Jason Hom

All Publications

• Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial. JAMA network open Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., Cool, J. A., Kanjee, Z., Parsons, A. S., Ahuja, N., Horvitz, E., Yang, D., Milstein, A., Olson, A. P., Rodman, A., Chen, J. H. 2024; 7 (10): e2440969

  Abstract

  Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves physician diagnostic reasoning.To assess the effect of an LLM on physicians' diagnostic reasoning compared with conventional resources.A single-blind randomized clinical trial was conducted from November 29 to December 29, 2023. Using remote video conferencing and in-person participation across multiple academic medical institutions, physicians with training in family medicine, internal medicine, or emergency medicine were recruited.Participants were randomized to either access the LLM in addition to conventional diagnostic resources or conventional resources only, stratified by career stage. Participants were allocated 60 minutes to review up to 6 clinical vignettes.The primary outcome was performance on a standardized rubric of diagnostic performance based on differential diagnosis accuracy, appropriateness of supporting and opposing factors, and next diagnostic evaluation steps, validated and graded via blinded expert consensus. Secondary outcomes included time spent per case (in seconds) and final diagnosis accuracy. All analyses followed the intention-to-treat principle. A secondary exploratory analysis evaluated the standalone performance of the LLM by comparing the primary outcomes between the LLM alone group and the conventional resource group.Fifty physicians (26 attendings, 24 residents; median years in practice, 3 [IQR, 2-8]) participated virtually as well as at 1 in-person site. The median diagnostic reasoning score per case was 76% (IQR, 66%-87%) for the LLM group and 74% (IQR, 63%-84%) for the conventional resources-only group, with an adjusted difference of 2 percentage points (95% CI, -4 to 8 percentage points; P = .60). The median time spent per case for the LLM group was 519 (IQR, 371-668) seconds, compared with 565 (IQR, 456-788) seconds for the conventional resources group, with a time difference of -82 (95% CI, -195 to 31; P = .20) seconds. The LLM alone scored 16 percentage points (95% CI, 2-30 percentage points; P = .03) higher than the conventional resources group.In this trial, the availability of an LLM to physicians as a diagnostic aid did not significantly improve clinical reasoning compared with conventional resources. The LLM alone demonstrated higher performance than both physician groups, indicating the need for technology and workforce development to realize the potential of physician-artificial intelligence collaboration in clinical practice.ClinicalTrials.gov Identifier: NCT06157944.

  View details for DOI 10.1001/jamanetworkopen.2024.40969

  View details for PubMedID 39466245

• Large Language Model Influence on Management Reasoning: A Randomized Controlled Trial. medRxiv : the preprint server for health sciences Goh, E., Gallo, R., Strong, E., Weng, Y., Kerman, H., Freed, J., Cool, J. A., Kanjee, Z., Lane, K. P., Parsons, A. S., Ahuja, N., Horvitz, E., Yang, D., Milstein, A., Olson, A. P., Hom, J., Chen, J. H., Rodman, A. 2024

  Abstract

  Large language model (LLM) artificial intelligence (AI) systems have shown promise in diagnostic reasoning, but their utility in management reasoning with no clear right answers is unknown.To determine whether LLM assistance improves physician performance on open-ended management reasoning tasks compared to conventional resources.Prospective, randomized controlled trial conducted from 30 November 2023 to 21 April 2024.Multi-institutional study from Stanford University, Beth Israel Deaconess Medical Center, and the University of Virginia involving physicians from across the United States.92 practicing attending physicians and residents with training in internal medicine, family medicine, or emergency medicine.Five expert-developed clinical case vignettes were presented with multiple open-ended management questions and scoring rubrics created through a Delphi process. Physicians were randomized to use either GPT-4 via ChatGPT Plus in addition to conventional resources (e.g., UpToDate, Google), or conventional resources alone.The primary outcome was difference in total score between groups on expert-developed scoring rubrics. Secondary outcomes included domain-specific scores and time spent per case.Physicians using the LLM scored higher compared to those using conventional resources (mean difference 6.5 %, 95% CI 2.7-10.2, p<0.001). Significant improvements were seen in management decisions (6.1%, 95% CI 2.5-9.7, p=0.001), diagnostic decisions (12.1%, 95% CI 3.1-21.0, p=0.009), and case-specific (6.2%, 95% CI 2.4-9.9, p=0.002) domains. GPT-4 users spent more time per case (mean difference 119.3 seconds, 95% CI 17.4-221.2, p=0.02). There was no significant difference between GPT-4-augmented physicians and GPT-4 alone (-0.9%, 95% CI -9.0 to 7.2, p=0.8).LLM assistance improved physician management reasoning compared to conventional resources, with particular gains in contextual and patient-specific decision-making. These findings indicate that LLMs can augment management decision-making in complex cases.ClinicalTrials.gov Identifier: NCT06208423 ; https://classic.clinicaltrials.gov/ct2/show/NCT06208423.Question: Does large language model (LLM) assistance improve physician performance on complex management reasoning tasks compared to conventional resources?Findings: In this randomized controlled trial of 92 physicians, participants using GPT-4 achieved higher scores on management reasoning compared to those using conventional resources (e.g., UpToDate).Meaning: LLM assistance enhances physician management reasoning performance in complex cases with no clear right answers.

  View details for DOI 10.1101/2024.08.05.24311485

  View details for PubMedID 39148822

  View details for PubMedCentralID PMC11326321

• The Undiagnosed Diseases Network: Characteristics of solvable applicants and diagnostic suggestions for non-accepted ones. Genetics in medicine : official journal of the American College of Medical Genetics Mulvihill, J. J., Findley, L., Ni, W., Sinsheimer, J. S., Cole, F. S., Esteves, C., Bernstein, J. A., Newman, J. H., Wheeler, M. T., Mokry, J. R. 2024: 101203

  Abstract

  Can certain characteristics identify as solvable some undiagnosed patients who seek extensive evaluation and thorough record review, like by the Undiagnosed Diseases Network (UDN)?The UDN is a national research resource to solve medical mysteries through team science. Applicants provide informed consent to access to their medical records. After review, expert panels assess if applicants meet inclusion and exclusion criteria to select participants. When not accepting applicants, UDN experts may offer suggestions for diagnostic efforts. Using minimal information from initial applications, we compare features in applicants not accepted with those accepted and either solved or still not solved by the UDN. The diagnostic suggestions offered to non-accepted applicants and their clinicians were tallied.Non-accepted applicants were more often female, older at first symptoms and application, and longer in review than accepted applicants. The accepted and successfully diagnosed applicants were younger in ages, shorter in review time, more often non-white, of Hispanic ethnicity, and presenting with nervous system features. Half of non-accepted applicants were given suggestions for further local diagnostic evaluation. A few seemed to have two major diagnoses or a provocative environmental exposure history.Comprehensive UDN record review generates possibly helpful advice.

  View details for DOI 10.1016/j.gim.2024.101203

  View details for PubMedID 38967101

• Merlin: A Vision Language Foundation Model for 3D Computed Tomography. Research square Blankemeier, L., Cohen, J. P., Kumar, A., Veen, D. V., Gardezi, S., Paschali, M., Chen, Z., Delbrouck, J. B., Reis, E., Truyts, C., Bluethgen, C., Jensen, M., Ostmeier, S., Varma, M., Valanarasu, J., Fang, Z., Huo, Z., Nabulsi, Z., Ardila, D., Weng, W. H., Junior, E. A., Ahuja, N., Fries, J., Shah, N., Johnston, A., Boutin, R., Wentland, A., Langlotz, C., Hom, J., Gatidis, S., Chaudhari, A. 2024

  Abstract

  Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on the abdomen. Given the current shortage of both general and specialized radiologists, there is a large impetus to use artificial intelligence to alleviate the burden of interpreting these complex imaging studies while simultaneously using the images to extract novel physiological insights. Prior state-of-the-art approaches for automated medical image interpretation leverage vision language models (VLMs) that utilize both the image and the corresponding textual radiology reports. However, current medical VLMs are generally limited to 2D images and short reports. To overcome these shortcomings for abdominal CT interpretation, we introduce Merlin - a 3D VLM that leverages both structured electronic health records (EHR) and unstructured radiology reports for pretraining without requiring additional manual annotations. We train Merlin using a high-quality clinical dataset of paired CT scans (6+ million images from 15,331 CTs), EHR diagnosis codes (1.8+ million codes), and radiology reports (6+ million tokens) for training. We comprehensively evaluate Merlin on 6 task types and 752 individual tasks. The non-adapted (off-the-shelf) tasks include zero-shot findings classification (31 findings), phenotype classification (692 phenotypes), and zero-shot cross-modal retrieval (image to findings and image to impressions), while model adapted tasks include 5-year chronic disease prediction (6 diseases), radiology report generation, and 3D semantic segmentation (20 organs). We perform internal validation on a test set of 5,137 CTs, and external validation on 7,000 clinical CTs and on two public CT datasets (VerSe, TotalSegmentator). Beyond these clinically-relevant evaluations, we assess the efficacy of various network architectures and training strategies to depict that Merlin has favorable performance to existing task-specific baselines. We derive data scaling laws to empirically assess training data needs for requisite downstream task performance. Furthermore, unlike conventional VLMs that require hundreds of GPUs for training, we perform all training on a single GPU. This computationally efficient design can help democratize foundation model training, especially for health systems with compute constraints. We plan to release our trained models, code, and dataset, pending manual removal of all protected health information.

  View details for DOI 10.21203/rs.3.rs-4546309/v1

  View details for PubMedID 38978576

  View details for PubMedCentralID PMC11230513

• Comparing IM Residency Application Personal Statements Generated by GPT-4 and Authentic Applicants. Journal of general internal medicine Nair, V., Nayak, A., Ahuja, N., Weng, Y., Keet, K., Hosamani, P., Hom, J. 2024

  View details for DOI 10.1007/s11606-024-08784-w

  View details for PubMedID 38689120

  View details for PubMedCentralID 10589311

• Influence of a Large Language Model on Diagnostic Reasoning: A Randomized Clinical Vignette Study. medRxiv : the preprint server for health sciences Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., Cool, J., Kanjee, Z., Parsons, A. S., Ahuja, N., Horvitz, E., Yang, D., Milstein, A., Olson, A. P., Rodman, A., Chen, J. H. 2024

  Abstract

  Diagnostic errors are common and cause significant morbidity. Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves diagnostic reasoning.To assess the impact of the GPT-4 LLM on physicians' diagnostic reasoning compared to conventional resources.Multi-center, randomized clinical vignette study.The study was conducted using remote video conferencing with physicians across the country and in-person participation across multiple academic medical institutions.Resident and attending physicians with training in family medicine, internal medicine, or emergency medicine.Participants were randomized to access GPT-4 in addition to conventional diagnostic resources or to just conventional resources. They were allocated 60 minutes to review up to six clinical vignettes adapted from established diagnostic reasoning exams.The primary outcome was diagnostic performance based on differential diagnosis accuracy, appropriateness of supporting and opposing factors, and next diagnostic evaluation steps. Secondary outcomes included time spent per case and final diagnosis.50 physicians (26 attendings, 24 residents) participated, with an average of 5.2 cases completed per participant. The median diagnostic reasoning score per case was 76.3 percent (IQR 65.8 to 86.8) for the GPT-4 group and 73.7 percent (IQR 63.2 to 84.2) for the conventional resources group, with an adjusted difference of 1.6 percentage points (95% CI -4.4 to 7.6; p=0.60). The median time spent on cases for the GPT-4 group was 519 seconds (IQR 371 to 668 seconds), compared to 565 seconds (IQR 456 to 788 seconds) for the conventional resources group, with a time difference of -82 seconds (95% CI -195 to 31; p=0.20). GPT-4 alone scored 15.5 percentage points (95% CI 1.5 to 29, p=0.03) higher than the conventional resources group.In a clinical vignette-based study, the availability of GPT-4 to physicians as a diagnostic aid did not significantly improve clinical reasoning compared to conventional resources, although it may improve components of clinical reasoning such as efficiency. GPT-4 alone demonstrated higher performance than both physician groups, suggesting opportunities for further improvement in physician-AI collaboration in clinical practice.

  View details for DOI 10.1101/2024.03.12.24303785

  View details for PubMedID 38559045

  View details for PubMedCentralID PMC10980135

• Adapted large language models can outperform medical experts in clinical text summarization. Nature medicine Van Veen, D., Van Uden, C., Blankemeier, L., Delbrouck, J. B., Aali, A., Bluethgen, C., Pareek, A., Polacin, M., Reis, E. P., Seehofnerová, A., Rohatgi, N., Hosamani, P., Collins, W., Ahuja, N., Langlotz, C. P., Hom, J., Gatidis, S., Pauly, J., Chaudhari, A. S. 2024

  Abstract

  Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP) tasks, their effectiveness on a diverse range of clinical summarization tasks remains unproven. Here we applied adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progress notes and doctor-patient dialogue. Quantitative assessments with syntactic, semantic and conceptual NLP metrics reveal trade-offs between models and adaptation methods. A clinical reader study with 10 physicians evaluated summary completeness, correctness and conciseness; in most cases, summaries from our best-adapted LLMs were deemed either equivalent (45%) or superior (36%) compared with summaries from medical experts. The ensuing safety analysis highlights challenges faced by both LLMs and medical experts, as we connect errors to potential medical harm and categorize types of fabricated information. Our research provides evidence of LLMs outperforming medical experts in clinical text summarization across multiple tasks. This suggests that integrating LLMs into clinical workflows could alleviate documentation burden, allowing clinicians to focus more on patient care.

  View details for DOI 10.1038/s41591-024-02855-5

  View details for PubMedID 38413730

  View details for PubMedCentralID 5593724

• Interpreter and limited-English proficiency patient training helps develop medical and physician assistant students' cross-cultural communication skills. BMC medical education Nguyen, Q., Flora, J., Basaviah, P., Bryant, M., Hosamani, P., Westphal, J., Kugler, J., Hom, J., Chi, J., Parker, J., DiGiammarino, A. 2024; 24 (1): 185

  Abstract

  The increasing linguistic and cultural diversity in the United States underscores the necessity of enhancing healthcare professionals' cross-cultural communication skills. This study focuses on incorporating interpreter and limited-English proficiency (LEP) patient training into the medical and physician assistant student curriculum. This aims to improve equitable care provision, addressing the vulnerability of LEP patients to healthcare disparities, including errors and reduced access. Though training is recognized as crucial, opportunities in medical curricula remain limited.To bridge this gap, a novel initiative was introduced in a medical school, involving second-year students in clinical sessions with actual LEP patients and interpreters. These sessions featured interpreter input, patient interactions, and feedback from interpreters and clinical preceptors. A survey assessed the perspectives of students, preceptors, and interpreters.Outcomes revealed positive reception of interpreter and LEP patient integration. Students gained confidence in working with interpreters and valued interpreter feedback. Preceptors recognized the sessions' value in preparing students for future clinical interactions.This study underscores the importance of involving experienced interpreters in training students for real-world interactions with LEP patients. Early interpreter training enhances students' communication skills and ability to serve linguistically diverse populations. Further exploration could expand languages and interpretation modes and assess long-term effects on students' clinical performance. By effectively training future healthcare professionals to navigate language barriers and cultural diversity, this research contributes to equitable patient care in diverse communities.

  View details for DOI 10.1186/s12909-024-05173-z

  View details for PubMedID 38395858

  View details for PubMedCentralID 9932446

• Quality improvement project to reduce medicare 1-day write-offs due to inappropriate admission orders. BMC health services research Oke, O., Sullivan, K. M., Hom, J., Svec, D., Weng, Y., Shieh, L. 2024; 24 (1): 204

  Abstract

  We identified that Stanford Health Care had a significant number of patients who after discharge are found by the utilization review committee not to meet Center for Mediare and Medicaid Services (CMS) 2-midnight benchmark for inpatient status. Some of the charges incurred during the care of these patients are written-off and known as Medicare 1-day write-offs. This study which aims to evaluate the use of a Best Practice Alert (BPA) feature on the electronic medical record, EPIC, to ensure appropriate designation of a patient's hospitalization status as either inpatient or outpatient in accordance with Center for Medicare and Medicaid services (CMS) 2 midnight length of stay benchmark thereby reducing the number of associated write-offs.We incorporated a best practice alert (BPA) into the Epic Electronic Medical Record (EMR) that would prompt the discharging provider and the case manager to review the patients' inpatient designation prior to discharge and change the patient's designation to observation when deemed appropriate. Patients who met the inclusion criteria (Patients must have Medicare fee-for-service insurance, inpatient length of stay (LOS) less than 2 midnights, inpatient designation as hospitalization status at time of discharge, was hospitalized to an acute level of care and belonged to one of 37 listed hospital services at the time of signing of the discharge order) were randomized to have the BPA either silent or active over a three-month period from July 18, 2019, to October 18, 2019.A total of 88 patients were included in this study: 40 in the control arm and 48 in the intervention arm. In the intervention arm, 8 (8/48, 16.7%) had an inpatient status designation despite potentially meeting Medicare guidelines for an observation stay, comparing to 23 patients (23/40, 57.5%) patients in the control group (p = 0.001). The estimated number of write-offs in the control arm was 17 (73.9%, out of 23 inpatient patients) while in the intervention arm was 1 (12.5%, out of 8 inpatient patient) after accounting for patients who may have met inpatient criteria for other reasons based on case manager note review.This is the first time to our knowledge that a BPA has been used in this manner to reduce the number of Medicare 1-day write-offs.

  View details for DOI 10.1186/s12913-024-10594-z

  View details for PubMedID 38355492

  View details for PubMedCentralID 6181108

• A Benchmark of Domain-Adapted Large Language Models for Generating Brief Hospital Course Summaries ArXIV Aali, A., Van Veen, D., Arefeen, Y., Hom, J., Bluethgen, C., Reis, E., Gatidis, S., Clifford, N., Daws, J., Tehrani, A., Kim, J., Chaudhari, A. 2024
• Detecting Underdiagnosed Medical Conditions with Deep Learning-Based Opportunistic CT Imaging arXIV Aali, A., Johnston, A., Blankemeier, L., Van Veen, D., Derry, L., Svec, D., Hom, J., Boutin, R., Akshay, C. 2024
• Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts. Research square Veen, D. V., Uden, C. V., Blankemeier, L., Delbrouck, J. B., Aali, A., Bluethgen, C., Pareek, A., Polacin, M., Reis, E. P., Seehofnerova, A., Rohatgi, N., Hosamani, P., Collins, W., Ahuja, N., Langlotz, C., Hom, J., Gatidis, S., Pauly, J., Chaudhari, A. 2023

  Abstract

  Sifting through vast textual data and summarizing key information from electronic health records (EHR) imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy on a diverse range of clinical summarization tasks has not yet been rigorously demonstrated. In this work, we apply domain adaptation methods to eight LLMs, spanning six datasets and four distinct clinical summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between models and adaptation methods in addition to instances where recent advances in LLMs may not improve results. Further, in a clinical reader study with ten physicians, we show that summaries from our best-adapted LLMs are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis highlights challenges faced by both LLMs and human experts. Lastly, we correlate traditional quantitative NLP metrics with reader study scores to enhance our understanding of how these metrics align with physician preferences. Our research marks the first evidence of LLMs outperforming human experts in clinical text summarization across multiple tasks. This implies that integrating LLMs into clinical workflows could alleviate documentation burden, empowering clinicians to focus more on personalized patient care and the inherently human aspects of medicine.

