David Scheinker is the Executive Director of Systems Design and Collaborative Research at the Stanford Lucile Packard Children's Hospital. He is the Founder and Director of SURF Stanford Medicine, a group that brings together students and faculty from the university with physicians, nurses, and administrators from the hospitals. SURF has implemented and published dozens of projects demonstrating improvements to the quality and efficiency of care. His areas of focus include clinical care delivery, technical improvements to hospital operations, sensor-based and algorithm-enabled telemedicine, and the socioeconomic factors that shape healthcare cost and quality.

Before coming to Stanford, he was a Joint Research Fellow at The MIT Sloan School of Management and Massachusetts General Hospital. He received a PhD in theoretical math from The University of California San Diego under Jim Agler. He advises Carta Healthcare, a healthcare analytics company started by former students.

Academic Appointments

Administrative Appointments

  • Executive Director of Systems Design and Collaborative Research, Lucile Packard Children's Hospital Stanford (2015 - Present)
  • Founder and Director, SURF Stanford Medicine (2015 - Present)
  • Faculty, Clinical Excellence Research Center (CERC) (2018 - Present)
  • Faculty, Master of Science in Clinical Informatics Management (MCiM) (2020 - Present)
  • Faculty, Clinical Excellence Leadership Training (CELT) (2016 - Present)

Boards, Advisory Committees, Professional Organizations

  • Advisor, Carta Healthcare (2017 - Present)

2023-24 Courses

Stanford Advisees

All Publications

  • Equitable implementation of a precision digital health program for glucose management in individuals with newly diagnosed type 1 diabetes. Nature medicine Prahalad, P., Scheinker, D., Desai, M., Ding, V. Y., Bishop, F. K., Lee, M. Y., Ferstad, J., Zaharieva, D. P., Addala, A., Johari, R., Hood, K., Maahs, D. M. 2024


    Few young people with type 1 diabetes (T1D) meet glucose targets. Continuous glucose monitoring improves glycemia, but access is not equitable. We prospectively assessed the impact of a systematic and equitable digital-health-team-based care program implementing tighter glucose targets (HbA1c < 7%), early technology use (continuous glucose monitoring starts <1 month after diagnosis) and remote patient monitoring on glycemia in young people with newly diagnosed T1D enrolled in the Teamwork, Targets, Technology, and Tight Control (4T Study 1). Primary outcome was HbA1c change from 4 to 12 months after diagnosis; the secondary outcome was achieving the HbA1c targets. The 4T Study 1 cohort (36.8% Hispanic and 35.3% publicly insured) had a mean HbA1c of 6.58%, 64% with HbA1c < 7% and mean time in the range (70-180 mg dl-1) of 68% at 1 year after diagnosis. Clinical implementation of the 4T Study 1 met the prespecified primary outcome and improved glycemia without unexpected serious adverse events. The strategies in the 4T Study 1 can be used to implement systematic and equitable care for individuals with T1D and translate to care for other chronic diseases. registration: NCT04336969 .

    View details for DOI 10.1038/s41591-024-02975-y

    View details for PubMedID 38702523

    View details for PubMedCentralID 9764665

  • Smart Start - Designing Powerful Clinical Trials Using Pilot Study Data. NEJM evidence Ferstad, J. O., Prahalad, P., Maahs, D. M., Zaharieva, D. P., Fox, E., Desai, M., Johari, R., Scheinker, D. 2024; 3 (2): EVIDoa2300164


    Using Pilot Study Data to Design Clinical TrialsDigital health interventions are often studied in a pilot trial before full evaluation in a randomized controlled trial. The authors introduce Smart Start, a framework for using pilot study data to optimize the intervention and design the subsequent randomized controlled trial to maximize the chance of success.

    View details for DOI 10.1056/EVIDoa2300164

    View details for PubMedID 38320487

  • WAVES - The Lucile Packard Children's Hospital Pediatric Physiological Waveforms Dataset. Scientific data Miller, D. R., Dhillon, G. S., Bambos, N., Shin, A. Y., Scheinker, D. 2023; 10 (1): 124


    WAVES is a large, single-center dataset comprising 9 years of high-frequency physiological waveform data from patients in intensive and acute care units at a large academic, pediatric medical center. The data comprise approximately 10.6 million hours of 1 to 20 concurrent waveforms over approximately 50,364 distinct patient encounters. The data have been de-identified, cleaned, and organized to facilitate research. Initial analyses demonstrate the potential of the data for clinical applications such as non-invasive blood pressure monitoring and methodological applications such as waveform-agnostic data imputation. WAVES is the largest pediatric-focused and second largest physiological waveform dataset available for research.

    View details for DOI 10.1038/s41597-023-02037-x

    View details for PubMedID 36882443

    View details for PubMedCentralID 3609896

  • A computer modeling method to analyze rideshare data for the surveillance of novel strains of SARS-CoV-2. Annals of epidemiology Safranek, C. W., Scheinker, D. 2022


    PURPOSE: No method is available to systematically study SARS-CoV-2 transmission dynamics using the data that rideshare companies share with government agencies. We developed a proof-of-concept method for the analysis of SARS-CoV-2 transmissions between rideshare passengers and drivers.METHOD: To assess whether this method could enable hypothesis testing about SARS-CoV-2, we repeated ten 200-day agent-based simulations of SARS-CoV-2 propagation within the Los Angeles County rideshare network. Assuming data access for 25% of infections, we estimated an epidemiologist's ability to analyze the observable infection patterns to correctly identify a baseline viral variant A, as opposed to viral variant A with mask use (50% reduction in viral particle exchange), or a more infectious viral variant B (300% higher cumulative viral load).RESULTS: Simulations had an average of 190,387 potentially infectious rideshare interactions, resulting in 409 average diagnosed infections. Comparison of the number of observed and expected passenger-to-driver infections under each hypothesis demonstrated our method's ability to consistently discern large infectivity differences (viral variant A versus viral variant B) given partial data from one large city, and to discern smaller infectivity differences (viral variant A versus viral variant A with masks) given partial data aggregated across multiple cities.CONCLUSIONS: This novel statistical method suggests that, for the present and subsequent pandemics, government-facilitated analysis of rideshare data combined with diagnosis records may augment efforts to better understand viral transmission dynamics and to measure changes in infectivity associated with non-pharmaceutical interventions and emergent viral strains.

    View details for DOI 10.1016/j.annepidem.2022.08.051

    View details for PubMedID 36087658

  • Algorithm-Enabled, Personalized Glucose Management for Type 1 Diabetes at the Population Scale: Prospective Evaluation in Clinical Practice. JMIR diabetes Scheinker, D., Gu, A., Grossman, J., Ward, A., Ayerdi, O., Miller, D., Leverenz, J., Hood, K., Lee, M. Y., Maahs, D. M., Prahalad, P. 2022; 7 (2): e27284


    BACKGROUND: The use of continuous glucose monitors (CGMs) is recommended as the standard of care by the American Diabetes Association for individuals with type 1 diabetes (T1D). Few hardware-agnostic, open-source, whole-population tools are available to facilitate the use of CGM data by clinicians such as physicians and certified diabetes educators.OBJECTIVE: This study aimed to develop a tool that identifies patients appropriate for contact using an asynchronous message through electronic medical records while minimizing the number of patients reviewed by a certified diabetes educator or physician using the tool.METHODS: We used consensus guidelines to develop timely interventions for diabetes excellence (TIDE), an open-source hardware-agnostic tool to analyze CGM data to identify patients with deteriorating glucose control by generating generic flags (eg, mean glucose [MG] >170 mg/dL) and personalized flags (eg, MG increased by >10 mg/dL). In a prospective 7-week study in a pediatric T1D clinic, we measured the sensitivity of TIDE in identifying patients appropriate for contact and the number of patients reviewed. We simulated measures of the workload generated by TIDE, including the average number of time in range (TIR) flags per patient per review period, on a convenience sample of eight external data sets, 6 from clinical trials and 2 donated by research foundations.RESULTS: Over the 7 weeks of evaluation, the clinical population increased from 56 to 64 patients. The mean sensitivity was 99% (242/245; SD 2.5%), and the mean reduction in the number of patients reviewed was 42.6% (182/427; SD 10.9%). The 8 external data sets contained 1365 patients with 30,017 weeks of data collected by 7 types of CGMs. The rates of generic and personalized TIR flags per patient per review period were, respectively, 0.15 and 0.12 in the data set with the lowest average MG (141 mg/dL) and 0.95 and 0.22 in the data set with the highest average MG (207 mg/dL).CONCLUSIONS: TIDE is an open-source hardware-agnostic tool for personalized analysis of CGM data at the clinical population scale. In a pediatric T1D clinic, TIDE identified 99% of patients appropriate for contact using an asynchronous message through electronic medical records while reducing the number of patients reviewed by certified diabetes care and education specialists by 43%. For each of the 8 external data sets, simulation of the use of TIDE produced fewer than 0.25 personalized TIR flags per patient per review period. The use of TIDE to support telemedicine-based T1D care may facilitate sensitive and efficient guideline-based population health management.

    View details for DOI 10.2196/27284

    View details for PubMedID 35666570

  • A New Technology-Enabled Care Model for Pediatric Type 1 Diabetes. NEJM catalyst innovations in care delivery Scheinker, D., Prahalad, P., Johari, R., Maahs, D. M., Majzun, R. 2022; 3 (5)


    In July 2018, pediatric type 1 diabetes (T1D) care at Stanford suffered many of the problems that plague U.S. health care. Patient outcomes lagged behind those of peer European nations, care was delivered primarily on a fixed cadence rather than as needed, continuous glucose monitors (CGMs) were largely unavailable for individuals with public insurance, and providers' primary access to CGM data was through long printouts. Stanford developed a new technology-enabled, telemedicine-based care model for patients with newly diagnosed T1D. They developed and deployed Timely Interventions for Diabetes Excellence (TIDE) to facilitate as-needed patient contact with the partially automated analysis of CGM data and used philanthropic funding to facilitate full access to CGM technology for publicly insured patients, for whom CGM is not readily available in California. A study of the use of CGM for patients with new-onset T1D (pilot Teamwork, Targets, and Technology for Tight Control [4T] study), which incorporated the use of TIDE, was associated with a 0.5%-point reduction in hemoglobin A1c compared with historical controls and an 86% reduction in screen time for providers reviewing patient data. Based on this initial success, Stanford expanded the use of TIDE to a total of 300 patients, including many outside the pilot 4T study, and made TIDE freely available as open-source software. Next steps include expanding the use of TIDE to support the care of approximately 1,000 patients, improving TIDE and the associated workflows to scale their use to more patients, incorporating data from additional sensors, and partnering with other institutions to facilitate their deployment of this care model.

    View details for DOI 10.1056/CAT.21.0438

    View details for PubMedID 36544715

  • The Dysfunctional Health Benefits Market and Implications for US Employers and Employees. JAMA Scheinker, D., Milstein, A., Schulman, K. 1800

    View details for DOI 10.1001/jama.2021.23258

    View details for PubMedID 34994781

  • Reducing administrative costs in US health care: Assessing single payer and its alternatives. Health services research Scheinker, D., Richman, B. D., Milstein, A., Schulman, K. A. 2021


    OBJECTIVE: Excess administrative costs in the US health care system are routinely referenced as a justification for comprehensive reform. While there is agreement that these costs are too high, there is little understanding of what generates administrative costs and what policy options might mitigate them.DATA SOURCES: Literature review and national utilization and expenditure data.STUDY DESIGN: We developed a simulation model of physician billing and insurance-related (BIR) costs to estimate how certain policy reforms would generate savings. Our model is based on structural elements of the payment process in the United States and considers each provider's number of health plan contracts, the number of features in each health plan, the clinical and nonclinical processes required to submit a bill for payment, and the compliance costs associated with medical billing.DATA EXTRACTION: For several types of visits, we estimated fixed and variable costs of the billing process. We used the model to estimate the BIR costs at a national level under a variety of policy scenarios, including variations of a single payer "Medicare-for-All" model that extends fee-for-service Medicare to the entire population and policy efforts to reduce administrative costs in a multi-payer model. We conducted sensitivity analyses of a wide variety of model parameters.PRINCIPAL FINDINGS: Our model estimates that national BIR costs are reduced between 33% and 53% in Medicare-for-All style single-payer models and between 27% and 63% in various multi-payer models. Under a wide range of assumptions and sensitivity analyses, standardizing contracts generates larger savings with less variance than savings from single-payer strategies.CONCLUSION: Although moving toward a single-payer system will reduce BIR costs, certain reforms to payer-provider contracts could generate at least as many administrative cost savings without radically reforming the entire health system. BIR costs can be meaningfully reduced without abandoning a multi-payer system.

    View details for DOI 10.1111/1475-6773.13649

    View details for PubMedID 33788283

  • The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE). Health care management science Yang, L., Zhang, T., Glynn, P., Scheinker, D. 2021


    Hospitals commonly project demand for their services by combining their historical share of regional demand with forecasts of total regional demand. Hospital-specific forecasts of demand that provide prediction intervals, rather than point estimates, may facilitate better managerial decisions, especially when demand overage and underage are associated with high, asymmetric costs. Regional point forecasts of patient demand are commonly available, e.g., for the number of people requiring hospitalization due to an epidemic such as COVID-19. However, even in this common setting, no probabilistic, consistent, computationally tractable forecast is available for the fraction of patients in a region that a particular institution should expect. We introduce such a forecast, DICE (Demand Intervals from Consistent Estimators). We describe its development and deployment at an academic medical center in California during the 'second wave' of COVID-19 in the Unite States. We show that DICE is consistent under mild assumptions and suitable for use with perfect, biased and unbiased regional forecasts. We evaluate its performance on empirical data from a large academic medical center as well as on synthetic data.

    View details for DOI 10.1007/s10729-021-09555-3

    View details for PubMedID 33751281

  • The design and evaluation of a novel algorithm for automated preference card optimization. Journal of the American Medical Informatics Association : JAMIA Scheinker, D., Hollingsworth, M., Brody, A., Phelps, C., Bryant, W., Pei, F., Petersen, K., Reddy, A., Wall, J. 2021


    BACKGROUND: Inaccurate surgical preference cards (supply lists) are associated with higher direct costs, waste, and delays. Numerous preference card improvement projects have relied on institution-specific, manual approaches of limited reproducibility. We developed and tested an algorithm to facilitate the first automated, informatics-based, fully reproducible approach.METHODS: The algorithm cross-references the supplies used in each procedure and listed on each preference card and uses a time-series regression to estimate the likelihood that each quantity listed on the preference card is inaccurate. Algorithm performance was evaluated by measuring changes in direct costs between preference cards revised with the algorithm and preference cards that were not revised or revised without use of the algorithm. Results were evaluated with a difference-in-differences (DID) multivariate fixed-effects model of costs during an 8-month pre-intervention and a 15-month post-intervention period.RESULTS: The accuracies of the quantities of 469155 surgeon-procedure-specific items were estimated. Nurses used these estimates to revise 309 preference cards across eight surgical services corresponding to, respectively, 1777 and 3106 procedures in the pre- and post-intervention periods. The average direct cost of supplies per case decreased by 8.38% ($352, SD $6622) for the intervention group and increased by 13.21% ($405, SD $14706) for the control group (P<.001). The DID analysis showed significant cost reductions only in the intervention group during the intervention period (P<.001).CONCLUSION: The optimization of preference cards with a variety of institution-specific, manually intensive approaches has led to cost savings. The automated algorithm presented here produced similar results that may be more readily reproducible.

