): 167-176

  Abstract

  To develop and validate a mathematical radiation dose optimization model for computed tomography (CT) of the chest, abdomen, and pelvis.This quality improvement project was determined not to constitute human subject research. A model for measuring water-equivalent diameter (DW) based on the topogram was developed and validated on each axial section in eight CT examinations of the chest, abdomen, and pelvis (500 images). A model for estimating image noise and size-specific dose estimates (SSDEs) using image and metadata was developed and validated in 16 examinations of anthropomorphic phantoms. A model to quantify radiologist image quality preferences was developed and applied to evaluations of 32 CT examinations of the abdomen and pelvis by 10 radiologists. The scanners' dose modulation algorithms were modeled and incorporated into an application capable of prediction of image noise and SSDE over a range of patient sizes. With use of the application, protocol techniques were recommended to achieve specific image noise targets. Comparisons were evaluated by using two-tailed nonpaired and paired t tests. Results: The mean difference between topogram- and axial-based DW estimates was -3.5% ± 2.2 (standard deviation). The mean difference between estimated and measured image noise and volume CT dose index on the anthropomorphic phantoms was -6.9% ± 5.5 and 0.8% ± 1.8, respectively. A three-dimensional radiologist image quality preference model was developed. For the prediction model validation studies, mean differences between predicted and actual effective tube current-time product, SSDE, and estimated image noise were -0.9% ± 9.3, -1.8% ± 10.6, and -0.5% ± 4.4, respectively.CT image quality and radiation dose can be mathematically predicted and optimized on the basis of patient size and radiologist-specific image noise target curves.

  View details for DOI 10.1148/radiol.13122320

  View details for Web of Science ID 000325000700019

  View details for PubMedID 23784878

• Comparison of radiation dose estimates, image noise, and scan duration in pediatric body imaging for volumetric and helical modes on 320-detector CT and helical mode on 64-detector CT PEDIATRIC RADIOLOGY Johnston, J. H., Podberesky, D. J., Yoshizumi, T. T., Angel, E., Toncheva, G., Larson, D. B., Egelhoff, J. C., Anderson-Evans, C., Nguyen, G. B., Barelli, A., Alsip, C., Salisbury, S. R., Frush, D. P. 2013; 43 (9): 1117-1127

  Abstract

  Advanced multidetector CT systems facilitate volumetric image acquisition, which offers theoretic dose savings over helical acquisition with shorter scan times.Compare effective dose (ED), scan duration and image noise using 320- and 64-detector CT scanners in various acquisition modes for clinical chest, abdomen and pelvis protocols.ED and scan durations were determined for 64-detector helical, 160-detector helical and volume modes under chest, abdomen and pelvis protocols on 320-detector CT with adaptive collimation and 64-detector helical mode on 64-detector CT without adaptive collimation in a phantom representing a 5-year-old child. Noise was measured as standard deviation of Hounsfield units.Compared to 64-detector helical CT, all acquisition modes on 320-detector CT resulted in lower ED and scan durations. Dose savings were greater for chest (27-46%) than abdomen/pelvis (18-28%) and chest/abdomen/pelvis imaging (8-14%). Noise was similar across scanning modes, although some protocols on 320-detector CT produced slightly higher noise.Dose savings can be achieved for chest, abdomen/pelvis and chest/abdomen/pelvis examinations on 320-detector CT compared to helical acquisition on 64-detector CT, with shorter scan durations. Although noise differences between some modes reached statistical significance, this is of doubtful diagnostic significance and will be studied further in a clinical setting.

  View details for DOI 10.1007/s00247-013-2690-5

  View details for Web of Science ID 000323275700007

  View details for PubMedID 23636537

• Quality Improvement and Confirmation Projects: Facilitating Rapid, Measurable Performance Improvement. Radiographics Hawkins, C. M., Alsip, C. N., Pryor, R. M., Leach, A. D., Larson, D. B. 2013: 135058-?

