Essays in primary care operations

Recent measures such as the Affordable Care Act in the United Sates have increased interest in innovative health care delivery models. This thesis explores various facets of potential innovations from both analytical and empirical approaches. In Chapter 1, we develop a model of patient health dynamics in which patient health follows Markovian transitions between healthy, intermediate, and sick states. In contrast to most currently used models, we treat patient demand for office visits as endogenous and managed by a physician via selection of a revisit frequency consistent with patient preferences. Our results quantify the overall impact of using e-visits and non-physician providers on physician\u27s choices, and, consequently, on physician\u27s expected earnings and patients\u27 expected health. In Chapter 2, we evaluate the effect of e-visit adoption on physician productivity and patient health using panel data from a large primary care provider in the US. We implement an instrumental variable analysis that leverages the differential propensities of physicians to adopt e-visits over time. Our estimates show that e-visit adoption increases the number of annual office and telephone visits by about three office visits and four telephone visits per year. We find no evidence, however, that e-visits affect patient health as measured by emergency room visit frequency, blood cholesterol levels, or blood glucose levels. In Chapter 3, we study timely access, which refers to the ability of patients to access their primary care providers when urgently needed. Without timely access to care, patient health can deteriorate and require emergency room care, which is both undesirable and expensive. We construct a new measure of timely access by using weekly variations in PCP workload in the Veterans Health Administration. Our health outcome measures are the patient\u27s likelihood of an emergency room visit, and conditional on a visit, the number of days until a follow-up appointment. We show that changes in timely access to primary care do not affect the frequency of emergency room visits, but they significantly delay post-emergency room follow-up care: a one standard deviation decrease in our measure leads to a 9.5 percent decrease in the odds of a follow-up visit with a patient\u27s assigned provider within 30 days

The Variance Learning Curve

The expansive learning curve literature in operations management has established how various facets of prior experience improve average performance. In this paper, we explore how increased cumulative experience affects performance variability or consistency. We use a two-stage estimation method of a heteroskedastic learning curve model to examine the relationship between experience and performance variability among paramedics at the London Ambulance Service. We find that, for paramedics with lower experience, an increase in experience of 500 jobs reduces the variance of task completion time by 8.7%, in addition to improving average completion times by 2.7%. Similar to prior results on the average learning curve, we find a diminishing impact of additional experience on the variance learning curve. We provide an evidence base for how to model the learning benefits of cumulative experience on performance in service systems. Our findings imply that the benefits of learning are substantially underestimated if the consistency effect is ignored. Specifically, our estimates indicate that queue lengths (or wait times) might be overestimated by as much as 4% by ignoring the impact of the variance learning curve in service systems. Furthermore, our results suggest that previously established drivers of productivity should be revisited to examine how they affect consistency, in addition to average performance. This paper was accepted by Charles Corbett, operations management. </jats:p