This dissertation investigates how healthcare provider networks are formed and their effects on patient health outcomes. The first chapter explores three types of hospital networks that are intended to improve coordination of patient care across different hospitals: integrated delivery systems, accountable care organizations, and electronic health records. Using 2007-2017 Healthcare Information and Management Systems Society IT data and Medicare data on accountable care organizations and hospital quality, I document several interesting patterns regarding the formation and potential effects of these networks in the United States. I find correlations consistent with assortative matching where higher quality hospitals match with higher quality groups, which may be inefficient if there are peer effects that mean higher quality groups could have more substantial influence on lower quality hospitals that have more room to improve. I show that accountable care organizations appear to be strategic about the network formation process, omitting hospitals that are natural members. They may do so for anticompetitive reasons–ordinary least square regressions find that accountable care organization market concentration is negatively correlated with hospital quality. These regressions additionally point to the need for caution in advocating for a unified electronic health record, as hospital quality is positively correlated with regional electronic health record market concentration–which is related to coordination abilities–but negatively correlated with national concentration–which is related to competition.
The second chapter takes inspiration from the descriptive results of the first chapter and establishes a causal effect of electronic health record networks at the patient level. I hypothesize that systematic, reliable transfer of patient medical history can improve clinical decisions and thus health outcomes, especially during medical emergencies. Thus, I identify patients who had emergency cardiovascular episodes in 2007-2014 Medicare claims and use a difference-in-differences strategy to estimate the causal effect of their primary care and emergency hospitals being in the same electronic health record network. I find that electronic health record compatibility decreases the mortality rate but increases the rate of other bad health outcomes by approximately the same amount, suggesting that compatibility makes it easier for patients to survive given poor health but does not overall improve health otherwise. This result highlights the importance of analyzing the effects of healthcare treatments on both the rates of mortality and negative outcomes in survivors. Only looking at the rate of negative outcomes in survivors, electronic health record compatibility would have appeared to be a harmful treatment, while it was actually reducing mortality.
The third chapter moves from hospital networks, which have only one type of agent, to look at physician-insurer networks, represented by a two-sided many-to-many matching market. I use Healthgrades and National Committee for Quality Assurance consumer ratings data to collect physician and insurance plan characteristics, respectively. Descriptive statistics indicate that higher quality physicians are in more insurance networks, while higher quality plans tend to be more restricted in the numbers of physicians they accept. There is a mild correlation between physician and plan quality, but there are many possible explanations for it. To test if it is due to assortative matching and to better understand how physicians and insurers decide with whom to contract, I estimate a structural many-to-many matching model using the matching maximum score estimator. Data quality and quantity appear to be obstacles in obtaining precise estimates, so I leave further exploration of this topic to future research.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-s8m8-2p18 |
| Date | January 2020 |
| Creators | Martin, Janet Jing |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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