  View details for DOI 10.21203/rs.3.rs-3483777/v1

  View details for PubMedID 37961377

  View details for PubMedCentralID PMC10635391

• Genomics Research with Undiagnosed Children: Ethical Challenges at the Boundaries of Research and Clinical Care JOURNAL OF PEDIATRICS Halley, M. C., Young, J. L., Tang, C., Mintz, K. T., Lucas-Griffin, S., Maghiro, A., Ashley, E. A., Tabor, H. K., Undiagnosed Diseases Network 2023; 261

  View details for DOI 10.1018/j.jpeds.2023.113537

  View details for Web of Science ID 001029333600001

• Chatbot vs Medical Student Performance on Free-Response Clinical Reasoning Examinations. JAMA internal medicine Strong, E., DiGiammarino, A., Weng, Y., Kumar, A., Hosamani, P., Hom, J., Chen, J. H. 2023

  View details for DOI 10.1001/jamainternmed.2023.2909

  View details for PubMedID 37459090

• TRAINING WITH INTERPRETERS AND LIMITED-ENGLISH PROFICIENCY PATIENTS IS VALUABLE TO DEVELOPING MEDICAL AND PHYSICIAN ASSISTANT STUDENTS' COMMUNICATION SKILLS Nguyen, Q. Q., Flora, J. L., Chi, J., Hom, J., Kugler, J., Bryant, M., Hosamani, P., Westphal, J., Basaviah, P., DiGiammarino, A. A., Parker, J. SPRINGER. 2023: S785

  View details for Web of Science ID 001043057203203

• Genomics Research with Undiagnosed Children: Ethical Challenges at the Boundaries of Research and Clinical Care. The Journal of pediatrics Halley, M. C., Young, J. L., Tang, C., Mintz, K. T., Lucas-Griffin, S., Maghiro, A. S., Ashley, E. A., Tabor, H. K. 2023: 113537

  Abstract

  To explore the perspectives of parents of undiagnosed children enrolled in genomic diagnosis research regarding their motivations for enrolling their children, their understanding of the potential burdens and benefits, and the extent to which their experiences ultimately aligned with or diverged from their original expectations.In-depth interviews were conducted with parents, audio-recorded and transcribed. A structured codebook was applied to each transcript, after which iterative memoing was used to identify themes.Fifty-four parents participated, including 17 (31.5%) whose child received a diagnosis through research. Themes describing parents' expectations and experiences of genomic diagnosis research included: 1) the extent to which parents' motivations for participation focused on their hope that it would directly benefit their child; 2) the ways in which parents' frustrations regarding the research process confused the dual clinical and research goals of their participation; and 3) the limited clinical benefits parents ultimately experienced for their children.Our results suggest that parents of undiagnosed children seeking enrollment in genomic diagnosis research are at risk of a form of therapeutic misconception - in this case, diagnostic misconception. These findings indicate the need to examine the processes and procedures associated with this research in order to appropriately communicate and balance the potential burdens and benefits of study participation.

  View details for DOI 10.1016/j.jpeds.2023.113537

  View details for PubMedID 37271495

• Participation in a national diagnostic research study: assessing the patient experience. Orphanet journal of rare diseases Rosenfeld, L. E., LeBlanc, K., Nagy, A., Ego, B. K., Undiagnosed Diseases Network, McCray, A. T., Acosta, M. T., Adam, M., Adams, D. R., Alvarez, R. L., Alvey, J., Amendola, L., Andrews, A., Ashley, E. A., Bacino, C. A., Bademci, G., Balasubramanyam, A., Baldridge, D., Bale, J., Bamshad, M., Barbouth, D., Bayrak-Toydemir, P., Beck, A., Beggs, A. H., Behrens, E., Bejerano, G., Bellen, H. J., Bennett, J., Berg-Rood, B., Bernstein, J. A., Berry, G. T., Bican, A., Bivona, S., Blue, E., Bohnsack, J., Bonner, D., Botto, L., Boyd, B., Briere, L. C., Brokamp, E., Brown, G., Burke, E. A., Burrage, L. C., Butte, M. J., Byers, P., Byrd, W. E., Carey, J., Carrasquillo, O., Cassini, T., Chang, T. C., Chanprasert, S., Chao, H., Clark, G. D., Coakley, T. R., Cobban, L. A., Cogan, J. D., Coggins, M., Cole, F. S., Colley, H. A., Cooper, C. M., Cope, H., Corona, R., Craigen, W. J., Crouse, A. B., Cunningham, M., D'Souza, P., Dai, H., Dasari, S., Davis, J., Dayal, J. G., Dell'Angelica, E. C., Dipple, K., Doherty, D., Dorrani, N., Doss, A. L., Douine, E. D., Duncan, L., Earl, D., Eckstein, D. J., Emrick, L. T., Eng, C. M., Falk, M., Fieg, E. L., Fisher, P. G., Fogel, B. L., Forghani, I., Gahl, W. A., Glass, I., Gochuico, B., Goddard, P. C., Godfrey, R. A., Golden-Grant, K., Grajewski, A., Hadley, D., Hahn, S., Halley, M. C., Hamid, R., Hassey, K., Hayes, N., High, F., Hing, A., Hisama, F. M., Holm, I. A., Hom, J., Horike-Pyne, M., Huang, A., Hutchison, S., Introne, W., Isasi, R., Izumi, K., Jamal, F., Jarvik, G. P., Jarvik, J., Jayadev, S., Jean-Marie, O., Jobanputra, V., Karaviti, L., Kennedy, J., Ketkar, S., Kiley, D., Kilich, G., Kobren, S. N., Kohane, I. S., Kohler, J. N., Korrick, S., Kozuira, M., Krakow, D., Krasnewich, D. M., Kravets, E., Lalani, S. R., Lam, B., Lam, C., Lanpher, B. C., Lanza, I. R., Lee, B. H., Levitt, R., Lewis, R. A., Liu, P., Liu, X. Z., Longo, N., Loo, S. K., Loscalzo, J., Maas, R. L., Macnamara, E. F., MacRae, C. A., Maduro, V. V., Mahoney, R., Malicdan, M. C., Mamounas, L. A., Manolio, T. A., Mao, R., Maravilla, K., Marom, R., Marth, G., Martin, B. A., Martin, M. G., Martinez-Agosto, J. A., Marwaha, S., McCauley, J., McConkie-Rosell, A., McGee, E., Mefford, H., Merritt, J. L., Might, M., Mirzaa, G., Morava, E., Moretti, P., Mulvihill, J., Nakano-Okuno, M., Nelson, S. F., Newman, J. H., Nicholas, S. K., Nickerson, D., Nieves-Rodriguez, S., Novacic, D., Oglesbee, D., Orengo, J. P., Pace, L., Pak, S., Pallais, J. C., Palmer, C. G., Papp, J. C., Parker, N. H., Phillips, J. A., Posey, J. E., Potocki, L., Swerdzewski, B. N., Quinlan, A., Rao, D. A., Raper, A., Raskind, W., Renteria, G., Reuter, C. M., Rives, L., Robertson, A. K., Rodan, L. H., Rosenfeld, J. A., Rosenwasser, N., Rossignol, F., Ruzhnikov, M., Sacco, R., Sampson, J. B., Saporta, M., Schaechter, J., Schedl, T., Schoch, K., Scott, D. A., Scott, C. R., Shashi, V., Shin, J., Silverman, E. K., Sinsheimer, J. S., Sisco, K., Smith, E. C., Smith, K. S., Solem, E., Solnica-Krezel, L., Solomon, B., Spillmann, R. C., Stoler, J. M., Sullivan, K., Sullivan, J. A., Sun, A., Sutton, S., Sweetser, D. A., Sybert, V., Tabor, H. K., Tan, Q. K., Tan, A. L., Tekin, M., Telischi, F., Thorson, W., Tifft, C. J., Toro, C., Tran, A. A., Ungar, R. A., Urv, T. K., Vanderver, A., Velinder, M., Viskochil, D., Vogel, T. P., Wahl, C. E., Walker, M., Wallace, S., Walley, N. M., Wambach, J., Wan, J., Wang, L., Wangler, M. F., Ward, P. A., Wegner, D., Hubshman, M. W., Wener, M., Wenger, T., Westerfield, M., Wheeler, M. T., Whitlock, J., Wolfe, L. A., Worley, K., Xiao, C., Yamamoto, S., Yang, J., Zhang, Z., Zuchner, S. 2023; 18 (1): 73

  Abstract

  INTRODUCTION: The Undiagnosed Diseases Network (UDN), a clinical research study funded by the National Institutes of Health, aims to provide answers for patients with undiagnosed conditions and generate knowledge about underlying disease mechanisms. UDN evaluations involve collaboration between clinicians and researchers and go beyond what is possible in clinical settings. While medical and research outcomes of UDN evaluations have been explored, this is the first formal assessment of the patient and caregiver experience.METHODS: We invited UDN participants and caregivers to participate in focus groups via email, newsletter, and a private participant Facebook group. We developed focus group questions based on research team expertise, literature focused on patients with rare and undiagnosed conditions, and UDN participant and family member feedback. In March 2021, we conducted, recorded, and transcribed four 60-min focus groups via Zoom. Transcripts were evaluated using a thematic analysis approach.RESULTS: The adult undiagnosed focus group described the UDN evaluation as validating and an avenue for access to medical providers. They also noted that the experience impacted professional choices and helped them rely on others for support. The adult diagnosed focus group described the healthcare system as not set up for rare disease. In the pediatric undiagnosed focus group, caregivers discussed a continued desire for information and gratitude for the UDN evaluation. They also described an ability to rule out information and coming to terms with not having answers. The pediatric diagnosed focus group discussed how the experience helped them focus on management and improved communication. Across focus groups, adults (undiagnosed/diagnosed) noted the comprehensiveness of the evaluation. Undiagnosed focus groups (adult/pediatric) discussed a desire for ongoing communication and care with the UDN. Diagnosed focus groups (adult/pediatric) highlighted the importance of the diagnosis they received in the UDN. The majority of the focus groups noted a positive future orientation after participation.CONCLUSION: Our findings are consistent with prior literature focused on the patient experience of rare and undiagnosed conditions and highlight benefits from comprehensive evaluations, regardless of whether a diagnosis is obtained. Focus group themes also suggest areas for improvement and future research related to the diagnostic odyssey.

  View details for DOI 10.1186/s13023-023-02695-5

  View details for PubMedID 37032333

• Performance of ChatGPT on free-response, clinical reasoning exams. medRxiv : the preprint server for health sciences Strong, E., DiGiammarino, A., Weng, Y., Basaviah, P., Hosamani, P., Kumar, A., Nevins, A., Kugler, J., Hom, J., Chen, J. H. 2023

  Abstract

  Studies show that ChatGPT, a general purpose large language model chatbot, could pass the multiple-choice US Medical Licensing Exams, but the model's performance on open-ended clinical reasoning is unknown.To determine if ChatGPT is capable of consistently meeting the passing threshold on free-response, case-based clinical reasoning assessments.Fourteen multi-part cases were selected from clinical reasoning exams administered to pre-clerkship medical students between 2019 and 2022. For each case, the questions were run through ChatGPT twice and responses were recorded. Two clinician educators independently graded each run according to a standardized grading rubric. To further assess the degree of variation in ChatGPT's performance, we repeated the analysis on a single high-complexity case 20 times.A single US medical school.ChatGPT.Passing rate of ChatGPT's scored responses and the range in model performance across multiple run throughs of a single case.12 out of the 28 ChatGPT exam responses achieved a passing score (43%) with a mean score of 69% (95% CI: 65% to 73%) compared to the established passing threshold of 70%. When given the same case 20 separate times, ChatGPT's performance on that case varied with scores ranging from 56% to 81%.ChatGPT's ability to achieve a passing performance in nearly half of the cases analyzed demonstrates the need to revise clinical reasoning assessments and incorporate artificial intelligence (AI)-related topics into medical curricula and practice.

  View details for DOI 10.1101/2023.03.24.23287731

  View details for PubMedID 37034742

  View details for PubMedCentralID PMC10081420

• Exploring Fraud, Waste and Abuse within Telehealth Shah, N., Hom, J., Ahuja, N., Hernandez-Broussard, T. Centers for Medicare & Medicaid Services.. 2023
• A concurrent dual analysis of genomic data augments diagnoses: experiences of two clinical sites in the Undiagnosed Diseases Network. Genetics in medicine : official journal of the American College of Medical Genetics Spillmann, R. C., Tan, Q. K., Reuter, C., Schoch, K., Kohler, J., Bonner, D., Zastrow, D., Alkelai, A., Baugh, E., Cope, H., Marwaha, S., Wheeler, M. T., Bernstein, J. A., Shashi, V. 2022

  Abstract

  Next generation sequencing (NGS) has revolutionized the diagnostic process for rare/ultra-rare conditions. However, diagnosis rates differ between analytical pipelines. In the NIH-Undiagnosed Diseases Network (UDN) study, each individual's NGS data are concurrently analyzed by the UDN sequencing core laboratory and the clinical sites. We examined the outcomes of this practice.A retrospective review was performed at two UDN clinical sites, to compare variants, and diagnoses/candidate genes identified with the dual analyses of the NGS data.Ninety-five individuals had 100 diagnoses/candidate genes. There was 59% concordance between the UDN sequencing core laboratories and the clinical sites in identifying diagnoses/candidate genes. The core laboratory provided more diagnoses, while the clinical sites prioritized more research variants/candidate genes (p <0.001). The clinical sites solely identified 15% of the diagnoses/candidate genes. The differences between the two pipelines were more often due to variant prioritization disparities, than variant detection.The unique dual analysis of NGS data in the UDN synergistically enhances outcomes. The core laboratory provides a clinical analysis with more diagnoses and the clinical sites prioritized more research variants/candidate genes. Implementing such concurrent dual analyses in other genomic research studies and clinical settings can improve both variant detection and prioritization.

  View details for DOI 10.1016/j.gim.2022.12.001

  View details for PubMedID 36481303

• Association between Obesity and Length of COVID-19 Hospitalization: Unexpected Insights from the American Heart Association National COVID-19 Registry. Journal of obesity & metabolic syndrome Collins, W. J., Chang, A. Y., Weng, Y., Dahlen, A., O'Brien, C. G., Hom, J., Ahuja, N., Rodriguez, F., Rohatgi, N. 2022

  Abstract

  Background: Observational analyses have noted an association between obesity and poor clinical outcome from Coronavirus Disease 2019 (COVID-19). The mechanism for this finding remains unclear.Methods: We analyzed data from 22,915 COVID-19 patients hospitalized in non-intensive care units using the American Heart Association National COVID Registry of adult COVID-19 admissions from March 2020 to April 2021. A multivariable Poisson model adjusted for age, sex, medical history, admission respiratory status, hospitalization characteristics, and select laboratory findings was used to calculate length of stay (LOS) as a function of body mass index (BMI) category. Additionally, 5,327 patients admitted to intensive care units were similarly analyzed for comparison.Results: Relative to normal BMI subjects, overweight, class I obese, and class II obese patients had approximately half-day reductions in LOS (-0.469 days, P<0.01; -0.480 days, P<0.01; -0.578 days, P<0.01, respectively).Conclusion: The model identified a dose-dependent, inverse relationship between BMI category and LOS for COVID-19, which was not seen when the model was applied to critically ill patients.

  View details for DOI 10.7570/jomes22042

  View details for PubMedID 36058896

• Detailed characterization of hospitalized patients infected with the Omicron variant of SARS-CoV-2. Journal of internal medicine Ozdalga, E., Ahuja, N., Sehgal, N., Hom, J., Weng, Y., Pinsky, B., Schulman, K. A., Collins, W. 2022

  View details for DOI 10.1111/joim.13501

  View details for PubMedID 35417053

• Fraud, Waste and Abuse in the Context of COVID-19. Dries, A., Ahuja, N., Hom, J., Hernandez-Boussard, T. Centers for Medicare & Medicaid Services. 2022
• Corrigendum to eP296-The yield of thorough record review in the Undiagnosed Diseases Network, Volume 132, Supplement 1, April 2021, Page S187, https://doi.org/10.1016/S1096-7192(21)00378-4. Molecular genetics and metabolism Findley, L., Mulvihill, J. J., Bentley, A., Bernstein, J. A., Bican, A., Botto, L., Briere, L., Butte, M. J., Cope, H., Fogel, B. L., Hom, J., Kravets, E., Mak, B. C., Martin, M. G., Martinez-Agosto, J. A., Nelson, S. F., Newman, J., Palmer, C. G., Parker, N. H., Rosenfeld, J. A., Ruzhnikov, M., Schoch, K., Spillmann, R., Undiagnosed Diseases Network 2021

  View details for DOI 10.1016/j.ymgme.2021.08.007

  View details for PubMedID 34663553

• Genetic counselor roles in the undiagnosed diseases network research study: Clinical care, collaboration, and curation. Journal of genetic counseling Kohler, J. N., Kelley, E. G., Boyd, B. M., Sillari, C. H., Marwaha, S., Undiagnosed Diseases Network, Wheeler, M. T., Acosta, M. T., Adam, M., Adams, D. R., Agrawal, P. B., Alejandro, M. E., Alvey, J., Amendola, L., Andrews, A., Ashley, E. A., Azamian, M. S., Bacino, C. A., Bademci, G., Baker, E., Balasubramanyam, A., Baldridge, D., Bale, J., Bamshad, M., Barbouth, D., Bayrak-Toydemir, P., Beck, A., Beggs, A. H., Behrens, E., Bejerano, G., Bennet, J., Berg-Rood, B., Bernstein, J. A., Berry, G. T., Bican, A., Bivona, S., Blue, E., Bohnsack, J., Bonnenmann, C., Bonner, D., Botto, L., Boyd, B., Briere, L. C., Brokamp, E., Brown, G., Burke, E. A., Burrage, L. C., Butte, M. J., Byers, P., Byrd, W. E., Carey, J., Carrasquillo, O., Chang, T. C., Chanprasert, S., Chao, H., Clark, G. D., Coakley, T. R., Cobban, L. A., Cogan, J. D., Coggins, M., Sessions Cole, F., Colley, H. A., Cooper, C. M., Cope, H., Craigen, W. J., Crouse, A. B., Cunningham, M., D'Souza, P., Dai, H., Dasari, S., Davis, J., Dayal, J. G., Deardorff, M., Dell'Angelica, E. C., Dhar, S. U., Dipple, K., Doherty, D., Dorrani, N., Doss, A. L., Douine, E. D., Draper, D. D., Duncan, L., Earl, D., Eckstein, D. J., Emrick, L. T., Eng, C. M., Esteves, C., Falk, M., Fernandez, L., Ferreira, C., Fieg, E. L., Findley, L. C., Fisher, P. G., Fogel, B. L., Forghani, I., Fresard, L., Gahl, W. A., Glass, I., Gochuico, B., Godfrey, R. A., Golden-Grant, K., Goldman, A. M., Goldrich, M. P., Goldstein, D. B., Grajewski, A., Groden, C. A., Gutierrez, I., Hahn, S., Hamid, R., Hanchard, N. A., Hassey, K., Hayes, N., High, F., Hing, A., Hisama, F. M., Holm, I. A., Hom, J., Horike-Pyne, M., Huang, A., Huang, Y., Huryn, L., Isasi, R., Jamal, F., Jarvik, G. P., Jarvik, J., Jayadev, S., Karaviti, L., Kennedy, J., Kiley, D., Kobren, S. N., Kohane, I. S., Kohler, J. N., Krakow, D., Krasnewich, D. M., Kravets, E., Korrick, S., Koziura, M., Krier, J. B., Lalani, S. R., Lam, B., Lam, C., LaMoure, G. L., Lanpher, B. C., Lanza, I. R., Latham, L., LeBlanc, K., Lee, B. H., Lee, H., Levitt, R., Lewis, R. A., Lincoln, S. A., Liu, P., Liu, X. Z., Longo, N., Loo, S. K., Loscalzo, J., Maas, R. L., MacDowall, J., Macnamara, E. F., MacRae, C. A., Maduro, V. V., Majcherska, M. M., Mak, B. C., Maclidan, M. C., Mamounas, L. A., Manolio, T. A., Mao, R., Maravilla, K., Markello, T. C., Marom, R., Marth, G., Martin, B. A., Martin, M. G., Martinez-Agosto, J. A., Marwaha, S., McCauley, J., McConkie-Rosell, A., McCormack, C. E., McCray, A. T., McGee, E., Mefford, H., Lawrence Merritt, J., Might, M., Mirzaa, G., Morava, E., Moretti, P. M., Mosbrook-Davis, D., Mulvihill, J. J., Murdock, D. R., Nagy, A., Nakano-Okuno, M., Nath, A., Nelson, S. F., Newman, J. H., Nicholas, S. K., Nickerson, D., Nieves-Rodriguez, S., Novacic, D., Oglesbee, D., Orengo, J. P., Pace, L., Pak, S., Carl Pallais, J., Palmer, C. G., Papp, J. C., Parker, N. H., Phillips, J. A., Posey, J. E., Potocki, L., Power, B., Pusey, B. N., Quinlan, A., Raskind, W., Raja, A. N., Rao, D. A., Renteria, G., Reuter, C. M., Rives, L., Robertson, A. K., Rodan, L. H., Rosenfeld, J. A., Rosenwasser, N., Rossignol, F., Ruzhnikov, M., Sacco, R., Sampson, J. B., Samson, S. L., Saporta, M., Ron Scott, C., Schaechter, J., Schedl, T., Schoch, K., Scott, D. A., Shashi, V., Shin, J., Signer, R., Silverman, E. K., Sinsheimer, J. S., Sisco, K., Smith, E. C., Smith, K. S., Solem, E., Solnica-Krezel, L., Solomon, B., Spillmann, R. C., Stoler, J. M., Sullivan, J. A., Sullivan, K., Sun, A., Sutton, S., Sweetser, D. A., Sybert, V., Tabor, H. K., Tan, A. L., Tan, Q. K., Tekin, M., Telischi, F., Thorson, W., Thurm, A., Tifft, C. J., Toro, C., Tran, A. A., Tucker, B. M., Urv, T. K., Vanderver, A., Velinder, M., Viskochil, D., Vogel, T. P., Wahl, C. E., Wallace, S., Walley, N. M., Walsh, C. A., Walker, M., Wambach, J., Wan, J., Wang, L., Wangler, M. F., Ward, P. A., Wegner, D., Wener, M., Wenger, T., Perry, K. W., Westerfield, M., Wheeler, M. T., Whitlock, J., Wolfe, L. A., Woods, J. D., Yamamoto, S., Yang, J., Yousef, M., Zastrow, D. B., Zein, W., Zhao, C., Zuchner, S. 2021