  Abstract

  As radiology departments continue to increase in size and complexity, the process of improving and maintaining excellent performance is becoming increasingly challenging. In response, a systematic process for efficiently implementing and sustaining measurable improvement in our radiology department has been developed, which targets focused aspects of individual performance that contribute to overall departmental quality. Projects designed to achieve such improvements have been called quality improvement and confirmation (QuIC) projects. The QuIC project process involves a project champion, medical expert, technical expert, quality improvement technologist specialist, and appropriate leaders, managers, and support personnel. The project champion conducts a preliminary investigation and organizes team members, who define the desired performance through consensus, establish data collection and analysis procedures, and prepare to launch the project. Once launched, the QuIC project process follows an execution period that is divided into four phases: (a) project launch phase, (b) support phase, (c) transition phase, and (d) maintenance phase. The first three phases focus on education, group-level feedback, and individual feedback, respectively. Weekly audits are performed to track performance improvement. Data collection, analysis, and dissemination processes are automated to the extent possible. To date, four such projects have been successfully conducted. The QuIC project concept is an attempt to apply the principles of process improvement to the process of process improvement by enabling any member of a radiology department to efficiently and reliably spearhead a quality improvement project. We consider this to be a work in progress and continue to refine the process with the goal of eventually being able to conduct many projects simultaneously. © RSNA, 2013.

  View details for PubMedID 24475763

• Commentary: Masters of Radiology Panel Discussion-How Do We Maintain Control Over Imaging? AMERICAN JOURNAL OF ROENTGENOLOGY Forman, H. P., Larson, D. B., Kazerooni, E. A., Norbash, A., Javitt, M. C., Beauchamp, N. J. 2013; 201 (1): 128-132

  View details for DOI 10.2214/AJR.13.10891

  View details for Web of Science ID 000320771900040

  View details for PubMedID 23789666

• Diagnostic Reference Ranges for Pediatric Abdominal CT RADIOLOGY Goske, M. J., Strauss, K. J., Coombs, L. P., Mandel, K. E., Towbin, A. J., Larson, D. B., Callahan, M. J., Darge, K., Podberesky, D. J., Frush, D. P., Westra, S. J., Prince, J. S. 2013; 268 (1): 208-218

  Abstract

  To develop diagnostic reference ranges (DRRs) and a method for an individual practice to calculate site-specific reference doses for computed tomographic (CT) scans of the abdomen or abdomen and pelvis in children on the basis of body width (BW).This HIPAA-compliant multicenter retrospective study was approved by institutional review boards of participating institutions; informed consent was waived. In 939 pediatric patients, CT doses were reviewed in 499 (53%) male and 440 (47%) female patients (mean age, 10 years). Doses were from 954 scans obtained from September 1 to December 1, 2009, through Quality Improvement Registry for CT Scans in Children within the National Radiology Data Registry, American College of Radiology. Size-specific dose estimate (SSDE), a dose estimate based on BW, CT dose index, dose-length product, and effective dose were analyzed. BW measurement was obtained with electronic calipers from the axial image at the splenic vein level after completion of the CT scan. An adult-sized patient was defined as a patient with BW of 34 cm. An appropriate dose range for each DRR was developed by reviewing image quality on a subset of CT scans through comparison with a five-point visual reference scale with increments of added simulated quantum mottle and by determining DRR to establish lower and upper bounds for each range.For 954 scans, DRRs (SSDEs) were 5.8-12.0, 7.3-12.2, 7.6-13.4, 9.8-16.4, and 13.1-19.0 mGy for BWs less than 15, 15-19, 20-24, 25-29, and 30 cm or greater, respectively. The fractions of adult doses, adult SSDEs, used within the consortium for patients with BWs of 10, 14, 18, 22, 26, and 30 cm were 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9, respectively.The concept of DRRs addresses the balance between the patient's risk (radiation dose) and benefit (diagnostic image quality). Calculation of reference doses as a function of BW for an individual practice provides a tool to help develop site-specific CT protocols that help manage pediatric patient radiation doses.

  View details for DOI 10.1148/radiol.13120730

  View details for Web of Science ID 000320761400023

  View details for PubMedID 23513245

• Masters of Radiology Panel Discussion: Defining a Quality Dashboard for Radiology-What Are the Right Metrics? AMERICAN JOURNAL OF ROENTGENOLOGY Forman, H. P., Larson, D. B., Kazerooni, E. A., Norbash, A., Crowe, J. K., Javitt, M. C., Beauchamp, N. J. 2013; 200 (4): 839-844

  View details for DOI 10.2214/AJR.12.10469

  View details for Web of Science ID 000316622100036

  View details for PubMedID 23521458

• Improving Consistency in Radiology Reporting through the Use of Department-wide Standardized Structured Reporting RADIOLOGY Larson, D. B., Towbin, A. J., Pryor, R. M., Donnelly, L. F. 2013; 267 (1): 240-250