    View details for DOI 10.1093/jamia/ocaa275

    View details for PubMedID 33497439

  • Differences in Central Line-Associated Bloodstream Infection Rates Based on the Criteria Used to Count Central Line Days. JAMA Scheinker, D., Ward, A., Shin, A. Y., Lee, G. M., Mathew, R., Donnelly, L. F. 2020; 323 (2): 183–85

    View details for DOI 10.1001/jama.2019.18616

    View details for PubMedID 31935018

  • Identification of Factors Associated With Variation in US County-Level Obesity Prevalence Rates Using Epidemiologic vs Machine Learning Models JAMA NETWORK OPEN Scheinker, D., Valencia, A., Rodriguez, F. 2019; 2 (4)
  • A Machine Learning Model for Week-Ahead Hypoglycemia Prediction From Continuous Glucose Monitoring Data. Journal of diabetes science and technology Giammarino, F., Senanayake, R., Prahalad, P., Maahs, D. M., Scheinker, D. 2024: 19322968241236208


    BACKGROUND: Remote patient monitoring (RPM) programs augment type 1 diabetes (T1D) care based on retrospective continuous glucose monitoring (CGM) data. Few methods are available to estimate the likelihood of a patient experiencing clinically significant hypoglycemia within one week.METHODS: We developed a machine learning model to estimate the probability that a patient will experience a clinically significant hypoglycemic event, defined as CGM readings below 54 mg/dL for at least 15 consecutive minutes, within one week. The model takes as input the patient's CGM time series over a given week, and outputs the predicted probability of a clinically significant hypoglycemic event the following week. We used 10-fold cross-validation and external validation (testing on cohorts different from the training cohort) to evaluate performance. We used CGM data from three different cohorts of patients with T1D: REPLACE-BG (226 patients), Juvenile Diabetes Research Foundation (JDRF; 355 patients) and Tidepool (120 patients).RESULTS: In 10-fold cross-validation, the average area under the receiver operating characteristic curve (ROC-AUC) was 0.77 (standard deviation [SD]: 0.0233) on the REPLACE-BG cohort, 0.74 (SD: 0.0188) on the JDRF cohort, and 0.76 (SD: 0.02) on the Tidepool cohort. In external validation, the average ROC-AUC across the three cohorts was 0.74 (SD: 0.0262).CONCLUSIONS: We developed a machine learning algorithm to estimate the probability of a clinically significant hypoglycemic event within one week. Predictive algorithms may provide diabetes care providers using RPM with additional context when prioritizing T1D patients for review.

    View details for DOI 10.1177/19322968241236208

    View details for PubMedID 38445628

  • Role and Perspective of Certified Diabetes Care and Education Specialists in the Development of the 4T Program. Diabetes spectrum : a publication of the American Diabetes Association Leverenz, J. C., Leverenz, B., Prahalad, P., Bishop, F. K., Sagan, P., Martinez-Singh, A., Conrad, B., Chmielewski, A., Senaldi, J., Scheinker, D., Maahs, D. M. 2024; 37 (2): 153-159

    View details for DOI 10.2337/ds23-0010

    View details for PubMedID 38756427

    View details for PubMedCentralID PMC11093765

  • Drivers of telemedicine in primary care clinics at a large academic medical centre. Journal of telemedicine and telecare Parameswaran, V., Koos, H., Kalwani, N., Qureshi, L., Rosengaus, L., Dash, R., Scheinker, D., Rodriguez, F., Johnson, C. B., Stange, K., Aron, D., Lyytinen, K., Sharp, C. 2023: 1357633X231219311


    COVID-19 disrupted healthcare routines and prompted rapid telemedicine implementation. We investigated the drivers of visit modality selection (telemedicine versus in-person) in primary care clinics at an academic medical centre.We used electronic medical record data from March 2020 to May 2022 from 13 primary care clinics (N = 21,031 new, N = 207,292 return visits), with 55% overall telemedicine use. Hierarchical logistic regression and cross-validation methods were used to estimate the variation in visit modality explained by the patient, clinician and visit factors as measured by the mean-test area under the curve (AUC).There was significant variation in telemedicine use across clinicians (ranging from 0-100%) for the same visit diagnosis. The strongest predictors of telemedicine were the clinician seen for new visits (mean AUC of 0.79) and the primary visit diagnosis for return visits (0.77). Models based on all patient characteristics combined accounted for relatively little variation in modality selection, 0.54 for new and 0.58 for return visits, respectively. Amongst patient characteristics, males, patients over 65 years, Asians and patient's with non-English language preferences used less telemedicine; however, those using interpreter services used significantly more telemedicine.Clinician seen and primary visit diagnoses were the best predictors of visit modality. The distinction between new and return visits and the minimal impact of patient characteristics on visit modality highlights the complexity of clinical care and warrants research approaches that go beyond linear models to uncover the emergent causal effects of specific technology features mediated by tasks, people and organisations.

    View details for DOI 10.1177/1357633X231219311

    View details for PubMedID 38130140

  • Suboptimal antimicrobial discharge prescriptions at a tertiary referral children's hospital. Antimicrobial stewardship & healthcare epidemiology : ASHE Zhang, Y. W., Paturi, S., Puckett, L. M., Scheinker, D., Schwenk, H. T., Joerger, T. A. 2023; 3 (1): e223


    To determine the rate of and factors associated with suboptimal discharge antimicrobial prescribing at a tertiary referral children's hospital.Retrospective cohort.Tertiary referral children's hospital.All enteral antimicrobial discharge prescriptions at Lucile Packard Children's Hospital Stanford from January 1st, 2021 through December 31st, 2021.All enteral discharge antimicrobials are routinely evaluated by our antimicrobial stewardship program within 48 hours of hospital discharge. Antimicrobials are determined to be optimal or suboptimal by an antimicrobial stewardship pharmacist after evaluating the prescribed choice of antimicrobial, dose, duration, dosing frequency, and formulation. The rate and factors associated with suboptimal antimicrobial discharge prescribing were evaluated.Of 2,593 antimicrobial prescriptions ordered at discharge, 19.7% were suboptimal. Suboptimal prescriptions were due to incorrect duration (72.2%), dose (31.0%), dose frequency (23.3%), drug choice (6.5%), or formulation (5.7%). In total, 87.2% of antimicrobials for perioperative prophylaxis and 13.5% of treatment antimicrobials were suboptimal. Antimicrobials with the highest rate of suboptimal prescriptions were amoxicillin-clavulanate (40.7%), clindamycin (36.6%), and cephalexin (36.6%).Suboptimal antimicrobial discharge prescriptions are common and present an opportunity for antimicrobial stewardship programs during hospital transition of care. Factors associated with suboptimal prescriptions differ by antimicrobial and prescribed indication, indicating that multiple stewardship interventions may be needed to improve prescribing.

    View details for DOI 10.1017/ash.2023.488

    View details for PubMedID 38156234

    View details for PubMedCentralID PMC10753499

  • SCAN for Abuse: Electronic Health Record-Based Universal Child Abuse Screening. Journal of pediatric surgery Martin, N. R., Claypool, A. L., Diyaolu, M., Chan, K. S., A'Neals, E., Iyer, K., Stewart, C. C., Egge, M., Bernacki, K., Hallinan, M., Zuo, L., Gupta, U., Naru, N., Scheinker, D., Morris, A. M., Brandeau, M. L., Chao, S. 2023


    Identification of physical abuse at the point of care without a systematic approach remains inherently subjective and prone to judgement error. This study examines the implementation of an electronic health record (EHR)-based universal child injury screen (CIS) to improve detection rates of child abuse.CIS was implemented in the EHR admission documentation for all patients age 5 or younger at a single medical center, with the following questions. 1) "Is this patient an injured/trauma patient?" 2) "If this is a trauma/injured patient, where did the injury occur?" A "Yes" response to Question 1 would alert a team of child abuse pediatricians and social workers to determine if a patient required formal child abuse clinical evaluation. Patients who received positive CIS responses, formal child abuse work-up, and/or reports to Child Protective Services (CPS) were reviewed for analysis. CPS rates from historical controls (2017-2018) were compared to post-implementation rates (2019-2021).Between 2019 and 2021, 14,150 patients were screened with CIS. 286 (2.0 %) patients screened received positive CIS responses. 166 (58.0 %) of these patients with positive CIS responses would not have otherwise been identified for child abuse evaluation by their treating teams. 18 (10.8 %) of the patients identified by the CIS and not by the treating team were later reported to CPS. Facility CPS reporting rates for physical abuse were 1.2 per 1000 admitted children age 5 or younger (pre-intervention) versus 4.2 per 1000 (post-intervention).Introduction of CIS led to increased detection suspected child abuse among children age 5 or younger.Level II.Study of Diagnostic Test.

    View details for DOI 10.1016/j.jpedsurg.2023.10.025

    View details for PubMedID 37953157

  • Surgical scheduling via optimization and machine learning with long-tailed data : Health care management science, in press. Health care management science Shi, Y., Mahdian, S., Blanchet, J., Glynn, P., Shin, A. Y., Scheinker, D. 2023


    Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a modeling framework to reduce recovery unit congestion. We estimate the LOS and its probability distribution using machine learning models, schedule procedures on a rolling basis using a variety of optimization models, and estimate performance with simulation. The machine learning models achieved only modest LOS prediction accuracy, despite access to a very rich set of patient characteristics. Compared to the current paper-based system used in the hospital, most optimization models failed to reduce congestion without increasing wait times for surgery. A conservative stochastic optimization with sufficient sampling to capture the long tail of the LOS distribution outperformed the current manual process and other stochastic and robust optimization approaches. These results highlight the perils of using oversimplified distributional models of LOS for scheduling procedures and the importance of using optimization methods well-suited to dealing with long-tailed behavior.

    View details for DOI 10.1007/s10729-023-09649-0

    View details for PubMedID 37665543

  • The evolving role of data & amp; safety monitoring boards for real-world clinical trials JOURNAL OF CLINICAL AND TRANSLATIONAL SCIENCE Bunning, B. J., Hedlin, H., Chen, J. H., Ciolino, J. D., Ferstad, J., Fox, E., Garcia, A., Go, A., Johari, R., Lee, J., Maahs, D. M., Mahaffey, K. W., Opsahl-Ong, K., Perez, M., Rochford, K., Scheinker, D., Spratt, H., Turakhia, M. P., Desai, M. 2023; 7 (1)
  • The evolving role of data & safety monitoring boards for real-world clinical trials. Journal of clinical and translational science Bunning, B. J., Hedlin, H., Chen, J. H., Ciolino, J. D., Ferstad, J. O., Fox, E., Garcia, A., Go, A., Johari, R., Lee, J., Maahs, D. M., Mahaffey, K. W., Opsahl-Ong, K., Perez, M., Rochford, K., Scheinker, D., Spratt, H., Turakhia, M. P., Desai, M. 2023; 7 (1): e179


    Clinical trials provide the "gold standard" evidence for advancing the practice of medicine, even as they evolve to integrate real-world data sources. Modern clinical trials are increasingly incorporating real-world data sources - data not intended for research and often collected in free-living contexts. We refer to trials that incorporate real-world data sources as real-world trials. Such trials may have the potential to enhance the generalizability of findings, facilitate pragmatic study designs, and evaluate real-world effectiveness. However, key differences in the design, conduct, and implementation of real-world vs traditional trials have ramifications in data management that can threaten their desired rigor.Three examples of real-world trials that leverage different types of data sources - wearables, medical devices, and electronic health records are described. Key insights applicable to all three trials in their relationship to Data and Safety Monitoring Boards (DSMBs) are derived.Insight and recommendations are given on four topic areas: A. Charge of the DSMB; B. Composition of the DSMB; C. Pre-launch Activities; and D. Post-launch Activities. We recommend stronger and additional focus on data integrity.Clinical trials can benefit from incorporating real-world data sources, potentially increasing the generalizability of findings and overall trial scale and efficiency. The data, however, present a level of informatic complexity that relies heavily on a robust data science infrastructure. The nature of monitoring the data and safety must evolve to adapt to new trial scenarios to protect the rigor of clinical trials.

    View details for DOI 10.1017/cts.2023.582

    View details for PubMedID 37745930

    View details for PubMedCentralID PMC10514684

  • Sociodemographic disparities in the use of cardiovascular ambulatory care and telemedicine during the COVID-19 pandemic. American heart journal Osmanlliu, E., Kalwani, N. M., Parameswaran, V., Qureshi, L., Dash, R., Scheinker, D., Rodriguez, F. 2023


    The COVID-19 pandemic accelerated adoption of telemedicine in cardiology clinics. Early in the pandemic, there were sociodemographic disparities in telemedicine use. It is unknown if these disparities persisted and whether they were associated with changes in the population of patients accessing care.We examined all adult cardiology visits at an academic and an affiliated community practice in Northern California from March 2019 to February 2020 (pre-COVID) and March 2020 to February 2021 (COVID). We compared patient sociodemographic characteristics between these periods. We used logistic regression to assess the association of patient/visit characteristics with visit modality (in-person vs telemedicine and video- vs phone-based telemedicine) during the COVID period.There were 54,948 pre-COVID and 58,940 COVID visits. Telemedicine use increased from <1% to 70.7% of visits (49.7% video, 21.0% phone) during the COVID period. Patient sociodemographic characteristics were similar during both periods. In adjusted analyses, visits for patients from some sociodemographic groups were less likely to be delivered by telemedicine, and when delivered by telemedicine, were less likely to be delivered by video versus phone. The observed disparities in the use of video-based telemedicine were greatest for patients aged ≥80 years (vs age <60, OR 0.24, 95% CI 0.21, 0.28), Black patients (vs non-Hispanic White, OR 0.64, 95% CI 0.56, 0.74), patients with limited English proficiency (vs English proficient, OR 0.52, 95% CI 0.46-0.59), and those on Medicaid (vs privately insured, OR 0.47, 95% CI 0.41-0.54).During the first year of the pandemic, the sociodemographic characteristics of patients receiving cardiovascular care remained stable, but the modality of care diverged across groups. There were differences in the use of telemedicine vs in-person care and most notably in the use of video- vs phone-based telemedicine. Future studies should examine barriers and outcomes in digital healthcare access across diverse patient groups.

    View details for DOI 10.1016/j.ahj.2023.06.011

    View details for PubMedID 37369269

    View details for PubMedCentralID PMC10290766

  • A quantitative model to ensure capacity sufficient for timely access to care in a remote patient monitoring program. Endocrinology, diabetes & metabolism Chang, A., Gao, M. Z., Ferstad, J. O., Dupenloup, P., Zaharieva, D. P., Maahs, D. M., Prahalad, P., Johari, R., Scheinker, D. 2023: e435


    Algorithm-enabled remote patient monitoring (RPM) programs pose novel operational challenges. For clinics developing and deploying such programs, no standardized model is available to ensure capacity sufficient for timely access to care. We developed a flexible model and interactive dashboard of capacity planning for whole-population RPM-based care for T1D.Data were gathered from a weekly RPM program for 277 paediatric patients with T1D at a paediatric academic medical centre. Through the analysis of 2 years of observational operational data and iterative interviews with the care team, we identified the primary operational, population, and workforce metrics that drive demand for care providers. Based on these metrics, an interactive model was designed to facilitate capacity planning and deployed as a dashboard.The primary population-level drivers of demand are the number of patients in the program, the rate at which patients enrol and graduate from the program, and the average frequency at which patients require a review of their data. The primary modifiable clinic-level drivers of capacity are the number of care providers, the time required to review patient data and contact a patient, and the number of hours each provider allocates to the program each week. At the institution studied, the model identified a variety of practical operational approaches to better match the demand for patient care.We designed a generalizable, systematic model for capacity planning for a paediatric endocrinology clinic providing RPM for T1D. We deployed this model as an interactive dashboard and used it to facilitate expansion of a novel care program (4 T Study) for newly diagnosed patients with T1D. This model may facilitate the systematic design of RPM-based care programs.