  Abstract

  Genetic counselors (GCs) are increasingly filling important positions on research study teams, but there is limited literature describing the roles of GCs in these settings. GCs on the Undiagnosed Diseases Network (UDN) study team serve in a variety of roles across the research network and provide an opportunity to better understand genetic counselor roles in research. To quantitatively characterize the tasks regularly performed and professional fulfillment derived from these tasks, two surveys were administered to UDN GCs in a stepwise fashion. Responses from the first, free-response survey elicited the scope of tasks which informed development of a second structured, multiple-select survey. In survey 2, respondents were asked to select which roles they performed. Across 19 respondents, roles in survey 2 received a total of 947 selections averaging approximately 10 selections per role. When asked to indicate what roles they performed, respondent selected a mean of 50 roles (range 22-70). Survey 2 data were analyzed via thematic coding of responses and hierarchical cluster analysis to identify patterns in responses. From the thematic analysis, 20 non-overlapping codes emerged in seven categories: clinical interaction and care, communication, curation, leadership, participant management, research, and team management. Three themes emerged from the categories that represented the roles of GCs in the UDN: clinical care, collaboration, and curation. Cluster analyses showed that responses were more similar among individuals at the same institution than between institutions. This study highlights the ways GCs apply their unique skill set in the context of a clinical translational research network. Additionally, findings from this study reinforce the wide applicability of core skills that are part of genetic counseling training. Clinical literacy, genomics expertise and analysis, interpersonal, psychosocial and counseling skills, education, professional practice skills, and an understanding of research processes make genetic counselors well suited for such roles and poised to positively impact research experiences and outcomes for participants.

  View details for DOI 10.1002/jgc4.1493

  View details for PubMedID 34374469

• A randomized study of a best practice alert for platelet transfusions. Vox sanguinis Murphy, C., Mou, E., Pang, E., Shieh, L., Hom, J., Shah, N. 2021

  Abstract

  BACKGROUND AND OBJECTIVES: Inappropriate platelet transfusions represent an opportunity for improvements in patient care. Use of a best practice alert (BPA) as clinical decision support (CDS) for red cell transfusions has successfully reduced unnecessary red blood cell (RBC) transfusions in prior studies. We studied the impact of a platelet transfusion BPA with visibility randomized by patient chart.MATERIALS AND METHODS: A BPA was built to introduce CDS at the time of platelet ordering in the electronic health record. Alert visibility was randomized at the patient encounter level. BPA eligible platelet transfusions for patients with both visible and non-visible alerts were recorded along with reasons given for override of the BPA. Focused interviews were performed with providers who interacted with the BPA to assess its impact on their decision making.RESULTS: Over a 9-month study period, 446 patient charts were randomized. The visible alert group used 25.3% fewer BPA eligible platelets. Mean monthly usage of platelets eligible for BPA display was 65.7 for the control group and 49.1 for the visible alert group (p=0.07). BPA-eligible platelets used per inpatient day at risk per month were not significantly different between groups (2.4 vs. 2.1, p=0.53).CONCLUSION: It is feasible to study CDS via chart-based randomization. A platelet BPA reduced total platelets used over the study period and may have resulted in $151,069 in yearly savings, although there were no differences when adjusted for inpatient days at risk. During interviews, providers offered additional workflow insights allowing further improvement of CDS for platelet transfusions.

  View details for DOI 10.1111/vox.13132

  View details for PubMedID 34081800

• A resource of lipidomics and metabolomics data from individuals with undiagnosed diseases SCIENTIFIC DATA Kyle, J. E., Stratton, K. G., Zink, E. M., Kim, Y., Bloodsworth, K. J., Monroe, M. E., Bacino, C. A., Bacino, C. A., Hanchard, N. A., Lewis, R. A., Rosenfeld, J. A., Scott, D. A., Tran, A. A., Ward, P. A., Burrage, L. C., Clark, G. D., Alejandro, M. E., Posey, J. E., Wangler, M. F., Lee, B. H., Craigen, W. J., Bellen, H. J., Nicholas, S. K., Bostwick, B. L., Samson, S. L., Goldman, A. M., Moretti, P. M., Eng, C. M., Muzny, D. M., Orengo, J. P., Vogel, T. P., Lalani, S. R., Murdock, D. R., Azamian, M. S., Orange, J. S., Emrick, L. T., Dhar, S. U., Balasubramanyam, A., Potocki, L., Yamamoto, S., Yang, Y., Chen, S., Jamal, F., Karaviti, L., Marom, R., Lincoln, S. A., Walsh, C. A., Beggs, A. H., Rodan, L. H., Stoler, J. M., Berry, G. T., Cobban, L. A., MacRae, C. A., Krier, J. B., Silverman, E. K., Fieg, E. L., Maas, R. L., Loscalzo, J., Aday, A., Korrick, S., Goldstein, D. B., Stong, N., Sullivan, J. A., Spillmann, R. C., Pena, L. M., Tan, Q., Walley, N. M., Jiang, Y., McConkie-Rosell, A., Schoch, K., Shashi, V., Cope, H., Holm, I. A., Kohane, I. S., McCray, A. T., Esteves, C., LeBlanc, K., Might, M., Kelley, E., Worthey, E. A., Dorset, D. C., Boone, B. E., Levy, S. E., Birch, C. L., Jones, A. L., Brown, D. M., Bick, D. P., Newberry, J., Lazar, J., May, T., Sweetser, D. A., Briere, L. C., Pallais, J., Cooper, C. M., High, F., Walker, M., Colley, H. A., Mamounas, L. A., Manolio, T. A., Burke, E. A., Godfrey, R. A., Groden, C. A., Gahl, W. A., Wolfe, L. A., Markello, T. C., Lau, C., Draper, D. D., Gould, S. E., Nehrebecky, M. E., Wahl, C. E., Batzli, G. F., Macnamara, E. F., Dayal, J. G., Eckstein, D. J., Mulvihill, J. J., Tifft, C. J., Urv, T. K., Wise, A. L., Murphy, J. L., Gropman, A. L., Howerton, E. M., Krasnewich, D. M., Johnston, J. M., Pusey, B. N., Adams, D. R., Maduro, V. V., Malicdan, M. V., Davids, M., Estwick, T., Novacic, D., Sharma, P., Toro, C., Yu, G., Behnam, B., D'Souza, P., Ferreira, C., Morimoto, M., Baker, E. H., Yang, J., Gourdine, J. F., Brush, M., Haendel, M., Ashley, E. A., Bernstein, J. A., Sampson, J. B., Zastrow, D. B., Friedman, N. D., Merker, J. D., McCormack, C. E., Fisher, P. G., Davidson, J. M., Dries, A. M., Enns, G. M., Majcherska, M. M., Reuter, C. M., Waggott, D. M., Kohler, J. N., Coakley, T. R., Smith, K. S., Wheeler, M. T., Bonner, D., Fernandez, L., Hom, J., Huang, Y., Marwaha, S., Zhao, C., Martinez-Agosto, J. A., Dell'Angelica, E. C., Papp, J. C., Douine, E. D., Nelson, S. F., Martin, M. G., Palmer, C., Parker, N. H., Butte, M. J., Yoon, A. J., Loo, S. K., Fogel, B. L., Dipple, K. M., Sinsheimer, J. S., Allard, P., Barseghyan, H., Dorrani, N., Lee, H., Vilain, E., Eskin, A., Renteria, G., Signer, R., Wan, J., Zheng, A., Westerfield, M., Phillips, J. A., Cogan, J. D., Newman, J. H., Robertson, A. K., Hamid, R., Bican, A., Brokamp, E., Duncan, L., Kozuira, M., Rives, L., Shakachite, L., Waters, K. M., Webb-Robertson, B. M., Koeller, D. M., Metz, T. O., Undiagnosed Dis Network 2021; 8 (1): 114

  Abstract

  Every year individuals experience symptoms that remain undiagnosed by healthcare providers. In the United States, these rare diseases are defined as a condition that affects fewer than 200,000 individuals. However, there are an estimated 7000 rare diseases, and there are an estimated 25-30 million Americans in total (7.6-9.2% of the population as of 2018) affected by such disorders. The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Mass spectrometry-based metabolomics and lipidomics analyses could advance the collective understanding of individual symptoms and advance diagnoses for individuals with heretofore undiagnosed disease. Here, we report the mass spectrometry-based metabolomics and lipidomics analyses of blood plasma, urine, and cerebrospinal fluid from 148 patients within the UDN and their families, as well as from a reference population of over 100 individuals with no known metabolic diseases. The raw and processed data are available to the research community so that they might be useful in the diagnoses of current or future patients suffering from undiagnosed disorders.

  View details for DOI 10.1038/s41597-021-00894-y

  View details for Web of Science ID 000642148100001

  View details for PubMedID 33883556

  View details for PubMedCentralID PMC8060404

• The yield of thorough record review in the Undiagnosed Diseases Network Findley, L., Rosenfeld, J., Spillman, R., Cope, H., Schoch, K., Briere, L., Bernstein, J., Hom, J., Ruzhnikov, M., Kravets, E., Botto, L., Bentley, A., Newman, J., Becan, A., Mak, B., Martinez-Agosto, J., Palmer, C., Nelson, S., Parker, N., Martin, M., Fogel, B., Butte, M. ACADEMIC PRESS INC ELSEVIER SCIENCE. 2021: S187

  View details for Web of Science ID 000639219800290

• Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases. Genetics in medicine : official journal of the American College of Medical Genetics Kobren, S. N., Baldridge, D., Velinder, M., Krier, J. B., LeBlanc, K., Esteves, C., Pusey, B. N., Zuchner, S., Blue, E., Lee, H., Huang, A., Bastarache, L., Bican, A., Cogan, J., Marwaha, S., Alkelai, A., Murdock, D. R., Liu, P., Wegner, D. J., Paul, A. J., Undiagnosed Diseases Network, Sunyaev, S. R., Kohane, I. S., Acosta, M. T., Adam, M., Adams, D. R., Agrawal, P. B., Alejandro, M. E., Alvey, J., Amendola, L., Andrews, A., Ashley, E. A., Azamian, M. S., Bacino, C. A., Bademci, G., Baker, E., Balasubramanyam, A., Baldridge, D., Bale, J., Bamshad, M., Barbouth, D., Bayrak-Toydemir, P., Beck, A., Beggs, A. H., Behrens, E., Bejerano, G., Bennett, J., Berg-Rood, B., Bernstein, J. A., Berry, G. T., Bican, A., Bivona, S., Blue, E., Bohnsack, J., Bonnenmann, C., Bonner, D., Botto, L., Boyd, B., Briere, L. C., Brokamp, E., Brown, G., Burke, E. A., Burrage, L. C., Butte, M. J., Byers, P., Byrd, W. E., Carey, J., Carrasquillo, O., Chang, T. C., Chanprasert, S., Chao, H., Clark, G. D., Coakley, T. R., Cobban, L. A., Cogan, J. D., Coggins, M., Cole, F. S., Colley, H. A., Cooper, C. M., Cope, H., Craigen, W. J., Crouse, A. B., Cunningham, M., D'Souza, P., Dai, H., Dasari, S., Davis, J., Daya, J. G., Deardorff, M., Dell'Angelica, E. C., Dhar, S. U., Dipple, K., Doherty, D., Dorrani, N., Doss, A. L., Douine, E. D., Draper, D. D., Duncan, L., Earl, D., Eckstein, D. J., Emrick, L. T., Eng, C. M., Esteves, C., Falk, M., Fernandez, L., Ferreira, C., Fieg, E. L., Findley, L. C., Fisher, P. G., Fogel, B. L., Forghani, I., Fresard, L., Gahl, W. A., Glass, I., Gochuico, B., Godfrey, R. A., Golden-Grant, K., Goldman, A. M., Goldrich, M. P., Goldstein, D. B., Grajewski, A., Groden, C. A., Gutierrez, I., Hahn, S., Hamid, R., Hanchard, N. A., Hassey, K., Hayes, N., High, F., Hing, A., Hisama, F. M., Holm, I. A., Hom, J., Horike-Pyne, M., Huang, A., Huang, Y., Huryn, L., Isasi, R., Jamal, F., Jarvik, G. P., Jarvik, J., Jayadev, S., Karaviti, L., Kennedy, J., Kiley, D., Kohane, I. S., Kohler, J. N., Korrick, S., Kozuira, M., Krakow, D., Krasnewich, D. M., Kravets, E., Krier, J. B., LaMoure, G. L., Lalani, S. R., Lam, B., Lam, C., Lanpher, B. C., Lanza, I. R., Latham, L., LeBlanc, K., Lee, B. H., Lee, H., Levitt, R., Lewis, R. A., Lincoln, S. A., Liu, P., Liu, X. Z., Longo, N., Loo, S. K., Loscalzo, J., Maas, R. L., MacDowall, J., MacRae, C. A., Macnamara, E. F., Maduro, V. V., Majcherska, M. M., Mak, B. C., Malicdan, M. C., Mamounas, L. A., Manolio, T. A., Mao, R., Maravilla, K., Markello, T. C., Marom, R., Marth, G., Martin, B. A., Martin, M. G., Martinez-Agosto, J. A., Marwaha, S., McCauley, J., McConkie-Rosell, A., McCormack, C. E., McCray, A. T., McGee, E., Mefford, H., Merritt, J. L., Might, M., Mirzaa, G., Morava, E., Moretti, P. M., Moretti, P., Mosbrook-Davis, D., Mulvihill, J. J., Murdock, D. R., Nagy, A., Nakano-Okuno, M., Nath, A., Nelson, S. F., Newman, J. H., Nicholas, S. K., Nickerson, D., Nieves-Rodriguez, S., Novacic, D., Oglesbee, D., Orengo, J. P., Pace, L., Pak, S., Pallais, J. C., Palmer, C. G., Papp, J. C., Parker, N. H., Phillips, J. A., Posey, J. E., Potocki, L., Power, B., Pusey, B. N., Quinlan, A., Raja, A. N., Rao, D. A., Raskind, W., Renteria, G., Reuter, C. M., Rives, L., Robertson, A. K., Rodan, L. H., Rosenfeld, J. A., Rosenwasser, N., Rossignol, F., Ruzhnikov, M., Sacco, R., Sampson, J. B., Samson, S. L., Saporta, M., Schaechter, J., Schedl, T., Schoch, K., Scott, C. R., Scott, D. A., Shashi, V., Shin, J., Signer, R. H., Silverman, E. K., Sinsheimer, J. S., Sisco, K., Smith, E. C., Smith, K. S., Solem, E., Solnica-Krezel, L., Ben Solomon, S., Spillmann, R. C., Stoler, J. M., Sullivan, J. A., Sullivan, K., Sun, A., Sutton, S., Sweetser, D. A., Sybert, V., Tabor, H. K., Tan, A. L., Tan, Q. K., Tekin, M., Telischi, F., Thorson, W., Thurm, A., Tifft, C. J., Toro, C., Tran, A. A., Tucker, B. M., Urv, T. K., Vanderver, A., Velinder, M., Viskochil, D., Vogel, T. P., Wahl, C. E., Walker, M., Wallace, S., Walley, N. M., Walsh, C. A., Wambach, J., Wan, J., Wang, L., Wangler, M. F., Ward, P. A., Wegner, D., Wener, M., Wenger, T., Perry, K. W., Westerfield, M., Wheeler, M. T., Whitlock, J., Wolfe, L. A., Woods, J. D., Yamamoto, S., Yang, J., Yousef, M., Zastrow, D. B., Zein, W., Zhao, C., Zuchner, S. 2021

  Abstract

  PURPOSE: Genomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful.METHODS: We collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols.RESULTS: We found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases.CONCLUSION: The largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.

  View details for DOI 10.1038/s41436-020-01084-8

  View details for PubMedID 33580225

• Student engagement in the online classroom: comparing preclinical medical student question-asking behaviors in a videoconference versus in-person learning environment. FASEB bioAdvances Caton, J. B., Chung, S., Adeniji, N., Hom, J., Brar, K., Gallant, A., Bryant, M., Hain, A., Basaviah, P., Hosamani, P. 2021; 3 (2): 110–17

  Abstract

  The COVID-19 pandemic forced medical schools to rapidly transform their curricula using online learning approaches. At our institution, the preclinical Practice of Medicine (POM) course was transitioned to large-group, synchronous, video-conference sessions. The aim of this study is to assess whether there were differences in learner engagement, as evidenced by student question-asking behaviors between in-person and videoconferenced sessions in one preclinical medical student course. In Spring, 2020, large-group didactic sessions in POM were converted to video-conference sessions. During these sessions, student microphones were muted, and video capabilities were turned off. Students submitted typed questions via a Q&A box, which was monitored by a senior student teaching assistant. We compared student question asking behavior in recorded video-conference course sessions from POM in Spring, 2020 to matched, recorded, in-person sessions from the same course in Spring, 2019. We found that, on average, the instructors answered a greater number of student questions and spent a greater percentage of time on Q&A in the online sessions compared with the in-person sessions. We also found that students asked a greater number of higher complexity questions in the online version of the course compared with the in-person course. The video-conference learning environment can promote higher student engagement when compared with the in-person learning environment, as measured by student question-asking behavior. Developing an understanding of the specific elements of the online learning environment that foster student engagement has important implications for instructional design in both the online and in-person setting.

