Leila Agha

Published Research
with David Molitor
Forthcoming at Review of Economics and Statistics

The Determinants of Productivity in Medical Testing: Intensity and Allocation of Care
with Jason Abaluck, and Christopher Kabrhel, Ali Raja, Arjun Venkatesh
2016, American Economic Review, 106(12): 3730-64.

[Previous title: Negative Tests and the Efficiency of Medical Care: What Determines Heterogeneity in Imaging Behavior?]

2015, Science, 348(6233):434-438.

2014, Journal of Health Economics, 34: p.19-30.

Current Research

Coordination within Teams and the Costs of Health Care
with Keith M. Ericson, Kimberly Geissler, Benjamin Lubin, and James Rebitzer

We examine how primary care physicians (PCPs) assemble teams of specialists to care for their patients. In our model, PCPs can invest in a relationship with specialists that requires upfront costs but has benefits for care coordination. PCPs who work with fewer specialists (have higher referral concentration) invest more in relationship-specific capital. Using the Massachusetts APCD, we show that this team-based coordination of care measure is virtually uncorrelated with existing patient-based coordination of care measures. We identify the effect of referral concentration on spending by comparing the spending of individuals who see the same specialist, but come from PCPs with different referral concentration. We use the same technique with standardized prices to distinguish whether the effects are a result of specialist prices or utilization effects. We find that a one standard deviation increase in coordination of care by a PCP (a change of 0.05 in HHI) reduces average costs by 2.2%. 

Physician Learning and Early Experimentation with New Medical Technologies
The adoption of new medical technologies has driven substantial gains in longevity as well as steep expenditure growth in the health care industry over recent decades. In this paper, I analyze the diffusion of positron emission tomography and deep brain stimulation, using data on Medicare claims from 1998-2005. I find that the mix of patient diagnoses treated with the new technologies changes substantially during the early stages of diffusion. Moreover, states that are late to adopt these technologies do not repeat the process of experimental learning undertaken by early adopters to discover which patients should receive the new treatment. Rather, late adopters demonstrate the same patterns of usage as the early adopters in a given year. This provides some evidence in favor of staggering reimbursement allowance to reduce the potential costs of early experimentation with a new technology.