David Scheinker
VerifiedStanford University · Rheumatology
Active 2010–2026
About
David Scheinker is a Clinical Professor of Pediatrics at Stanford University and a member of the Center for Artificial Intelligence in Medicine & Imaging (AIMI). His work focuses on the application of artificial intelligence in medicine and imaging, contributing to advancements in healthcare through innovative research and education. As a faculty member at Stanford, he is involved in various initiatives aimed at integrating AI technologies into clinical practice, fostering collaboration between academia and industry, and training the next generation of healthcare professionals in AI-driven medicine.
Research topics
- Internal medicine
- Machine Learning
- Medicine
- Computer Science
- Endocrinology
- Environmental health
- Surgery
- Demography
Selected publications
Research Code Sharing in Support of Gold Standard Science
Journal of Diabetes Science and Technology · 2026-01-14 · 1 citations
articleOpen accessSharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH's Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article's cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH's GSS principles.
Integrating the Glycemia Risk Index Into Clinical Practice and Research: A Consensus Report
Journal of Diabetes Science and Technology · 2026-03-07
articleOpen accessA panel of experts in the use of continuous glucose monitoring (CGM) data in the treatment of diabetes met in Burlingame, California on October 27, 2025 to discuss the utility of the glycemia risk index (GRI) for clinical care research and population health management. The GRI composite metric is a single number (on a 0-100 percentile scale-lower is better) based on an expert-determined weighting of the seven individual components in the existing ambulatory glucose profile (AGP). The GRI describes the quality of glycemia based on glucose values collected in a 14-day CGM tracing, thus providing additional insights into CGM profiles beyond the AGP. During the meeting, the mathematical derivation of the GRI metric was presented along with its use for adult and pediatric individuals with diabetes and cancer who require medications that can adversely affect the glucose concentration. Examples where the GRI provided useful insights into the quality of CGM tracings were also discussed by the expert panel. In addition, a new smartphone application, the GRI Calculator, was presented. This app calculates the GRI of a CGM tracing and provides visualization of sequential CGM tracings for a specific individual. The GRI provides a reference measurement for the accuracy of artificial intelligence (AI) models assigning levels of glycemic quality to CGM tracings intended to match the assessments of clinicians. The GRI is now part of the data visualization panel for the Integration of Connected Diabetes Device Data into the Electronic Health Record (iCoDE-2) project, which standardizes both CGM and insulin dosing data. Further exploration of the potential value of the GRI for non-insulin users needs to be undertaken. The panel unanimously recommended that CGM manufacturers and developers of data visualization software for CGMs add the GRI to their data platforms for insulin users.
JMIR Diabetes · 2026-02-02
articleOpen accessSenior authorUnlabelled: Clinics continue to adopt care models shaped by the algorithmic analysis of continuous glucose monitoring (CGM) data, such as remote patient monitoring for type 1 diabetes. No clinic-facing quantitative framework currently exists to track the impact of such algorithm-directed care on patient outcomes and clinical workload. We used CGM data from the Teamwork, Targets, Technology, and Tight Control (4T) Study (Pilot n=135 and Study 1 n=133), in which algorithms enable precision, whole-population care by directing clinician attention to patients with deteriorating glucose management. Youth meeting criteria for clinical review are then contacted by Certified Diabetes Care and Education Specialists. Through iterative data analysis and meetings with a variety of stakeholders, we identified metrics for reviewing and revising clinical workloads, glucose management, and timeliness of care. For each metric, we developed an interactive dashboard to provide clinical and administrative leaders with an overview of the program. The metrics to track clinical workload were the total number of youths (1) in the program, (2) in each study, and (3) cared for by each clinician. The metrics to track glucose management were the number of youths meeting each criterion for review, including (4) total, (5) for each clinician, and (6) for each study. The metric to track timeliness of care was (7) the number of days since meeting criteria for clinical review. When presented at regular program leadership meetings, the metrics facilitated data-driven decision-making about clinical and operational components of the program. In this paper, we describe the process of developing and operationalizing this reproducible, clinician-facing key performance indicator tool to monitor an algorithm-enabled remote patient monitoring program. As the role of algorithms grows in directing clinical effort and prioritizing patients for care, this framework may help clinics track clinical workload, patient outcomes, and the timeliness of care.