  View details for DOI 10.1096/fba.2020-00089

  View details for PubMedID 33615156

• Effect of electronic clinical decision support on inappropriate prescriptions in older adults. Journal of the American Geriatrics Society Singhal, S., Krishnamurthy, A., Wang, B., Weng, Y., Sharp, C., Shah, N., Ahuja, N., Hosamani, P., Periyakoil, V. S., Hom, J. 2021

  View details for DOI 10.1111/jgs.17608

  View details for PubMedID 34877652

• OrderRex clinical user testing: a randomized trial of recommender system decision support on simulated cases. Journal of the American Medical Informatics Association : JAMIA Kumar, A., Aikens, R. C., Hom, J., Shieh, L., Chiang, J., Morales, D., Saini, D., Musen, M., Baiocchi, M., Altman, R., Goldstein, M. K., Asch, S., Chen, J. H. 2020

  Abstract

  OBJECTIVE: To assess usability and usefulness of a machine learning-based order recommender system applied to simulated clinical cases.MATERIALS AND METHODS: 43 physicians entered orders for 5 simulated clinical cases using a clinical order entry interface with or without access to a previously developed automated order recommender system. Cases were randomly allocated to the recommender system in a 3:2 ratio. A panel of clinicians scored whether the orders placed were clinically appropriate. Our primary outcome included the difference in clinical appropriateness scores. Secondary outcomes included total number of orders, case time, and survey responses.RESULTS: Clinical appropriateness scores per order were comparable for cases randomized to the order recommender system (mean difference -0.11 order per score, 95% CI: [-0.41, 0.20]). Physicians using the recommender placed more orders (median 16 vs 15 orders, incidence rate ratio 1.09, 95%CI: [1.01-1.17]). Case times were comparable with the recommender system. Order suggestions generated from the recommender system were more likely to match physician needs than standard manual search options. Physicians used recommender suggestions in 98% of available cases. Approximately 95% of participants agreed the system would be useful for their workflows.DISCUSSION: User testing with a simulated electronic medical record interface can assess the value of machine learning and clinical decision support tools for clinician usability and acceptance before live deployments.CONCLUSIONS: Clinicians can use and accept machine learned clinical order recommendations integrated into an electronic order entry interface in a simulated setting. The clinical appropriateness of orders entered was comparable even when supported by automated recommendations.

  View details for DOI 10.1093/jamia/ocaa190

  View details for PubMedID 33106874

• Carotid plaque imaging and the risk of atherosclerotic cardiovascular disease. Cardiovascular diagnosis and therapy Zhu, G., Hom, J., Li, Y., Jiang, B., Rodriguez, F., Fleischmann, D., Saloner, D., Porcu, M., Zhang, Y., Saba, L., Wintermark, M. 2020; 10 (4): 1048-1067

  Abstract

  Carotid artery plaque is a measure of atherosclerosis and is associated with future risk of atherosclerotic cardiovascular disease (ASCVD), which encompasses coronary, cerebrovascular, and peripheral arterial diseases. With advanced imaging techniques, computerized tomography (CT) and magnetic resonance imaging (MRI) have shown their potential superiority to routine ultrasound to detect features of carotid plaque vulnerability, such as intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), fibrous cap (FC), and calcification. The correlation between imaging features and histological changes of carotid plaques has been investigated. Imaging of carotid features has been used to predict the risk of cardiovascular events. Other techniques such as nuclear imaging and intra-vascular ultrasound (IVUS) have also been proposed to better understand the vulnerable carotid plaque features. In this article, we review the studies of imaging specific carotid plaque components and their correlation with risk scores.

  View details for DOI 10.21037/cdt.2020.03.10

  View details for PubMedID 32968660

  View details for PubMedCentralID PMC7487384

• Carotid plaque imaging and the risk of atherosclerotic cardiovascular disease CARDIOVASCULAR DIAGNOSIS AND THERAPY Zhu, G., Hom, J., Li, Y., Jiang, B., Rodriguez, F., Fleischmann, D., Saloner, D., Porcu, M., Zhang, Y., Saba, L., Wintermark, M. 2020; 10 (4): 1048–67

  View details for DOI 10.21037/cdt.2020.03.10

  View details for Web of Science ID 000561681200004

• Assessment of the Radiology Support, Communication and Alignment Network to Reduce Medical Imaging Overutilization: A Multipractice Cohort Study. Journal of the American College of Radiology : JACR Rezaii, P. G., Fredericks, N., Lincoln, C. M., Hom, J., Willis, M., Burleson, J., Haines, G. R., Chatfield, M., Boothroyd, D., Ding, V. Y., Bello, J. A., McGinty, G. B., Smith, C. D., Yucel, E. K., Hillman, B., Thorwarth, W. T., Wintermark, M. 2020; 17 (5): 597–605

  Abstract

  PURPOSE: The aim of this study was to determine whether participation in Radiology Support, Communication and Alignment Network (R-SCAN) results in a reduction of inappropriate imaging in a wide range of real-world clinical environments.METHODS: This quality improvement study used imaging data from 27 US academic and private practices that completed R-SCAN projects between January 25, 2015, and August 8, 2018. Each project consisted of baseline, educational (intervention), and posteducational phases. Baseline and posteducational imaging cases were rated as high, medium, or low value on the basis of validated ACR Appropriateness Criteria. Four cohorts were generated: a comprehensive cohort that included all eligible practices and three topic-specific cohorts that included practices that completed projects of specific Choosing Wisely topics (pulmonary embolism, adnexal cyst, and low back pain). Changes in the proportion of high-value cases after R-SCAN intervention were assessed for each cohort using generalized estimating equation logistic regression, and changes in the number of low-value cases were analyzed using Poisson regression.RESULTS: Use of R-SCAN in the comprehensive cohort resulted in a greater proportion of high-value imaging cases (from 57% to 79%; odds ratio, 2.69; 95% confidence interval, 1.50-4.86; P= .001) and 345 fewer low-value cases after intervention (incidence rate ratio, 0.45; 95% confidence interval, 0.29-0.70; P < .001). Similar changes in proportion of high-value cases and number of low-value cases were found for the pulmonary embolism, adnexal cyst, and low back pain cohorts.CONCLUSIONS: R-SCAN participation was associated with a reduced likelihood of inappropriate imaging and is thus a promising tool to enhance the quality of patient care and promote wise use of health care resources.

  View details for DOI 10.1016/j.jacr.2020.02.011

  View details for PubMedID 32371000

• Everything Every Radiologist Always Wanted (and Needs) to Know About Clinical Decision Support. Journal of the American College of Radiology : JACR Wintermark, M., Willis, M. H., Hom, J., Franceschi, A. M., Fotos, J. S., Mosher, T., Cruciata, G., Reuss, T., Horton, R., Fredericks, N., Burleson, J., Haines, B., Bruno, M. 2020; 17 (5): 568–73

  Abstract

  As of January 2020, clinical decision support needs to be implemented across US health systems for advanced diagnostic imaging services. This article reviews the history, importance, and hurdles of clinical decision support and discusses a few pearls and pitfalls regarding its implementation.

  View details for DOI 10.1016/j.jacr.2020.03.016

  View details for PubMedID 32370997

• A minimalist electronic health record-based intervention to reduce standing lab utilisation. Postgraduate medical journal Chin, K., Krishnamurthy, A., Zubair, T., Ramaswamy, T., Hom, J., Maggio, P., Shieh, L. 2020

  Abstract

  BACKGROUND: Repetitive laboratory testing in stable patients is low-value care. Electronic health record (EHR)-based interventions are easy to disseminate but can be restrictive.OBJECTIVE: To evaluate the effect of a minimally restrictive EHR-based intervention on utilisation.SETTING: One year before and after intervention at a 600-bed tertiary care hospital. 18000 patients admitted to General Medicine, General Surgery and the Intensive Care Unit (ICU).INTERVENTION: Providers were required to specify the number of times each test should occur instead of being able to order them indefinitely.MEASUREMENTS: For eight tests, utilisation (number of labs performed per patient day) and number of associated orders were measured.RESULTS: Utilisation decreased for some tests on all services. Notably, complete blood count with differential decreased 9% (p<0.001) on General Medicine and 21% (p<0.001) in the ICU.CONCLUSIONS: Requiring providers to specify the number of occurrences of labs changes significantly reduces utilisation in some cases.

  View details for DOI 10.1136/postgradmedj-2019-136992

  View details for PubMedID 32051280

• Portable Ultrasound Device Usage and Learning Outcomes Among Internal Medicine Trainees: A Parallel-Group Randomized Trial. Journal of hospital medicine Kumar, A., Weng, Y., Wang, L., Bentley, J., Almli, M., Hom, J., Witteles, R., Ahuja, N., Kugler, J. 2020; 15 (2): e1–e6

  Abstract

  BACKGROUND: Little is known about how to effectively train residents with point-of-care ultrasonography (POCUS) despite increasing usage.OBJECTIVE: This study aimed to assess whether handheld ultrasound devices (HUDs), alongside a year-long lecture series, improved trainee image interpretation skills with POCUS.METHODS: Internal medicine intern physicians (N = 149) at a single academic institution from 2016 to 2018 participated in the study. The 2017 interns (n = 47) were randomized 1:1 to receive personal HUDs (n = 24) for patient care vs no-HUDs (n = 23). All 2017 interns received a repeated lecture series regarding cardiac, thoracic, and abdominal POCUS. Interns were assessed on their ability to interpret POCUS images of normal/abnormal findings. The primary outcome was the difference in end-of-the-year assessment scores between interns randomized to receive HUDs vs not. Secondary outcomes included trainee scores after repeating lectures and confidence with POCUS. Intern scores were also compared with historical (2016, N = 50) and contemporaneous (2018, N = 52) controls who received no lectures.RESULTS: Interns randomized to HUDs did not have significantly higher image interpretation scores (median HUD score: 0.84 vs no-HUD score: 0.84; P = .86). However, HUD interns felt more confident in their abilities. The 2017 cohort had higher scores (median 0.84), compared with the 2016 historical control (median 0.71; P = .001) and 2018 contemporaneous control (median 0.48; P < .001). Assessment scores improved after first-time exposure to the lecture series, while repeated lectures did not improve scores.CONCLUSIONS: Despite feeling more confident, personalized HUDs did not improve interns' POCUS-related knowledge or interpretive ability. Repeated lecture exposure without further opportunities for deliberate practice may not be beneficial for mastering POCUS.

  View details for DOI 10.12788/jhm.3351

  View details for PubMedID 32118565

• Clinical sites of the Undiagnosed Diseases Network: unique contributions to genomic medicine and science. Genetics in medicine : official journal of the American College of Medical Genetics Schoch, K. n., Esteves, C. n., Bican, A. n., Spillmann, R. n., Cope, H. n., McConkie-Rosell, A. n., Walley, N. n., Fernandez, L. n., Kohler, J. N., Bonner, D. n., Reuter, C. n., Stong, N. n., Mulvihill, J. J., Novacic, D. n., Wolfe, L. n., Abdelbaki, A. n., Toro, C. n., Tifft, C. n., Malicdan, M. n., Gahl, W. n., Liu, P. n., Newman, J. n., Goldstein, D. B., Hom, J. n., Sampson, J. n., Wheeler, M. T., Cogan, J. n., Bernstein, J. A., Adams, D. R., McCray, A. T., Shashi, V. n. 2020

  Abstract

  The NIH Undiagnosed Diseases Network (UDN) evaluates participants with disorders that have defied diagnosis, applying personalized clinical and genomic evaluations and innovative research. The clinical sites of the UDN are essential to advancing the UDN mission; this study assesses their contributions relative to standard clinical practices.We analyzed retrospective data from four UDN clinical sites, from July 2015 to September 2019, for diagnoses, new disease gene discoveries and the underlying investigative methods.Of 791 evaluated individuals, 231 received 240 diagnoses and 17 new disease-gene associations were recognized. Straightforward diagnoses on UDN exome and genome sequencing occurred in 35% (84/240). We considered these tractable in standard clinical practice, although genome sequencing is not yet widely available clinically. The majority (156/240, 65%) required additional UDN-driven investigations, including 90 diagnoses that occurred after prior nondiagnostic exome sequencing and 45 diagnoses (19%) that were nongenetic. The UDN-driven investigations included complementary/supplementary phenotyping, innovative analyses of genomic variants, and collaborative science for functional assays and animal modeling.Investigations driven by the clinical sites identified diagnostic and research paradigms that surpass standard diagnostic processes. The new diagnoses, disease gene discoveries, and delineation of novel disorders represent a model for genomic medicine and science.

  View details for DOI 10.1038/s41436-020-00984-z

  View details for PubMedID 33093671

• Physician Usage and Acceptance of a Machine Learning Recommender System for Simulated Clinical Order Entry. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Chiang, J., Kumar, A., Morales, D., Saini, D., Hom, J., Shieh, L., Musen, M., Goldstein, M. K., Chen, J. H. 2020; 2020: 89–97

  Abstract

  Clinical decision support tools that automatically disseminate patterns of clinical orders have the potential to improve patient care by reducing errors of omission and streamlining physician workflows. However, it is unknown if physicians will accept such tools or how their behavior will be affected. In this randomized controlled study, we exposed 34 licensed physicians to a clinical order entry interface and five simulated emergency cases, with randomized availability of a previously developed clinical order recommender system. With the recommender available, physicians spent similar time per case (6.7 minutes), but placed more total orders (17.1 vs. 15.8). The recommender demonstrated superior recall (59% vs 41%) and precision (25% vs 17%) compared to manual search results, and was positively received by physicians recognizing workflow benefits. Further studies must assess the potential clinical impact towards a future where electronic health records automatically anticipate clinical needs.

  View details for PubMedID 32477627

• Characteristics and Financial Impact of Potentially Inappropriate Platelet Transfusion in the Inpatient Hospital Setting Mou, E., Murphy, C., Hom, J., Shieh, L., Shah, N. AMER SOC HEMATOLOGY. 2019

  View details for DOI 10.1182/blood-2019-125949

  View details for Web of Science ID 000577160404054

• Carotid Artery Imaging Is More Strongly Associated With the 10-Year Atherosclerotic Cardiovascular Disease Score Than Coronary Artery Imaging. Journal of computer assisted tomography Li, Y., Zhu, G., Ding, V., Jiang, B., Boothroyd, D., Rodriguez, F., Fleischmann, D., Desai, M., Saloner, D., Saba, L., Hom, J., Wintermark, M. 2019; 43 (5): 679–85

  Abstract

  PURPOSE: The aim of this study was to compare coronary and carotid artery imaging and determine which one shows the strongest association with atherosclerotic cardiovascular disease (ASCVD) score.PATIENTS AND METHODS: Two separate series patients who underwent either coronary computed tomography angiography (CTA) or carotid CTA were included. We recorded the ASCVD scores and assessed the CTA imaging. Two thirds were used to build predictive models, and the remaining one third generated predicted ASCVD scores. The Bland-Altman analysis analyzed the concordance.RESULTS: A total of 110 patients were included in each group. There was no significant difference between clinical characteristics. Three imaging variables were included in the carotid model. Two coronary models (presence of calcium or Agatston score) were created. The bias between true and predicted ASCVD scores was 0.37 ± 5.72% on the carotid model, and 2.07 ± 7.18% and 2.47 ± 7.82% on coronary artery models, respectively.CONCLUSIONS: Both carotid and coronary artery imaging features can predict ASCVD score. The carotid artery was more associated to the ASCVD score than the coronary artery.

  View details for DOI 10.1097/RCT.0000000000000920

  View details for PubMedID 31609291

• Effect of Electronic Clinical Decision Support on 25(OH) Vitamin D Testing JOURNAL OF GENERAL INTERNAL MEDICINE Chin, K., Hom, J., Tan, M., Sharp, C., Wang, S., Chen, Y., Chen, D. 2019; 34 (9): 1697–99

  View details for DOI 10.1007/s11606-019-05057-9

  View details for Web of Science ID 000483539200013

• Extracutaneous manifestations in phacomatosis cesioflammea and cesiomarmorata: Case series and literature review AMERICAN JOURNAL OF MEDICAL GENETICS PART A Kumar, A., Zastrow, D. B., Kravets, E. J., Beleford, D., Ruzhnikov, M. Z., Grove, M. E., Dries, A. M., Kohler, J. N., Waggott, D. M., Yang, Y., Huang, Y., Mackenzie, K. M., Eng, C. M., Fisher, P. G., Ashley, E. A., Teng, J. M., Stevenson, D. A., Shieh, J. T., Wheeler, M. T., Bernstein, J. A., Adams, D. R., Aday, A., Alejandro, M. E., Allard, P., Azamian, M. S., Bacino, C. A., Baker, E., Balasubramanyam, A., Barseghyan, H., Batzli, G. F., Beggs, A. H., Behnam, B., Bellen, H. J., Bican, A., Bick, D. P., Birch, C. L., Bonner, D., Boone, B. E., Bostwick, B. L., Briere, L. C., Brokamp, E., Brown, D. M., Brush, M., Burke, E. A., Burrage, L. C., Butte, M. J., Chen, S., Clark, G. D., Coakley, T. R., Cogan, J. D., Colley, H. A., Cooper, C. M., Cope, H., Craigen, W. J., D'Souza, P., Davids, M., Davidson, J. M., Dayal, J. G., Dell'Angelica, E. C., Dhar, S. U., Dipple, K. M., Donnell-Fink, L. A., Dorrani, N., Dorset, D. C., Douine, E. D., Draper, D. D., Eckstein, D. J., Emrick, L. T., Enns, G. M., Eskin, A., Esteves, C., Estwick, T., Fairbrother, L., Fernandez, L., Ferreira, C., Fieg, E. L., Fogel, B. L., Friedman, N. D., Gahl, W. A., Glanton, E., Godfrey, R. A., Goldman, A. M., Goldstein, D. B., Gould, S. E., Gourdine, J. F., Groden, C. A., Gropman, A. L., Haendel, M., Hamid, R., Hanchard, N. A., High, F., Holm, I. A., Hom, J., Howerton, E. M., Jamal, F., Jiang, Y., Johnston, J. M., Jones, A. L., Karaviti, L., Koeller, D. M., Kohane, I. S., Krasnewich, D. M., Korrick, S., Koziura, M., Krier, J. B., Kyle, J. E., Lalani, S. R., Lau, C., Lazar, J., LeBlanc, K., Lee, B. H., Lee, H., Levy, S. E., Lewis, R. A., Lincoln, S. A., Loo, S. K., Loscalzo, J., Maas, R. L., Macnamara, E. F., MacRae, C. A., Maduro, V. V., Majcherska, M. M., Malicdan, M., Mamounas, L. A., Manolio, T. A., Markello, T. C., Marom, R., Martin, M. G., Martinez-Agosto, J. A., Marwaha, S., May, T., McConkie-Rosell, A., McCormack, C. E., McCray, A. T., Merker, J. D., Metz, T. O., Might, M., Moretti, P. M., Morimoto, M., Mulvihill, J. J., Murdock, D. R., Murphy, J. L., Muzny, D. M., Nehrebecky, M. E., Nelson, S. F., Newberry, J., Newman, J. H., Nicholas, S. K., Novacic, D., Orange, J. S., Orengo, J. P., Pallais, J., Palmer, C. S., Papp, J. C., Parker, N. H., Pena, L. M., Phillips, J. A., Posey, J. E., Postlethwait, J. H., Potocki, L., Pusey, B. N., Reuter, C. M., Rives, L., Robertson, A. K., Rodan, L. H., Rosenfeld, J. A., Sampson, J. B., Samson, S. L., Schoch, K., Scott, D. A., Shakachite, L., Sharma, P., Shashi, V., Signer, R., Silverman, E. K., Sinsheimer, J. S., Smith, K. S., Spillmann, R. C., Staler, J. M., Stong, N., Sullivan, J. A., Sweetser, D. A., Tan, Q., Tifft, C. J., Toro, C., Tran, A. A., Urv, T. K., Vilain, E., Vogel, T. P., Wahl, C. E., Walley, N. M., Walsh, C. A., Walker, M., Wan, J., Wangler, M. F., Ward, P. A., Waters, K. M., Webb-Robertson, B. M., Westerfield, M., Wise, A. L., Wolfe, L. A., Worthey, E. A., Yamamoto, S., Yang, J., Yoon, A. J., Yu, G., Zhao, C., Zheng, A., Undiagnosed Dis Network 2019; 179 (6): 966–77