    View details for DOI 10.1002/edm2.435

    View details for PubMedID 37345227

  • Identification of Factors Associated With 30-Day Readmissions After Posterior Lumbar Fusion Using Machine Learning and Traditional Models: A National Longitudinal Database Study. Spine Rezaii, P. G., Herrick, D., Ratliff, J. K., Rusu, M., Scheinker, D., Desai, A. M. 2023


    STUDY DESIGN: Retrospective cohort study.OBJECTIVE: To identify factors associated with readmissions after PLF using machine learning and logistic regression (LR) models.SUMMARY OF BACKGROUND DATA: Readmissions following posterior lumbar fusion (PLF) place significant health and financial burden on the patient and overall healthcare system.METHODS: The Optum Clinformatics Data Mart database was used to identify patients who underwent posterior lumbar laminectomy, fusion, and instrumentation between 2004 and 2017. Four machine learning models and a multivariable LR model were used to assess factors most closely associated with 30-day readmission. These models were also evaluated in terms of ability to predict unplanned 30-day readmissions. The top performing model (Gradient Boosting Machine; GBM) was then compared to the validated LACE index in terms of potential cost savings associated with implementation of the model.RESULTS: A total of 18,981 patients were included, of which 3,080 (16.2%) were readmitted within 30 days of initial admission. Discharge status, prior admission, and geographic division were most influential for the LR model, while discharge status, length of stay, and prior admissions had greatest relevance for the GBM model. GBM outperformed LR in predicting unplanned 30-day readmission (mean AUC 0.865 vs. 0.850, P<0.0001). Use of GBM also achieved a projected 80% decrease in readmission-associated costs relative to those achieved by the LACE index model.CONCLUSIONS: Factors associated with readmission vary in terms of predictive influence based on standard logistic regression and machine learning models used, highlighting the complementary roles these models have in identifying relevant factors for prediction of 30-day readmissions. For posterior lumbar fusion procedures, Gradient Boosting Machine yielded greatest predictive ability and associated cost savings for readmission.LEVEL OF EVIDENCE: 3.

    View details for DOI 10.1097/BRS.0000000000004664

    View details for PubMedID 37027190

  • Disparities in Hemoglobin A1c Levels in the First Year After Diagnosis Among Youths With Type 1 Diabetes Offered Continuous Glucose Monitoring. JAMA network open Addala, A., Ding, V., Zaharieva, D. P., Bishop, F. K., Adams, A. S., King, A. C., Johari, R., Scheinker, D., Hood, K. K., Desai, M., Maahs, D. M., Prahalad, P. 2023; 6 (4): e238881


    Continuous glucose monitoring (CGM) is associated with improvements in hemoglobin A1c (HbA1c) in youths with type 1 diabetes (T1D); however, youths from minoritized racial and ethnic groups and those with public insurance face greater barriers to CGM access. Early initiation of and access to CGM may reduce disparities in CGM uptake and improve diabetes outcomes.To determine whether HbA1c decreases differed by ethnicity and insurance status among a cohort of youths newly diagnosed with T1D and provided CGM.This cohort study used data from the Teamwork, Targets, Technology, and Tight Control (4T) study, a clinical research program that aims to initiate CGM within 1 month of T1D diagnosis. All youths with new-onset T1D diagnosed between July 25, 2018, and June 15, 2020, at Stanford Children's Hospital, a single-site, freestanding children's hospital in California, were approached to enroll in the Pilot-4T study and were followed for 12 months. Data analysis was performed and completed on June 3, 2022.All eligible participants were offered CGM within 1 month of diabetes diagnosis.To assess HbA1c change over the study period, analyses were stratified by ethnicity (Hispanic vs non-Hispanic) or insurance status (public vs private) to compare the Pilot-4T cohort with a historical cohort of 272 youths diagnosed with T1D between June 1, 2014, and December 28, 2016.The Pilot-4T cohort comprised 135 youths, with a median age of 9.7 years (IQR, 6.8-12.7 years) at diagnosis. There were 71 boys (52.6%) and 64 girls (47.4%). Based on self-report, participants' race was categorized as Asian or Pacific Islander (19 [14.1%]), White (62 [45.9%]), or other race (39 [28.9%]); race was missing or not reported for 15 participants (11.1%). Participants also self-reported their ethnicity as Hispanic (29 [21.5%]) or non-Hispanic (92 [68.1%]). A total of 104 participants (77.0%) had private insurance and 31 (23.0%) had public insurance. Compared with the historical cohort, similar reductions in HbA1c at 6, 9, and 12 months postdiagnosis were observed for Hispanic individuals (estimated difference, -0.26% [95% CI, -1.05% to 0.43%], -0.60% [-1.46% to 0.21%], and -0.15% [-1.48% to 0.80%]) and non-Hispanic individuals (estimated difference, -0.27% [95% CI, -0.62% to 0.10%], -0.50% [-0.81% to -0.11%], and -0.47% [-0.91% to 0.06%]) in the Pilot-4T cohort. Similar reductions in HbA1c at 6, 9, and 12 months postdiagnosis were also observed for publicly insured individuals (estimated difference, -0.52% [95% CI, -1.22% to 0.15%], -0.38% [-1.26% to 0.33%], and -0.57% [-2.08% to 0.74%]) and privately insured individuals (estimated difference, -0.34% [95% CI, -0.67% to 0.03%], -0.57% [-0.85% to -0.26%], and -0.43% [-0.85% to 0.01%]) in the Pilot-4T cohort. Hispanic youths in the Pilot-4T cohort had higher HbA1c at 6, 9, and 12 months postdiagnosis than non-Hispanic youths (estimated difference, 0.28% [95% CI, -0.46% to 0.86%], 0.63% [0.02% to 1.20%], and 1.39% [0.37% to 1.96%]), as did publicly insured youths compared with privately insured youths (estimated difference, 0.39% [95% CI, -0.23% to 0.99%], 0.95% [0.28% to 1.45%], and 1.16% [-0.09% to 2.13%]).The findings of this cohort study suggest that CGM initiation soon after diagnosis is associated with similar improvements in HbA1c for Hispanic and non-Hispanic youths as well as for publicly and privately insured youths. These results further suggest that equitable access to CGM soon after T1D diagnosis may be a first step to improve HbA1c for all youths but is unlikely to eliminate disparities Identifier: NCT04336969.

    View details for DOI 10.1001/jamanetworkopen.2023.8881

    View details for PubMedID 37074715

    View details for PubMedCentralID PMC10116368

  • The Association Between Central Line-Associated Bloodstream Infection and Central Line Access. Critical care medicine Ward, A., Chemparathy, A., Seneviratne, M., Gaskari, S., Mathew, R., Wood, M., Donnelly, L. F., Lee, G. M., Scheinker, D., Shin, A. Y. 2023


    OBJECTIVES: Identifying modifiable risk factors associated with central line-associated bloodstream infections (CLABSIs) may lead to modifications to central line (CL) management. We hypothesize that the number of CL accesses per day is associated with an increased risk for CLABSI and that a significant fraction of CL access may be substituted with non-CL routes.DESIGN: We conducted a retrospective cohort study of patients with at least one CL device day from January 1, 2015, to December 31, 2019. A multivariate mixed-effects logistic regression model was used to estimate the association between the number of CL accesses in a given CL device day and prevalence of CLABSI within the following 3 days.SETTING: A 395-bed pediatric academic medical center.PATIENTS: Patients with at least one CL device day from January 1, 2015, to December 31, 2019.INTERVENTIONS: None.MEASUREMENTS AND MAIN RESULTS: There were 138,411 eligible CL device days across 6,543 patients, with 639 device days within 3 days of a CLABSI (a total of 217 CLABSIs). The number of per-day CL accesses was independently associated with risk of CLABSI in the next 3 days (adjusted odds ratio, 1.007; 95% CI, 1.003-1.012; p = 0.002). Of medications administered through CLs, 88% were candidates for delivery through a peripheral line. On average, these accesses contributed a 6.3% increase in daily risk for CLABSI.CONCLUSIONS: The number of daily CL accesses is independently associated with risk of CLABSI in the next 3 days. In the pediatric population examined, most medications delivered through CLs could be safely administered peripherally. Efforts to reduce CL access may be an important strategy to include in contemporary CLABSI-prevention bundles.

    View details for DOI 10.1097/CCM.0000000000005838

    View details for PubMedID 36920081

  • CGM Metrics Identify Dysglycemic States in Participants From the TrialNet Pathway to Prevention Study. Diabetes care Wilson, D. M., Pietropaolo, S. L., Acevedo-Calado, M., Huang, S., Anyaiwe, D., Scheinker, D., Steck, A. K., Vasudevan, M. M., McKay, S. V., Sherr, J. L., Herold, K. C., Dunne, J. L., Greenbaum, C. J., Lord, S. M., Haller, M. J., Schatz, D. A., Atkinson, M. A., Nelson, P. W., Pietropaolo, M. 2023


    Continuous glucose monitoring (CGM) parameters may identify individuals at risk for progression to overt type 1 diabetes. We aimed to determine whether CGM metrics provide additional insights into progression to clinical stage 3 type 1 diabetes.One hundred five relatives of individuals in type 1 diabetes probands (median age 16.8 years; 89% non-Hispanic White; 43.8% female) from the TrialNet Pathway to Prevention study underwent 7-day CGM assessments and oral glucose tolerance tests (OGTTs) at 6-month intervals. The baseline data are reported here. Three groups were evaluated: individuals with 1) stage 2 type 1 diabetes (n = 42) with ≥2 diabetes-related autoantibodies and abnormal OGTT; 2) stage 1 type 1 diabetes (n = 53) with ≥2 diabetes-related autoantibodies and normal OGTT; and 3) negative test for all diabetes-related autoantibodies and normal OGTT (n = 10).Multiple CGM metrics were associated with progression to stage 3 type 1 diabetes. Specifically, spending ≥5% time with glucose levels ≥140 mg/dL (P = 0.01), ≥8% time with glucose levels ≥140 mg/dL (P = 0.02), ≥5% time with glucose levels ≥160 mg/dL (P = 0.0001), and ≥8% time with glucose levels ≥160 mg/dL (P = 0.02) were all associated with progression to stage 3 disease. Stage 2 participants and those who progressed to stage 3 also exhibited higher mean daytime glucose values; spent more time with glucose values over 120, 140, and 160 mg/dL; and had greater variability.CGM could aid in the identification of individuals, including those with a normal OGTT, who are likely to rapidly progress to stage 3 type 1 diabetes.

    View details for DOI 10.2337/dc22-1297

    View details for PubMedID 36730530

  • A model to design financially sustainable algorithm-enabled remote patient monitoring for pediatric type 1 diabetes care. Frontiers in endocrinology Dupenloup, P., Pei, R. L., Chang, A., Gao, M. Z., Prahalad, P., Johari, R., Schulman, K., Addala, A., Zaharieva, D. P., Maahs, D. M., Scheinker, D. 2022; 13: 1021982


    Population-level algorithm-enabled remote patient monitoring (RPM) based on continuous glucose monitor (CGM) data review has been shown to improve clinical outcomes in diabetes patients, especially children. However, existing reimbursement models are geared towards the direct provision of clinic care, not population health management. We developed a financial model to assist pediatric type 1 diabetes (T1D) clinics design financially sustainable RPM programs based on algorithm-enabled review of CGM data.Data were gathered from a weekly RPM program for 302 pediatric patients with T1D at Lucile Packard Children's Hospital. We created a customizable financial model to calculate the yearly marginal costs and revenues of providing diabetes education. We consider a baseline or status quo scenario and compare it to two different care delivery scenarios, in which routine appointments are supplemented with algorithm-enabled, flexible, message-based contacts delivered according to patient need. We use the model to estimate the minimum reimbursement rate needed for telemedicine contacts to maintain revenue-neutrality and not suffer an adverse impact to the bottom line.The financial model estimates that in both scenarios, an average reimbursement rate of roughly $10.00 USD per telehealth interaction would be sufficient to maintain revenue-neutrality. Algorithm-enabled RPM could potentially be billed for using existing RPM CPT codes and lead to margin expansion.We designed a model which evaluates the financial impact of adopting algorithm-enabled RPM in a pediatric endocrinology clinic serving T1D patients. This model establishes a clear threshold reimbursement value for maintaining revenue-neutrality, as well as an estimate of potential RPM reimbursement revenue which could be billed for. It may serve as a useful financial-planning tool for a pediatric T1D clinic seeking to leverage algorithm-enabled RPM to provide flexible, more timely interventions to its patients.

    View details for DOI 10.3389/fendo.2022.1021982

    View details for PubMedID 36440201

    View details for PubMedCentralID PMC9691757

  • Criteria for Early Pacemaker Implantation in Patients With Postoperative Heart Block After Congenital Heart Surgery. Circulation. Arrhythmia and electrophysiology Duong, S. Q., Shi, Y., Giacone, H., Navarre, B., Gal, D., Han, B., Sganga, D., Ma, M., Reddy, C. D., Shin, A., Kwiatkowski, D. M., Dubin, A. M., Scheinker, D., Algaze, C. A. 2022: e011145


    Guidelines recommend observation for atrioventricular node recovery until postoperative days (POD) 7 to 10 before permanent pacemaker placement (PPM) in patients with heart block after congenital cardiac surgery. To aid in surgical decision-making for early PPM, we established criteria to identify patients at high risk of requiring PPM.We reviewed all cases of second degree and complete heart block (CHB) on POD 0 from August 2009 through December 2018. A decision tree model was trained to predict the need for PPM amongst patients with persistent CHB and prospectively validated from January 2019 through March 2021. Separate models were developed for all patients on POD 0 and those without recovery by POD 4.Of the 139 patients with postoperative heart block, 68 required PPM. PPM was associated with older age (3.2 versus 1.0 years; P=0.018) and persistent CHB on POD 0 (versus intermittent CHB or second degree heart block; 87% versus 58%; P=0.001). Median days [IQR] to atrioventricular node recovery was 2 [0-5] and PPM was 9 [6-11]. Of the 100 cases of persistent CHB (21 in the validation cohort), 59 (59%) required PPM. A decision tree model identified 4 risk factors for PPM in patients with persistent CHB: (1) aortic valve replacement, subaortic stenosis repair, or Konno procedure; (2) ventricular L-looping; (3) atrioventricular valve replacement; (4) and absence of preoperative antiarrhythmic agent (in POD 0 model only). The POD 4 model specificity was 0.89 [0.67-0.99] and positive predictive value was 0.94 [95% CI 0.81-0.98], which was stable in prospective validation (positive predictive value 1.0).A data-driven analysis led to actionable criteria to identify patients requiring PPM. Patients with left ventricular outflow tract surgery, atrioventricular valve replacement, or ventricular L-Looping could be considered for PPM on POD 4 to reduce risks of temporary pacing and improve care efficiency.

    View details for DOI 10.1161/CIRCEP.122.011145

    View details for PubMedID 36306332

  • Drivers of variation in telemedicine use during the COVID-19 pandemic: The experience of a large academic cardiovascular practice. Journal of telemedicine and telecare Koos, H., Parameswaran, V., Claire, S., Chen, C., Kalwani, N., Osmanlliu, E., Qureshi, L., Dash, R., Scheinker, D., Rodriguez, F. 2022: 1357633X221130288


    BACKGROUND: COVID-19 spurred rapid adoption and expansion of telemedicine. We investigated the factors driving visit modality (telemedicine vs. in-person) for outpatient visits at a large cardiovascular center.METHODS: We used electronic health record data from March 2020 to February 2021 from four cardiology subspecialties (general cardiology, electrophysiology, heart failure, and interventional cardiology) at a large academic health system in Northern California. There were 21,912 new and return visits with 69% delivered by telemedicine. We used hierarchical logistic regression and cross-validation methods to estimate the variation in visit modality explained by patient, clinician, and visit factors as measured by the mean area under the curve.RESULTS: Across all subspecialties, the clinician seen was the strongest predictor of telemedicine usage, while primary visit diagnosis was the next most predictive. In general cardiology, the model based on clinician seen had a mean area under the curve of 0.83, the model based on the primary diagnosis had a mean area under the curve of 0.69, and the model based on all patient characteristics combined had a mean area under the curve of 0.56. There was significant variation in telemedicine use across clinicians within each subspecialty, even for visits with the same primary visit diagnosis.CONCLUSION: Individual clinician practice patterns had the largest influence on visit modality across subspecialties in a large cardiovascular medicine practice, while primary diagnosis was less predictive, and patient characteristics even less so. Cardiovascular clinics should reduce variability in visit modality selection through standardized processes that integrate clinical factors and patient preference.