  Abstract

  To successfully develop a department-wide standardized structured reporting program and achieve widespread adoption throughout the radiology department.A structured reporting work group was formed in February 2010 to oversee development of standardized structured reports for a radiology department of 36 radiologists at a tertiary care children's hospital. The committee reached consensus on report organization and provided written guidelines and checklists for division representatives to aid in creation of the structured reports. Report drafts were reviewed by a subcommittee and revised until agreement was reached with the report author. Each report was vetted by all radiologists who would be using the report, and further revisions were made, as appropriate. Reports were then entered into the speech recognition system so that each report was associated with a procedure code or a group of codes from the radiology information system. This enabled automatic report population within the speech recognition system. The initiative was completed by September 2011. Quarterly audits were performed to evaluate for adherence to the standard report format and use of the normal report in cases in which the radiologist believed the study was normal. In August 2012, radiologists were surveyed as to their impressions of the structured reporting program.A total of 228 standardized structured reports were created within 2 years after initiation of the project, corresponding to 199,000 (94%) of 212,000 departmental studies by volume. By the end of the implementation period in September 2011, all 223 (100%) audited reports adhered to the standard report format and 80 (99%) of 81 reports adhered to the normal report. Radiologist feedback was largely favorable.Standardized department-wide structured reporting can be implemented in a radiology department, with a high rate of adoption by the radiologists.

  View details for DOI 10.1148/radiol.12121502

  View details for Web of Science ID 000316565000025

  View details for PubMedID 23329657

• Practice Policy and Quality Initiatives Decreasing Variability in Turnaround Time for Radiographic Studies from the Emergency Department RADIOGRAPHICS Towbin, A. J., Iyer, S. B., Brown, J., Varadarajan, K., Perry, L. A., Larson, D. B. 2013; 33 (2): 361-371

  Abstract

  A study was performed to evaluate use of quality improvement techniques to decrease the variability in turnaround time (TAT) for radiology reports on emergency department (ED) radiographs. An interdepartmental improvement team applied multiple interventions. Statistical process control charts were used to evaluate for improvement in mean TAT for ED radiographs, percentage of ED radiographs read within 35 minutes, and standard deviation of the mean TAT. To determine if the changes in the radiology department had an effect on the ED, the average time from when an ED physician first met with the patient to the time when the final treatment decision was made was also measured. There was a significant improvement in mean TAT for ED radiographs (from 23.9 to 14.6 minutes), percentage of ED radiographs read within 35 minutes (from 82.2% to 92.9%), and standard deviation of the mean TAT (from 22.8 to 12.7). The mean time from when an ED physician first met with the patient to the time a final treatment decision was made decreased from 88.7 to 79.8 minutes. Quality improvement techniques were used to decrease mean TAT and the variability in TAT for ED radiographs. This change was associated with an improvement in ED throughput.

  View details for DOI 10.1148/rg.332125738

  View details for Web of Science ID 000315998700007

  View details for PubMedID 23479701

• Standardization of Quality Initiative Reporting RADIOGRAPHICS Duncan, J. R., Larson, D. B., Kruskal, J. B. 2013; 33 (2): 373-374

  View details for DOI 10.1148/rg.332125034

  View details for Web of Science ID 000315998700008

  View details for PubMedID 23479702

• Masters of Radiology Panel Discussion: Hyperefficient Radiology-Can We Maintain The Pace? AMERICAN JOURNAL OF ROENTGENOLOGY Forman, H. P., Larson, D. B., Kazerooni, E. A., Norbash, A., Crowe, J. K., Javitt, M. C., Beauchamp, N. J., Mendelson, E. B. 2012; 199 (4): 838-843

  View details for DOI 10.2214/AJR.12.9648

  View details for Web of Science ID 000309117300036

  View details for PubMedID 22997376

• Masters of Radiology Panel Discussion: The Future of the Radiology Job Market AMERICAN JOURNAL OF ROENTGENOLOGY Forman, H. P., Larson, D. B., Kaye, A. D., Kazerooni, E. A., Norbash, A., Crowe, J. K., Javitt, M. C., Beauchamp, N. J., Mendelson, E. B. 2012; 199 (1): 127-132

  View details for DOI 10.2214/AJR.12.9019

  View details for Web of Science ID 000305804000040

  View details for PubMedID 22733903

• Masters of Radiology Panel Discussion: Maintaining Maintenance of Certification in the Field of Radiology AMERICAN JOURNAL OF ROENTGENOLOGY Forman, H. P., Larson, D. B., Kazerooni, E. A., Norbash, A., Crowe, J. K., Javitt, M. C., Beauchamp, N. J., Mendelson, E. B. 2012; 198 (4): 854-857

