Of the 120 million patients seen annually in US Emergency Departments, 85% are discharged home, as are the vast majority of the one billion patients seen in ambulatory clinics. While rates of in-hospital deaths are routinely measured and reported, deaths outside of health facilities are far more challenging to capture because of fragmented health information systems both within and among health care organizations. As a result, providers have no systematic way of knowing when a patient dies unexpectedly after being sent home, and policy makers cannot readily measure how frequently these catastrophic events occur. I propose a series of linked research projects to track and analyze unexpected deaths after medical encounters, using data from a large academic hospital system as well as Medicare claims, in order to identify factors related to individual patients, providers, and system that increase the likelihood of such events. I will subsequently adapt the methods to an international setting, where clinical and administrative data are less complete than in the US. This work would have important implications for improving both clinical care and health services design. In addition, a systematic method for tracking and reporting rates of unexpected early deaths would be a useful way to measure health services performance, and assess trade-offs between quality and cost.

Public Health Relevance

Most of the 1.1 billion patients seen annually in emergency facilities or clinics return home after their visit, but neither doctors nor policy makers have any systematic way of identifying patients who die unexpectedly after being sent home. My project aims to track and analyze these events, with the goals of helping providers identify high-risk patients;understanding the health system factors that make errors of risk assessment more likely;and exploring whether rates of unexpected death after discharge can be used to measure and publicly report on the quality of care delivered by health services.

Agency
National Institute of Health (NIH)
Institute
Office of The Director, National Institutes of Health (OD)
Type
Early Independence Award (DP5)
Project #
1DP5OD012161-01
Application #
8416137
Study Section
Special Emphasis Panel (ZRG1-BBBP-E (53))
Program Officer
Basavappa, Ravi
Project Start
2012-09-25
Project End
2017-08-31
Budget Start
2012-09-25
Budget End
2013-08-31
Support Year
1
Fiscal Year
2012
Total Cost
$317,500
Indirect Cost
$67,500
Name
Brigham and Women's Hospital
Department
Type
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
02115
Obermeyer, Ziad; Lee, Thomas H (2017) Lost in Thought - The Limits of the Human Mind and the Future of Medicine. N Engl J Med 377:1209-1211
Mullainathan, Sendhil; Obermeyer, Ziad (2017) Does Machine Learning Automate Moral Hazard and Error? Am Econ Rev 107:476-480
Obermeyer, Ziad; Cohn, Brent; Wilson, Michael et al. (2017) Early death after discharge from emergency departments: analysis of national US insurance claims data. BMJ 356:j239
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Obermeyer, Ziad; Clarke, Alissa C; Makar, Maggie et al. (2016) Emergency Care Use and the Medicare Hospice Benefit for Individuals with Cancer with a Poor Prognosis. J Am Geriatr Soc 64:323-9
Lakin, Joshua R; Robinson, Margaret G; Bernacki, Rachelle E et al. (2016) Estimating 1-Year Mortality for High-Risk Primary Care Patients Using the ""Surprise"" Question. JAMA Intern Med 176:1863-1865

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