    View details for DOI 10.1177/1357633X221130288

    View details for PubMedID 36214200

  • Predictive Ability of the Braden QD Scale for Hospital-Acquired Venous Thromboembolism in Hospitalized Children JOINT COMMISSION JOURNAL ON QUALITY AND PATIENT SAFETY Gonzalez, A., Mulet, Y., Song, N., Loh, L., Scheinker, D., Shin, A. Y., Donnelly, L. F. 2022; 48 (10): 513-520


    Hospital-acquired venous thromboembolisms (HA-VTEs) are increasingly common in pediatric inpatients and associated with significant morbidity and cost. The Braden QD Scale was created to predict the risk of hospital-acquired pressure injury (HAPI) and is used broadly in children's hospitals. This study evaluated the ability of the Braden QD Total score to predict risk of HA-VTE at a quaternary children's hospital.To analyze the predictive potential of the Braden QD Total score and subscores for HA-VTEs, the researchers performed univariate logistic regressions. The increase in a patient's odds of developing an HA-VTE for every 1-point increase in each Braden QD score was evaluated. Each model was evaluated using a 5-fold cross-validated area-under-the-curve of the corresponding receiver operating characteristic curve (AUROC).This study analyzed 27,689 pediatric inpatients. HA-VTE occurred in 135 patients. The odds of HA-VTE incidence increased by 29% (odds ratio 1.29, 95% confidence interval [CI] 1.25-1.34, p < 0.001) for every 1-point increase in a patient's Braden QD Total score. The AUROC was 0.81 (95% CI 0.77-0.85).The Braden QD Scale is a predictor for HA-VTE, outperforming its original intended use for predicting HAPI and performing similarly to other HA-VTE predictive models. As the Braden QD Total score is currently recorded in the electronic health records of many children's hospitals, it could be practically and easily implemented as a tool to predict which patients are at risk for HA-VTE.

    View details for DOI 10.1016/j.jcjq.2022.05.007

    View details for Web of Science ID 000873971100004

    View details for PubMedID 35963770

  • Trends in national and county-level Hispanic mortality in the United States, 2011-2020. Scientific reports Sarraju, A., Ngo, S., Ashland, M., Scheinker, D., Rodriguez, F. 2022; 12 (1): 11812


    Hispanic populations generally experience more adverse socioeconomic conditions yet demonstrate lower mortality compared with Non-Hispanic White (NHW) populations in the US. This finding of a mortality advantage is well-described as the "Hispanic paradox." The Coronavirus Disease 2019 (COVID-19) pandemic has disproportionately affected Hispanic populations. To quantify these effects, we evaluated US national and county-level trends in Hispanic versus NHW mortality from 2011 through 2020. We found that a previously steady Hispanic mortality advantage significantly decreased in 2020, potentiallydriven by COVID-19-attributable Hispanic mortality. Nearly 16% of US counties experienced a reversal of their pre-pandemic Hispanic mortality advantage such that their Hispanic mortality exceeded NHW mortality in 2020. An additional 50% experienced a decrease in a pre-pandemic Hispanic mortality advantage. Our work provides a quantitative understanding of the disproportionate burden of the pandemic on Hispanic health and the Hispanic paradox and provides a renewed impetus to tackle the factors driving these concerning disparities.

    View details for DOI 10.1038/s41598-022-15916-x

    View details for PubMedID 35821236

  • DRIVERS OF VARIATION IN TELEMEDICINE USE AT AN ACADEMIC CARDIOVASCULAR CENTER DURING THE COVID-19 PANDEMIC Koos, H., Parameswaran, V., Claire, S., Chen, C., Kalwani, N., Osmanlliu, E., Dash, R., Scheinker, D., Rodriguez, F. ELSEVIER SCIENCE INC. 2022: 2046
  • Performance of a Commonly Used Pressure Injury Risk Model Under Changing Incidence JOINT COMMISSION JOURNAL ON QUALITY AND PATIENT SAFETY Fleming, S., McFarlane, K., Thapa, I., Johnson, A. K., Kruger, J. F., Shin, A. Y., Scheinker, D., Donnelly, L. F. 2022; 48 (3): 131-138


    Hospital-acquired pressure injuries (HAPIs) cause patient harm and increase health care costs. We sought to evaluate the performance of the Braden QD Scale-associated changes in HAPI incidence.Using electronic health records data from a quaternary children's hospital, we evaluated the association between Braden QD scores and patient risk of HAPI. We analyzed how this relationship changed during a hospitalwide quality HAPI reduction initiative.Of 23,532 unique patients, 108 (0.46%, 95% confidence interval [CI] = 0.38%-0.55%) experienced a HAPI. Every 1-point increase in the Braden QD score was associated with a 41% increase in the patient's odds of developing a HAPI (odds ratio [OR] = 1.41, 95% CI = 1.36-1.46, p < 0.001). HAPI incidence declined significantly following implementation of a HAPI-reduction initiative (β = -0.09, 95% CI = -0.11 - -0.07, p < 0.001), as did Braden QD positive predictive value (β = -0.29, 95% CI = -0.44 - -0.14, p < 0.001) and specificity (β = -0.28, 95% CI = -0.43 - -0.14, p < 0.001), while sensitivity (β = 0.93, 95% CI = 0.30-1.75, p = 0.01) and the concordance statistic (β = 0.18, 95% CI = 0.15-0.21, p < 0.001) increased significantly.Decreases in HAPI incidence following a quality improvement initiative were associated with (1) significant deterioration in threshold-dependent performance measures such as specificity and precision and (2) significant improvements in threshold-independent performance measures such as the concordance statistic. The performance of the Braden QD Scale is more stable as a tool that continuously measures risk than as a prediction tool.

    View details for DOI 10.1016/j.jcjq.2021.10.008

    View details for Web of Science ID 000763291600002

    View details for PubMedID 34866024

  • Visualizing Opioid-Use Variation in a Pediatric Perioperative Dashboard. Applied clinical informatics Safranek, C. W., Feitzinger, L., Joyner, A. K., Woo, N., Smith, V., Souza, E. D., Vasilakis, C., Anderson, T. A., Fehr, J., Shin, A. Y., Scheinker, D., Wang, E., Xie, J. 2022; 13 (2): 370-379


    BACKGROUND: Anesthesiologists integrate numerous variables to determine an opioid dose that manages patient nociception and pain while minimizing adverse effects. Clinical dashboards that enable physicians to compare themselves to their peers can reduce unnecessary variation in patient care and improve outcomes. However, due to the complexity of anesthetic dosing decisions, comparative visualizations of opioid-use patterns are complicated by case-mix differences between providers.OBJECTIVES: This single-institution case study describes the development of a pediatric anesthesia dashboard and demonstrates how advanced computational techniques can facilitate nuanced normalization techniques, enabling meaningful comparisons of complex clinical data.METHODS: We engaged perioperative-care stakeholders at a tertiary care pediatric hospital to determine patient and surgical variables relevant to anesthesia decision-making and to identify end-user requirements for an opioid-use visualization tool. Case data were extracted, aggregated, and standardized. We performed multivariable machine learning to identify and understand key variables. We integrated interview findings and computational algorithms into an interactive dashboard with normalized comparisons, followed by an iterative process of improvement and implementation.RESULTS: The dashboard design process identified two mechanisms-interactive data filtration and machine-learning-based normalization-that enable rigorous monitoring of opioid utilization with meaningful case-mix adjustment. When deployed with real data encompassing 24,332 surgical cases, our dashboard identified both high and low opioid-use outliers with associated clinical outcomes data.CONCLUSION: A tool that gives anesthesiologists timely data on their practice patterns while adjusting for case-mix differences empowers physicians to track changes and variation in opioid administration over time. Such a tool can successfully trigger conversation amongst stakeholders in support of continuous improvement efforts. Clinical analytics dashboards can enable physicians to better understand their practice and provide motivation to change behavior, ultimately addressing unnecessary variation in high impact medication use and minimizing adverse effects.

    View details for DOI 10.1055/s-0042-1744387

    View details for PubMedID 35322398

  • Changes in telemedicine use and ambulatory visit volumes at a multispecialty cardiovascular center during the COVID-19 pandemic. Journal of telemedicine and telecare Kalwani, N. M., Osmanlliu, E., Parameswaran, V., Qureshi, L., Dash, R., Heidenreich, P. A., Scheinker, D., Rodriguez, F. 1800: 1357633X211073428


    Early in the COVID-19 pandemic, cardiology clinics rapidly implemented telemedicine to maintain access to care. Little is known about subsequent trends in telemedicine use and visit volumes across cardiology subspecialties. We conducted a retrospective cohort study including all patients with ambulatory visits at a multispecialty cardiovascular center in Northern California from March 2019 to February 2020 (pre-COVID) and March 2020 to February 2021 (COVID). Telemedicine use increased from 3.5% of visits (1200/33,976) during the pre-COVID period to 63.0% (21,251/33,706) during the COVID period. Visit volumes were below pre-COVID levels from March to May 2020 but exceeded pre-COVID levels after June 2020, including when local COVID-19 cases peaked. Telemedicine use was above 75% of visits in all cardiology subspecialties in April 2020 and stabilized at rates ranging from over 95% in electrophysiology to under 25% in heart transplant and vascular medicine. From June 2020 to February 2021, subspecialties delivering a greater percentage of visits through telemedicine experienced larger increases in new patient visits (r=0.81, p=0.029). Telemedicine can be used to deliver a significant proportion of outpatient cardiovascular care though utilization varies across subspecialties. Higher rates of telemedicine adoption may increase access to care in cardiology clinics.

    View details for DOI 10.1177/1357633X211073428

    View details for PubMedID 35108126

  • Personalizing cholesterol treatment recommendations for primary cardiovascular disease prevention. Scientific reports Sarraju, A., Ward, A., Li, J., Valencia, A., Palaniappan, L., Scheinker, D., Rodriguez, F. 2022; 12 (1): 23


    Statin therapy is the cornerstone of preventing atherosclerotic cardiovascular disease (ASCVD), primarily by reducing low density lipoprotein cholesterol (LDL-C) levels. Optimal statin therapy decisions rely on shared decision making and may be uncertain for a given patient. In areas of clinical uncertainty, personalized approaches based on real-world data may help inform treatment decisions. We sought to develop a personalized statin recommendation approach for primary ASCVD prevention based on historical real-world outcomes in similar patients. Our retrospective cohort included adults from a large Northern California electronic health record (EHR) aged 40-79 years with no prior cardiovascular disease or statin use. The cohort was split into training and test sets. Weighted-K-nearest-neighbor (wKNN) regression models were used to identify historical EHR patients similar to a candidate patient. We modeled four statin decisions for each patient: none, low-intensity, moderate-intensity, and high-intensity. For each candidate patient, the algorithm recommended the statin decision that was associated with the greatest percentage reduction in LDL-C after 1 year in similar patients. The overall cohort consisted of 50,576 patients (age 54.6 ± 9.8 years) with 55% female, 48% non-Hispanic White, 32% Asian, and 7.4% Hispanic patients. Among 8383 test-set patients, 52%, 44%, and 4% were recommended high-, moderate-, and low-intensity statins, respectively, for a maximum predicted average 1-yr LDL-C reduction of 16.9%, 20.4%, and 14.9%, in each group, respectively. Overall, using aggregate EHR data, a personalized statin recommendation approach identified the statin intensity associated with the greatest LDL-C reduction in historical patients similar to a candidate patient. Recommendations included low- or moderate-intensity statins for maximum LDL-C lowering in nearly half the test set, which is discordant with their expected guideline-based efficacy. A data-driven personalized statin recommendation approach may inform shared decision making in areas of uncertainty, and highlight unexpected efficacy-effectiveness gaps.

    View details for DOI 10.1038/s41598-021-03796-6

    View details for PubMedID 34996943

  • Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities. Frontiers in endocrinology Zaharieva, D. P., Senanayake, R., Brown, C., Watkins, B., Loving, G., Prahalad, P., Ferstad, J. O., Guestrin, C., Fox, E. B., Maahs, D. M., Scheinker, D. 2022; 13: 1096325


    Algorithm-enabled patient prioritization and remote patient monitoring (RPM) have been used to improve clinical workflows at Stanford and have been associated with improved glucose time-in-range in newly diagnosed youth with type 1 diabetes (T1D). This novel algorithm-enabled care model currently integrates continuous glucose monitoring (CGM) data to prioritize patients for weekly reviews by the clinical diabetes team. The use of additional data may help clinical teams make more informed decisions around T1D management. Regular exercise and physical activity are essential to increasing cardiovascular fitness, increasing insulin sensitivity, and improving overall well-being of youth and adults with T1D. However, exercise can lead to fluctuations in glycemia during and after the activity. Future iterations of the care model will integrate physical activity metrics (e.g., heart rate and step count) and physical activity flags to help identify patients whose needs are not fully captured by CGM data. Our aim is to help healthcare professionals improve patient care with a better integration of CGM and physical activity data. We hypothesize that incorporating exercise data into the current CGM-based care model will produce specific, clinically relevant information such as identifying whether patients are meeting exercise guidelines. This work provides an overview of the essential steps of integrating exercise data into an RPM program and the most promising opportunities for the use of these data.

    View details for DOI 10.3389/fendo.2022.1096325

    View details for PubMedID 36714600

  • Monitoring Approaches for a Pediatric Chronic Kidney Disease Machine Learning Model. Applied clinical informatics Morse, K. E., Brown, C., Fleming, S., Todd, I., Powell, A., Russell, A., Scheinker, D., Sutherland, S. M., Lu, J., Watkins, B., Shah, N. H., Pageler, N. M., Palma, J. P. 2022; 13 (2): 431-438


    OBJECTIVE: The purpose of this study is to evaluate the ability of three metrics to monitor for a reduction in performance of a chronic kidney disease (CKD) model deployed at a pediatric hospital.METHODS: The CKD risk model estimates a patient's risk of developing CKD 3 to 12 months following an inpatient admission. The model was developed on a retrospective dataset of 4,879 admissions from 2014 to 2018, then run silently on 1,270 admissions from April to October, 2019. Three metrics were used to monitor its performance during the silent phase: (1) standardized mean differences (SMDs); (2) performance of a "membership model"; and (3) response distribution analysis. Observed patient outcomes for the 1,270 admissions were used to calculate prospective model performance and the ability of the three metrics to detect performance changes.RESULTS: The deployed model had an area under the receiver-operator curve (AUROC) of 0.63 in the prospective evaluation, which was a significant decrease from an AUROC of 0.76 on retrospective data (p=0.033). Among the three metrics, SMDs were significantly different for 66/75 (88%) of the model's input variables (p <0.05) between retrospective and deployment data. The membership model was able to discriminate between the two settings (AUROC=0.71, p <0.0001) and the response distributions were significantly different (p <0.0001) for the two settings.CONCLUSION: This study suggests that the three metrics examined could provide early indication of performance deterioration in deployed models' performance.