  View details for DOI 10.2214/AJR.11.8375

  View details for Web of Science ID 000302129000033

  View details for PubMedID 22451551

• Masters of Radiology Panel Discussion: Women in Radiology-How Can We Encourage More Women to Join the Field and Become Leaders? AMERICAN JOURNAL OF ROENTGENOLOGY Forman, H. P., Larson, D. B., Kaye, A. D., Kazerooni, E. A., Norbash, A., Crowe, J. K., Javitt, M. C., Beauchamp, N. J. 2012; 198 (1): 145-149

  View details for DOI 10.2214/AJR.11.8053

  View details for Web of Science ID 000298884100049

  View details for PubMedID 22194490

• What Is the Role of the Radiologist in Holding Down Health Care Cost Growth? AMERICAN JOURNAL OF ROENTGENOLOGY Forman, H. P., Beauchamp, N. J., Kaye, A., Larson, D. B., Norbash, A. 2011; 197 (4): 919-922

  View details for DOI 10.2214/AJR.11.7491

  View details for Web of Science ID 000295081000062

  View details for PubMedID 21940579

• Changing Radiologists' Expectations: False Information versus Years of Experience RADIOLOGY Larson, D. B. 2011; 261 (1): 327-327

  View details for DOI 10.1148/radiol.11110794

  View details for Web of Science ID 000295039000039

  View details for PubMedID 21931145

• Masters of Radiology Panel Discussion: Encouraging and Fostering Mentorship-How We Can Ensure That No Faculty Member Is Left Behind and That Leaders Do Not Fail AMERICAN JOURNAL OF ROENTGENOLOGY Forman, H. P., Larson, D. B., Norbash, A., Javitt, M. C., Beauchamp, N. J., Monsees, B., Messinger, N. 2011; 197 (1): 149-153

  View details for DOI 10.2214/AJR.11.7090

  View details for Web of Science ID 000291991200036

  View details for PubMedID 21701023

• Rethinking Peer Review: What Aviation Can Teach Radiology about Performance Improvement RADIOLOGY Larson, D. B., Nance, J. J. 2011; 259 (3): 626-632

  View details for DOI 10.1148/radiol.11102222

  View details for Web of Science ID 000290898100003

  View details for PubMedID 21602501

• Rising Use of CT in Child Visits to the Emergency Department in the United States, 1995-2008 RADIOLOGY Larson, D. B., Johnson, L. W., Schnell, B. M., Goske, M. J., Salisbury, S. R., Forman, H. P. 2011; 259 (3): 793-801

  Abstract

  To describe nationwide trends and factors associated with the use of computed tomography (CT) in children visiting emergency departments (EDs) in the United States between 1995 and 2008.This study was exempt from institutional review board oversight. Data from the 1995-2008 National Hospital Ambulatory Medical Care Survey were used to evaluate the number and percentage of visits associated with CT for patients younger than 18 years. A mean of 7375 visits were sampled each year. Data were subcategorized according to multiple patient and hospital characteristics. The Rao-Scott χ(2) test was performed to determine whether CT use was similar across subpopulations.From 1995 to 2008, the number of pediatric ED visits that included CT examination increased from 0.33 to 1.65 million, a fivefold increase, with a compound annual growth rate of 13.2%. The percentage of visits associated with CT increased from 1.2% to 5.9%, a 4.8-fold increase, with a compound annual growth rate of 12.8%. The number of visits associated with CT at pediatric-focused and non-pediatric-focused EDs increased from 14,895 and 316,133, respectively, in 1995 to 212,716 and 1,438,413, respectively, in 2008. By the end of the study period, top chief complaints among those undergoing CT included head injury, abdominal pain, and headache.Use of CT in children who visit the ED has increased substantially and occurs primarily at non-pediatric-focused facilities. This underscores the need for special attention to this vulnerable population to ensure that imaging is appropriately ordered, performed, and interpreted.

  View details for DOI 10.1148/radiol.11101939

  View details for Web of Science ID 000290898100019

  View details for PubMedID 21467249

• Reliability of Renal Length Measurements Made With Ultrasound Compared With Measurements From Helical CT Multiplanar Reformat Images AMERICAN JOURNAL OF ROENTGENOLOGY Larson, D. B., Meyers, M. L., O'Hara, S. M. 2011; 196 (5): W592-W597

