The Determinants of Productivity in Medical Testing: Intensity and Allocation of Care

Abaluck, Jason; Agha, Leila; Kabrhel, Chris; Raja, Ali S.; Venkatesh, Arjun K.

doi:10.1257/aer.20140260

Cited by 73 publications

(62 citation statements)

References 43 publications

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“…The same mechanism may explain the weak relationship between decision rates and outcomes observed in other settings. 1 Perhaps most closely related to our paper are evaluations by Abaluck et al (2016) and Currie and MacLeod (2017), both of which examine diagnostic decision-making in health care. Abaluck et al (2016) assume that physicians have the same diagnostic skill (i.e., the same ranking of cases) but may differ in where they set their thresholds for diagnosis.…”

Section: Introductionmentioning

confidence: 97%

Selection with Variation in Diagnostic Skill: Evidence from Radiologists

Chan¹,

Gentzkow²,

Yu³

2019

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Physicians, judges, teachers, and agents in many other settings differ systematically in the decisions they make when faced with similar cases. Standard approaches to interpreting and exploiting such differences assume they arise solely from variation in preferences. We develop an alternative framework that allows variation in both preferences and diagnostic skill, and show that both dimensions are identified in standard settings under quasi-random assignment. We apply this framework to study pneumonia diagnoses by radiologists. Diagnosis rates vary widely among radiologists, and descriptive evidence suggests that a large component of this variation is due to differences in diagnostic skill. Our estimated model suggests that radiologists view failing to diagnose a patient with pneumonia as more costly than incorrectly diagnosing one without, and that this leads less-skilled radiologists to optimally choose lower diagnosis thresholds. Variation in skill can explain 44 percent of the variation in diagnostic decisions, and policies that improve skill perform better than uniform decision guidelines. Failing to account for skill variation can lead to highly misleading results in research designs that use agent assignments as instruments.

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Section: Introductionmentioning

confidence: 97%

Selection with Variation in Diagnostic Skill: Evidence from Radiologists

Chan¹,

Gentzkow²,

Yu³

2019

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“…While our model and related tests cannot distinguish between statistical and taste‐ or bias‐based discrimination, using intuition similar to Abaluck et al . () provides additional insight. Our results indicate that following the alert level increase, police officers frisked and used force against relatively more members of the group ‘Other’ without an increase in the arrest rate.…”

Section: Resultsmentioning

confidence: 96%

How Do NYPD Officers Respond to Terror Threats?

Lehrer

Lepage

2019

Economica

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Using data from the Stop‐and‐Frisk programme of the New York Police Department (NYPD), we evaluate the impact of a specific terrorist attack threat from Al Qaeda on policing behaviour in New York City. We find that after the Department of Homeland Security raised the alert level in response to this threat, people categorized as ‘Other’ by the NYPD, including Arabs, were significantly more likely to be frisked and have force used against them, yet were not more likely to be arrested. These individuals were in turn less likely to be frisked or have force used against them immediately after the alert level returned to its baseline level. Further, evidence suggests that these impacts were larger in magnitude in police precincts that have higher concentrations of mosques. Our results are consistent with profiling by police officers leading to low‐productivity stops, but we cannot rule out that it constitutes efficient policing given important differences between deterrence of terrorism versus other crimes.

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“…Not ordering useful tests is also problematic, but the data available cannot identify this. A related economic literature addresses medical screening and imaging (e.g., Aas, 2009;Abaluck, Agha, Kabrhel, Raja, & Venkatesh, 2016;Hackl, Halla, Hummer, & Pruckner, 2015). 3 For the United States, see http://www.choosingwisely.org/, and for Canada, see http://www.choosingwiselycanada.org/.…”

Section: Introductionmentioning

confidence: 99%

Payment models in primary health care: A driver of the quantity and quality of medical laboratory utilization

Chami

Sweetman

2019

Health Economics

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Physician payment models' incentives regarding many aspects of primary health care are not well understood. We focus on the case of medical laboratory utilization and examine how physicians' laboratory test ordering patterns change following a switch to a blended capitation payment model from one with fee for service enhanced with pay for performance. Also, within blended capitation, we examine differences between traditional staffing and interdisciplinary teams. Using a propensity score weighted fixed‐effects specification to address selection, it is estimated that the switch to capitation leads to a short‐run average of 3% fewer laboratory requisitions per patient. Patients' laboratory utilization also becomes more concentrated with the rostering physician. More importantly, using diabetes‐related laboratory tests as a case study, after joining the blended model, physicians order 3% fewer inappropriate/redundant tests, and the addition of an interdisciplinary care team makes the reduction about 9%. Advances in both continuity and quality seem to be associated with blended capitation.

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The Determinants of Productivity in Medical Testing: Intensity and Allocation of Care

Cited by 73 publications

References 43 publications

Selection with Variation in Diagnostic Skill: Evidence from Radiologists

Selection with Variation in Diagnostic Skill: Evidence from Radiologists

How Do NYPD Officers Respond to Terror Threats?

Payment models in primary health care: A driver of the quantity and quality of medical laboratory utilization

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