Bio

Bio


David Scheinker is the 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 to improve the quality of care using operations research methodology. He received a PhD in theoretical math from The University of California San Diego under Jim Agler. Before coming to Stanford, he was a Joint Research Fellow at The MIT Sloan School of Management and Massachusetts General Hospital. His current areas of research include applications of operations research in healthcare, healthcare policy, mathematical control theory, and functional analysis.

Concurrently with his university appointments, David has spent time teaching theoretical math to gifted 11 and 12 year old students for the Johns Hopkins Center for Talented Youth. He is writing a popular math book titled Infinity in Wonderland with the intent to bring the material of these courses to a wider audience.

Academic Appointments


Administrative Appointments


  • Founder and Director, SURF Stanford Medicine (2015 - Present)
  • Director of Systems Design and Collaborative Research, Lucile Packard Children's Hospital Stanford (2015 - Present)
  • Faculty, Clinical Excellence Research Center (CERC) (2018 - Present)

Boards, Advisory Committees, Professional Organizations


  • Advisor, Carta Healthcare (2017 - Present)

Teaching

2020-21 Courses


Publications

All Publications


  • 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

  • PERSONALIZED INTER-DONATION INTERVALS TO MANAGE RISK OF IRON-RELATED ADVERSE EVENTS IN REPEAT BLOOD DONORS Russell, W., Scheinker, D., Custer, B. SAGE PUBLICATIONS INC. 2020: E111–E112
  • 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)
  • 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

    Abstract

    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

  • EXPLAINING VARIATION IN US COUNTY-LEVEL OBESITY PREVALENCE Valencia, A., Rodriguez, F., Scheinker, D. ELSEVIER SCIENCE INC. 2019: 1762
  • 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): e192884

    Abstract

    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

  • Anesthesiologist Surgery Assignments Using Policy Learning Ward, A., Zhou, Z., Bambos, N., Scheinker, D., Wang, E., IEEE IEEE. 2019
  • CGM Initiation Soon After Type 1 Diabetes Diagnosis Results in Sustained CGM Use and Wear Time. Diabetes care Prahalad, P., Addala, A., Scheinker, D., Hood, K. K., Maahs, D. M. 2019

    View details for DOI 10.2337/dc19-1205

    View details for PubMedID 31558548

  • 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

    Abstract

    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 (theaba.org). 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

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

    Abstract

    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

  • 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

    Abstract

    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

    Abstract

    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., Scheinker, D., 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

    Abstract

    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

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