Circulation Population Health and Outcomes · 2026-01-13 · 1 citations
articleBACKGROUND: The impact of telemedicine use on heart failure (HF) care is unknown. We assessed the association between telemedicine use and rates of diagnostic testing and prescribing for patients with HF. METHODS: We extracted electronic health record data for patients with HF at an academic cardiovascular center in Northern California. We assigned patients to the cardiologist providing >50% of their visits from March 2019 to May 2023. This interval was divided into 8 nonoverlapping 6-month periods, excluding March through May of 2020. All patients seen and orders placed in each period were included. Orders of interest included diagnostic tests (Holter monitors, electrocardiograms, echocardiograms, natriuretic peptide tests, chemistry panels) and new prescriptions (beta blockers, aldosterone antagonists, renin angiotensin inhibitors, hydralazine, nitrates, total guideline-directed medical therapy, diuretics). We assessed the association between clinician-period ordering rates and telemedicine use (proportion of visits via video/phone) using negative binomial regression with adjustment for the period, number of patients, and clinician random intercepts. We report incident rate ratios of per-patient ordering associated with a 50-percentage-point increase in telemedicine use. Subgroup analyses were conducted for patients with reduced/preserved ejection fraction. RESULTS: There were 7741 patients seen by 44 clinicians (1941 patients with HF with reduced ejection fraction seen by 28 clinicians). Mean age was 65 years (SD, 16.8) with 41% female. A 50-percentage-point increase in telemedicine use was associated with significantly reduced ordering of electrocardiograms (incident rate ratio, 0.30, [95% CI 0.25-0.35]), echocardiograms (0.70 [0.59-0.82]), natriuretic peptide tests (0.66 [0.49-0.88]), and chemistry panels (0.66 [0.56-0.76]). For the HF with reduced ejection fraction subgroup, this increase in telemedicine use was associated with reduced ordering of aldosterone antagonists (0.72 [0.52-0.99]) and total guideline-directed medical therapy (0.80 [0.69-0.94]). CONCLUSIONS: Greater clinician telemedicine use was associated with decreased diagnostic testing for patients with HF and reduced guideline-directed medical therapy initiation for patients with HF with reduced ejection fraction. Novel interventions are needed to ensure guideline-concordant care regardless of care modality.
Diabetes Care · 2026-03-25
articleOpen accessOBJECTIVE: The use of continuous glucose monitoring (CGM) with remote patient monitoring (RPM) continues to grow. We evaluated the cost-effectiveness of CGM with RPM compared with self-monitoring of blood glucose (SMBG) and CGM alone. RESEARCH DESIGN AND METHODS: We simulated type 1 diabetes progression with a Markov model in 5-year-old patients over a 20-year, 50-year, and lifetime horizon. We tracked diabetic ketoacidosis (DKA), severe hypoglycemia (SH), and seven chronic complications: retinopathy, neuropathy, nephropathy, cardiovascular disease, end-stage renal disease, lower-extremity amputation, and blindness. We compared three interventions: SMBG, CGM, and CGM with RPM. Efficacy estimates were derived from meta-analyses of pediatric CGM studies and the results of the Teamwork, Targets, Technology, and Tight Glycemia Study (4T Study 1). We evaluated quality-adjusted life years (QALYs) and health care costs (2022 U.S. dollars) discounted at 3% annually. We performed extensive sensitivity analyses. RESULTS: Compared with SMBG, CGM increased QALYs by 0.09 and costs by $8,900 over 20 years; CGM with RPM increased QALYs by 0.37, and costs by $10,300. CGM with RPM yielded more QALYs at a lower incremental cost-effectiveness ratio compared with CGM ($27,400/QALY vs. $103,700/QALY, respectively). Results were robust across sensitivity analyses and time horizons. CGM with RPM remained cost-effective when achieving at least 30% of 4T's clinical efficacy. CONCLUSIONS: CGM with RPM delivers superior health outcomes compared with SMBG and CGM and is likely cost-effective for patients with newly diagnosed type 1 diabetes. Despite higher intervention costs, CGM with RPM can reduce complications costs and generate net health care savings.
Variation in Commercial Insurer Prior Authorization Rules
Annals of Internal Medicine · 2026-05-18
articleSenior authorResearch Code Sharing in Support of Gold Standard Science.
UNC Libraries · 2026-03-17
articleOpen accessSharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH's Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article's cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH's GSS principles.