  View details for DOI 10.1002/ajmg.a.61134

  View details for Web of Science ID 000468322800015

• Developing a genomics rotation: Practical training around variant interpretation for genetic counseling students JOURNAL OF GENETIC COUNSELING Grove, M. E., White, S., Fisk, D. G., Rego, S., Dagan-Rosenfeld, O., Kohler, J. N., Reuter, C. M., Bonner, D., Wheeler, M. T., Bernstein, J. A., Ormond, K. E., Hanson-Kahn, A. K., Undiagnosed Dis Network 2019; 28 (2): 466–76

  View details for DOI 10.1002/jgc4.1094

  View details for Web of Science ID 000463993600030

• A toolkit for genetics providers in follow-up of patients with non-diagnostic exome sequencing JOURNAL OF GENETIC COUNSELING Zastrow, D. B., Kohler, J. N., Bonner, D., Reuter, C. M., Fernandez, L., Grove, M. E., Fisk, D. G., Yang, Y., Eng, C. M., Ward, P. A., Bick, D., Worthey, E. A., Fisher, P. G., Ashley, E. A., Bernstein, J. A., Wheeler, M. T., Adams, D. R., Aday, A., Alejandro, M. E., Allard, P., Ashley, E. A., Azamian, M. S., Bacino, C. A., Baker, E., Balasubramanyam, A., Barseghyan, H., Batzli, G. F., Beggs, A. H., Behnam, B., Bellen, H. J., Bernstein, J. A., Bican, A., Bick, D. P., Birch, C. L., Boone, B. E., Bostwick, B. L., Briere, L. C., Brokamp, E., Brown, D. M., Brush, M., Burke, E. A., Burrage, L. C., Butte, M. J., Chen, S., Clark, G. D., Coakley, T. R., Cogan, J. D., Colley, H. A., Cooper, C. M., Cope, H., Craigen, W. J., D'Souza, P., Davids, M., Dayal, J. G., Dell'Angelica, E. C., Dhar, S. U., Dipple, K. M., Donnell-Fink, L. A., Dorrani, N., Dorset, D. C., Douine, E. D., Draper, D. D., Dries, A. M., Eckstein, D. J., Emrick, L. T., Eng, C. M., Enns, G. M., Eskin, A., Esteves, C., Estwick, T., Fairbrother, L., Ferreira, C., Fieg, E. L., Fisher, P. G., Fogel, B. L., Gahl, W. A., Glanton, E., Godfrey, R. A., Goldman, A. M., Goldstein, D. B., Gould, S. E., Gourdine, J. F., Groden, C. A., Gropman, A. L., Haendel, M., Hamid, R., Hanchard, N. A., High, F., Holm, I. A., Hom, J., Howerton, E. M., Huang, Y., Jamal, F., Jiang, Y., Johnston, J. M., Jones, A. L., Karaviti, L., Koeller, D. M., Kohane, I. S., Krasnewich, D. M., Korrick, S., Koziura, M., Krier, J. B., Kyle, J. E., Lalani, S. R., Lau, C., Lazar, J., LeBlanc, K., Lee, B. H., Lee, H., Levy, S. E., Lewis, R. A., Lincoln, S. A., Loo, S. K., Loscalzo, J., Maas, R. L., Macnamara, E. F., MacRae, C. A., Maduro, V. V., Majcherska, M. M., Malicdan, M. V., Mamounas, L. A., Manolio, T. A., Markello, T. C., Marom, R., Martin, G., Martinez-Agosto, J. A., Marwaha, S., May, T., McConkie-Rosell, A., McCormack, C. E., McCray, A. T., Merker, J. D., Metz, T. O., Might, M., Moretti, P. M., Morimoto, M., Nehrebecky, M. E., Nelson, S. F., Newberry, J., Newman, J. H., Nicholas, S. K., Novacic, D., Orange, J. S., Orengo, J. P., Pallais, J., Palmer, C. S., Papp, J. C., Postlethwait, J. H., Potocki, L., Pusey, B. N., Rives, L., Robertson, A. K., Rodan, L. H., Rosenfeld, J. A., Sampson, J. B., Samson, S. L., Schoch, K., Scott, D. A., Shakachite, L., Sharma, P., Shashi, V., Signer, R., Silverman, E. K., Sinsheimer, J. S., Smith, K. S., Spillmann, R. C., Stoler, J. M., Stong, N., Sullivan, J. A., Sweetser, D. A., Tan, Q., Tifft, C. J., Toro, C., Tran, A. A., Urv, T. K., Vilain, E., Vogel, T. P., Waggott, D. M., Wahl, C. E., Walley, N. M., Walsh, C. A., Walker, M., Wan, J., Wangler, M. F., Ward, P. A., Waters, K. M., Webb-Robertson, B. M., Westerfield, M., Wheeler, M. T., Wise, A. L., Wolfe, L. A., Worthey, E. A., Yamamoto, S., Yang, J., Yang, Y., Yoon, A. J., Yu, G., Zhao, C., Zheng, A., Undiagnosed Dis Network 2019; 28 (2): 213–28

  View details for DOI 10.1002/jgc4.1119

  View details for Web of Science ID 000463993600005

• Characterizing electronic health record usage patterns of inpatient medicine residents using event log data PLOS ONE Wang, J. K., Ouyang, D., Hom, J., Chi, J., Chen, J. H. 2019; 14 (2)

  View details for DOI 10.1371/journal.pone.0205379

  View details for Web of Science ID 000457874000003

• Characteristics and Financial Impact of Potentially Inappropriate Platelet Transfusion in the Inpatient Hospital Setting. Mou, E., Murphy, C., Hom, J., Shieh, L., Shah, N. 2019
• An Electronic Best Practice Alert Based on Choosing Wisely Guidelines Reduces Thrombophilia Testing in the Outpatient Setting JOURNAL OF GENERAL INTERNAL MEDICINE Jun, T., Kwang, H., Mou, E., Berube, C., Bentley, J., Shieh, L., Hom, J. 2019; 34 (1): 29-30

  View details for DOI 10.1007/s11606-018-4663-8

  View details for Web of Science ID 000454888300013

• Thrombophilia testing in the inpatient setting: impact of an educational intervention. BMC medical informatics and decision making Kwang, H. n., Mou, E. n., Richman, I. n., Kumar, A. n., Berube, C. n., Kaimal, R. n., Ahuja, N. n., Harman, S. n., Johnson, T. n., Shah, N. n., Witteles, R. n., Harrington, R. n., Shieh, L. n., Hom, J. n. 2019; 19 (1): 167

  Abstract

  Thrombophilia testing is frequently ordered in the inpatient setting despite its limited impact on clinical decision-making and unreliable results in the setting of acute thrombosis or ongoing anticoagulation. We sought to determine the effect of an educational intervention in reducing inappropriate thrombophilia testing for hospitalized patients.During the 2014 academic year, we implemented an educational intervention with a phase implementation design for Internal Medicine interns at Stanford University Hospital. The educational session covering epidemiology, appropriate thrombophilia evaluation and clinical rationale behind these recommendations. Their ordering behavior was compared with a contemporaneous control (non-medicine and private services) and a historical control (interns from prior academic year). From the analyzed data, we determined the proportion of inappropriate thrombophilia testing of each group. Logistic generalized estimating equations were used to estimate odds ratios for inappropriate thrombophilia testing associated with the intervention.Of 2151 orders included, 934 were deemed inappropriate (43.4%). The two intervention groups placed 147 orders. A pooled analysis of ordering practices by intervention groups revealed a trend toward reduction of inappropriate ordering (p = 0.053). By the end of the study, the intervention groups had significantly lower rates of inappropriate testing compared to historical or contemporaneous controls.A brief educational intervention was associated with a trend toward reduction in inappropriate thrombophilia testing. These findings suggest that focused education on thrombophilia testing can positively impact inpatient ordering practices.

  View details for DOI 10.1186/s12911-019-0889-6

  View details for PubMedID 31429747

• Genomics in medicine: a novel elective rotation for internal medicine residents. Postgraduate medical journal Geng, L. N., Kohler, J. N., Levonian, P. n., Bernstein, J. A., Ford, J. M., Ahuja, N. n., Witteles, R. n., Hom, J. n., Wheeler, M. n. 2019

  Abstract

  It is well recognised that medical training globally and at all levels lacks sufficient incorporation of genetics and genomics education to keep up with the rapid advances and growing application of genomics to clinical care. However, the best strategy to implement these desired changes into postgraduate medical training and engage learners is still unclear. We developed a novel elective rotation in 'Genomic Medicine and Undiagnosed Diseases' for categorical Internal Medicine Residents to address this educational gap and serve as an adaptable model for training that can be applied broadly across different specialties and at other institutions. Key curriculum goals achieved include increased understanding about genetic testing modalities and tools available for diagnosis and risk analysis, the role of genetics-trained allied health professionals, and indications and limitations of genetic and genomic testing in both rare and common conditions.

  View details for DOI 10.1136/postgradmedj-2018-136355

  View details for PubMedID 31439813

• Effect of Electronic Clinical Decision Support on Imaging for the Evaluation of Acute Low Back Pain in the Ambulatory Care Setting. World neurosurgery Chen, D. n., Bhambhvani, H. P., Hom, J. n., Mahoney, M. n., Wintermark, M. n., Sharp, C. n., Ratliff, J. n., Chen, Y. R. 2019

  Abstract

  To assess the effectiveness of a clinical decision support tool consisting of an electronic medical record Best Practice Alert (BPA) on the frequency of lumbar imaging in patients with acute low back pain (LBP) in the ambulatory care setting. To understand why providers order imaging outside of clinical guidelines.We implemented a BPA pop-up alert on 3/23/16 that informed the ordering physician of the Choosing Wisely recommendation to not order imaging within the first 6 weeks of low back pain in the absence of red flags. We measured imaging rates 1 year before and after implementation of the BPA. To override the BPA, providers could ignore the alert or explain their rationale for ordering imaging using either pre-set options or free-text submission. We tracked pre-set options and manually reviewed 125 free-text submissions.Significant decreases in both total imaging rate (9.6% decrease, p = 0.02) and MRI rate (14.9% decrease, p < 0.01) were observed after implementation of the BPA. No change was found in the rates of x-ray or CT ordering. 64% of providers used pre-set options in overriding the BPA, while 36% of providers entered a free-text submission. Among those providers using a free-text submission, 56% entered a non-guideline supported rationale.The present study demonstrates the effectiveness of a simple, low-cost clinical decision support tool in reducing imaging rates for patients with acute low back pain. We additionally identify reasons providers order imaging outside of clinical guidelines.

  View details for DOI 10.1016/j.wneu.2019.11.031

  View details for PubMedID 31733384

• A Patient with Sjogren's Syndrome and Subsequent Diagnosis of Inclusion Body Myositis and Light-Chain Amyloidosis. Journal of general internal medicine Hom, J. n., Marwaha, S. n., Postolova, A. n., Kittle, J. n., Vasquez, R. n., Davidson, J. n., Kohler, J. n., Dries, A. n., Fernandez-Betancourt, L. n., Majcherska, M. n., Dearlove, J. n., Raghavan, S. n., Vogel, H. n., Bernstein, J. A., Fisher, P. n., Ashley, E. n., Sampson, J. n., Wheeler, M. n. 2019

  Abstract

  We discuss a challenging case of a 58-year-old Vietnamese-American woman who presented to her new primary care provider with an 8-year history of slowly progressive dysphagia, hoarseness, muscle weakness with associated frequent falls, and weight loss. She eventually reported dry eyes and dry mouth, and she was diagnosed with Sjogren's syndrome. Subsequently, she was additionally diagnosed with inclusion body myositis and gastric light-chain (AL) amyloidosis. Although inclusion body myositis has been previously associated with Sjogren's syndrome, inclusion body myositis is rare in non-Caucasians, and the trio of Sjogren's syndrome, inclusion body myositis, and AL amyloidosis has not been previously reported. Sjogren's syndrome is a systemic autoimmune condition characterized by ocular and oral dryness. It is one of the most common rheumatologic disorders in the USA and worldwide. Early diagnosis of Sjogren's is particularly important given the frequency and variety of associated autoimmune diseases and extraglandular manifestations. Furthermore, although inclusion body myositis has a low prevalence, it is the most common inflammatory myopathy in older adults and is unfortunately associated with long delays in diagnosis, so knowledge of this disorder is also crucial for practicing internists.

  View details for PubMedID 30887439

• Characterizing electronic health record usage patterns of inpatient medicine residents using event log data. PloS one Wang, J. K., Ouyang, D. n., Hom, J. n., Chi, J. n., Chen, J. H. 2019; 14 (2): e0205379

  Abstract

  Amid growing rates of burnout, physicians report increasing electronic health record (EHR) usage alongside decreasing clinical facetime with patients. There exists a pressing need to improve physician-computer-patient interactions by streamlining EHR workflow. To identify interventions to improve EHR design and usage, we systematically characterize EHR activity among internal medicine residents at a tertiary academic hospital across various inpatient rotations and roles from June 2013 to November 2016. Logged EHR timestamps were extracted from Stanford Hospital's EHR system (Epic) and cross-referenced against resident rotation schedules. We tracked the quantity of EHR logs across 24-hour cycles to reveal daily usage patterns. In addition, we decomposed daily EHR time into time spent on specific EHR actions (e.g. chart review, note entry and review, results review).In examining 24-hour usage cycles from general medicine day and night team rotations, we identified a prominent trend in which night team activity promptly ceased at the shift's end, while day team activity tended to linger post-shift. Across all rotations and roles, residents spent on average 5.38 hours (standard deviation = 2.07) using the EHR. PGY1 (post-graduate year one) interns and PGY2+ residents spent on average 2.4 and 4.1 times the number of EHR hours on information review (chart, note, and results review) as information entry (note and order entry).Analysis of EHR event log data can enable medical educators and programs to develop more targeted interventions to improve physician-computer-patient interactions, centered on specific EHR actions.

  View details for PubMedID 30726208

• Effect of Electronic Clinical Decision Support on 25(OH) Vitamin D Testing. Journal of general internal medicine Chin, K. K., Hom, J. n., Tan, M. n., Sharp, C. n., Wang, S. n., Chen, Y. R., Chen, D. n. 2019

  View details for PubMedID 31090033

• Semiautomated Characterization of Carotid Artery Plaque Features From Computed Tomography Angiography to Predict Atherosclerotic Cardiovascular Disease Risk Score. Journal of computer assisted tomography Zhu, G. n., Li, Y. n., Ding, V. n., Jiang, B. n., Ball, R. L., Rodriguez, F. n., Fleischmann, D. n., Desai, M. n., Saloner, D. n., Gupta, A. n., Saba, L. n., Hom, J. n., Wintermark, M. n. 2019

  Abstract

  To investigate whether selected carotid computed tomography angiography (CTA) quantitative features can predict 10-year atherosclerotic cardiovascular disease (ASCVD) risk scores.One hundred seventeen patients with calculated ASCVD risk scores were considered. A semiautomated imaging analysis software was used to segment and quantify plaque features. Eighty patients were randomly selected to build models using 14 imaging variables and the calculated ASCVD risk score as the end point (continuous and binarized). The remaining 37 patients were used as the test set to generate predicted ASCVD scores. The predicted and observed ASCVD risk scores were compared to assess properties of the predictive model.Nine of 14 CTA imaging variables were included in a model that considered the plaque features in a continuous fashion (model 1) and 6 in a model that considered the plaque features dichotomized (model 2). The predicted ASCVD risk scores were 18.87% ± 13.26% and 18.39% ± 11.6%, respectively. There were strong correlations between the observed ASCVD and the predicted ASCVDs, with r = 0.736 for model 1 and r = 0.657 for model 2. The mean biases between observed ASCVD and predicted ASCVDs were -1.954% ± 10.88% and -1.466% ± 12.04%, respectively.Selected quantitative imaging carotid features extracted from the semiautomated carotid artery analysis can predict the ASCVD risk scores.

  View details for PubMedID 31082978

• Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nature medicine Frésard, L. n., Smail, C. n., Ferraro, N. M., Teran, N. A., Li, X. n., Smith, K. S., Bonner, D. n., Kernohan, K. D., Marwaha, S. n., Zappala, Z. n., Balliu, B. n., Davis, J. R., Liu, B. n., Prybol, C. J., Kohler, J. N., Zastrow, D. B., Reuter, C. M., Fisk, D. G., Grove, M. E., Davidson, J. M., Hartley, T. n., Joshi, R. n., Strober, B. J., Utiramerur, S. n., Lind, L. n., Ingelsson, E. n., Battle, A. n., Bejerano, G. n., Bernstein, J. A., Ashley, E. A., Boycott, K. M., Merker, J. D., Wheeler, M. T., Montgomery, S. B. 2019

  Abstract

  It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution.

  View details for DOI 10.1038/s41591-019-0457-8

  View details for PubMedID 31160820

• Comparison of Outcomes for Adult Inpatients With Sickle Cell Disease Cared for by Hospitalists Versus Hematologists. American journal of medical quality : the official journal of the American College of Medical Quality Slade, J. n., Rohatgi, N. n., Weng, Y. n., Hom, J. n., Ahuja, N. n. 2019: 1062860619892060

  View details for DOI 10.1177/1062860619892060

  View details for PubMedID 31856577

• Developing a genomics rotation: Practical training around variant interpretation for genetic counseling students. Journal of genetic counseling Grove, M. E., White, S. n., Fisk, D. G., Rego, S. n., Dagan-Rosenfeld, O. n., Kohler, J. N., Reuter, C. M., Bonner, D. n., Wheeler, M. T., Bernstein, J. A., Ormond, K. E., Hanson-Kahn, A. K. 2019

  Abstract

  With the wide adoption of next-generation sequencing (NGS)-based genetic tests, genetic counselors require increased familiarity with NGS technology, variant interpretation concepts, and variant assessment tools. The use of exome and genome sequencing in clinical care has expanded the reach and diversity of genetic testing. Regardless of the setting where genetic counselors are performing variant interpretation or reporting, most of them have learned these skills from colleagues, while on the job. Though traditional, lecture-based learning around these topics is important, there has been growing need for the inclusion of case-based, experiential training of genomics and variant interpretation for genetic counseling students, with the goal of creating a strong foundation in variant interpretation for new genetic counselors, regardless of what area of practice they enter. To address this need, we established a genomics and variant interpretation rotation for Stanford's genetic counseling training program. In response to changes in the genomics landscape, this has now evolved into three unique rotation experiences, each focused on variant interpretation in the context of various genomic settings, including clinical laboratory, research laboratory, and healthy genomic analysis studies. Here, we describe the goals and learning objectives that we have developed for these variant interpretation rotations, and illustrate how these concepts are applied in practice.

