David Chan, MD, PhD, is an Assistant Professor of Medicine at the Stanford School of Medicine, an investigator at the Department of Veterans Affairs, and a Faculty Research Fellow at the National Bureau of Economic Research. Drawing on labor and organizational economics, he is interested in studying how information is used in health care, how this affects productivity, and implications for design. He is the recipient of the 2014 NIH Director’s High-Risk, High-Reward Early Independence Award to study the optimal balance of information in health information technology for patient care.

Dr. Chan received master’s degrees in policy and economics from the London School of Economics and Oxford University, where he studied as a Marshall scholar. He holds a medical degree from UCLA and a PhD in economics from MIT. He trained in internal medicine at Brigham and Women’s Hospital and was an instructor of medicine at Harvard Medical School, prior to coming to Palo Alto, where he currently is a hospitalist at the Department of Veterans Affairs, Palo Alto.

Academic Appointments


2017-18 Courses


All Publications

  • The Efficiency of Slacking Off: Evidence from the Emergency Department Econometrica Chan, D. C. 2018; Forthcoming
  • Teamwork and Moral Hazard: Evidence from the Emergency Department JOURNAL OF POLITICAL ECONOMY Chan, D. C. 2016; 124 (3): 734-770

    View details for DOI 10.1086/685910

    View details for Web of Science ID 000376987700004

  • The Impact of Massachusetts Health Care Reform on Access, Quality, and Costs of Care for the Already-Insured. Health services research Joynt, K. E., Chan, D. C., Zheng, J., Orav, E. J., Jha, A. K. 2014


    To assess the impact of Massachusetts Health Reform (MHR) on access, quality, and costs of outpatient care for the already-insured.Medicare data from before (2006) and after (2009) MHR implementation.We performed a retrospective difference-in-differences analysis of quantity of outpatient visits, proportion of outpatient quality metrics met, and costs of care for Medicare patients with ≥1 chronic disease in 2006 versus 2009. We used the remaining states in New England as controls.We used existing Medicare claims data provided by the Centers for Medicare and Medicaid Services.MHR was not associated with a decrease in outpatient visits per year compared to controls (9.4 prereform to 9.6 postreform in MA vs. 9.4-9.5 in controls, p = .32). Quality of care in MA improved more than controls for hemoglobin A1c monitoring, mammography, and influenza vaccination, and similarly to controls for diabetic eye examination, colon cancer screening, and pneumococcal vaccination. Average costs for patients in Massachusetts increased from $9,389 to $10,668, versus $8,375 to $9,114 in control states (p < .001).MHR was not associated with worsening in access or quality of outpatient care for the already-insured, and it had modest effects on costs. This has implications for other states expanding insurance coverage under the Affordable Care Act.

    View details for DOI 10.1111/1475-6773.12228

    View details for PubMedID 25219772

  • Insurance Expansion In Massachusetts Did Not Reduce Access Among Previously Insured Medicare Patients HEALTH AFFAIRS Joynt, K. E., Chan, D., Orav, E. J., Jha, A. K. 2013; 32 (3): 571-578


    Critics of Massachusetts's health reform, a model for the Affordable Care Act, have argued that insurance expansion probably had a negative spillover effect leading to worse outcomes among already insured patients, such as vulnerable Medicare patients. Using Medicare data from 2004 to 2009, we examined trends in preventable hospitalizations for conditions such as uncontrolled hypertension and diabetes--markers of access to effective primary care--in Massachusetts compared to control states. We found that after Massachusetts's health reform, preventable hospitalization rates for Medicare patients actually decreased more in Massachusetts than in control states (a reduction of 101 admissions per 100,000 patients per quarter compared to a reduction of 83 admissions). Therefore, we found no evidence that Massachusetts's insurance expansion had a deleterious spillover effect on preventable hospitalizations among the previously insured. Our findings should offer some reassurance that it is possible to expand access to uninsured Americans without negatively affecting important clinical outcomes for those who are already insured.

    View details for DOI 10.1377/hlthaff.2012.1018

    View details for Web of Science ID 000316557900017

    View details for PubMedID 23459737

  • Patient, Physician, and Payment Predictors of Statin Adherence MEDICAL CARE Chan, D. C., Shrank, W. H., Cutler, D., Jan, S., Fischer, M. A., Liu, J., Avorn, J., Solomon, D., Brookhart, A., Choudhry, N. K. 2010; 48 (3): 196-202


    Although many patient, physician, and payment predictors of adherence have been described, knowledge of their relative strength and overall ability to explain adherence is limited.To measure the contributions of patient, physician, and payment predictors in explaining adherence to statins.Retrospective cohort study using administrative data.A total of 14,257 patients insured by Horizon Blue Cross Blue Shield of New Jersey who were newly prescribed a statin cholesterol-lowering medication.Adherence to statin medication was measured during the year after the initial prescription, based on proportion of days covered. The impact of patient, physician, and payment predictors of adherence were evaluated using multivariate logistic regression. The explanatory power of these models was evaluated with C statistics, a measure of the goodness of fit.Overall, 36.4% of patients were fully adherent. Older patient age, male gender, lower neighborhood percent black composition, higher median income, and fewer number of emergency department visits were significant patient predictors of adherence. Having a statin prescribed by a cardiologist, a patient's primary care physician, or a US medical graduate were significant physician predictors of adherence. Lower copayments also predicted adherence. All of our models had low explanatory power. Multivariate models including patient covariates only had greater explanatory power (C = 0.613) than models with physician variables only (C = 0.566) or copayments only (C = 0.543). A fully specified model had only slightly more explanatory power (C = 0.633) than the model with patient characteristics alone.Despite relatively comprehensive claims data on patients, physicians, and out-of-pocket costs, our overall ability to explain adherence remains poor. Administrative data likely do not capture many complex mechanisms underlying adherence.