  Abstract

  The purpose of this article is to determine the reliability of sonographic renal length measurements compared with measurements obtained from helical CT multiplanar reformat images and compared with standard renal growth curves.A retrospective review was performed of 76 subjects who underwent both renal ultrasound and abdominal CT within 2 weeks of one another. Renal lengths were measured using oblique coronal reformat images of helically acquired CT data by two observers on two occasions. Intraobserver and interobserver error for these measurements were calculated. Ultrasound renal length measurements were compared with CT measurements. Measurement variation was compared with standard renal growth curves.The mean (± SD) of the absolute value of interobserver error of CT measurements was 0.9 ± 0.8 mm. Compared with CT, individual ultrasound measurements underestimated renal length by 1.5 ± 5.6 mm on average, with a 95% CI of -12.5 to 9.5 mm. When the maximum of three ultrasound renal length measurements was used, the SD was 4.7 mm, with a 95% CI of -8.2 to 10.1 mm of the reported renal length. This corresponds to greater or less than 3.3 years of normal renal growth.Lack of renal growth can be asserted only when renal length falls below the growth curve, taking into account the corresponding measurement error limits, which we found to be greater or less than 9.3 mm. If the follow-up measurement falls within these limits, one should not infer lack of appropriate renal growth, even if the renal length measurement decreases or remains unchanged for up to 3 years.

  View details for DOI 10.2214/AJR.10.5486

  View details for Web of Science ID 000289769000014

  View details for PubMedID 21512050

• Masters of Radiology Panel Discussion: The Commoditization of Radiology AMERICAN JOURNAL OF ROENTGENOLOGY Forman, H. P., Larson, D. B., Kaye, A. D., Kazerooni, E. A., Norbash, A., Crowe, J. K., Javitt, M. C., Beauchamp, N. J. 2011; 196 (4): 843-847

  View details for DOI 10.2214/AJR.10.6393

  View details for Web of Science ID 000288650600041

  View details for PubMedID 21427333

• National Trends in CT Use in the Emergency Department: 1995-2007 RADIOLOGY Larson, D. B., Johnson, L. W., Schnell, B. M., Salisbury, S. R., Forman, H. P. 2011; 258 (1): 164-173

  Abstract

  To identify nationwide trends and factors associated with the use of computed tomography (CT) in the emergency department (ED).This study was exempt from institutional review board approval. Data from the 1995-2007 National Hospital Ambulatory Medical Care Survey were used to evaluate the numbers and percentages of ED visits associated with CT. A mean of 30 044 visits were sampled each year. Data were also subcategorized according to multiple patient and hospital characteristics. The Rao-Scott χ(2) test was performed to determine whether CT use was similar across subpopulations. Data were evaluated according to exponential and logistic growth models.From 1995 to 2007, the number of ED visits that included a CT examination increased from 2.7 million to 16.2 million, constituting a 5.9-fold increase and a compound annual growth rate of 16.0%. The percentage of visits associated with CT increased from 2.8% to 13.9%, constituting a 4.9-fold increase and a compound annual growth rate of 14.2%. The exponential growth model provided the best fit for the trend in CT use. CT use was greater in older patients, white patients, patients admitted to the hospital, and patients at facilities in metropolitan regions. By the end of the study period, the top chief complaints among those who underwent CT were abdominal pain, headache, and chest pain. The percentage of patient visits associated with CT for all evaluated chief complaints increased-most substantially among those who underwent CT for flank, abdominal, or chest pain.Use of CT has increased at a higher rate in the ED than in other settings. The overall use of CT had not begun to taper by 2007.

  View details for DOI 10.1148/radiol.10100640

  View details for Web of Science ID 000285574200019

  View details for PubMedID 21115875

• Masters of Radiology Panel Discussion: Models for Health Care Performance in Radiology-How Do We Measure Our Productivity and Ourselves? AMERICAN JOURNAL OF ROENTGENOLOGY Forman, H. P., Norbash, A., Beauchamp, N. J., Thrall, J. H., Larson, D. B., Kazerooni, E. A., Hricak, H., Monsees, B., Javitt, M. C., Crowe, J. K. 2011; 196 (1): 130-135

  View details for DOI 10.2214/AJR.10.5611

  View details for Web of Science ID 000286018800018

  View details for PubMedID 21178057

• Masters of Radiology Panel Discussion: Who Is Accountable for the Appropriateness of Studies-The Radiologist, the Referring Physician, or Both? AMERICAN JOURNAL OF ROENTGENOLOGY Forman, H. P., Beauchamp, N. J., Kazerooni, E. A., Larson, D. B., Javitt, M. C., Norbash, A. 2010; 195 (4): 968-973