    View details for DOI 10.1055/s-0042-1746168

    View details for PubMedID 35508197

  • A personalized decision aid for prostate cancer shared decision making. BMC medical informatics and decision making Bagshaw, H. P., Martinez, A., Heidari, N., Scheinker, D., Pollack, A., Stoyanova, R., Horwitz, E., Morton, G., Kishan, A. U., Buyyounouski, M. K. 1800; 21 (1): 374


    BACKGROUND: A shared decision-making model is preferred for engaging prostate cancer patients in treatment decisions. However, the process of assessing an individual's preferences and values is challenging and not formalized. The purpose of this study is to develop an automated decision aid for patient-centric treatment decision-making using decision analysis, preference thresholds and value elicitations to maximize the compatibility between a patient's treatment expectations and outcome.METHODS: A template for patient-centric medical decision-making was constructed. The inputs included prostate cancer risk group, pre-treatment health state, treatment alternatives (primarily focused on radiation in this model), side effects (erectile dysfunction, urinary incontinence, nocturia and bowel incontinence), and treatment success (5-year freedom from biochemical failure). A linear additive value function was used to combine the values for each attribute (side effects, success and the alternatives) into a value for all prospects. The patient-reported toxicity probabilities were derived from phase II and III trials. The probabilities are conditioned on the starting state for each of the side effects. Toxicity matrices for erectile dysfunction, urinary incontinence, nocturia and bowel incontinence were created for the treatment alternatives. Toxicity probability thresholds were obtained by identifying the patient's maximum acceptable threshold for each of the side effects. Results are represented as a visual. R and Rstudio were used to perform analyses, and R Shiny for application creation.RESULTS: We developed a web-based decision aid. Based on preliminary use of the application, every treatment alternative could be the best choice for a decision maker with a particular set of preferences. This result implies that no treatment has determinist dominance over the remaining treatments and that a preference-based approach can help patients through their decision-making process, potentially affecting compliance with treatment, tolerance of side effects and satisfaction with the decision.CONCLUSIONS: We present a unique patient-centric prostate cancer treatment decision aid that systematically assesses and incorporates a patient's preferences and values to rank treatment options by likelihood of achieving the preferred outcome. This application enables the practice and study of personalized medicine. This model can be expanded to include additional inputs, such as genomics, as well as competing, concurrent or sequential therapies.

    View details for DOI 10.1186/s12911-021-01732-2

    View details for PubMedID 34972513

  • Workforce demographics and unit structure in paediatric cardiac critical care in the United States. Cardiology in the young Horak, R. V., Bai, S., Marino, B. S., Werho, D. K., Rhodes, L. A., Costello, J. M., Cabrera, A. G., Cooper, D. S., Tan, Y., Tabbutt, S., Krawczeski, C. D. 2021: 1-5


    OBJECTIVE: To assess current demographics and duties of physicians as well as the structure of paediatric cardiac critical care in the United States.DESIGN: REDCap surveys were sent by email from May till August 2019 to medical directors ("directors") of critical care units at the 120 United States centres submitting data to the Society of Thoracic Surgeons Congenital Heart Surgery Database and to associated faculty from centres that provided email lists. Faculty and directors were asked about personal attributes and clinical duties. Directors were additionally asked about unit structure.MEASUREMENTS AND MAIN RESULTS: Responses were received from 66% (79/120) of directors and 62% (294/477) of contacted faculty. Seventy-six percent of directors and 54% of faculty were male, however, faculty <40 years old were predominantly women. The majority of both groups were white. Median bed count (n = 20) was similar in ICUs and multi-disciplinary paediatric ICUs. The median service expectation for one clinical full-time equivalent was 14 weeks of clinical service (interquartile range 12, 16), with the majority of programmes (86%) providing in-house attending night coverage. Work hours were high during service and non-service weeks with both directors (37%) and faculty (45%).CONCLUSIONS: Racial and ethnic diversity is markedly deficient in the paediatric cardiac critical care workforce. Although the majority of faculty are male, females make up the majority of the workforce younger than 40 years old. Work hours across all age groups and unit types are high both on- and off-service, with most units providing attending in-house night coverage.

    View details for DOI 10.1017/S1047951121004753

    View details for PubMedID 34857058

  • Teamwork, Targets, Technology, and Tight Control in Newly Diagnosed Type 1 Diabetes: Pilot 4T Study. The Journal of clinical endocrinology and metabolism Prahalad, P., Ding, V. Y., Zaharieva, D. P., Addala, A., Johari, R., Scheinker, D., Desai, M., Hood, K., Maahs, D. M. 2021


    CONTEXT: Youth with type 1 diabetes (T1D) do not meet hemoglobin A1c (HbA1c) targets.OBJECTIVE: To assess HbA1c outcomes in children with new onset T1D enrolled in the Teamwork, Targets, Technology and Tight Control (4T) Study.METHOD: HbA1c levels were compared between the 4T and Historical cohorts. HbA1c differences between cohorts were estimated using locally estimated scatter plot smoothing (LOESS). The change from nadir HbA1c (month 4) to 12 months post-diagnosis was estimated by cohort using a piecewise mixed effects regression model accounting for age at diagnosis, sex, ethnicity, and insurance type.SETTING AND PARTICIPANTS: We recruited 135 youth with newly diagnosed T1D at Stanford Children's Health.INTERVENTION: Starting July 2018, all youth within the first month of T1D diagnosis were offered continuous glucose monitoring (CGM) initiation and remote CGM data review was added in March 2019.MAIN OUTCOME MEASURE: HbA1c.RESULTS: HbA1c at 6, 9, and 12 months post-diagnosis was lower in the 4T cohort than in the Historic cohort (-0.54%, -0.52%, and -0.58%, respectively). Within the 4T cohort, HbA1c at 6, 9, and 12 months post-diagnosis was lower in those patients with Remote Monitoring than those without (-0.14%, -0.18%, -0.14%, respectively). Multivariable regression analysis showed that the 4T cohort experienced a significantly lower increase in HbA1c between months 4 and 12 (p < 0.001).CONCLUSIONS: A technology-enabled team-based approach to intensified new onset education involving target setting, CGM initiation, and remote data review significantly decreased HbA1c in youth with T1D 12 months post-diagnosis.

    View details for DOI 10.1210/clinem/dgab859

    View details for PubMedID 34850024

  • Machine learning approaches improve risk stratification for secondary cardiovascular disease prevention in multiethnic patients. Open heart Sarraju, A., Ward, A., Chung, S., Li, J., Scheinker, D., Rodriguez, F. 2021; 8 (2)


    OBJECTIVES: Identifying high-risk patients is crucial for effective cardiovascular disease (CVD) prevention. It is not known whether electronic health record (EHR)-based machine-learning (ML) models can improve CVD risk stratification compared with a secondary prevention risk score developed from randomised clinical trials (Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention, TRS 2°P).METHODS: We identified patients with CVD in a large health system, including atherosclerotic CVD (ASCVD), split into 80% training and 20% test sets. A rich set of EHR patient features was extracted. ML models were trained to estimate 5-year CVD event risk (random forests (RF), gradient-boosted machines (GBM), extreme gradient-boosted models (XGBoost), logistic regression with an L2 penalty and L1 penalty (Lasso)). ML models and TRS 2°P were evaluated by the area under the receiver operating characteristic curve (AUC).RESULTS: The cohort included 32 192 patients (median age 74 years, with 46% female, 63% non-Hispanic white and 12% Asian patients and 23 475 patients with ASCVD). There were 4010 events over 5 years of follow-up. ML models demonstrated good overall performance; XGBoost demonstrated AUC 0.70 (95% CI 0.68 to 0.71) in the full CVD cohort and AUC 0.71 (95% CI 0.69 to 0.73) in patients with ASCVD, with comparable performance by GBM, RF and Lasso. TRS 2°P performed poorly in all CVD (AUC 0.51, 95% CI 0.50 to 0.53) and ASCVD (AUC 0.50, 95% CI 0.48 to 0.52) patients. ML identified nontraditional predictive variables including education level and primary care visits.CONCLUSIONS: In a multiethnic real-world population, EHR-based ML approaches significantly improved CVD risk stratification for secondary prevention.

    View details for DOI 10.1136/openhrt-2021-001802

    View details for PubMedID 34667093

  • Prediction of Iron-related Adverse Donation Outcomes for Repeat Blood Donors Based on Donation Interval Russell, W., Scheinker, D., Custer, B. WILEY. 2021: 11A-12A
  • Effect of Time of Daily Data Collection on the Calculation of Catheter-associated Urinary Tract Infection Rates. Pediatric quality & safety Donnelly, L. F., Wood, M., Loh, L., Tekic, N., Shin, A. Y., Scheinker, D. 2021; 6 (5): e466


    Introduction: According to the National Healthcare Safety Network (NHSN) definitions for Catheter-associated urinary tract infections (CAUTI) rates, determination of the number of urinary catheter days must occur by calculating the number of catheters in place "for each day of the month, at the same time of day" but does not define at what time of day this occurs. The purpose of this review was to determine if a data collection time of 11 am would yield a greater collection of urinary catheter days than that done at midnight.Methods: During a 20-month period, the number of urinary catheter days was calculated using once-a-day electronic measurements to identify a urinary catheter presence. We used data collected at 11 am and collected at midnight (our historic default) in comparing the calculated urinary catheter days and resultant CAUTI rates.Results: There were 7,548 patients who had a urinary tract catheter. The number of urinary catheter days captured using the 11 am collection time was 15,425, and using the midnight collection time was 10,234, resulting in a 50.7% increase. The CAUTI rate per 1,000 urinary catheter days calculated using the 11 am collection method was 0.58, and using the midnight collection method was 0.88, a reduced CAUTI rate of 33.6%.Conclusion: The data collection time can significantly impact the calculation of urinary catheter days and on calculated CAUTI rates. Variations in how healthcare systems define their denominator per current National Healthcare Safety Network policy may result in significant differences in reported rates.

    View details for DOI 10.1097/pq9.0000000000000466

    View details for PubMedID 34476317

  • Population-level management of Type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health. Pediatric diabetes Ferstad, J. O., Vallon, J. J., Jun, D., Gu, A., Vitko, A., Morales, D. P., Leverenz, J., Lee, M. Y., Leverenz, B., Vasilakis, C., Osmanlliu, E., Prahalad, P., Maahs, D. M., Johari, R., Scheinker, D. 2021


    OBJECTIVE: To develop and scale algorithm-enabled patient prioritization to improve population-level management of type 1 diabetes (T1D) in a pediatric clinic with fixed resources, using telemedicine and remote monitoring of patients via continuous glucose monitor (CGM) data review.RESEARCH DESIGN AND METHODS: We adapted consensus glucose targets for T1D patients using CGM to identify interpretable clinical criteria to prioritize patients for weekly provider review. The criteria were constructed to manage the number of patients reviewed weekly and identify patients who most needed provider contact. We developed an interactive dashboard to display CGM data relevant for the patients prioritized for review.RESULTS: The introduction of the new criteria and interactive dashboard was associated with a 60% reduction in the mean time spent by diabetes team members who remotely and asynchronously reviewed patient data and contacted patients, from 3.2±0.20 to 1.3±0.24minutes per patient per week. Given fixed resources for review, this corresponded to an estimated 147% increase in weekly clinic capacity. Patients who qualified for and received remote review (n=58) have associated 8.8 percentage points (pp) (95% CI=0.6-16.9pp) greater time-in-range (70-180mg/dL) glucoses compared to 25 control patients who did not qualify at twelve months after T1D onset.CONCLUSIONS: An algorithm-enabled prioritization of T1D patients with CGM for asynchronous remote review reduced provider time spent per patient and was associated with improved time-in-range. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1111/pedi.13256

    View details for PubMedID 34374183

  • Development and Implementation of a Real-time Bundle-adherence Dashboard for Central Line-associated Bloodstream Infections. Pediatric quality & safety Chemparathy, A., Seneviratne, M. G., Ward, A., Mirchandani, S., Li, R., Mathew, R., Wood, M., Shin, A. Y., Donnelly, L. F., Scheinker, D., Lee, G. M. 2021; 6 (4): e431


    Introduction: Central line-associated bloodstream infections (CLABSIs) are the most common hospital-acquired infection in pediatric patients. High adherence to the CLABSI bundle mitigates CLABSIs. At our institution, there did not exist a hospital-wide system to measure bundle-adherence. We developed an electronic dashboard to monitor CLABSI bundle-adherence across the hospital and in real time.Methods: Institutional stakeholders and areas of opportunity were identified through interviews and data analyses. We created a data pipeline to pull adherence data from twice-daily bundle checks and populate a dashboard in the electronic health record. The dashboard was developed to allow visualization of overall and individual element bundle-adherence across units. Monthly dashboard accesses and element-level bundle-adherence were recorded, and the nursing staff's feedback about the dashboard was obtained.Results: Following deployment in September 2018, the dashboard was primarily accessed by quality improvement, clinical effectiveness and analytics, and infection prevention and control. Quality improvement and infection prevention and control specialists presented dashboard data at improvement meetings to inform unit-level accountability initiatives. All-element adherence across the hospital increased from 25% in September 2018 to 44% in December 2019, and average adherence to each bundle element increased between 2018 and 2019.Conclusions: CLABSI bundle-adherence, overall and by element, increased across the hospital following the deployment of a real-time electronic data dashboard. The dashboard enabled population-level surveillance of CLABSI bundle-adherence that informed bundle accountability initiatives. Data transparency enabled by electronic dashboards promises to be a useful tool for infectious disease control.

    View details for DOI 10.1097/pq9.0000000000000431

    View details for PubMedID 34235355

  • Extremum seeking for optimal control problems with unknown time-varying systems and unknown objective functions INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING Scheinker, A., Scheinker, D. 2021; 35 (7): 1143-1161

    View details for DOI 10.1002/acs.3097

    View details for Web of Science ID 000672432200002

  • Improved individual and population-level HbA1c estimation using CGM data and patient characteristics. Journal of diabetes and its complications Grossman, J., Ward, A., Crandell, J. L., Prahalad, P., Maahs, D. M., Scheinker, D. 2021: 107950


    Machine learning and linear regression models using CGM and participant data reduced HbA1c estimation error by up to 26% compared to the GMI formula, and exhibit superior performance in estimating the median of HbA1c at the cohort level, potentially of value for remote clinical trials interrupted by COVID-19.

    View details for DOI 10.1016/j.jdiacomp.2021.107950

    View details for PubMedID 34127370

  • Prediction of Prolonged Opioid Use After Surgery in Adolescents: Insights From Machine Learning. Anesthesia and analgesia Ward, A., Jani, T., De Souza, E., Scheinker, D., Bambos, N., Anderson, T. A. 2021


    BACKGROUND: Long-term opioid use has negative health care consequences. Patients who undergo surgery are at risk for prolonged opioid use after surgery (POUS). While risk factors have been previously identified, no methods currently exist to determine higher-risk patients. We assessed the ability of a variety of machine-learning algorithms to predict adolescents at risk of POUS and to identify factors associated with this risk.METHODS: A retrospective cohort study was conducted using a national insurance claims database of adolescents aged 12-21 years who underwent 1 of 1297 surgeries, with general anesthesia, from January 1, 2011 to December 30, 2017. Logistic regression with an L2 penalty and with a logistic regression with an L1 lasso (Lasso) penalty, random forests, gradient boosting machines, and extreme gradient boosted models were trained using patient and provider characteristics to predict POUS (≥1 opioid prescription fill within 90-180 days after surgery) risk. Predictive capabilities were assessed using the area under the receiver-operating characteristic curve (AUC)/C-statistic, mean average precision (MAP); individual decision thresholds were compared using sensitivity, specificity, Youden Index, F1 score, and number needed to evaluate. The variables most strongly associated with POUS risk were identified using permutation importance.RESULTS: Of 186,493 eligible patient surgical visits, 8410 (4.51%) had POUS. The top-performing algorithm achieved an overall AUC of 0.711 (95% confidence interval [CI], 0.699-0.723) and significantly higher AUCs for certain surgeries (eg, 0.823 for spinal fusion surgery and 0.812 for dental surgery). The variables with the strongest association with POUS were the days' supply of opioids and oral morphine milligram equivalents of opioids in the year before surgery.CONCLUSIONS: Machine-learning models to predict POUS risk among adolescents show modest to strong results for different surgeries and reveal variables associated with higher risk. These results may inform health care system-specific identification of patients at higher risk for POUS and drive development of preventative measures.

