Unpacking Dimensions of Performance in Healthcare Delivery
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CitationSaynisch, Philip. 2019. Unpacking Dimensions of Performance in Healthcare Delivery. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractIn complex healthcare settings, the optimal choice of treatment can be highly ambiguous. As a consequence, the marginal patient may face radically different care depending upon the choice of provider. Moreover, the complexity of treatment in these settings heightens information asymmetries between patient and provider at the same time as the stakes are at their highest. In light of these challenges, this dissertation aims to identify the drivers of performance for healthcare organizations and providers, and the mechanisms by which this performance can change over time. It does so in two contexts: the decision-making and surgical performance of kidney transplant teams, and in primary care practices adopting the patient-centered medical home model.
Chapter One (work with Robert S. Huckman and Nikoloas K. Trichakis) explores how kidney transplant center volume impacts two dimensions of performance: decision-making and surgical execution. Using learning curve models, I find evidence that larger transplant centers have better post-transplant outcomes. However, patients at these centers are less likely to receive a better organ and more likely to die or be removed from the transplant waitlist after an offer is declined, indicating lower-quality decision-making. This tension between improved execution and reduced decision quality implies that practice may not make perfect in complex medical decision-making.
In Chapter Two (work with Guy David and Aaron Smith-McLallen), I contribute to the literature on the impact of the patient-centered medical home (PCMH) model. Using data on the specific capabilities adopted by practices, I employ hierarchical clustering to group practices based on their approach to the PCMH. By evaluating the clusters as separate interventions, I find that treating the PCMH as a single model obscures important variation in patient outcomes.
Chapter Three (work with Guy David, Aaron Smith-McLallen, Spencer Luster and Ravi Chawla) focuses on how the PCMH model effects one important patient outcome – medication adherence. To do so, I identify a subset of six PCMH features which were most directly related to that outcome. We find that adoption of the PCMH model was associated with improvements in medication adherence, and that these gains were concentrated in practices that adopted four or more of the adherence-related capabilities.
In Chapter Four (work with Guy David, Benjamin Ukert, Abiy Agiro, Sarah Hudson Scholle and Tyler Oberlander), I use the approach outlined in Chapter Two to study the PCMH model, but extend the work to a larger sample of patients and practices, with the sample covering more years and geographic regions. We find an overall effect on healthcare utilization (including an 8% reduction in total expenditures), but also identify significant heterogeneity by cluster, with a reduction in emergency department utilization driven entirely by one group of practices emphasizing the adoption of enhanced electronic communications capabilities.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:42029777
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