  View details for DOI 10.2214/AJR.10.4997

  View details for Web of Science ID 000282033600026

  View details for PubMedID 20858826

• Masters of Radiology Panel Discussion: Radiology Extenders-Challenges and Opportunities to Balance the Demands of Our Changing Work Environment AMERICAN JOURNAL OF ROENTGENOLOGY Forman, H. P., Javitt, M. C., Monsees, B., Larson, D. B., Norbash, A., Kaye, A., Beauchamp, N. J., Messinger, N. 2010; 195 (1): 170-175

  View details for DOI 10.2214/AJR.10.4619

  View details for Web of Science ID 000278998200023

  View details for PubMedID 20566812

• Masters of Radiology Panel Discussion: Role of Communication in Today's Radiologic Practices AMERICAN JOURNAL OF ROENTGENOLOGY Forman, H. P., Javitt, M. C., Monsees, B., Crowe, J. K., Beauchamp, N. J., Larson, D. B., Kaye, A., Kazerooni, E. A., Norbash, A., Messinger, N., Hricak, H., Thrall, J. H. 2010; 194 (4): 1014-1017

  View details for DOI 10.2214/AJR.09.4060

  View details for Web of Science ID 000275863300023

  View details for PubMedID 20308504

• Masters of Radiology Panel Discussion: Responding to Health Care Reform and Other Market Pressures AMERICAN JOURNAL OF ROENTGENOLOGY Forman, H. P., Crowe, J. K., Messinger, N., Javitt, M. C., Larson, D. B., Norbash, A., Kaye, A., Thrall, J. H., Hricak, H., Kazerooni, E. A. 2010; 194 (1): 173-177

  View details for DOI 10.2214/AJR.09.3715

  View details for Web of Science ID 000272990500024

  View details for PubMedID 20028920

• RADPEER scoring white paper. Journal of the American College of Radiology Jackson, V. P., Cushing, T., Abujudeh, H. H., Borgstede, J. P., Chin, K. W., Grimes, C. K., Larson, D. B., Larson, P. A., Pyatt, R. S., Thorwarth, W. T. 2009; 6 (1): 21-25

  Abstract

  The ACR's RADPEER program began in 2002; the electronic version, e-RADPEER, was offered in 2005. To date, more than 10,000 radiologists and more than 800 groups are participating in the program. Since the inception of RADPEER, there have been continuing discussions regarding a number of issues, including the scoring system, the subspecialty-specific subcategorization of data collected for each imaging modality, and the validation of interfacility scoring consistency. This white paper reviews the task force discussions, the literature review, and the new recommended scoring process and lexicon for RADPEER.

  View details for DOI 10.1016/j.jacr.2008.06.011

  View details for PubMedID 19111267

• My old Kentucky home, goodnight: Potential impact of planned changes in the radiology board certification process AMERICAN JOURNAL OF ROENTGENOLOGY Larson, D. B., Saket, D. D. 2008; 190 (5): 1149-1151

  View details for DOI 10.2214/AJR.07.3981

  View details for Web of Science ID 000255185100002

  View details for PubMedID 18430822

• Informing parents about CT radiation exposure in children: It's OK to tell them AMERICAN JOURNAL OF ROENTGENOLOGY Larson, D. B., Rader, S. B., Forman, H. P., Fenton, L. Z. 2007; 189 (2): 271-275

  Abstract

  The purpose of our study was to determine how parents' understanding of and willingness to allow their children to undergo CT change after receiving information regarding radiation dose and risk.One hundred parents of children undergoing nonemergent CT studies at a tertiary-care children's hospital were surveyed before and after reading an informational handout describing radiation risk. Parental knowledge of whether CT uses radiation or increases lifetime risk of cancer was assessed, as was willingness to permit their child to undergo both a CT examination that their child's doctor recommended and one for which their doctor thought observation might be equally effective.Of the 100 parents who were surveyed, 66% believed CT uses radiation before reading the handout, versus 99% afterward (p < 0.01). Before reading the handout, 13% believed CT increases the lifetime risk of cancer, versus 86% afterward (p < 0.01). After reading the handout, parents became less willing to have their child undergo CT given a hypothetic situation in which their doctor believed that either CT or observation would be equally effective (p < 0.01), but their willingness to have their child undergo CT recommended by their doctor did not significantly change. After reading the handout, 62% of parents reported no change in level of concern. No parent refused or requested to defer CT after reading the handout.A brief informational handout can improve parental understanding of the potential increased risk of cancer related to pediatric CT without causing parents to refuse studies recommended by the referring physician.