    View details for DOI 10.1213/ANE.0000000000005527

    View details for PubMedID 33939656

  • Modeling Intraoperative Opioid Administration Variation: A Single-Center Retrospective Cohort Study of Children Safranek, C., Poole, S., De Souza, B., Hong, T., Wang, E., Scheinker, D., Anderson, T. A. LIPPINCOTT WILLIAMS & WILKINS. 2021: 795-797
  • Quantifying Pediatric Intensive Care Unit Staffing Levels at a Pediatric Academic Medical Center: A Mixed Methods Approach. Journal of nursing management Ostberg, N., Ling, J., Winter, S. G., Som, S., Vasilakis, C., Shin, A. Y., Cornell, T. T., Scheinker, D. 2021


    AIM: Identify, simulate, and evaluate the formal and informal patient-level and unit-level factors that nurse managers use to determine the number of nurses for each shift.BACKGROUND: Nurse staffing schedules are commonly set based on metrics such as midnight census that do not account for seasonality or midday turnover, resulting in last-minute adjustments or inappropriate staffing levels.METHODS: Staffing schedules at a pediatric intensive care unit (PICU) were simulated based on nurse-to-patient assignment rules from interviews with nursing management. Multivariate regression modeled the discrepancies between scheduled and historical staffing levels and constructed rules to reduce these discrepancies. The primary outcome was the median difference between simulated and historical staffing levels.RESULTS: Nurse-to-patient ratios underestimated staffing by a median of 1.5 nurses per shift. Multivariate regression identified patient turnover as the primary factor accounting for this difference and subgroup analysis revealed that patient age and weight were also important. New rules reduced the difference to a median of 0.07 nurses per shift.CONCLUSION: Measurable, predictable indicators of patient acuity and historical trends may allow for schedules that better match demand.IMPLICATIONS FOR NURSING MANAGEMENT: Data-driven methods can quantify what drives unit demand and generate nurse schedules that require fewer last-minute adjustments.

    View details for DOI 10.1111/jonm.13346

    View details for PubMedID 33894027

  • Racial and Ethnic Disparities in Household Contact with Individuals at Higher Risk of Exposure to COVID-19. Journal of general internal medicine Kong, S. T., Lee, R. Y., Rodriguez, F., Scheinker, D. 2021

    View details for DOI 10.1007/s11606-021-06656-1

    View details for PubMedID 33674919

  • County-Level Factors Associated With Cardiovascular Mortality by Race/Ethnicity. Journal of the American Heart Association Zuma, B. Z., Parizo, J. T., Valencia, A., Spencer-Bonilla, G., Blum, M. R., Scheinker, D., Rodriguez, F. 2021: e018835


    Background Persistent racial/ethnic disparities in cardiovascular disease (CVD) mortality are partially explained by healthcare access and socioeconomic, demographic, and behavioral factors. Little is known about the association between race/ethnicity-specific CVD mortality and county-level factors. Methods and Results Using 2017 county-level data, we studied the association between race/ethnicity-specific CVD age-adjusted mortality rate (AAMR) and county-level factors (demographics, census region, socioeconomics, CVD risk factors, and healthcare access). Univariate and multivariable linear regressions were used to estimate the association between these factors; R2 values were used to assess the factors that accounted for the greatest variation in CVD AAMR by race/ethnicity (non-Hispanic White, non-Hispanic Black, and Hispanic/Latinx individuals). There were 659740 CVD deaths among non-Hispanic White individuals in 2698 counties; 100475 deaths among non-Hispanic Black individuals in 717 counties; and 49493 deaths among Hispanic/Latinx individuals across 267 counties. Non-Hispanic Black individuals had the highest mean CVD AAMR (320.04 deaths per 100000 individuals), whereas Hispanic/Latinx individuals had the lowest (168.42 deaths per 100000 individuals). The highest CVD AAMRs across all racial/ethnic groups were observed in the South. In unadjusted analyses, the greatest variation (R2) in CVD AAMR was explained by physical inactivity for non-Hispanic White individuals (32.3%), median household income for non-Hispanic Black individuals (24.7%), and population size for Hispanic/Latinx individuals (28.4%). In multivariable regressions using county-level factor categories, the greatest variation in CVD AAMR was explained by CVD risk factors for non-Hispanic White individuals (35.3%), socioeconomic factors for non-Hispanic Black (25.8%), and demographic factors for Hispanic/Latinx individuals (34.9%). Conclusions The associations between race/ethnicity-specific age-adjusted CVD mortality and county-level factors differ significantly. Interventions to reduce disparities may benefit from being designed accordingly.

    View details for DOI 10.1161/JAHA.120.018835

    View details for PubMedID 33653083

  • The Hispanic paradox in the prevalence of obesity at the county-level. Obesity science & practice Valencia, A., Zuma, B. Z., Spencer-Bonilla, G., López, L., Scheinker, D., Rodriguez, F. 2021; 7 (1): 14-24


    The percentage of Hispanics in a county has a negative association with prevalence of obesity. Because Hispanic individuals are unevenly distributed in the United States, this study examined whether this protective association persists when stratifying counties into quartiles based on the size of the Hispanic population and after adjusting for county-level demographic, socioeconomic, healthcare, and environmental factors.Data were extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings. Counties were categorized into quartiles based on their percentage of Hispanics, 0%-5% (n = 1794), 5%-20% (n = 962), 20%-50% (n = 283), and >50% (n = 99). For each quartile, univariate and multivariate regression models were used to evaluate the association between prevalence of obesity and demographic, socioeconomic, healthcare, and environmental factors.Counties with the top quartile of Hispanic individuals had the lowest prevalence of obesity compared to counties at the bottom quartile (28.4 ± 3.6% vs. 32.7 ± 4.0%). There was a negative association between county-level percentage of Hispanics and prevalence of obesity in unadjusted analyses that persisted after adjusting for all county-level factors.Counties with a higher percentage of Hispanics have lower levels of obesity, even after controlling for demographic, socioeconomic, healthcare, and environmental factors. More research is needed to elucidate why having more Hispanics in a county may be protective against county-level obesity.

    View details for DOI 10.1002/osp4.461

    View details for PubMedID 33680488

    View details for PubMedCentralID PMC7909595

  • Quantifying Electronic Health Record Data: A Potential Risk for Cognitive Overload. Hospital pediatrics Gal, D. B., Han, B., Longhurst, C., Scheinker, D., Shin, A. Y. 2021


    OBJECTIVES: To quantify and describe patient-generated health data.METHODS: This is a retrospective, single-center study of patients hospitalized in the pediatric cardiovascular ICU between February 1, 2020, and February 15, 2020. The number of data points generated over a 24-hour period per patient was collected from the electronic health record. Data were analyzed by type, and frontline provider exposure to data was extrapolated on the basis of patient-to-provider ratios.RESULTS: Thirty patients were eligible for inclusion. Nineteen were hospitalized after cardiac surgery, whereas 11 were medical patients. Patients generated an average of 1460 (SD 509) new data points daily, resulting in frontline providers being presented with an average of 4380 data points during a day shift (7:00 am to 7:00 pm). Overnight, because of a higher patient-to-provider ratio, frontline providers were exposed to an average of 16060 data points. There was no difference in data generation between medical and surgical patients. Structured data accounted for >80% of the new data generated.CONCLUSIONS: Health care providers face significant generation of new data daily through the contemporary electronic health record, likely contributing to cognitive burden and putting them at risk for cognitive overload. This study represents the first attempt to quantify this volume in the pediatric setting. Most data generated are structured and amenable to data-optimization systems to mitigate the potential for cognitive overload and its deleterious effects on patient safety and health care provider well-being.

    View details for DOI 10.1542/hpeds.2020-002402

    View details for PubMedID 33500357

  • Application of the Quadruple Aim to evaluate the operational impact of a telemedicine program. Healthcare (Amsterdam, Netherlands) Kalwani, N. M., Wang, K. M., Johnson, A. N., Deb, J. D., Gold, T., Maddukuri, A. K., Savage, E. G., Parameswaran, V., Dash, R., Scheinker, D., Rodriguez, F. 2021; 9 (4): 100593


    In response to the COVID-19 pandemic, telemedicine utilization has increased dramatically, yet most institutions lack a standardized approach to determine how much to invest in these programs.We used the Quadruple Aim to evaluate the operational impact of CardioClick, a program replacing in-person follow-up visits with video visits in a preventive cardiology clinic. We examined data for 134 patients enrolled in CardioClick with 181 video follow-up visits and 276 patients enrolled in the clinic's traditional prevention program with 694 in-person follow-up visits.Patients in CardioClick and the cohort receiving in-person care were similar in terms of age (43 vs 45 years), gender balance (74% vs 79% male), and baseline clinical characteristics. Video follow-up visits were shorter than in-person visits in terms of clinician time (median 22 vs 30 min) and total clinic time (median 22 vs 68 min). Video visits were more likely to end on time than in-person visits (71 vs 11%, p < .001). Physicians more often completed video visit documentation on the day of the visit (56 vs 42%, p = .002).Implementation of video follow-up visits in a preventive cardiology clinic was associated with operational improvements in the areas of efficiency, patient experience, and clinician experience. These benefits in three domains of the Quadruple Aim justify expanded use of telemedicine at our institution.The Quadruple Aim provides a framework to evaluate telemedicine programs recently implemented in many health systems.Level III (retrospective comparative study).

    View details for DOI 10.1016/j.hjdsi.2021.100593

    View details for PubMedID 34749227

  • Individualized risk trajectories for iron-related adverse outcomes in repeat blood donors. Transfusion Russell, W. A., Scheinker, D., Custer, B. 2021


    Despite a fingerstick hemoglobin requirement and 56-day minimum donation interval, repeat blood donation continues to cause and exacerbate iron deficiency.Using data from the REDS-II Donor Iron Status Evaluation study, we developed multiclass prediction models to estimate the competing risk of hemoglobin deferral and collecting blood from a donor with sufficient hemoglobin but low or absent underlying iron stores. We compared models developed with and without two biomarkers not routinely measured in most blood centers: ferritin and soluble transferrin receptor. We generated and analyzed "individual risk trajectories": estimates of how each donors' risk developed as a function of the time interval until their next donation attempt.With standard biomarkers, the top model had a multiclass area under the receiver operator characteristic curve (AUC) of 77.6% (95% CI [77.3%-77.8%]). With extra biomarkers, multiclass AUC increased to 82.8% (95% CI [82.5%-83.1%]). In the extra biomarkers model, ferritin was the single most important variable, followed by the donation interval. We identified three risk archetypes: "fast recoverers" (<10% risk of any adverse outcome on post-donation day 56), "slow recoverers" (>60% adverse outcome risk on day 56 that declines to <35% by day 250), and "chronic high-risk" (>85% risk of the adverse outcome on day 250).A longer donation interval reduced the estimated risk of iron-related adverse outcomesfor most donors, but risk remained high for some. Tailoring safeguards to individual risk estimates could reduce blood collections from donors with low or absent iron stores.

    View details for DOI 10.1111/trf.16740

    View details for PubMedID 34783364

  • Assessment of physician training and prediction of workforce needs in paediatric cardiac intensive care in the United States. Cardiology in the young Horak, R. V., Marino, B. S., Werho, D. K., Rhodes, L. A., Costello, J. M., Cabrera, A. G., Cooper, D. S., Bai, S., Tabbutt, S., Rao, I., Scheinker, D., Shin, A. Y., Krawczeski, C. D. 2021: 1-6


    To assess the training and the future workforce needs of paediatric cardiac critical care faculty.REDCap surveys were sent May-August 2019 to medical directors and faculty at the 120 US centres participating in the Society of Thoracic Surgeons Congenital Heart Surgery Database. Faculty and directors were asked about personal training pathway and planned employment changes. Directors were additionally asked for current faculty numbers, expected job openings, presence of training programmes, and numbers of trainees. Predictive modelling of the workforce was performed using respondents' data. Patient volume was projected from US Census data and compared to projected provider availability.Sixty-six per cent (79/120) of directors and 62% (294/477) of contacted faculty responded. Most respondents had training that incorporated critical care medicine with the majority completing training beyond categorical fellowship. Younger respondents and those in dedicated cardiac ICUs were more significantly likely to have advanced training or dual fellowships in cardiology and critical care medicine. An estimated 49-63 faculty enter the workforce annually from various training pathways. Based on modelling, these faculty will likely fill current and projected open positions over the next 5 years.Paediatric cardiac critical care training has evolved, such that the majority of faculty now have dual fellowship or advanced training. The projected number of incoming faculty will likely fill open positions within the next 5 years. Institutions with existing or anticipated training programmes should be cognisant of these data and prepare graduates for an increasingly competitive market.

    View details for DOI 10.1017/S1047951121004893

    View details for PubMedID 34924098

  • Clinically serious hypoglycemia is rare and not associated with time-in-range in youth with new-onset type 1 diabetes. The Journal of clinical endocrinology and metabolism Addala, A., Zaharieva, D. P., Gu, A. J., Prahalad, P., Scheinker, D., Buckingham, B., Hood, K. K., Maahs, D. M. 2021


    Early initiation of continuous glucose monitoring (CGM) is advocated for youth with type 1 diabetes (T1D). Data to guide CGM use on time-in-range (TIR), hypoglycemia, and the role of partial clinical remission (PCR) are limited. Our aims were to assess whether: 1) an association between increased TIR and hypoglycemia exists, and 2) how time in hypoglycemia varies by PCR status.We analyzed 80 youth who were started on CGM shortly after T1D diagnosis and were followed for up to 1-year post-diagnosis. TIR and hypoglycemia rates were determined by CGM data and retrospectively analyzed. PCR was defined as (visit-HbA1c)+(4*units/kg/day) <9.Youth were started on CGM 8.0 (IQR 6.0-13.0) days post-diagnosis. Time spent <70mg/dL remained low despite changes in TIR (highest TIR 74.6±16.7%, 2.4±2.4% hypoglycemia at 1 month post-diagnosis; lowest TIR 61.3±20.3%, 2.1±2.7% hypoglycemia at 12 months post-diagnosis). No events of severe hypoglycemia occurred. Hypoglycemia was rare and there was minimal difference for PCR versus non-PCR youth (54-70mg/dL: 1.8% vs 1.2%, p=0.04; <54mg/dL: 0.3% vs 0.3%, p=0.55). Approximately 50% of the time spent in hypoglycemia was in the 65-70mg/dL range.As TIR gradually decreased over 12 months post-diagnosis, hypoglycemia was limited with no episodes of severe hypoglycemia. Hypoglycemia rates did not vary in a clinically meaningful manner by PCR status. With CGM being started earlier, consideration needs to be given to modifying CGM hypoglycemia education, including alarm settings. These data support a trial in the year post-diagnosis to determine alarm thresholds for youth who wear CGM.