  View details for PubMedID 30706981

• Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests. JAMA network open Xu, S. n., Hom, J. n., Balasubramanian, S. n., Schroeder, L. F., Najafi, N. n., Roy, S. n., Chen, J. H. 2019; 2 (9): e1910967

  Abstract

  Laboratory testing is an important target for high-value care initiatives, constituting the highest volume of medical procedures. Prior studies have found that up to half of all inpatient laboratory tests may be medically unnecessary, but a systematic method to identify these unnecessary tests in individual cases is lacking.To systematically identify low-yield inpatient laboratory testing through personalized predictions.In this retrospective diagnostic study with multivariable prediction models, 116 637 inpatients treated at Stanford University Hospital from January 1, 2008, to December 31, 2017, a total of 60 929 inpatients treated at University of Michigan from January 1, 2015, to December 31, 2018, and 13 940 inpatients treated at the University of California, San Francisco from January 1 to December 31, 2018, were assessed.Diagnostic accuracy measures, including sensitivity, specificity, negative predictive values (NPVs), positive predictive values (PPVs), and area under the receiver operating characteristic curve (AUROC), of machine learning models when predicting whether inpatient laboratory tests yield a normal result as defined by local laboratory reference ranges.In the recent data sets (July 1, 2014, to June 30, 2017) from Stanford University Hospital (including 22 664 female inpatients with a mean [SD] age of 58.8 [19.0] years and 22 016 male inpatients with a mean [SD] age of 59.0 [18.1] years), among the top 20 highest-volume tests, 792 397 were repeats of orders within 24 hours, including tests that are physiologically unlikely to yield new information that quickly (eg, white blood cell differential, glycated hemoglobin, and serum albumin level). The best-performing machine learning models predicted normal results with an AUROC of 0.90 or greater for 12 stand-alone laboratory tests (eg, sodium AUROC, 0.92 [95% CI, 0.91-0.93]; sensitivity, 98%; specificity, 35%; PPV, 66%; NPV, 93%; lactate dehydrogenase AUROC, 0.93 [95% CI, 0.93-0.94]; sensitivity, 96%; specificity, 65%; PPV, 71%; NPV, 95%; and troponin I AUROC, 0.92 [95% CI, 0.91-0.93]; sensitivity, 88%; specificity, 79%; PPV, 67%; NPV, 93%) and 10 common laboratory test components (eg, hemoglobin AUROC, 0.94 [95% CI, 0.92-0.95]; sensitivity, 99%; specificity, 17%; PPV, 90%; NPV, 81%; creatinine AUROC, 0.96 [95% CI, 0.96-0.97]; sensitivity, 93%; specificity, 83%; PPV, 79%; NPV, 94%; and urea nitrogen AUROC, 0.95 [95% CI, 0.94, 0.96]; sensitivity, 87%; specificity, 89%; PPV, 77%; NPV 94%).The findings suggest that low-yield diagnostic testing is common and can be systematically identified through data-driven methods and patient context-aware predictions. Implementing machine learning models appear to be able to quantify the level of uncertainty and expected information gained from diagnostic tests explicitly, with the potential to encourage useful testing and discourage low-value testing that incurs direct costs and indirect harms.

  View details for DOI 10.1001/jamanetworkopen.2019.10967

  View details for PubMedID 31509205

• Assessing the Relationship between Atherosclerotic Cardiovascular Disease Risk Score and Carotid Artery Imaging Findings JOURNAL OF NEUROIMAGING Li, Y., Zhu, G., Ding, V., Huang, Y., Jiang, B., Ball, R. L., Rodriguez, F., Fleischmann, D., Desai, M., Saloner, D., Saba, L., Hom, J., Wintermark, M. 2019; 29 (1): 119–25

  View details for DOI 10.1111/jon.12573

  View details for Web of Science ID 000454959000014

• Association model data learned from clinicians stratified by patient mortality outcomes at a Tertiary Academic Center. Data in brief Wang, J. K., Hom, J., Balasubramanian, S., Chen, J. H. 2018; 21: 1669–73

  Abstract

  In this data article, we learn clinical order patterns from inpatient electronic health record (EHR) data at a tertiary academic center from three different cohorts of providers: (1) Clinicians with lower-than-expected patient mortality rates, (2) clinicians with higher-than-expected patient mortality rates, and (3) an unfiltered population of clinicians. We extract and make public these order patterns learned from each clinician cohort associated with six common admission diagnoses (e.g. pneumonia, chest pain, etc.). We also share a reusable reference standard or benchmark for evaluating automatically-learned clinical order patterns for each admission diagnosis, based on a manual review of clinical practice literature. The data shared in this article can support further study, evaluation, and translation of data-driven CDS systems. Further interpretation and discussion of this data can be found in Wang et al. (2018).

  View details for PubMedID 30505898

• Association model data learned from clinicians stratified by patient mortality outcomes at a Tertiary Academic Center DATA IN BRIEF Wang, J. K., Hom, J., Balasubramanian, S., Chen, J. H. 2018; 21: 1669–73

  View details for DOI 10.1016/j.dib.2018.10.163

  View details for Web of Science ID 000457925900230

• Effect of Genetic Diagnosis on Patients with Previously Undiagnosed Disease NEW ENGLAND JOURNAL OF MEDICINE Splinter, K., Adams, D. R., Bacino, C. A., Bellen, H. J., Bernstein, J. A., Cheatle-Jarvela, A. M., Eng, C. M., Esteves, C., Gahl, W. A., Hamid, R., Jacob, H. J., Kikani, B., Koeller, D. M., Kohane, I. S., Lee, B. H., Loscalzo, J., Luo, X., McCray, A. T., Metz, T. O., Mulvihill, J. J., Nelson, S. F., Palmer, C. S., Phillips, J. A., Pick, L., Postlethwait, J. H., Reuter, C., Shashi, V., Sweetser, D. A., Tifft, C. J., Walley, N. M., Wangler, M. F., Westerfield, M., Wheeler, M. T., Wise, A. L., Worthey, E. A., Yamamoto, S., Ashley, E. A., Undiagnosed Dis Network 2018; 379 (22): 2131–39

  View details for DOI 10.1056/NEJMoa1714458

  View details for Web of Science ID 000451402500008

• Assessing the Relationship Between American Heart Association Atherosclerotic Cardiovascular Disease Risk Score and Coronary Artery Imaging Findings JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY Li, Y., Zhu, G., Ding, V., Jiang, B., Ball, R. L., Ahuja, N., Rodriguez, F., Fleischmann, D., Desai, M., Saloner, D., Saba, L., Wintermark, M., Hom, J. 2018; 42 (6): 898–905

  View details for DOI 10.1097/RCT.0000000000000823

  View details for Web of Science ID 000451395400012

• Reducing Telemetry Use Is Safe: A Retrospective Analysis of Rapid Response Team and Code Events After a Successful Intervention to Reduce Telemetry Use. American journal of medical quality : the official journal of the American College of Medical Quality Xie, L., Garg, T., Svec, D., Hom, J., Kaimal, R., Ahuja, N., Barnes, J., Shieh, L. 2018: 1062860618805189

  Abstract

  Interventions guiding appropriate telemetry utilization have successfully reduced use at many hospitals, but few studies have examined their possible adverse outcomes. The authors conducted a successful intervention to reduce telemetry use in 2013 on a hospitalist service using educational modules, routine review, and financial incentives. The association of reduced telemetry use with the incidence of rapid response team (RRT) and code activations was assessed in a retrospective cohort study of 210 patients who experienced a total of 233 RRT and code events on the inpatient internal medicine services from January 2012 through March 2015 at a tertiary care center. The incidence of adverse events for the hospitalist service was not significantly different during the intervention and postintervention period as compared to the preintervention period. Reducing inappropriate telemetry use was not associated with an increase in the incidence rates of RRT and code events.

  View details for PubMedID 30293436

• An evaluation of clinical order patterns machine-learned from clinician cohorts stratified by patient mortality outcomes JOURNAL OF BIOMEDICAL INFORMATICS Wang, J. K., Hom, J., Balasubramanian, S., Schuler, A., Shah, N. H., Goldstein, M. K., Baiocchi, M. M., Chen, J. H. 2018; 86: 109–19

  View details for DOI 10.1016/j.jbi.2018.09.005

  View details for Web of Science ID 000460600800011

• An Electronic Best Practice Alert Based on Choosing Wisely Guidelines Reduces Thrombophilia Testing in the Outpatient Setting. Journal of general internal medicine Jun, T., Kwang, H., Mou, E., Berube, C., Bentley, J., Shieh, L., Hom, J. 2018

  View details for PubMedID 30215176

• An Evaluation of Clinical Order Patterns Machine-Learned from Clinician Cohorts Stratified by Patient Mortality Outcomes. Journal of biomedical informatics Wang, J. K., Hom, J., Balasubramanian, S., Schuler, A., Shah, N. H., Goldstein, M. K., Baiocchi, M. T., Chen, J. H. 2018

  Abstract

  OBJECTIVE: Evaluate the quality of clinical order practice patterns machine-learned from clinician cohorts stratified by patient mortality outcomes.MATERIALS AND METHODS: Inpatient electronic health records from 2010-2013 were extracted from a tertiary academic hospital. Clinicians (n=1,822) were stratified into low-mortality (21.8%, n=397) and high-mortality (6.0%, n=110) extremes using a two-sided P-value score quantifying deviation of observed vs. expected 30-day patient mortality rates. Three patient cohorts were assembled: patients seen by low-mortality clinicians, high-mortality clinicians, and an unfiltered crowd of all clinicians (n=1,046, 1,046, and 5,230 post-propensity score matching, respectively). Predicted order lists were automatically generated from recommender system algorithms trained on each patient cohort and evaluated against i) real-world practice patterns reflected in patient cases with better-than-expected mortality outcomes and ii) reference standards derived from clinical practice guidelines.RESULTS: Across six common admission diagnoses, order lists learned from the crowd demonstrated the greatest alignment with guideline references (AUROC range=0.86-0.91), performing on par or better than those learned from low-mortality clinicians (0.79-0.84, P<10-5) or manually-authored hospital order sets (0.65-0.77, P<10-3). The same trend was observed in evaluating model predictions against better-than-expected patient cases, with the crowd model (AUROC mean=0.91) outperforming the low-mortality model (0.87, P<10-16) and order set benchmarks (0.78, P<10-35).DISCUSSION: Whether machine-learning models are trained on all clinicians or a subset of experts illustrates a bias-variance tradeoff in data usage. Defining robust metrics to assess quality based on internal (e.g. practice patterns from better-than-expected patient cases) or external reference standards (e.g. clinical practice guidelines) is critical to assess decision support content.CONCLUSION: Learning relevant decision support content from all clinicians is as, if not more, robust than learning from a select subgroup of clinicians favored by patient outcomes.

  View details for PubMedID 30195660

• Lean-Based Redesign of Multidisciplinary Rounds on General Medicine Service JOURNAL OF HOSPITAL MEDICINE Kane, M., Rohatgi, N., Heidenreich, P. A., Thakur, A., Winget, M., Shum, K., Hereford, J., Shieh, L., Lew, T., Hom, J., Chi, J., Weinacker, A., Seay-Morrison, T., Ahuja, N. 2018; 13 (7): 482–85

  View details for DOI 10.12788/jhm.2908

  View details for Web of Science ID 000437294500006

• Assessing the Relationship between Atherosclerotic Cardiovascular Disease Risk Score and Carotid Artery Imaging Findings. Journal of neuroimaging : official journal of the American Society of Neuroimaging Li, Y. n., Zhu, G. n., Ding, V. n., Huang, Y. n., Jiang, B. n., Ball, R. L., Rodriguez, F. n., Fleischmann, D. n., Desai, M. n., Saloner, D. n., Saba, L. n., Hom, J. n., Wintermark, M. n. 2018

  Abstract

  To characterize the relationship between computed tomography angiography (CTA) imaging characteristics of carotid artery and the 10-year risk of atherosclerotic cardiovascular disease (ASCVD) score.We retrospectively identified all patients who underwent a cervical CTA at our institution from January 2013 to July 2016, extracted clinical information, and calculated the 10-year ASCVD score using the Pooled Cohort Equations from the 2013 ACC/AHA guidelines. We compared the imaging features of artery atherosclerosis derived from the CTAs between low and high risk.One hundred forty-six patients met our inclusion criteria. Patients with an ASCVD score ≥7.5% (64.4%) had significantly more arterial stenosis than patients with an ASCVD score <7.5% (35.6%, P < .001). Maximal plaque thickness was significantly higher (mean 2.33 vs. .42 mm, P < .001) and soft plaques (55.3% vs. 13.5%, P < .001) were significantly more frequent in patients with an ASCVD score ≥7.5%. However, among patients with a 10-year ASCVD score ≥7.5%, 33 (35.1%) had no arterial stenosis, 35 (37.2%) had a maximal plaque thickness less than. 9 mm, and 42 (44.7%) had no soft plaque. Furthermore, among the patients with a 10-year ASCVD score <7.5%, 8 (15.4%) had some arterial stenosis, 8 (15.4%) had a maximal plaque thickness more than. 9 mm, and 7 (13.5%) had soft plaque.There is some concordance but not a perfect overlap between the 10-year ASCVD risk scores calculated from clinical and blood assessment and carotid artery imaging findings.

  View details for PubMedID 30357980

• Effect of Genetic Diagnosis on Patients with Previously Undiagnosed Disease. The New England journal of medicine Splinter, K. n., Adams, D. R., Bacino, C. A., Bellen, H. J., Bernstein, J. A., Cheatle-Jarvela, A. M., Eng, C. M., Esteves, C. n., Gahl, W. A., Hamid, R. n., Jacob, H. J., Kikani, B. n., Koeller, D. M., Kohane, I. S., Lee, B. H., Loscalzo, J. n., Luo, X. n., McCray, A. T., Metz, T. O., Mulvihill, J. J., Nelson, S. F., Palmer, C. G., Phillips, J. A., Pick, L. n., Postlethwait, J. H., Reuter, C. n., Shashi, V. n., Sweetser, D. A., Tifft, C. J., Walley, N. M., Wangler, M. F., Westerfield, M. n., Wheeler, M. T., Wise, A. L., Worthey, E. A., Yamamoto, S. n., Ashley, E. A. 2018

  Abstract

  Many patients remain without a diagnosis despite extensive medical evaluation. The Undiagnosed Diseases Network (UDN) was established to apply a multidisciplinary model in the evaluation of the most challenging cases and to identify the biologic characteristics of newly discovered diseases. The UDN, which is funded by the National Institutes of Health, was formed in 2014 as a network of seven clinical sites, two sequencing cores, and a coordinating center. Later, a central biorepository, a metabolomics core, and a model organisms screening center were added.We evaluated patients who were referred to the UDN over a period of 20 months. The patients were required to have an undiagnosed condition despite thorough evaluation by a health care provider. We determined the rate of diagnosis among patients who subsequently had a complete evaluation, and we observed the effect of diagnosis on medical care.A total of 1519 patients (53% female) were referred to the UDN, of whom 601 (40%) were accepted for evaluation. Of the accepted patients, 192 (32%) had previously undergone exome sequencing. Symptoms were neurologic in 40% of the applicants, musculoskeletal in 10%, immunologic in 7%, gastrointestinal in 7%, and rheumatologic in 6%. Of the 382 patients who had a complete evaluation, 132 received a diagnosis, yielding a rate of diagnosis of 35%. A total of 15 diagnoses (11%) were made by clinical review alone, and 98 (74%) were made by exome or genome sequencing. Of the diagnoses, 21% led to recommendations regarding changes in therapy, 37% led to changes in diagnostic testing, and 36% led to variant-specific genetic counseling. We defined 31 new syndromes.The UDN established a diagnosis in 132 of the 382 patients who had a complete evaluation, yielding a rate of diagnosis of 35%. (Funded by the National Institutes of Health Common Fund.).

  View details for PubMedID 30304647

• Predicting Low Information Laboratory Diagnostic Tests. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science Roy, S. K., Hom, J. n., Mackey, L. n., Shah, N. n., Chen, J. H. 2018; 2017: 217–26

  Abstract

  Escalating healthcare costs and inconsistent quality is exacerbated by clinical practice variability. Diagnostic testing is the highest volume medical activity, but human intuition is typically unreliable for quantitative inferences on diagnostic performance characteristics. Electronic medical records from a tertiary academic hospital (2008-2014) allow us to systematically predict laboratory pre-test probabilities of being normal under different conditions. We find that low yield laboratory tests are common (e.g., ~90% of blood cultures are normal). Clinical decision support could triage cases based on available data, such as consecutive use (e.g., lactate, potassium, and troponin are >90% normal given two previously normal results) or more complex patterns assimilated through common machine learning methods (nearly 100% precision for the top 1% of several example labs).

  View details for PubMedID 29888076

• Lean-Based Redesign of Multidisciplinary Rounds on General Medicine Service. Journal of hospital medicine Kane, M. n., Rohatgi, N. n., Heidenreich, P. n., Thakur, A. n., Winget, M. n., Shum, K. n., Hereford, J. n., Shieh, L. n., Lew, T. n., Horn, J. n., Chi, J. n., Weinacker, A. n., Seay-Morrison, T. n., Ahuja, N. n. 2018

  Abstract

  Multidisciplinary rounds (MDR) facilitate timely communication amongst the care team and with patients. We used Lean techniques to redesign MDR on the teaching general medicine service.To examine if our Lean-based new model of MDR was associated with change in the primary outcome of length of stay (LOS) and secondary outcomes of discharges before noon, documentation of estimated discharge date (EDD), and patient satisfaction.This is a pre-post study. The preperiod (in which the old model of MDR was followed) comprised 4000 patients discharged between September 1, 2013, and October 22, 2014. The postperiod (in which the new model of MDR was followed) comprised 2085 patients between October 23, 2014, and April 30, 2015.Lean-based redesign of MDR.LOS, discharges before noon, EDD, and patient satisfaction.There was no change in the mean LOS. Discharges before noon increased from 6.9% to 10.7% (P < .001). Recording of EDD increased from 31.4% to 41.3% (P < .001). There was no change in patient satisfaction.Lean-based redesign of MDR was associated with an increase in discharges before noon and in recording of EDD.

  View details for PubMedID 29394300

• Assessing the Relationship Between American Heart Association Atherosclerotic Cardiovascular Disease Risk Score and Coronary Artery Imaging Findings. Journal of computer assisted tomography Li, Y. n., Zhu, G. n., Ding, V. n., Jiang, B. n., Ball, R. L., Ahuja, N. n., Rodriguez, F. n., Fleischmann, D. n., Desai, M. n., Saloner, D. n., Saba, L. n., Wintermark, M. n., Hom, J. n. 2018

  Abstract

  The aim of this study was to characterize the relationship between computed tomography angiography imaging characteristics of coronary artery and atherosclerotic cardiovascular disease (ASCVD) score.We retrospectively identified all patients who underwent a coronary computed tomography angiography at our institution from December 2013 to July 2016, then we calculated the 10-year ASCVD score. We characterized the relationship between coronary artery imaging findings and ASCVD risk score.One hundred fifty-one patients met our inclusion criteria. Patients with a 10-year ASCVD score of 7.5% or greater had significantly more arterial segments showing stenosis (46.4%, P = 0.008) and significantly higher maximal plaque thickness (1.25 vs 0.53, P = 0.001). However, among 56 patients with a 10-year ASCVD score of 7.5% or greater, 30 (53.6%) had no arterial stenosis. Furthermore, among the patients with a 10-year ASCVD score of less than 7.5%, 24 (25.3%) had some arterial stenosis.There is some concordance but not a perfect overlap between 10-year ASCVD risk scores and coronary artery imaging findings.

