Ziad Obermeyer
Biographic Data
| ID | 5546849 |
|---|---|
| NAME | Ziad Obermeyer |
| GIVEN NAMES | Ziad |
| FAMILY NAME | Obermeyer |
| SIGNATURE | OBERMEYER Z |
| AFFILIATIONS | Harvard University |
| ORCID | 0000-0002-4563-5849 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Overuse and Underuse of Health Care
Rampant overuse of health care in the US is plain to see. The traditional explanation from health economics is straightforward: bad incentives. Paying physicians too much leads to more carewhether patients need it or not. But paying physicians too little risks limiting patients' access to care
Dissecting racial bias in an algorithm used to manage the health of populations
Racial bias in health algorithms The U.S. health care system uses commercial algorithms to guide health decisions. Obermeyer et al. find evidence of racial bias in one widely used algorithm, such that Black patients assigned the same level of risk by the algorithm are sicker than White patients (see the Perspective by Benjamin). The authors estimated that this racial bias reduces the number of Black patients identified for extra care by more than…
Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Este trabajo pretende analizar la practica del trabajo social dentro de los procesos de gestion del capital simbolico tal y como fue definido por Pierre Bourdieu. El texto analiza para ello dos practicas diferentes de gestion del capital simbolico por parte de trabajadores sociales. El texto concluye explicando como estas practicas persiguen la transformacion de los habitus.
Short-term Outcomes for Medicare Beneficiaries After Low-acuity Visits to Emergency Departments and Clinics
BACKGROUND: There is substantial interest in identifying low-acuity visits to emergency departments (EDs) that could be treated more appropriately in other settings. Systematic differences in illness severity between ED patients and comparable patients elsewhere could make such strategies unsafe, but little evidence exists to guide policy makers. OBJECTIVE: To compare illness severity between patients visiting EDs and outpatient clinics, by compa…
Prediction Policy Problems
Most empirical policy work focuses on causal inference. We argue an important class of policy problems does not require causal inference but instead requires predictive inference. Solving these “prediction policy problems” requires more than simple regression techniques, since these are tuned to generating unbiased estimates of coefficients rather than minimizing prediction error. We argue that new developments in the field of “machine learning” …
No prominent works on this page.
Prediction Policy Problems
Most empirical policy work focuses on causal inference. We argue an important class of policy problems does not require causal inference but instead requires predictive inference. Solving these “prediction policy problems” requires more than simple regression techniques, since these are tuned to generating unbiased estimates of coefficients rather than minimizing prediction error. We argue that new developments in the field of “machine learning” …
Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Este trabajo pretende analizar la practica del trabajo social dentro de los procesos de gestion del capital simbolico tal y como fue definido por Pierre Bourdieu. El texto analiza para ello dos practicas diferentes de gestion del capital simbolico por parte de trabajadores sociales. El texto concluye explicando como estas practicas persiguen la transformacion de los habitus.
Short-term Outcomes for Medicare Beneficiaries After Low-acuity Visits to Emergency Departments and Clinics
BACKGROUND: There is substantial interest in identifying low-acuity visits to emergency departments (EDs) that could be treated more appropriately in other settings. Systematic differences in illness severity between ED patients and comparable patients elsewhere could make such strategies unsafe, but little evidence exists to guide policy makers. OBJECTIVE: To compare illness severity between patients visiting EDs and outpatient clinics, by compa…
Dissecting racial bias in an algorithm used to manage the health of populations
Racial bias in health algorithms The U.S. health care system uses commercial algorithms to guide health decisions. Obermeyer et al. find evidence of racial bias in one widely used algorithm, such that Black patients assigned the same level of risk by the algorithm are sicker than White patients (see the Perspective by Benjamin). The authors estimated that this racial bias reduces the number of Black patients identified for extra care by more than…
Overuse and Underuse of Health Care
Rampant overuse of health care in the US is plain to see. The traditional explanation from health economics is straightforward: bad incentives. Paying physicians too much leads to more carewhether patients need it or not. But paying physicians too little risks limiting patients' access to care
Computer Science (3 works) · Economics (3 works) · Health Systems, Economic Evaluations, Quality of Life (3 works) · Health care (2 works) · Healthcare cost, quality, practices (2 works) · Healthcare Policy and Management (2 works) · Machine learning (2 works) · Medicine (2 works) · Psychology (2 works) · Actuarial science (1 work)