    View details for DOI 10.1210/clinem/dgab522

    View details for PubMedID 34265059

  • Multimethod, multidataset analysis reveals paradoxical relationships between sociodemographic factors, Hispanic ethnicity and diabetes. BMJ open diabetes research & care Knight, G. M., Spencer-Bonilla, G., Maahs, D. M., Blum, M. R., Valencia, A., Zuma, B. Z., Prahalad, P., Sarraju, A., Rodriguez, F., Scheinker, D. 2020; 8 (2)


    INTRODUCTION: Population-level and individual-level analyses have strengths and limitations as do 'blackbox' machine learning (ML) and traditional, interpretable models. Diabetes mellitus (DM) is a leading cause of morbidity and mortality with complex sociodemographic dynamics that have not been analyzed in a way that leverages population-level and individual-level data as well as traditional epidemiological and ML models. We analyzed complementary individual-level and county-level datasets with both regression and ML methods to study the association between sociodemographic factors and DM.RESEARCH DESIGN AND METHODS: County-level DM prevalence, demographics, and socioeconomic status (SES) factors were extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings and merged with US Census data. Analogous individual-level data were extracted from 2007 to 2016 National Health and Nutrition Examination Survey studies and corrected for oversampling with survey weights. We used multivariate linear (logistic) regression and ML regression (classification) models for county (individual) data. Regression and ML models were compared using measures of explained variation (area under the receiver operating characteristic curve (AUC) and R2).RESULTS: Among the 3138 counties assessed, the mean DM prevalence was 11.4% (range: 3.0%-21.1%). Among the 12824 individuals assessed, 1688 met DM criteria (13.2% unweighted; 10.2% weighted). Age, gender, race/ethnicity, income, and education were associated with DM at the county and individual levels. Higher county Hispanic ethnic density was negatively associated with county DM prevalence, while Hispanic ethnicity was positively associated with individual DM. ML outperformed regression in both datasets (mean R2 of 0.679 vs 0.610, respectively (p<0.001) for county-level data; mean AUC of 0.737 vs 0.727 (p<0.0427) for individual-level data).CONCLUSIONS: Hispanic individuals are at higher risk of DM, while counties with larger Hispanic populations have lower DM prevalence. Analyses of population-level and individual-level data with multiple methods may afford more confidence in results and identify areas for further study.

    View details for DOI 10.1136/bmjdrc-2020-001725

    View details for PubMedID 33229378

  • The Hispanic paradox in the prevalence of obesity at the county-level OBESITY SCIENCE & PRACTICE Valencia, A., Zuma, B. Z., Spencer-Bonilla, G., Lopez, L., Scheinker, D., Rodriguez, F. 2020

    View details for DOI 10.1002/osp4.461

    View details for Web of Science ID 000581091800001

  • Target Based Care: An Intervention to Reduce Variation in Postoperative Length of Stay. The Journal of pediatrics Shin, A. Y., Rao, I. J., Bassett, H. K., Chadwick, W., Kim, J., Kipps, A. K., Komra, K., Loh, L., Maeda, K., Mafla, M., Presnell, L., Sharek, P. J., Steffen, K. M., Scheinker, D., Algaze, C. A. 2020


    OBJECTIVES: To derive care targets and evaluate the impact of displaying them at the point of care on postoperative length of stay (LOS).STUDY DESIGN: A prospective cohort study using 2 years of historical controls within a freestanding, academic children's hospital. Patients undergoing benchmark cardiac surgery between May 4, 2014 and August 15, 2016 (preintervention) and September 6, 2016 to September 30, 2018 (postintervention) were included. The intervention consisted of displaying at the point of care targets for the timing of extubation, transfer from the intensive care unit (ICU), and hospital discharge. Family satisfaction, reintubation, and readmission rates were tracked.RESULTS: The postintervention cohort consisted of 219 consecutive patients. There was a reduction in variation for ICU (difference in SD -2.56, p < 0.01), and total LOS (difference in SD -2.84, P < .001). Patients stayed on average 0.97 fewer days (p<0.001) in the ICU (median -1.01 [IQR -2.15,-0.39], 0.7 fewer days (p<0.001) on mechanical ventilation (median -0.54 [IQR -0.77,-0.50], and 1.18 fewer days (p<0.001) for the total LOS (median -2.25 [IQR -3.69,-0.15]. Log transformed multivariable linear regression demonstrated the intervention to be associated with shorter ICU LOS (beta coefficient -0.19, SE 0.059, p<0.001), total postoperative LOS (beta coefficient -0.12, SE 0.052, p=0.02), and ventilator duration (beta coefficient -0.21, SE 0.048, p<0.001). Balancing metrics did not differ after the intervention.CONCLUSIONS: Target based care is a simple, novel intervention associated with reduced variation in LOS and absolute LOS across a diverse spectrum of complex cardiac surgeries.

    View details for DOI 10.1016/j.jpeds.2020.09.017

    View details for PubMedID 32920104

  • Uninterrupted Continuous Glucose Monitoring Access is Associated with a Decrease in HbA1c in Youth with Type 1 Diabetes and Public Insurance. Pediatric diabetes Addala, A., Maahs, D. M., Scheinker, D., Chertow, S., Leverenz, B., Prahalad, P. 2020


    OBJECTIVE: Continuous glucose monitor (CGM) use is associated with improved glucose control. We describe the effect of continued and interrupted CGM use on hemoglobin A1c (HbA1c) in youth with public insurance.METHODS: We reviewed 956 visits from 264 youth with type 1 diabetes (T1D) and public insurance. Demographic data, HbA1c and two-week CGM data were collected. Youth were classified as never user, consistent user, insurance discontinuer, and self-discontinuer. Visits were categorized as never-user visit, visit before CGM start, visit after CGM start, visit with continued CGM use, visit with initial loss of CGM, visit with continued loss of CGM, and visit where CGM is regained after loss. Multivariate regression adjusting for age, sex, race, diabetes duration, initial HbA1c, and BMI were used to calculate adjusted mean and delta HbA1c.RESULTS: Adjusted mean HbA1c was lowest for the consistent user group (HbA1c 8.6%;[95%CI 7.9,9.3]). Delta HbA1c (calculated from visit before CGM start) was lower for visit after CGM start (-0.39%;[95%CI -0.78,-0.02]) and visit with continued CGM use (-0.29%;[95%CI -0.61,0.02]) whereas it was higher for visit with initial loss of CGM (0.40%;[95%CI -0.06,0.86]), visit with continued loss of CGM (0.46%;[95%CI 0.06,0.85]), and visit where CGM is regained after loss (0.57%;[95%CI 0.06,1.10]).CONCLUSIONS: Youth with public insurance using CGM have improved HbA1c, but only when CGM use is uninterrupted. Interruptions in use, primarily due to gaps in insurance coverage of CGM, were associated with increased HbA1c. These data support both initial and ongoing coverage of CGM for youth with T1D and public insurance. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1111/pedi.13082

    View details for PubMedID 32681582

  • Early CGM Initiation Improves HbA1c in T1D Youth over the First 15 Months Prahalad, P., Ding, V., Addala, A., New, C., Conrad, B. P., Chmielewski, A., Geels, E., Leverenz, J., Martinez-Singh, A., Sagan, P., Senaldi, J., Freeman, A., Scheinker, D., Hood, K. K., Desai, M., Maahs, D. M. AMER DIABETES ASSOC. 2020
  • The Association between Time-in-Range, Mean Glucose, and Incidence of Hypoglycemia in Youth with Newly Diagnosed T1D Gu, A., Prahalad, P., Maahs, D. M., Addala, A., Scheinker, D. AMER DIABETES ASSOC. 2020
  • A Telemedicine-CGM Recommendation System for Personalized Population Health Management Vallon, J., Ward, A. T., Prahalad, P., Hood, K. K., Maahs, D. M., Scheinker, D. AMER DIABETES ASSOC. 2020
  • Newly Diagnosed Pediatric Patients with Type 1 Diabetes Show Steady Decline in Glucose Time-in-Range (TIR) over 1 Year: Pilot Study Zaharieva, D., Prahalad, P., Addala, A., Scheinker, D., Desai, M., Hood, K. K., Leverenz, B., Maahs, D. M. AMER DIABETES ASSOC. 2020
  • Clinically Significant Hypoglycemia Is Rare in Youth with T1D during Partial Clinical Remission Addala, A., Gu, A., Zaharieva, D., Prahalad, P., Buckingham, B. A., Scheinker, D., Maahs, D. M. AMER DIABETES ASSOC. 2020
  • Implementing Analytics Projects in a Hospital: Successes, Failures, and Opportunities INTERFACES Scheinker, D., Brandeau, M. L. 2020; 50 (3): 176–89
  • Baseline creatinine determination method impacts association between acute kidney injury and clinical outcomes. Pediatric nephrology (Berlin, Germany) Russell, W. A., Scheinker, D. n., Sutherland, S. M. 2020


    Current consensus definition for acute kidney injury (AKI) does not specify how baseline serum creatinine should be determined. We assessed how baseline determination impacted AKI incidence and association between AKI and clinical outcomes.We retrospectively applied empirical (measured serum creatinine) and imputed (age/height) baseline estimation methods to pediatric patients discharged between 2014 and 2019 from an academic hospital. Using each method, we estimated AKI incidence and assessed area under ROC curve (AUROC) for AKI as a predictor of three clinical outcomes: application of AKI billing code (proxy for more clinically overt disease), inpatient mortality, and post-hospitalization chronic kidney disease.Incidence was highly variable across baseline methods (12.2-26.7%). Incidence was highest when lowest pre-admission creatinine was used if available and Schwartz bedside equation was used to impute one otherwise. AKI was more predictive of application of an AKI billing code when baseline was imputed universally, regardless of pre-admission values (AUROC 80.7-84.9%) than with any empirical approach (AUROC 64.5-76.6%). AKI was predictive of post-hospitalization CKD when using universal imputation baseline methods (AUROC 67.0-74.6%); AKI was not strongly predictive of post-hospitalization CKD when using empirical baseline methods (AUROC 46.4-58.5%). Baseline determination method did not affect the association between AKI and inpatient mortality.Method of baseline determination influences AKI incidence and association between AKI and clinical outcomes, illustrating the need for standard criteria. Imputing baseline for all patients, even when preadmission creatinine is available, may identify a more clinically relevant subset of the disease.

    View details for DOI 10.1007/s00467-020-04789-9

    View details for PubMedID 33095322

  • Extremum Seeking for Creating Optimal Feedback Controls of Unknown Systems by Tuning Basis Functions Scheinker, A., Scheinker, D., IEEE IEEE. 2020: 44–49
  • Improving Clinical Outcomes in Newly Diagnosed Pediatric Type 1 Diabetes: Teamwork, Targets, Technology, and Tight Control-The 4T Study. Frontiers in endocrinology Prahalad, P. n., Zaharieva, D. P., Addala, A. n., New, C. n., Scheinker, D. n., Desai, M. n., Hood, K. K., Maahs, D. M. 2020; 11: 360


    Many youth with type 1 diabetes (T1D) do not achieve hemoglobin A1c (HbA1c) targets. The mean HbA1c of youth in the USA is higher than much of the developed world. Mean HbA1c in other nations has been successfully modified following benchmarking and quality improvement methods. In this review, we describe the novel 4T approach-teamwork, targets, technology, and tight control-to diabetes management in youth with new-onset T1D. In this program, the diabetes care team (physicians, nurse practitioners, certified diabetes educators, dieticians, social workers, psychologists, and exercise physiologists) work closely to deliver diabetes education from diagnosis. Part of the education curriculum involves early integration of technology, specifically continuous glucose monitoring (CGM), and developing a curriculum around using the CGM to maintain tight control and optimize quality of life.

    View details for DOI 10.3389/fendo.2020.00360

    View details for PubMedID 32733375

    View details for PubMedCentralID PMC7363838

  • Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population. NPJ digital medicine Ward, A., Sarraju, A., Chung, S., Li, J., Harrington, R., Heidenreich, P., Palaniappan, L., Scheinker, D., Rodriguez, F. 2020; 3 (1): 125


    The pooled cohort equations (PCE) predict atherosclerotic cardiovascular disease (ASCVD) risk in patients with characteristics within prespecified ranges and has uncertain performance among Asians or Hispanics. It is unknown if machine learning (ML) models can improve ASCVD risk prediction across broader diverse, real-world populations. We developed ML models for ASCVD risk prediction for multi-ethnic patients using an electronic health record (EHR) database from Northern California. Our cohort included patients aged 18 years or older with no prior CVD and not on statins at baseline (n = 262,923), stratified by PCE-eligible (n = 131,721) or PCE-ineligible patients based on missing or out-of-range variables. We trained ML models [logistic regression with L2 penalty and L1 lasso penalty, random forest, gradient boosting machine (GBM), extreme gradient boosting] and determined 5-year ASCVD risk prediction, including with and without incorporation of additional EHR variables, and in Asian and Hispanic subgroups. A total of 4309 patients had ASCVD events, with 2077 in PCE-ineligible patients. GBM performance in the full cohort, including PCE-ineligible patients (area under receiver-operating characteristic curve (AUC) 0.835, 95% confidence interval (CI): 0.825-0.846), was significantly better than that of the PCE in the PCE-eligible cohort (AUC 0.775, 95% CI: 0.755-0.794). Among patients aged 40-79, GBM performed similarly before (AUC 0.784, 95% CI: 0.759-0.808) and after (AUC 0.790, 95% CI: 0.765-0.814) incorporating additional EHR data. Overall, ML models achieved comparable or improved performance compared to the PCE while allowing risk discrimination in a larger group of patients including PCE-ineligible patients. EHR-trained ML models may help bridge important gaps in ASCVD risk prediction.

    View details for DOI 10.1038/s41746-020-00331-1

    View details for PubMedID 34552202

  • Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population. NPJ digital medicine Ward, A. n., Sarraju, A. n., Chung, S. n., Li, J. n., Harrington, R. n., Heidenreich, P. n., Palaniappan, L. n., Scheinker, D. n., Rodriguez, F. n. 2020; 3: 125


    The pooled cohort equations (PCE) predict atherosclerotic cardiovascular disease (ASCVD) risk in patients with characteristics within prespecified ranges and has uncertain performance among Asians or Hispanics. It is unknown if machine learning (ML) models can improve ASCVD risk prediction across broader diverse, real-world populations. We developed ML models for ASCVD risk prediction for multi-ethnic patients using an electronic health record (EHR) database from Northern California. Our cohort included patients aged 18 years or older with no prior CVD and not on statins at baseline (n = 262,923), stratified by PCE-eligible (n = 131,721) or PCE-ineligible patients based on missing or out-of-range variables. We trained ML models [logistic regression with L2 penalty and L1 lasso penalty, random forest, gradient boosting machine (GBM), extreme gradient boosting] and determined 5-year ASCVD risk prediction, including with and without incorporation of additional EHR variables, and in Asian and Hispanic subgroups. A total of 4309 patients had ASCVD events, with 2077 in PCE-ineligible patients. GBM performance in the full cohort, including PCE-ineligible patients (area under receiver-operating characteristic curve (AUC) 0.835, 95% confidence interval (CI): 0.825-0.846), was significantly better than that of the PCE in the PCE-eligible cohort (AUC 0.775, 95% CI: 0.755-0.794). Among patients aged 40-79, GBM performed similarly before (AUC 0.784, 95% CI: 0.759-0.808) and after (AUC 0.790, 95% CI: 0.765-0.814) incorporating additional EHR data. Overall, ML models achieved comparable or improved performance compared to the PCE while allowing risk discrimination in a larger group of patients including PCE-ineligible patients. EHR-trained ML models may help bridge important gaps in ASCVD risk prediction.