  View details for DOI 10.2214/AJR.07.2248

  View details for Web of Science ID 000248624400006

  View details for PubMedID 17646450

• Major changes in radiology residency program requirements are coming AMERICAN JOURNAL OF ROENTGENOLOGY Larson, D. B. 2007; 188 (1): 3-4

  View details for DOI 10.2214/AJR.07.6100

  View details for Web of Science ID 000245647900002

  View details for PubMedID 17179338

• The AFIP and the tragedy of the commons. Journal of the American College of Radiology Larson, D. B. 2007; 4 (1): 8-10

  View details for PubMedID 17412218

• Non-enhancing pilocytic astrocytoma of the spinal cord PEDIATRIC RADIOLOGY Larson, D. B., Hedlund, G. L. 2006; 36 (12): 1312-1315

  Abstract

  Pilocytic astrocytomas are among the most common intramedullary spinal cord tumors in the pediatric age group. The presence of contrast enhancement is a major factor used to distinguish these tumors from other spinal cord lesions. We present a case of histologically proved non-enhancing intramedullary spinal cord pilocytic astrocytoma in a 12-year-old girl. This case represents an exception to the conventional wisdom that pediatric spinal neoplasms enhance with administration of intravenous contrast material.

  View details for DOI 10.1007/s00247-006-0301-4

  View details for Web of Science ID 000242831000012

  View details for PubMedID 17021719

• A comprehensive portrait of teleradiology in radiology practices: Results from the American College of Radiology's 1999 survey AMERICAN JOURNAL OF ROENTGENOLOGY Larson, D. B., Cypel, Y. S., Forman, H. P., Sunshine, J. H. 2005; 185 (1): 24-35

  Abstract

  This article presents a comprehensive portrait of the characteristics of teleradiology systems of radiology practices as of 1999. Our purposes are to help profile a rapidly evolving area of radiology that has been underexamined to date and to provide a baseline with which future findings can be compared.In 1999, the American College of Radiology surveyed 970 practices by mail. A response rate of 66% was achieved. Responses were weighted to represent all radiology practices in the United States. Data from nine questions specifically designed to profile the use of teleradiology were analyzed using descriptive statistical methods and multivariate regression analyses.Seventy-one percent of multiradiologist practices had teleradiology systems in place, using them to interpret 5% of their studies. For solo practices, corresponding statistics were 30% and 14%. Ninety-two percent of multiradiologist practices with teleradiology systems used them for preliminary on-call interpretation. Other major uses included consultation with other radiologists (20%) and primary interpretation of studies (18%). Ninety-five percent of multiradiologist practices with teleradiology systems used them to interpret CT, 84% used them for sonography, 69% for nuclear medicine, 47% for MRI, and 43% for conventional radiographs.Teleradiology had already become a fixture in most practices by 1999, though it was used for only a small fraction of image interpretations. Its widespread presence positioned teleradiology to become a key element of radiology practice nationwide.

  View details for Web of Science ID 000229951900005

  View details for PubMedID 15972394

• Graduate medical education financing: its effect on radiologists at all career levels AMERICAN JOURNAL OF ROENTGENOLOGY Larson, D. B. 2004; 182 (4): A9-A10

  View details for Web of Science ID 000220382800001

  View details for PubMedID 15085869

• MD/MBA programs in the United States: Evidence of a change in health care leadership ACADEMIC MEDICINE Larson, D. B., Chandler, M., Forman, H. P. 2003; 78 (3): 335-341

  Abstract

  Managerial sciences are playing an increasingly prominent role in the organization and delivery of health care. Despite popular media reports that a rising number of physicians are acquiring a background in this discipline through MD/MBA (medical and master of business administration) programs, no recent study has verified this. This study measured changes in the number and nature of the affiliations between management and medicine in the form of MD/MBA programs in the United States.Surveys of admission officers of 125 U.S. allopathic medical schools and of the overseers of each joint MD/MBA degree program were administered in May-October 2001. Main outcome measures included program growth, curriculum and degree requirements, application and admission requirements, and program leadership and organization.The number of MD/MBA programs grew from six to 33 between 1993 and 2001, and 17 more medical schools were considering establishing the joint-degree program. Ten, 15, and 20 programs produced 27, 42, and 61 graduates in 1999, 2000, and 2001, respectively, and over 100 students were expected to graduate per year when all 33 programs matured. Program structures and oversight indicate a spectrum of philosophies regarding the appropriate level of integration of the two degrees. MD/MBA programs apparently attempt to complement medical education with management education rather than the converse.The growth in the numbers of MD/MBA programs and participants indicates rising cooperation between medical and business schools and increasing interest in management education early in the careers of graduating physicians.