    View details for DOI 10.1097/MLR.0b013e3181c132ad

    View details for Web of Science ID 000275198200002

    View details for PubMedID 19890219

  • How Sensitive are Low Income Families to Health Plan Prices? American Economic Review Papers and Proceedings Chan, D., Gruber, J. 2010: 292-296

    View details for DOI 10.1257/aer.100.2.292

  • Improving Safety And Eliminating Redundant Tests: Cutting Costs In U. S. Hospitals HEALTH AFFAIRS Jha, A. K., Chan, D. C., Ridgway, A. B., Franz, C., Bates, D. W. 2009; 28 (5): 1475-1484


    High costs and unsafe care are major challenges for U.S. hospitals. Two sources of raised costs and unsafe care are adverse events in hospitals and tests ordered by several different physicians. After reviewing rates of these two occurrences in U.S. hospitals and simulating their costs, we estimated that in 2004 alone, eliminating readily preventable adverse events would have resulted in direct savings of more than $16.6 billion (5.5 percent of total inpatient costs). Eliminating redundant tests would have saved an additional $8 billion (2.7 percent). Addressing these situations could generate major savings to the system while improving patient care.

    View details for DOI 10.1377/hlthaff.28.5.1475

    View details for Web of Science ID 000269646100031

    View details for PubMedID 19738266

  • Quantitative Risk Stratification in Markov Chains with Limiting Conditional Distributions 29th Annual Meeting of the Society-for-Medical-Decision-Making Chan, D. C., Pollett, P. K., Weinstein, M. C. SAGE PUBLICATIONS INC. 2009: 532–40


    Many clinical decisions require patient risk stratification. The authors introduce the concept of limiting conditional distributions, which describe the equilibrium proportion of surviving patients occupying each disease state in a Markov chain with death. Such distributions can quantitatively describe risk stratification.The authors first establish conditions for the existence of a positive limiting conditional distribution in a general Markov chain and describe a framework for risk stratification using the limiting conditional distribution. They then apply their framework to a clinical example of a treatment indicated for high-risk patients, first to infer the risk of patients selected for treatment in clinical trials and then to predict the outcomes of expanding treatment to other populations of risk.For the general chain, a positive limiting conditional distribution exists only if patients in the earliest state have the lowest combined risk of progression or death. The authors show that in their general framework, outcomes and population risk are interchangeable. For the clinical example, they estimate that previous clinical trials have selected the upper quintile of patient risk for this treatment, but they also show that expanded treatment would weakly dominate this degree of targeted treatment, and universal treatment may be cost-effective.Limiting conditional distributions exist in most Markov models of progressive diseases and are well suited to represent risk stratification quantitatively. This framework can characterize patient risk in clinical trials and predict outcomes for other populations of risk.

    View details for DOI 10.1177/0272989X08330121

    View details for Web of Science ID 000268291200015

    View details for PubMedID 19336745

  • Heart failure disease management programs: A cost-effectiveness analysis AMERICAN HEART JOURNAL Chan, D. C., Heidenreich, P. A., Weinstein, M. C., Fonarow, G. C. 2008; 155 (2): 332-338


    Heart failure (HF) disease management programs have shown impressive reductions in hospitalizations and mortality, but in studies limited to short time frames and high-risk patient populations. Current guidelines thus only recommend disease management targeted to high-risk patients with HF.This study applied a new technique to infer the degree to which clinical trials have targeted patients by risk based on observed rates of hospitalization and death. A Markov model was used to assess the incremental life expectancy and cost of providing disease management for high-risk to low-risk patients. Sensitivity analyses of various long-term scenarios and of reduced effectiveness in low-risk patients were also considered.The incremental cost-effectiveness ratio of extending coverage to all patients was $9700 per life-year gained in the base case. In aggregate, universal coverage almost quadrupled life-years saved as compared to coverage of only the highest quintile of risk. A worst case analysis with simultaneous conservative assumptions yielded an incremental cost-effectiveness ratio of $110,000 per life-year gained. In a probabilistic sensitivity analysis, 99.74% of possible incremental cost-effectiveness ratios were <$50,000 per life-year gained.Heart failure disease management programs are likely cost-effective in the long-term along the whole spectrum of patient risk. Health gains could be extended by enrolling a broader group of patients with HF in disease management.

    View details for DOI 10.1016/j.ahj.2007.10.001

    View details for Web of Science ID 000252812800024

    View details for PubMedID 18215605