    View details for DOI 10.1038/s41746-020-00331-1

    View details for PubMedID 33043149

    View details for PubMedCentralID PMC7511400

  • Improving the efficiency of the operating room environment with an optimization and machine learning model HEALTH CARE MANAGEMENT SCIENCE Fairley, M., Scheinker, D., Brandeau, M. L. 2019; 22 (4): 756–67
  • Practice Characteristics of Board-certified Pediatric Anesthesiologists in the US: A Nationwide Survey. Cureus Muffly, M., Scheinker, D., Muffly, T., Singleton, M., Agarwal, R., Honkanen, A. 2019; 11 (9): e5745


    Introduction We conducted a survey to describe the practice characteristics of anesthesiologists who have passed the American Board of Anesthesiology (ABA) Pediatric Anesthesiology Certification Examination. Methods In July 2017, a list of anesthesiologists who had taken the ABA Pediatric Anesthesiology Certification Examination (hereafter referred to as "pediatric anesthesiologists") was obtained from the American Board of Anesthesiologists ( Email contact information for these individuals was collected from departmental rosters, email distribution lists, hospital or anesthesia group profiles, manuscript author contact information, website source code, and other publicly available online sources. The survey was designed using Qualtrics (Qualtrics, Provo, Utah; Seattle, Washington), a web-based tool, to ascertain residency/fellowship training history and current practice characteristics that includes: years in practice, clinical work hours per week, primary hospital setting, practice type, supervision model, estimated percentage of cases by patient age group, and percentage of respondents who cared for any patient undergoing a fellowship-level index cases within the previous year. The invitation to complete the survey included a financial incentive - the chance to win one of twenty $50 Amazon gift cards. Results There were 3,492 anesthesiologists who had taken the Pediatric Anesthesiology Certification Examination since 2013. Surveys were sent to those whom an email address was identified (2,681) and 962 complete survey responses were received (35.9%, 962/2,681). Over 80% (785) of respondents completed a pediatric anesthesiology fellowship. Of these, 485 respondents (50.4%) work in academic practice, 212 (22.0%) in private practice, 233 (24.2%) in private practice and have academic affiliations, and 32 (3.3%) as locum tenens or in other practice settings. The majority of respondents (64.3%) in academic practice work in freestanding children's hospitals. Pediatric anesthesiologists in academic practice and private practice with academic affiliations reported caring for a greater number of younger children and doing a wider variety of index cases than respondents in private practice. Conclusion The extent to which pediatric anesthesiologists care for pediatric patients - particularly young children and those undergoing complex cases - varies. The variability in practice characteristics is likely a result of differences in hospital type, anesthesia practice type, geographic location, and other factors.

    View details for DOI 10.7759/cureus.5745

    View details for PubMedID 31723506

    View details for PubMedCentralID PMC6825435

  • Practice Characteristics of Board-certified Pediatric Anesthesiologists in the US: A Nationwide Survey CUREUS Muffly, M., Scheinker, D., Muffly, T., Singleton, M., Agarwal, R., Honkanen, A. 2019; 11 (9)
  • Personalized Diabetes Management Using Data from Continuous Glucose Monitors Miller, D. R., Ward, A. T., Maahs, D. M., Scheinker, D. AMER DIABETES ASSOC. 2019

    View details for DOI 10.2337/db19-960-P

    View details for Web of Science ID 000501366902323

  • Non-clinical delays in transfer out of the surgical ICU are associated with increased hospital length of stay and delayed progress of care JOURNAL OF CRITICAL CARE Safavi, K., Furtado, J., Langle, A., Scheinker, D., Schmidt, U., Daily, B., Levi, R., Dunn, P. 2019; 50: 126–31


    The impact of non-clinical transfer delay (TD) from the ICU to a general care unit on the progress of the patient's care is unknown. We measured the association between TD and: (1) the patient's subsequent hospital length of stay (LOS); (2) the timing of care decisions that would advance patient care.This was a single center retrospective study in the United States of patients admitted to the surgical and neurosurgical ICUs during 2013 and 2015. The primary outcome was hospital LOS after transfer request. The secondary outcome was the timing of provider orders representing care decisions (milestones) that would advance the patient's care. Patient, surgery, and bed covariates were accounted for in a multivariate regression and propensity matching analysis.Out of the cohort of 4,926 patients, 1,717 met inclusion criteria. 670 (39%) experienced ≥12 hours of TD. For each day of TD, there was an average increase of 0.70 days in LOS (P < 0.001). The last milestone occurred on average 0.35 days later (P < 0.001). Propensity matching analyses were confirmatory (P < 0.001, P < 0.001).TD is associated with longer LOS and delays in milestone clinical decisions that progress care. Eliminating delays in milestones could mitigate TD's impact on LOS.

    View details for DOI 10.1016/j.jcrc.2018.11.025

    View details for Web of Science ID 000458375800021

    View details for PubMedID 30530264

  • Identification of Factors Associated With Variation in US County-Level Obesity Prevalence Rates Using Epidemiologic vs Machine Learning Models. JAMA network open Scheinker, D. n., Valencia, A. n., Rodriguez, F. n. 2019; 2 (4): e192884


    Obesity is a leading cause of high health care expenditures, disability, and premature mortality. Previous studies have documented geographic disparities in obesity prevalence.To identify county-level factors associated with obesity using traditional epidemiologic and machine learning methods.Cross-sectional study using linear regression models and machine learning models to evaluate the associations between county-level obesity and county-level demographic, socioeconomic, health care, and environmental factors from summarized statistical data extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings and merged with US Census data from each of 3138 US counties. The explanatory power of the linear multivariate regression and the top performing machine learning model were compared using mean R2 measured in 30-fold cross validation.County-level demographic factors (population; rural status; census region; and race/ethnicity, sex, and age composition), socioeconomic factors (median income, unemployment rate, and percentage of population with some college education), health care factors (rate of uninsured adults and primary care physicians), and environmental factors (access to healthy foods and access to exercise opportunities).County-level obesity prevalence in 2018, its association with each county-level factor, and the percentage of variation in county-level obesity prevalence explained by linear multivariate and gradient boosting machine regression measured with R2.Among the 3138 counties studied, the mean (range) obesity prevalence was 31.5% (12.8%-47.8%). In multivariate regressions, demographic factors explained 44.9% of variation in obesity prevalence; socioeconomic factors, 33.0%; environmental factors, 15.5%; and health care factors, 9.1%. The county-level factors with the strongest association with obesity were census region, median household income, and percentage of population with some college education. R2 values of univariate regressions of obesity prevalence were 0.238 for census region, 0.218 for median household income, and 0.160 for percentage of population with some college education. Multivariate linear regression and gradient boosting machine regression (the best-performing machine learning model) of obesity prevalence using all county-level demographic, socioeconomic, health care, and environmental factors had R2 values of 0.58 and 0.66, respectively (P < .001).Obesity prevalence varies significantly between counties. County-level demographic, socioeconomic, health care, and environmental factors explain the majority of variation in county-level obesity prevalence. Using machine learning models may explain significantly more of the variation in obesity prevalence..

    View details for PubMedID 31026030

  • Hemoglobin A1c Trajectory in Pediatric Patients with Newly Diagnosed Type 1 Diabetes. Diabetes technology & therapeutics Prahalad, P. n., Yang, J. n., Scheinker, D. n., Desai, M. n., Hood, K. n., Maahs, D. M. 2019


    Despite advances in diabetes technology and treatment, a majority of children and adolescents with type 1 diabetes (T1D) fail to meet hemoglobin A1c (HbA1c) targets. Among high-income nations, the United States has one of the highest mean HbA1c values. We tracked the HbA1c values of 261 patients diagnosed with T1D in our practice over a 2.5-year period to identify inflection points in the HbA1c trajectory. The HbA1c declined until 5 months postdiagnosis. There was a rise in the HbA1c between the fifth and sixth month postdiagnosis. The HbA1c continued to steadily rise and by 18 months postdiagnosis, the mean HbA1c was 8.2%, which is also our clinic mean. Understanding the HbA1c trajectory early in the course of diabetes has helped to identify opportunities for intensification of diabetes management to flatten the trajectory of HbA1c and improve clinical outcomes.

    View details for DOI 10.1089/dia.2019.0065

    View details for PubMedID 31180244

  • Hospitalization Patterns for Inpatient Surgery and Procedures in California:2000 – 2016 Anesthesia and Analgesia Muffly, M. K., Honkanen, A., Scheinker, D., Wang, T., Saynina, O., Singleton, M. A., , Wang, C. J., Sanders, L. M. 2019
  • CGM Initiation Soon After Type 1 Diabetes Diagnosis Results in Sustained CGM Use and Wear Time. Diabetes care Prahalad, P. n., Addala, A. n., Scheinker, D. n., Hood, K. K., Maahs, D. M. 2019

    View details for DOI 10.2337/dc19-1205

    View details for PubMedID 31558548

  • Anesthesiologist Surgery Assignments Using Policy Learning Ward, A., Zhou, Z., Bambos, N., Scheinker, D., Wang, E., IEEE IEEE. 2019
  • A Retrospective Review of a Bed-mounted Projection System for Managing Pediatric Preoperative Anxiety. Pediatric quality & safety Caruso, T. J., Tsui, J. H., Wang, E., Scheinker, D., Sharek, P. J., Cunningham, C., Rodriguez, S. T. 2018; 3 (4): e087


    Introduction: Most children undergoing anesthesia experience significant preoperative anxiety. We developed a bedside entertainment and relaxation theater (BERT) as an alternative to midazolam for appropriate patients undergoing anesthesia. The primary aim of this study was to determine if BERT was as effective as midazolam in producing cooperative patients at anesthesia induction. Secondary aims reviewed patient emotion and timeliness of BERT utilization.Methods: We conducted a retrospective cohort study of pediatric patients undergoing anesthesia at Lucile Packard Children's Hospital Stanford between February 1, 2016, and October 1, 2016. Logistic regression compared induction cooperation between groups. Multinomial logistic regression compared patients' emotion at induction. Ordinary least squares regression compared preoperative time.Results: Of the 686 eligible patients, 163 were in the BERT group and 150 in the midazolam. Ninety-three percentage of study patients (290/313) were cooperative at induction, and the BERT group were less likely to be cooperative (P = 0.04). The BERT group was more likely to be "playful" compared with "sedated" (P < 0.001). There was a reduction of 14.7 minutes in preoperative patient readiness associated with BERT (P = 0.001).Conclusions: Although most patients were cooperative for induction in both groups, the midazolam group was more cooperative. The BERT reduced the preinduction time and was associated with an increase in patients feeling "playful."

    View details for PubMedID 30229198

  • The Pediatric Anesthesiology Workforce: Projecting Supply and Trends 2015-2035 ANESTHESIA AND ANALGESIA Muffly, M. K., Singleton, M., Agarwal, R., Scheinker, D., Miller, D., Muffly, T. M., Honkanen, A. 2018; 126 (2): 568–78


    A workforce analysis was conducted to predict whether the projected future supply of pediatric anesthesiologists is balanced with the requirements of the inpatient pediatric population. The specific aims of our analysis were to (1) project the number of pediatric anesthesiologists in the future workforce; (2) project pediatric anesthesiologist-to-pediatric population ratios (0-17 years); (3) project the mean number of inpatient pediatric procedures per pediatric anesthesiologist; and (4) evaluate the effect of alternative projections of individual variables on the model projections through 2035.The future number of pediatric anesthesiologists is determined by the current supply, additions to the workforce, and departures from the workforce. We previously compiled a database of US pediatric anesthesiologists in the base year of 2015. The historical linear growth rate for pediatric anesthesiology fellowship positions was determined using the Accreditation Council for Graduate Medical Education Data Resource Books from 2002 to 2016. The future number of pediatric anesthesiologists in the workforce was projected given growth of pediatric anesthesiology fellowship positions at the historical linear growth rate, modeling that 75% of graduating fellows remain in the pediatric anesthesiology workforce, and anesthesiologists retire at the current mean retirement age of 64 years old. The baseline model projections were accompanied by age- and gender-adjusted anesthesiologist supply, and sensitivity analyses of potential variations in fellowship position growth, retirement, pediatric population, inpatient surgery, and market share to evaluate the effect of each model variable on the baseline model. The projected ratio of pediatric anesthesiologists to pediatric population was determined using the 2012 US Census pediatric population projections. The projected number of inpatient pediatric procedures per pediatric anesthesiologist was determined using the Kids' Inpatient Database historical data to project the future number of inpatient procedures (including out of operating room procedures).In 2015, there were 5.4 pediatric anesthesiologists per 100,000 pediatric population and a mean (±standard deviation [SD]) of 262 ±8 inpatient procedures per pediatric anesthesiologist. If historical trends continue, there will be an estimated 7.4 pediatric anesthesiologists per 100,000 pediatric population and a mean (±SD) 193 ±6 inpatient procedures per pediatric anesthesiologist in 2035. If pediatric anesthesiology fellowship positions plateau at 2015 levels, there will be an estimated 5.7 pediatric anesthesiologists per 100,000 pediatric population and a mean (±SD) 248 ±7 inpatient procedures per pediatric anesthesiologist in 2035.If historical trends continue, the growth in pediatric anesthesiologist supply may exceed the growth in both the pediatric population and inpatient procedures in the 20-year period from 2015 to 2035.

    View details for PubMedID 29116973

  • Constrained extremum seeking stabilization of systems not affine in control INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL Scheinker, A., Scheinker, D. 2018; 28 (2): 568–81

    View details for DOI 10.1002/rnc.3886

    View details for Web of Science ID 000418409100012

  • Promise and Perils of Big Data and Artificial Intelligence in Clinical Medicine and Biomedical Research. Circulation research Rodriguez, F. n., Scheinker, D. n., Harrington, R. A. 2018; 123 (12): 1282–84

    View details for PubMedID 30566055

  • A quality improvement initiative to optimize dosing of surgical antimicrobial prophylaxis. Paediatric anaesthesia Caruso, T. J., Wang, E., Schwenk, H. T., Scheinker, D., Yeverino, C., Tweedy, M., Maheru, M., Sharek, P. J. 2017; 27 (7): 702-710


    The risk of surgical site infections is reduced with appropriate timing and dosing of preoperative antimicrobials. Based on evolving national guidelines, we increased the preoperative dose of cefazolin from 25 to 30 mg·kg(-1) . This quality improvement project describes an improvement initiative to develop standard work processes to ensure appropriate dosing.The primary aim was to deliver cefazolin 30 mg·kg(-1) to at least 90% of indicated patients. The secondary aim was to determine differences between accuracy of cefazolin doses when given as an electronic order compared to a verbal order.Data were collected from January 1, 2012 to May 31, 2016. A quality improvement team of perioperative physicians, nurses, and pharmacists implemented a series of interventions including new electronic medical record order sets, personal provider antibiotic dose badges, and utilization of pharmacists to prepare antibiotics to increase compliance with the recommended dose. Process compliance was measured using a statistical process control chart, and dose compliance was measured through electronic analysis of the electronic medical record. Secondary aim data were displayed as percentage of dose compliance. An unpaired t-test was used to determine differences between groups.Between January 1, 2012 and May 31, 2016, cefazolin was administered to 9086 patients. The mean compliance of cefazolin at 30 mg·kg(-1) from May 2013 to March 2014 was 40%, which prompted initiation of this project. From April 2014 to May 2016, a series of interventions were deployed. The mean compliance from September 2015 to May 2016 was 93% with significantly reduced variation and no special cause variation, indicating that the process was in control at the target primary aim. There were 649 cefazolin administrations given verbally and 1929 given with an electronic order between October 1, 2014 and May 31, 2016. During this time period, the rate of compliance of administering cefazolin at 30 mg·kg(-1) was significantly higher when given after an electronic order than when given verbally, 94% vs 76%.This comprehensive quality improvement project improved practitioner compliance with evidence-based preoperative antimicrobial dosing recommendations to reduce the risk of surgical site infections.

    View details for DOI 10.1111/pan.13137

    View details for PubMedID 28321988

  • Detecting Inaccurate Predictions of Pediatric Surgical Durations Zhou, Z., Miller, D., Master, N., Scheinker, D., Bambos, N., Glynn, P., IEEE IEEE. 2016: 452–57