2026-03-25
article<p dir="ltr"><b>Objective: </b>The use of continuous glucose monitoring (CGM) with remote patient monitoring (RPM) continues to grow. We evaluated the cost-effectiveness of CGM with RPM compared to self-monitoring of blood glucose (SMBG) and CGM alone.</p><p dir="ltr"><b>Research design and methods: </b>We simulated type 1 diabetes progression with a Markov model in 5-year-old patients over a 20-year, 50-year, and age 100 horizon. We tracked diabetic ketoacidosis (DKA), severe hypoglycemia (SH), and seven chronic complications: retinopathy, neuropathy, nephropathy, cardiovascular disease, end-stage renal disease, lower-extremity amputation, and blindness. We compared three interventions: SMBG, CGM, and CGM with RPM. Efficacy estimates were derived from meta-analyses of pediatric CGM studies and the results of the Teamwork, Targets, Technology, and Tight Glycemia Study (4T Study 1). We evaluated quality-adjusted life years (QALYs) and healthcare costs (2022 U.S. dollars) discounted at 3% annually. We performed extensive sensitivity analyses.</p><p dir="ltr"><b>Results: </b>Compared to SMBG, CGM increased QALYs by 0.09 and costs by $8,900 over 20 years; CGM with RPM increased QALYs by 0.37 and costs by $10,300 CGM with RPM yielded more QALYs at a lower incremental cost-effectiveness ratio compared to CGM ($27,400/QALY vs $103,700/QALY, respectively). Results were robust across sensitivity analyses and time horizons. CGM with RPM remained cost-effective when achieving at least 30% of 4T’s clinical efficacy.</p><p dir="ltr"><b>Conclusions: </b>CGM with RPM delivers superior health outcomes compared to SMBG and CGM and is likely cost-effective for patients with newly diagnosed type 1 diabetes. Despite higher intervention costs, CGM with RPM can reduce complications costs and generate net healthcare savings.</p>
2026-03-25
article<p dir="ltr"><b>Objective: </b>The use of continuous glucose monitoring (CGM) with remote patient monitoring (RPM) continues to grow. We evaluated the cost-effectiveness of CGM with RPM compared to self-monitoring of blood glucose (SMBG) and CGM alone.</p><p dir="ltr"><b>Research design and methods: </b>We simulated type 1 diabetes progression with a Markov model in 5-year-old patients over a 20-year, 50-year, and age 100 horizon. We tracked diabetic ketoacidosis (DKA), severe hypoglycemia (SH), and seven chronic complications: retinopathy, neuropathy, nephropathy, cardiovascular disease, end-stage renal disease, lower-extremity amputation, and blindness. We compared three interventions: SMBG, CGM, and CGM with RPM. Efficacy estimates were derived from meta-analyses of pediatric CGM studies and the results of the Teamwork, Targets, Technology, and Tight Glycemia Study (4T Study 1). We evaluated quality-adjusted life years (QALYs) and healthcare costs (2022 U.S. dollars) discounted at 3% annually. We performed extensive sensitivity analyses.</p><p dir="ltr"><b>Results: </b>Compared to SMBG, CGM increased QALYs by 0.09 and costs by $8,900 over 20 years; CGM with RPM increased QALYs by 0.37 and costs by $10,300 CGM with RPM yielded more QALYs at a lower incremental cost-effectiveness ratio compared to CGM ($27,400/QALY vs $103,700/QALY, respectively). Results were robust across sensitivity analyses and time horizons. CGM with RPM remained cost-effective when achieving at least 30% of 4T’s clinical efficacy.</p><p dir="ltr"><b>Conclusions: </b>CGM with RPM delivers superior health outcomes compared to SMBG and CGM and is likely cost-effective for patients with newly diagnosed type 1 diabetes. Despite higher intervention costs, CGM with RPM can reduce complications costs and generate net healthcare savings.</p>
Optimal Control for Remote Patient Monitoring with Multidimensional Health States
2025-06-08
articleSenior authorSelecting the right monitoring level in Remote Patient Monitoring (RPM) systems for e-healthcare is crucial for balancing patient outcomes, various resources, and patient's quality of life. A prior work has used one-dimensional health representations, but patient health is inherently multidimensional and typically consists of many measurable physiological factors. In this paper, we introduce a multidimensional health state model within the RPM framework and use dynamic programming to study optimal monitoring strategies. Our analysis reveals that the optimal control is characterized by switching curves (for two-dimensional health states) or switching hyper-surfaces (in general): patients switch to intensive monitoring when health measurements cross a specific multidimensional surface. We further study how the optimal switching curve varies for different medical conditions and model parameters. This finding of the optimal control structure provides actionable insights for clinicians and aids in resource planning. The tunable modeling framework enhances the applicability and effectiveness of RPM services across various medical conditions.
Frequent coauthors
- 105 shared
David M. Maahs
- 102 shared
Priya Prahalad
Stanford University
- 67 shared
Ananta Addala
University of Fort Lauderdale
- 65 shared
Ramesh Johari
- 64 shared
Dessi P. Zaharieva
Stanford University
- 62 shared
Kevin A. Schulman
Stanford University
- 53 shared
Annie Chang
Icahn School of Medicine at Mount Sinai
- 52 shared
Paul Dupenloup
University of Bari Aldo Moro
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