Phil Saynisch is the Phyllis Torda Health Care Quality Fellow at the National Committee for Quality Assurance. He is also 2019 graduate of the Health Policy (Management) PhD at the Harvard Business School and Graduate School of Arts and Sciences. His work aims to identify the drivers of performance for healthcare organizations and providers, and the mechanisms by which this performance can change over time. In 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. He explores these issues in two settings: the decision-making and surgical performance of kidney transplant teams and primary care practices adopting the patient-centered medical home model.
In his work on performance in kidney transplantation, Phil explores whether the widely-documented volume-outcome relationship is a consequence of better decision-making (such as in diagnosis or evaluating treatment alternatives) or better execution of the chosen treatments. He finds evidence of a puzzle: larger transplant centers have better post-transplant outcomes; but 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.
His second project is an ongoing collaboration to evaluate the impact of the patient-centered medical home (PCMH) model, a leading model for primary care delivery reform. His work notes that the mixed evidence on the PCMH model may be a consequence of heterogeneity in implementation, and seeks to incorporate this heterogeneity into impact evaluations. Using techniques like hierarchical clustering to categorize medical home practices, he finds that evaluations which treat the PCMH as a single, undifferentiated intervention may conceal differences across PCMH types that explain important differences in patterns of healthcare utilization.
Phil has also provided support to an on-going international effort sponsored by the National Academy of Medicine and World Bank to improve preparedness for infectious disease outbreaks. This work aims to spur both national governments and industry to action by strengthening the economic case for investment in disease surveillance and other capabilities. Prior to joining the PhD Program, Phil spent three years working as a research assistant in the Center for Outcomes Research at Children’s Hospital of Philadelphia, working on projects exploring the relationship between obesity and surgical outcomes and the effectiveness of routine well visits for preterm infants. He also contributed to a project implementing a novel methods for evaluating hospital performance with template matching. Philip graduated summa cum laude from the University of Pennsylvania in 2009.