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Marriott Marquis, Grand Ballroom 4
Hosted By:
American Economic Association
Provider Decision-Making and Productivity in Health Care
Paper Session
Friday, Jan. 3, 2020 2:30 PM - 4:30 PM (PDT)
- Chair: David Chan Jr., Stanford University
Administration Above Administrators: The Changing Technology of Healthcare Management
Abstract
This paper measure the costs and types of administrative inputs in health care. We use data on labor and non-labor inputs by industry and categorize them as administrative or not. We find that non-labor inputs are a critical part of administrative spending, over and above labor inputs. Trends in non-labor administrative input spending have differed dramatically from that of labor input spending for hospitals over the last 20 years. Hospitals have substituted away from office workers, and towards externally purchased inputs. The share of managers and technical workers in administration has grown. The technology of healthcare administration is changing.Triage Judgments in the Emergency Department
Abstract
Efforts to use technology to aid human decisions have typically tried to emulate average human decisions (i.e., "crowdsourcing"), but to improve outcomes, algorithms need to emulate good human decisions. We adopt an approach combining quasi-experimental variation and machine learning to study this question in the setting of emergency department triage. We use rich data from the Veterans Health Administration (VHA) on approximately 11 million emergency visits in 130 VHA health care systems, including triage nurse identities and patient bed assignments, ESI scores, vital signs, comorbidities, and disposition and health outcomes. We show that patients are as good as randomly assigned to triage nurses based on the time of arrival to the emergency department, and we show that triage nurses have a causal effect on patient mortality. Among patients with predicted mortality greater than 0, one standard-deviation increase in triage nurse value added results in a 20% increase in patient mortality. We next characterize the mechanisms by which triages may have a mortality effect, along two key dimensions of triage behavior: First, triage nurses control the amount of time patients spend waiting to receive care. Second, triage nurses communicate the severity of patients' conditions by assigning an Emergency Severity Index (ESI) level to each patient. We find significant discretion and variation in both triage activities across triage nurses. We use machine learning and empirical Bayes methods to project high-dimensional variables describing these behaviors onto smaller-dimensional space. We find that these mechanisms together explain up to 80% of triage nurse effects on mortality. These results suggest that, relative to crowdsourcing, algorithms that link behaviors with outcomes could potentially capture a large amount of the effect of triage on outcomes.Time Dependency in Physician Decision-Making
Abstract
Using data with over 250,000 emergency department (ED) visits, we study the time-of-day effect on physician decision-making and patient outcomes. After controlling for patient characteristics, physician fixed effects and work hours, we find that cases treated at night have significantly lower probability of inpatient admission, and fewer medical tests. Moreover, these cases also exhibit a higher likelihood of a revisit to the ED in support of the decline of physician performance. While we cannot completely rule out the possibility of limited hospital arrangements, our findings are most consistent with the detrimental effect of disrupted circadian rhythm during night shifts.Discussant(s)
Robert Gibbons
,
Massachusetts Institute of Technology
Mark Shepard
,
Harvard University
David Silver
,
Princeton University
Alice Chen
,
University of Southern California
JEL Classifications
- I1 - Health
- D2 - Production and Organizations