  View details for Web of Science ID 000181465500017

  View details for PubMedID 12634220

• Technical skills for weight loss: Preliminary data from a randomized trial PREVENTIVE MEDICINE Katz, D. L., Chan, W., Gonzalez, M., Larson, D., Nawaz, H., Abdulrahman, M., Yeh, M. C. 2002; 34 (6): 608-615

  Abstract

  Optimal behavioral interventions for sustainable weight loss are uncertain. We therefore conducted a study among overweight/obese women comparing conventional dietary counseling of individuals (counseling-based intervention) to a novel, group-based skill-building intervention.Eighty subjects were randomly assigned to either the counseling-based or to the skill-building intervention. Outcomes included weight loss, dietitian hours per group and per unit weight loss, and dollars spent per group and per unit weight lost.Weight loss at 6 months (follow-up rate 61.3%) in the counseling-based group was 8.8 lb (P = 0.0001), and in the skill-building group was 3.8 lb (P = 0.01). A total of 160 dietitian hours were required for the counseling-based group, and 131 for the skilled-building group. The counseling-based group cost an average of $21 per pound lost, while the skill-building cost an average of $48 per pound lost (P = 0.16).At 6 months, individualized office-based counseling produced more weight loss than a skill-building approach and cost less than half as much per pound of weight loss. Longer-term follow-up is required to determine if, as hypothesized, the skill-building intervention produces more sustainable weight loss.

  View details for DOI 10.1006/pmed.2002.1025

  View details for Web of Science ID 000176029600007

  View details for PubMedID 12052021

• Self-reported weight and height - Implications for obesity research AMERICAN JOURNAL OF PREVENTIVE MEDICINE Nawaz, H., Chan, W., Abdulrahman, M., Larson, D., Katz, D. L. 2001; 20 (4): 294-298

  Abstract

  Self-reported weight and height are under- and over-reported, respectively, in epidemiologic studies. This tendency, which may adversely affect study operations, has not been evaluated among subjects being enrolled into a weight-loss program.Self-reported weight, height, and body mass index (BMI) were compared to measured values in 97 overweight or obese (BMI>27.3) women being enrolled into a randomized, controlled trial of two behavioral interventions for weight loss. The effects of demographic factors, baseline weight, baseline height, and baseline BMI on weight and height reporting were assessed.There was a significant difference between measured and reported weight (mean difference=-3.75 lb, p=0.0001) and height (mean difference=+0.35 in., p=0.0007). The mean difference between measured and reported BMI was -1.14 kg/m(2) (p=0.0001). Unemployed, retired, or disabled women were more likely to under-report their BMI than employed women (p=0.001). Six percent of subjects who were initially considered eligible for the study on the basis of the self-report were eventually excluded from the study because they did not meet the inclusion criterion for BMI.Obese women who seek weight-loss assistance tend to under-report their weight and over-report their height, suggesting that self-reported data are likely to be inaccurate. Misreporting is apparently influenced by employment and disability and has the potential to complicate recruitment of subjects for research studies.

  View details for Web of Science ID 000168351400010

  View details for PubMedID 11331120

• Decreasing Stroke Code to CT Time in Patients Presenting with Stroke Symptoms. Radiographics : a review publication of the Radiological Society of North America, Inc Kalnins, A. n., Mickelsen, L. J., Marsh, D. n., Zorich, C. n., Casal, S. n., Tai, W. A., Vora, N. n., Olalia, G. n., Wintermark, M. n., Larson, D. B. ; 37 (5): 1559–68

  Abstract

  Guided quality improvement (QI) programs present an effective means to streamline stroke code to computed tomography (CT) times in a comprehensive stroke center. Applying QI methods and a multidisciplinary team approach may decrease the stroke code to CT time in non-prenotified emergency department (ED) patients presenting with symptoms of stroke. The aim of this project was to decrease this time for non-prenotified stroke code patients from a baseline mean of 20 minutes to one less than 15 minutes during an 18-week period by applying QI methods in the context of a structured QI program. By reducing this time, it was expected that the door-to-CT time guideline of 25 minutes could be met more consistently. Through the structured QI program, we gained an understanding of the process that enabled us to effectively identify key drivers of performance to guide project interventions. As a result of these interventions, the stroke code to CT time for non-prenotified stroke code patients decreased to a mean of less than 14 minutes. This article reports these methods and results so that others can similarly improve the time it takes to perform nonenhanced CT studies in non-prenotified stroke code patients in the ED. (©)RSNA, 2017.

  View details for PubMedID 28820652