  View details for PubMedID 30407249

• A high value care curriculum for interns: a description of curricular design, implementation and housestaff feedback POSTGRADUATE MEDICAL JOURNAL Hom, J., Kumar, A., Evans, K. H., Svec, D., Richman, I., Fang, D., Smeraglio, A., Holubar, M., Johnson, T., Shah, N., Renault, C., Ahuja, N., Witteles, R., Harman, S., Shieh, L. 2017; 93 (1106): 725–29

  View details for DOI 10.1136/postgradmedj-2016-134617

  View details for Web of Science ID 000416160200005

• A high value care curriculum for interns: a description of curricular design, implementation and housestaff feedback. Postgraduate medical journal Hom, J. n., Kumar, A. n., Evans, K. H., Svec, D. n., Richman, I. n., Fang, D. n., Smeraglio, A. n., Holubar, M. n., Johnson, T. n., Shah, N. n., Renault, C. n., Ahuja, N. n., Witteles, R. n., Harman, S. n., Shieh, L. n. 2017

  Abstract

  Most residency programmes do not have a formal high value care curriculum. Our goal was to design and implement a multidisciplinary high value care curriculum specifically targeted at interns.Our curriculum was designed with multidisciplinary input from attendings, fellows and residents at Stanford. Curricular topics were inspired by the American Board of Internal Medicine's Choosing Wisely campaign, Alliance for Academic Internal Medicine, American College of Physicians and Society of Hospital Medicine. Our topics were as follows: introduction to value-based care; telemetry utilisation; lab ordering; optimal approach to thrombophilia work-ups and fresh frozen plasma use; optimal approach to palliative care referrals; antibiotic stewardship; and optimal approach to imaging for low back pain. Our curriculum was implemented at the Stanford Internal Medicine residency programme over the course of two academic years (2014 and 2015), during which 100 interns participated in our high value care curriculum. After each high value care session, interns were offered the opportunity to complete surveys regarding feedback on the curriculum, self-reported improvements in knowledge, skills and attitudinal module objectives, and quiz-based knowledge assessments.The overall survey response rate was 67.1%. Overall, the material was rated as highly useful on a 5-point Likert scale (mean 4.4, SD 0.6). On average, interns reported a significant improvement in their self-rated knowledge, skills and attitudes after the six seminars (mean improvement 1.6 points, SD 0.4 (95% CI 1.5 to 1.7), p<0.001).We successfully implemented a novel high value care curriculum that specifically targets intern physicians.

  View details for PubMedID 28663352

• Using Electronic Best Practice Alerts to Improve Thrombophilia Testing Based on ASH Choosing Wisely Guidelines. Blood Jun, T., Kwang, H., Mou, E., Berube, C., Shah, N., Kaimal, R., Bentley, J., Ahuja, N., Shieh, L., Hom, J. 2017; 130 (3355)
• Magnitude of Potentially Inappropriate Thrombophilia Testing in the Inpatient Hospital Setting. Journal of hospital medicine Mou, E. n., Kwang, H. n., Hom, J. n., Shieh, L. n., Kumar, A. n., Richman, I. n., Berube, C. n. 2017; 12 (9): 735–38

  Abstract

  Laboratory costs of thrombophilia testing exceed an estimated $650 million (in US dollars) annually. Quantifying the prevalence and financial impact of potentially inappropriate testing in the inpatient hospital setting represents an integral component of the effort to reduce healthcare expenditures. We conducted a retrospective analysis of our electronic medical record to evaluate 2 years' worth of inpatient thrombophilia testing measured against preformulated appropriateness criteria. Cost data were obtained from the Centers for Medicare and Medicaid Services 2016 Clinical Laboratory Fee Schedule. Of the 1817 orders analyzed, 777 (42.7%) were potentially inappropriate, with an associated cost of $40,422. The tests most frequently inappropriately ordered were Factor V Leiden, prothrombin gene mutation, protein C and S activity levels, antithrombin activity levels, and the lupus anticoagulant. Potentially inappropriate thrombophilia testing is common and costly. These data demonstrate a need for institution-wide changes in order to reduce unnecessary expenditures and improve patient care.

  View details for PubMedID 28914278

• Prevalence and Financial Impact of Inappropriate Thrombophilia Testing in the Inpatient Hospital Setting: A Retrospective Analysis Mou, E., Kwang, H., Hom, J., Shieh, L., Ahuja, N., Harman, S., Johnson, T., Kumar, A., Shah, N., Witteles, R., Berube, C. AMER SOC HEMATOLOGY. 2016

  View details for Web of Science ID 000394452508066

• The State of Medical Student Performance Evaluations: Improved Transparency or Continued Obfuscation? Academic medicine Hom, J., Richman, I., Hall, P., Ahuja, N., Harman, S., Harrington, R., Witteles, R. 2016; 91 (11): 1534-1539

  Abstract

  The medical student performance evaluation (MSPE), a letter summarizing academic performance, is included in each medical student's residency application. The extent to which medical schools follow Association of American Medical Colleges (AAMC) recommendations for comparative and transparent data is not known. This study's purpose was to describe the content, interpretability, and transparency of MSPEs.This cross-sectional study examined one randomly selected MSPE from every Liaison Committee on Medical Education-accredited U.S. medical school from which at least one student applied to the Stanford University internal medical residency program during the 2013-2014 application cycle. The authors described the number, distribution, and range of key words and clerkship grades used in the MSPEs and the proportions of schools with missing or incomplete data.The sample included MSPEs from 117 (89%) of 131 medical schools. Sixty schools (51%) provided complete information about clerkship grade and key word distributions. Ninety-six (82%) provided comparative data for clerkship grades, and 71 (61%) provided complete key word data. Key words describing overall performance were extremely heterogeneous, with a total of 72 used and great variation in the assignment of the top designation (median: 24% of students; range: 1%-60%). There was also great variation in the proportion of students awarded the top internal medicine clerkship grade (median: 29%; range: 2%-90%).The MSPE is a critical component of residency applications, yet data contained within MSPEs are incomplete and variable. Approximately half of U.S. medical schools do not follow AAMC guidelines for MSPEs.

  View details for PubMedID 26703411

• The State of Medical Student Performance Evaluations: Improved Transparency or Continued Obfuscation? ACADEMIC MEDICINE Hom, J., Richman, I., Hall, P., Ahuja, N., Harman, S., Harrington, R., Witteles, R. 2016; 91 (11): 1534–39

  View details for DOI 10.1097/ACM.0000000000001034

  View details for Web of Science ID 000387208300024

• R-SCAN: Imaging for Low Back Pain. Journal of the American College of Radiology Hom, J., Smith, C. D., Ahuja, N., Wintermark, M. 2016; 13 (11): 1385-1386 e1

  View details for DOI 10.1016/j.jacr.2016.06.043

  View details for PubMedID 27595195

• R-SCAN: Imaging for Uncomplicated Acute Rhinosinusitis. Journal of the American College of Radiology Kroll, H., Hom, J., Ahuja, N., Smith, C. D., Wintermark, M. 2016

  View details for DOI 10.1016/j.jacr.2016.08.018

  View details for PubMedID 27744010

• Patient Outcomes when Housestaff Exceed 80 Hours per Week. American journal of medicine Ouyang, D., Chen, J. H., Krishnan, G., Hom, J., Witteles, R., Chi, J. 2016; 129 (9): 993-999 e1

  Abstract

  It has been posited that high workload and long work hours for trainees could affect the quality and efficiency of patient care. Duty hour restrictions seek to balance patient care and resident education by limiting resident work hours. Through a retrospective cohort study, we investigate whether patient care on an inpatient general medicine service at a large academic medical center is impacted when housestaff work greater than eighty hours per week METHODS: We identified all admissions to a housestaff-run general medicine service between June 25, 2013 and June 29, 2014. Each hospitalization was classified by whether or not the patient was admitted by housestaff who have worked more than eighty hours a week during their hospitalization. Housestaff computer activity and duty hours were calculated by institutional electronic heath record audit, as well as length of stay and a composite of in-hospital mortality, ICU transfer rate, and 30-day readmission rate.We identified 4,767 hospitalizations by 3,450 unique patients; of which 40.9% of hospitalizations were managed by housestaff who worked more than eighty hours that week during their hospitalization. There was a significantly higher rate of the composite outcome (19.2% vs. 16.7%, p = 0.031) for patients admitted by housestaff working more than eighty hours a week during their hospitalization. We found a statistically significant higher length of stay (5.12 vs. 4.66 days, p = 0.048) and rate of ICU transfer (3.18% vs. 2.38%, p = 0.029). There was no statistically significant difference in 30-day readmission rate (13.7% vs. 12.8%, p = 0.395), or in-hospital mortality rate (3.18% vs. 2.42%, p = 0.115).There was no correlation with team census on admission and patient outcomes.Patients taken care of by housestaff working more than eighty hours a week had increased length of stay and number of ICU transfers. There was no association between resident work-hours and patient in-hospital mortality or 30-day readmission rate.

  View details for DOI 10.1016/j.amjmed.2016.03.023

  View details for PubMedID 27103047

• Effect of opioid prescribing guidelines in primary care. Medicine Chen, J. H., Hom, J., Richman, I., Asch, S. M., Podchiyska, T., Johansen, N. A. 2016; 95 (35)

  Abstract

  Long-term opioid use for noncancer pain is increasingly prevalent yet controversial given the risks of addiction, diversion, and overdose. Prior literature has identified the problem and proposed management guidelines, but limited evidence exists on the actual effectiveness of implementing such guidelines in a primary care setting.A multidisciplinary working group of institutional experts assembled comprehensive guidelines for chronic opioid prescribing, including monitoring and referral recommendations. The guidelines were disseminated in September 2013 to our medical center's primary care clinics via in person and electronic education.We extracted electronic medical records for patients with noncancer pain receiving opioid prescriptions (Rxs) in seasonally matched preintervention (11/1/2012-6/1/2013) and postintervention (11/1/2013-6/1/2014) periods. For patients receiving chronic (3 or more) opioid Rxs, we assessed the rates of drug screening, specialty referrals, clinic visits, emergency room visits, and quantity of opioids prescribed.After disseminating guidelines, the percentage of noncancer clinic patients receiving any opioid Rxs dropped from 3.9% to 3.4% (P = 0.02). The percentage of noncancer patients receiving chronic opioid Rxs decreased from 2.0% to 1.6% (P = 0.03). The rate of urine drug screening increased from 9.2% to 17.3% (P = 0.005) amongst noncancer chronic opioid patients. No significant differences were detected for other metrics or demographics assessed.An educational intervention for primary care opioid prescribing is feasible and was temporally associated with a modest reduction in overall opioid Rx rates. Provider use of routine drug screening increased, but overall rates of screening and specialty referral remained low despite the intervention. Despite national pressures to introduce opioid prescribing guidelines for chronic pain, doing so alone does not necessarily yield substantial changes in clinical practice.

  View details for DOI 10.1097/MD.0000000000004760

  View details for PubMedID 27583928

• Fulfilling outpatient medicine responsibilities during internal medicine residency: a quantitative study of housestaff participation with between visit tasks BMC MEDICAL EDUCATION Hom, J., Richman, I., Chen, J. H., Singh, B., Crump, C., Chi, J. 2016; 16

  Abstract

  Internal Medicine residents experience conflict between inpatient and outpatient medicine responsibilities. Outpatient "between visit" responsibilities such as reviewing lab and imaging data, responding to medication refill requests and replying to patient inquiries compete for time and attention with inpatient duties. By examining Electronic Health Record (EHR) audits, our study quantitatively describes this balance between competing responsibilities, focusing on housestaff participation with "between visit" outpatient responsibilities.We examined EHR log-in data from 2012-2013 for 41 residents (R1 to R3) assigned to a large academic center's continuity clinic. From the EHR log-in data, we examined housestaff compliance with "between visit" tasks, based on official clinic standards. We used generalized estimating equations to evaluate housestaff compliance with between visit tasks and amount of time spent on tasks. We examined the relationship between compliance with between visit tasks and resident year of training, rotation type (elective or required) and interest in primary care.Housestaff compliance with logging in to complete "between visit" tasks varied significantly depending on rotation, with overall compliance of 45 % during core inpatient rotations compared to 68 % during electives (p = 0.01). Compliance did not significantly vary by interest in primary care or training level. Once logged in, housestaff spent a mean 53 min per week logged in while on electives, compared to 55 min on required rotations (p = 0.90).Our study quantitatively highlights the difficulty of attending to outpatient responsibilities during busy core inpatient rotations, which comprise the bulk of residency at our institution and at others. Our results reinforce the need to continue development and study of innovative systems for coverage of "between visit" responsibilities, including shared coverage models among multiple residents and shared coverage models between residents and clinic attendings, both of which require a balance between clinic efficiency and resident ownership, autonomy and learning.

  View details for DOI 10.1186/s12909-016-0665-6

  View details for PubMedID 27160008

• LOS OUTLIERS: A CHALLENGING PROBLEM FOR BOTH THE TEACHING AND PRIVATE NON-TEACHING GENERAL MEDICINE SERVICES AT STANFORD HOSPITAL Ketchersid, J., Shieh, L., Ahuja, N. K., Chi, J., Hom, J. SPRINGER. 2016: S294

  View details for Web of Science ID 000392201600372

• Internal Medicine Resident Computer Usage: An Electronic Audit of an Inpatient Service. JAMA internal medicine Ouyang, D., Chen, J. H., Hom, J., Chi, J. 2016; 176 (2): 252-254

  View details for DOI 10.1001/jamainternmed.2015.6831

  View details for PubMedID 26642261

• Prevalence and Financial Impact of Inappropriate Thrombophilia Testing in the Inpatient Hospital Setting: A Retrospective Analysis Blood Mou, E., Kwang, H., Hom, J., Shieh, L., Ahuja, N., Harman, S., Johnson, T., Kumar, A., Shah, N., Witteles, R., Berube, C. 2016; 128 (22 2230)
• R-SCAN: Imaging for Headache. Journal of the American College of Radiology : JACR Hom, J. n., Ahuja, N. n., Smith, C. D., Wintermark, M. n. 2016; 13 (12 Pt A): 1534–35.e1

  View details for PubMedID 28341311

• Hospitalist intervention for appropriate use of telemetry reduces length of stay and cost. Journal of hospital medicine Svec, D., Ahuja, N., Evans, K. H., Hom, J., Garg, T., Loftus, P., Shieh, L. 2015; 10 (9): 627-632

  Abstract

  Telemetry monitoring is a widely used, labor-intensive, and often-limited resource. Little is known of the effectiveness of methods to guide appropriate use.Our intervention for appropriate use included: (1) a hospitalist-led, daily review of bed utilization, (2) hospitalist-driven education module for trainees, (3) quarterly feedback of telemetry usage, and (4) financial incentives.Hospitalists were encouraged to discuss daily telemetry utilization on rounds. A module on appropriate telemetry usage was taught by hospitalists during the intervention period (January 2013-August 2013) on medicine wards. Pre- and post-evaluations measured changes regarding telemetry use. We compared hospital bed-use data between the baseline period (January 2012-December 2012), intervention period, and extension period (September 2014-March 2015). During the intervention period, hospital bed-use data were sent to the hospitalist group quarterly. Financial incentives were provided after a decrease in hospitalist telemetry utilization.Stanford Hospital, a 444-bed, academic medical center in Stanford, California.Hospitalists saw reductions for both length of stay (LOS) (2.75 vs 2.13 days, P = 0.005) and total cost (22.5% reduction) for telemetry bed utilization in the intervention period. Nonhospitalists telemetry bed utilization remained unchanged. We saw significant improvements in trainee knowledge of the most cost-saving action (P = 0.002) and the least cost-saving action (P = 0.003) in the pre- and post-evaluation analyses. Results were sustained in the hospitalist group, with telemetry LOS of 1.93 days in the extension period.A multipronged, hospitalist-driven intervention to improve appropriate use of telemetry reduces LOS and cost, and increases knowledge of cost-saving actions among trainees.

  View details for DOI 10.1002/jhm.2411

  View details for PubMedID 26149105

• Hospitalist intervention for appropriate use of telemetry reduces length of stay and cost JOURNAL OF HOSPITAL MEDICINE Svec, D., Ahuja, N., Evans, K. H., Hom, J., Garg, T., Loftus, P., Shieh, L. 2015; 10 (9): 627-632

  View details for DOI 10.1002/jhm.2411

  View details for Web of Science ID 000360836000012

• Leakage Effects. MR and CT Perfusion and Pharmacokinetic Imaging Donahue, K., Hom, J., Boxerman, J., Bammer, R. Lippincott. 2014
• Hemorrhage MR and CT Perfusion and Pharmacokinetic Imaging Wijman, C., Kassner, A., Hom, J. Lippincott. 2014
• The electronic health record as a healthcare management strategy and implications for obstetrics and gynecologic practice. Current opinion in obstetrics & gynecology Eisenberg, M., Hom, J., Sharp, C. 2013; 25 (6): 476-481

  Abstract

  To review the current trends, utilities, impacts and strategy for electronic health records (EHRs) as related to obstetrics and gynecology.Adoption and utilization of EHRs are increasing rapidly but variably, given pressures of financial incentives, policy and technological advancement. Adoption is outpacing published evidence, but there is a growing body of descriptive literature regarding incentives, benefits, risks and costs of adoption and utilization. Further, there is a rising body of evidence that EHRs can bring benefits to processes and outcomes, and that their implementation can be considered as a healthcare management strategy. Obstetrics and gynecology practices have specific needs, which must be addressed in the adoption of such technology. Specialty specific literature is sparse but should be considered as part of any strategy aimed at achieving quality improvement and practice behavior change.Obstetrics and gynecologic practice presents unique challenges to the effective adoption and use of EHR technologies, but there is promise as the technologies, integration and usability are rapidly improving. This technology will have an increasing impact on the practice of obstetrics and gynecology in the coming years.

  View details for DOI 10.1097/GCO.0000000000000029

  View details for PubMedID 24185005

• Multiparametric MRI and CT Models of Infarct Core and Favorable Penumbral Imaging Patterns in Acute Ischemic Stroke STROKE Kidwell, C. S., Wintermark, M., De Silva, D. A., Schaewe, T. J., Jahan, R., Starkman, S., Jovin, T., Hom, J., Jumaa, M., Schreier, J., Gornbein, J., Liebeskind, D. S., Alger, J. R., Saver, J. L. 2013; 44 (1): 73-79

  Abstract

  Objective imaging methods to identify optimal candidates for late recanalization therapies are needed. The study goals were (1) to develop magnetic resonance imaging (MRI) and computed tomography (CT) multiparametric, voxel-based predictive models of infarct core and penumbra in acute ischemic stroke patients, and (2) to develop patient-level imaging criteria for favorable penumbral pattern based on good clinical outcome in response to successful recanalization.An analysis of imaging and clinical data was performed on 2 cohorts of patients (one screened with CT, the other with MRI) who underwent successful treatment for large vessel, anterior circulation stroke. Subjects were divided 2:1 into derivation and validation cohorts. Pretreatment imaging parameters independently predicting final tissue infarct and final clinical outcome were identified.The MRI and CT models were developed and validated from 34 and 32 patients, using 943 320 and 1 236 917 voxels, respectively. The derivation MRI and 2-branch CT models had an overall accuracy of 74% and 80%, respectively, and were independently validated with an accuracy of 71% and 79%, respectively. The imaging criteria of (1) predicted infarct core ≤90 mL and (2) ratio of predicted infarct tissue within the at-risk region ≤70% identified patients as having a favorable penumbral pattern with 78% to 100% accuracy.Multiparametric voxel-based MRI and CT models were developed to predict the extent of infarct core and overall penumbral pattern status in patients with acute ischemic stroke who may be candidates for late recanalization therapies. These models provide an alternative approach to mismatch in predicting ultimate tissue fate.

  View details for DOI 10.1161/STROKEAHA.112.670034

  View details for Web of Science ID 000312883800014

  View details for PubMedID 23233383

• MRI Blood-Brain Barrier Permeability Measurements to Predict Hemorrhagic Transformation in a Rat Model of Ischemic Stroke TRANSLATIONAL STROKE RESEARCH Hoffmann, A., Bredno, J., Wendland, M. F., Derugin, N., Hom, J., Schuster, T., Zimmer, C., Su, H., Ohara, P. T., Young, W. L., Wintermark, M. 2012; 3 (4): 508-516

  View details for DOI 10.1007/s12975-012-0212-7

  View details for Web of Science ID 000311407000013

• Delay correction for the assessment of blood-brain barrier permeability using first-pass dynamic perfusion CT. AJNR. American journal of neuroradiology Schneider, T., Hom, J., Bredno, J., Dankbaar, J. W., Cheng, S., Wintermark, M. 2011; 32 (7): E134-8

  Abstract

  Hemorrhagic transformation is a serious potential complication of ischemic stroke with damage to the BBB as one of the contributing mechanisms. BBB permeability measurements extracted from PCT by using the Patlak model can provide a valuable assessment of the extent of BBB damage. Unfortunately, Patlak assumptions require extended PCT acquisition, increasing the risk of motion artifacts. A necessary correction is presented for obtaining accurate BBB permeability measurements from first-pass PCT.

  View details for DOI 10.3174/ajnr.A2152

  View details for PubMedID 20538824

• Dynamic perfusion-CT assessment of early changes in blood brain barrier permeability of acute ischaemic stroke patients JOURNAL OF NEURORADIOLOGY Dankbaar, J. W., Hom, J., Schneider, T., Cheng, S., Bredno, J., LAU, B. C., van der Schaaf, I. C., Wintermark, M. 2011; 38 (3): 161-166

  Abstract

  Damage to the blood brain barrier (BBB) may lead to haemorrhagic transformation after ischaemic stroke. The purpose of this study was to evaluate the effect of patient characteristics and stroke severity on admission BBB permeability (BBBP) values measured with perfusion-CT (PCT) in acute ischaemic stroke patients.We retrospectively identified 65 patients with proven ischaemic stroke admitted within 12 hours after symptom onset. Patients' charts were reviewed for demographic variables and vascular risk factors. The Patlak's model was applied to calculate BBBP values from the PCT data in the infarct core, penumbra and non-ischaemic tissue in the contralateral hemisphere. Mean BBBP values and their 95% confidence intervals (CI) were calculated in the different tissue types. Effects of demographic variables and risk factors on BBBP were analyzed using a multivariate, generalized estimating equations (GEE) model.BBBP values in the infarct core (mean [95%CI]: 2.48 [2.16-2.85]) and penumbra (2.48 [2.21-2.79]) were significantly higher than in non-ischaemic tissue (2.12 [1.88-2.39]). Multivariate analysis demonstrated that collateral filling has effect on BBBP. Less elevated BBBP values were associated with more than 50% collateral filling.BBBP values are increased in ischaemic brain tissue on the admission PCT scan of acute ischaemic stroke patients. Less abnormally elevated BBBP values were observed in patients with more than 50% collateral filling, possibly explaining why there is a relationship between more collateral filling and a lower incidence of haemorrhagic transformation.

  View details for DOI 10.1016/j.neurad.2010.08.001

  View details for Web of Science ID 000293209800005

  View details for PubMedID 20950860

• Validation of In Vivo Magnetic Resonance Imaging Blood-Brain Barrier Permeability Measurements by Comparison With Gold Standard Histology STROKE Hoffmann, A., Bredno, J., Wendland, M. F., Derugin, N., Hom, J., Schuster, T., Su, H., Ohara, P. T., Young, W. L., Wintermark, M. 2011; 42 (7): 2054-2060

  Abstract

  We sought to validate the blood-brain barrier permeability measurements extracted from perfusion-weighted MRI through a relatively simple and frequently applied model, the Patlak model, by comparison with gold standard histology in a rat model of ischemic stroke.Eleven spontaneously hypertensive rats and 11 Wistar rats with unilateral 2-hour filament occlusion of the right middle cerebral artery underwent imaging during occlusion at 4 hours and 24 hours after reperfusion. Blood-brain barrier permeability was imaged by gradient echo imaging after the first pass of the contrast agent bolus and quantified by a Patlak analysis. Blood-brain barrier permeability was shown on histology by the extravasation of Evans blue on fluorescence microscopy sections matching location and orientation of MR images. Cresyl-violet staining was used to detect and characterize hemorrhage. Landmark-based elastic image registration allowed a region-by-region comparison of permeability imaging at 24 hours with Evans blue extravasation and hemorrhage as detected on histological slides obtained immediately after the 24-hour image set.Permeability values in the nonischemic tissue (marginal mean ± SE: 0.15 ± 0.019 mL/min 100 g) were significantly lower compared to all permeability values in regions of Evans blue extravasation or hemorrhage. Permeability values in regions of weak Evans blue extravasation (0.23 ± 0.016 mL/min 100 g) were significantly lower compared to permeability values of in regions of strong Evans blue extravasation (0.29 ± 0.020 mL/min 100 g) and macroscopic hemorrhage (0.35 ± 0.049 mL/min 100 g). Permeability values in regions of microscopic hemorrhage (0.26 ± 0.024 mL/min 100 g) only differed significantly from values in regions of nonischemic tissue (0.15 ± 0.019 mL/min 100 g).Areas of increased permeability measured in vivo by imaging coincide with blood-brain barrier disruption and hemorrhage observed on gold standard histology.

  View details for DOI 10.1161/STROKEAHA.110.597997

  View details for Web of Science ID 000292090900054

  View details for PubMedID 21636816

• Stroke Imaging Research Road Map NEUROIMAGING CLINICS OF NORTH AMERICA Leiva-Salinas, C., Hom, J., Warach, S., Wintermark, M. 2011; 21 (2): 239-?

  Abstract

  Although acute stroke imaging has made significant progress in the last few years, several improvements and validation steps are needed to make stroke-imaging techniques fully operational and appropriate in daily clinical practice. This review outlines the needs in the stroke-imaging field and describes a consortium that was founded to provide them.

  View details for DOI 10.1016/j.nic.2011.01.009

  View details for Web of Science ID 000292007900005

  View details for PubMedID 21640297

• Blood-Brain Barrier Permeability Assessed by Perfusion CT Predicts Symptomatic Hemorrhagic Transformation and Malignant Edema in Acute Ischemic Stroke AMERICAN JOURNAL OF NEURORADIOLOGY Hom, J., Dankbaar, J. W., Soares, B. P., Schneider, T., Cheng, S., Bredno, J., LAU, B. C., Smith, W., Dillon, W. P., Wintermark, M. 2011; 32 (1): 41-48

  Abstract

  SHT and ME are feared complications in patients with acute ischemic stroke. They occur >10 times more frequently in tPA-treated versus placebo-treated patients. Our goal was to evaluate the sensitivity and specificity of admission BBBP measurements derived from PCT in predicting the development of SHT and ME in patients with acute ischemic stroke.We retrospectively analyzed a dataset consisting of 32 consecutive patients with acute ischemic stroke with appropriate admission and follow-up imaging. We calculated admission BBBP by using delayed-acquisition PCT data and the Patlak model. Collateral flow was assessed on the admission CTA, while recanalization and reperfusion were assessed on the follow-up CTA and PCT, respectively. SHT and ME were defined according to ECASS III criteria. Clinical data were obtained from chart review. In our univariate and forward selection-based multivariate analysis for predictors of SHT and ME, we incorporated both clinical and imaging variables, including age, admission NIHSS score, admission blood glucose level, admission blood pressure, time from symptom onset to scanning, treatment type, admission PCT-defined infarct volume, admission BBBP, collateral flow, recanalization, and reperfusion. Optimal sensitivity and specificity for SHT and ME prediction were calculated by using ROC analysis.In our sample of 32 patients, 3 developed SHT and 3 developed ME. Of the 3 patients with SHT, 2 received IV tPA, while 1 received IA tPA and treatment with the Merci device; of the 3 patients with ME, 2 received IV tPA, while 1 received IA tPA and treatment with the Merci device. Admission BBBP measurements above the threshold were 100% sensitive and 79% specific in predicting SHT and ME. Furthermore, all patients with SHT and ME--and only those with SHT and ME--had admission BBBP measurements above the threshold, were older than 65 years of age, and received tPA. Admission BBBP, age, and tPA were the independent predictors of SHT and ME in our forward selection-based multivariate analysis. Of these 3 variables, only BBBP measurements and age were known before making the decision of administering tPA and thus are clinically meaningful.Admission BBBP, a pretreatment measurement, was 100% sensitive and 79% specific in predicting SHT and ME.

  View details for DOI 10.3174/ajnr.A2244

  View details for Web of Science ID 000287016200008

  View details for PubMedID 20947643

• Reperfusion Is a More Accurate Predictor of Follow-Up Infarct Volume Than Recanalization A Proof of Concept Using CT in Acute Ischemic Stroke Patients STROKE Soares, B. P., Tong, E., Hom, J., Cheng, S., Bredno, J., Boussel, L., Smith, W. S., Wintermark, M. 2010; 41 (1): E34-E40

  Abstract

  The purpose of this study was to compare recanalization and reperfusion in terms of their predictive value for imaging outcomes (follow-up infarct volume, infarct growth, salvaged penumbra) and clinical outcome in acute ischemic stroke patients. Material andTwenty-two patients admitted within 6 hours of stroke onset were retrospectively included in this study. These patients underwent a first stroke CT protocol including CT-angiography (CTA) and perfusion-CT (PCT) on admission, and similar imaging after treatment, typically around 24 hours, to assess recanalization and reperfusion. Recanalization was assessed by comparing arterial patency on admission and posttreatment CTAs; reperfusion, by comparing the volumes of CBV, CBF, and MTT abnormality on admission and posttreatment PCTs. Collateral flow was graded on the admission CTA. Follow-up infarct volume was measured on the discharge noncontrast CT. The groups of patients with reperfusion, no reperfusion, recanalization, and no recanalization were compared in terms of imaging and clinical outcomes.Reperfusion (using an MTT reperfusion index >75%) was a more accurate predictor of follow-up infarct volume than recanalization. Collateral flow and recanalization were not accurate predictors of follow-up infarct volume. An interaction term was found between reperfusion and the volume of the admission penumbra >50 mL.Our study provides evidence that reperfusion is a more accurate predictor of follow-up infarct volume in acute ischemic stroke patients than recanalization. We recommend an MTT reperfusion index >75% to assess therapy efficacy in future acute ischemic stroke trials that use perfusion-CT.

  View details for DOI 10.1161/STROKEAHA.109.568766

  View details for Web of Science ID 000273093400042

  View details for PubMedID 19910542

• Age- and anatomy-related values of blood-brain barrier permeability measured by perfusion-CT in non-stroke patients JOURNAL OF NEURORADIOLOGY Dankbaar, J. W., Hom, J., Schneider, T., Cheng, S., LAU, B. C., van der Schaaf, I., Virmani, S., Pohlman, S., Wintermark, M. 2009; 36 (4): 219-227

  Abstract

  The goal of this study was to determine blood-brain barrier permeability (BBBP) values extracted from perfusion-CT (PCT) using the Patlak model and possible variations related to age, gender, race, vascular risk factors and their treatment and anatomy in non-stroke patients.We retrospectively identified 96 non-stroke patients who underwent a PCT study using a prolonged acquisition time up to 3 minutes. Patients' charts were reviewed for demographic data, vascular risk factors and their treatment. The Patlak model was applied to calculate BBBP values in regions of interest drawn within the basal ganglia and the gray and white matter of the different cerebral lobes. Differences in BBBP values were analyzed using a multivariate analysis considering clinical variables and anatomy.Mean absolute BBBP values were 1.2 ml 100 g(-1) min(-1) and relative BBBP/CBF values were 3.5%. Statistical differences between gray and white matter were not clinically relevant. BBBP values were influenced by age, history of diabetes and/or hypertension and aspirin intake.This study reports ranges of BBBP values in non-stroke patients calculated from delayed phase PCT data using the Patlak model. These ranges will be useful to detect abnormal BBBP values when assessing patients with cerebral infarction for the risk of hemorrhagic transformation.

  View details for DOI 10.1016/j.neurad.2009.01.001

  View details for Web of Science ID 000271524500005

  View details for PubMedID 19251320

• Optimal Duration of Acquisition for Dynamic Perfusion CT Assessment of Blood-Brain Barrier Permeability Using the Patlak Model AMERICAN JOURNAL OF NEURORADIOLOGY Hom, J., Dankbaar, J. W., Schneider, T., Cheng, S., Bredno, J., Wintermark, M. 2009; 30 (7): 1366-1370

  Abstract

  A previous study demonstrated the need to use delayed acquisition rather than first-pass data for accurate blood-brain barrier permeability surface product (BBBP) calculation from perfusion CT (PCT) according to the Patlak model, but the optimal duration of the delayed acquisition has not been established. Our goal was to determine the optimal duration of the delayed PCT acquisition to obtain accurate BBBP measurements while minimizing potential motion artifacts and radiation dose.We retrospectively identified 23 consecutive patients with acute ischemic anterior circulation stroke who underwent a PCT study with delayed acquisition. The Patlak model was applied for the full delayed acquisition (90-240 seconds) and also for truncated analysis windows (90-210, 90-180, 90-150, 90-120 seconds). Linear regression of Patlak plots was performed separately for the full and truncated analysis windows, and the slope of these regression lines was used to indicate BBBP. The full and truncated analysis windows were compared in terms of the resulting BBBP values and the quality of the Patlak fitting.BBBP values in the infarct and penumbra were similar for the full 90- to 240-second acquisition (95% confidence intervals for the infarct and penumbra: 1.62-2.47 and 1.75-2.41 mL x100 g(-1) x min(-1), respectively) and the 90- to 210-second analysis window (1.82-2.76 and 2.01-2.74 mL x 100 g(-1) x min(-1), respectively). BBBP values increased significantly with shorter acquisitions. The quality of the Patlak fit was excellent for the full 90- to 240-second and 90- to 210-second acquisitions, but it degraded with shorter acquisitions.The duration for the delayed PCT acquisition should be at least 210 seconds, because acquisitions shorter than 210 seconds lead to significantly overestimated BBBP values.

  View details for DOI 10.3174/ajnr.A1592

  View details for Web of Science ID 000269169600020

  View details for PubMedID 19369610

• Dynamic Perfusion CT Assessment of the Blood-Brain Barrier Permeability: First Pass versus Delayed Acquisition AMERICAN JOURNAL OF NEURORADIOLOGY Dankbaar, J. W., Hom, J., Schneider, T., Cheng, S., LAU, B. C., van der Schaaf, I., Virmani, S., Pohlman, S., Dillon, W. P., Wintermark, M. 2008; 29 (9): 1671-1676

  Abstract

  The Patlak model has been applied to first-pass perfusion CT (PCT) data to extract information on blood-brain barrier permeability (BBBP) to predict hemorrhagic transformation in patients with acute stroke. However, the Patlak model was originally described for the delayed steady-state phase of contrast circulation. The goal of this study was to assess whether the first pass or the delayed phase of a contrast bolus injection better respects the assumptions of the Patlak model for the assessment of BBBP in patients with acute stroke by using PCT.We retrospectively identified 125 consecutive patients (29 with acute hemispheric stroke and 96 without) who underwent a PCT study by using a prolonged acquisition time up to 3 minutes. The Patlak model was applied to calculate BBBP in ischemic and nonischemic brain tissue. Linear regression of the Patlak plot was performed separately for the first pass and for the delayed phase of the contrast bolus injection. Patlak linear regression models for the first pass and the delayed phase were compared in terms of their respective square root mean squared errors (square root MSE) and correlation coefficients (R) by using generalized estimating equations with robust variance estimation.BBBP values calculated from the first pass were significantly higher than those from the delayed phase, both in nonischemic brain tissue (2.81 mL x 100 g(-1) x min(-1) for the first pass versus 1.05 mL x 100 g(-1) x min(-1) for the delayed phase, P < .001) and in ischemic tissue (7.63 mL x 100 g(-1) x min(-1) for the first pass versus 1.31 mL x 100 g(-1) x min(-1) for the delayed phase, P < .001). Compared with regression models from the first pass, Patlak regression models obtained from the delayed data were of better quality, showing significantly lower square root MSE and higher R.Only the delayed phase of PCT acquisition respects the assumptions of linearity of the Patlak model in patients with and without stroke.

  View details for DOI 10.3174/ajnr.A1203

  View details for Web of Science ID 000260023800015

  View details for PubMedID 18635616

• Accuracy and Anatomical Coverage of Perfusion CT Assessment of the Blood-Brain Barrier Permeability: One Bolus versus Two Boluses CEREBROVASCULAR DISEASES Dankbaar, J. W., Hom, J., Schneider, T., Cheng, S., Lau, B. C., van der Schaaf, I., Virmani, S., Pohlman, S., Dillon, W. P., Wintermark, M. 2008; 26 (6): 600-605

  Abstract

  To assess whether blood-brain barrier permeability (BBBP) values, extracted with the Patlak model from the second perfusion CT (PCT) contrast bolus, are significantly lower than the values extracted from the first bolus in the same patient.125 consecutive patients (29 with acute hemispheric stroke and 96 without stroke) who underwent a PCT study using a prolonged acquisition time up to 3 min were retrospectively identified. The Patlak model was applied to calculate the rate of contrast leakage out of the vascular compartment. Patlak plots were created from the arterial and parenchymal time enhancement curves obtained in multiple regions of interest drawn in ischemic brain tissue and in nonischemic brain tissue. The slope of a regression line fit to the Patlak plot was used as an indicator of BBBP. Square roots of the mean squared errors and correlation coefficients were used to describe the quality of the linear regression model. This was performed separately for the first and the second PCT bolus. Results from the first and the second bolus were compared in terms of BBBP values and the quality of the linear model fitted to the Patlak plot, using generalized estimating equations with robust variance estimation.BBBP values from the second bolus were not lower than BBBP values from the first bolus in either nonischemic brain tissue [estimated mean with 95% confidence interval: 1.42 (1.10-1.82) ml x 100 g(-1) x min(-1) for the first bolus versus 1.64 (1.31-2.05) ml x 100 g(-1) x min(-1) for the second bolus, p = 1.00] or in ischemic tissue [1.04 (0.97-1.12) ml x 100 g(-1) x min(-1) for the first bolus versus 1.19 (1.11-1.28) ml x 100 g(-1)min(-1) for the second bolus, p = 0.79]. Compared to regression models from the first bolus, the Patlak regression models obtained from the second bolus were of similar or slightly better quality. This was true both in nonischemic and ischemic brain tissue.The contrast material from the first bolus of contrast for PCT does not negatively influence measurements of BBBP values from the second bolus. The second bolus can thus be used to increase anatomical coverage of BBBP assessment using PCT.

  View details for DOI 10.1159/000165113

  View details for Web of Science ID 000261132400005

  View details for PubMedID 18946215

• Quantitative Assessment of the Impact of the Service-Learning Course “Mental Health and the Veteran Population: Case Study and Practicum” on Undergraduate Students. Stanford Undergraduate Research Journal Hom J., Bahl M. 2006; 5: 24-30
• Service-Learning Courses at Stanford: How to Make a Good Thing Even Better. Public Service Education at Stanford: The Haas Center's First Twenty Years Hom, J. Stanford University Press. 2005