Topics on the Implementation of Health Technology

Abstract

In this thesis I include work that addresses three different stages of health technology implementation and policy.In chapter 2, I analyze policy implementation limitations that hinder the adoption of Electronic Health Records (EHR). As more hospitals adopt EHR, focus has shifted to how these records can be used to improve patient care. One barrier to this improvement is limited information exchange between providers. In this work I examine the role of EHR vendors, hypothesizing that vendors strategically control the exchange of clinicalhospital exchanging clinical summaries with hospitals outside its health system increases as the percentage ofhospitals with the same EHR vendor in the region increases. When reviewing the relationship of vendor market concentration at the state level I find a positive significantrelationship with the percentage of hospitals that share clinical care summaries within a state. However, I find no significant impact from state policies designed to incentivizeinformation exchange through the State Health Information Exchange Cooperative Program. In order to avoid closed networks that foreclose some hospitals, it is importantthat future regulation attempt to be more inclusive of hospitals that do not use large vendors and are therefore unable to use proprietary methods for exchange.Chapter 3 explores the mental models of patients and how it may affect the implementation of tools to enhance adherence to Antiretroviral Therapy (ART). Highlevels of adherence to ART are necessary to prevent the emergence of drug-resistant HIV virus and delay disease progression. For this reason, a number of interventionshave been designed to support adherence for people living with HIV (PLWH). However, widely used adherence interventions, though successful for some populations, still fail certain vulnerable groups. The mental model approach allows us to go beyond current decision-making models to understand context specific aspects of behavior mostrelevant to this group. I interview nine high-risk non-adherents and compare their mental models to non-adherence models as seen by experts. In this study I identified how scarcity conditions and the several ways in which adhering to ARVs induces negative affect can influence the cost-benefit analysis that decision makers engage in when deciding to take their medication. Further work needs to be done to understand theprevalence of this decision-making biases in order to design more inclusive interventions. Chapter 4 explores the use of future self interventions (FSI) and the possibleunintended consequences of their use in health decision making due to negative perceptions of aging. Many leading causes of mortality and morbidity in developedcountries stem from health risk factors that are influenced by individual choices. Improving decision makers’ understanding of how benefits will accrue to themselves inthe future could inform health choices over their lifespan. However, negative attitudes toward aging related to the view of declining health or illness during this period could beuniquely relevant when the decision maker determines the utility of future health. The goal of this study is to examine how the relationships between future self connectiongenerated by FSI along with expectations of aging and aging anxiety influence the anticipated valuation of future health. Participants between the ages of 18 and 45 wererecruited via Amazon’s Mechanical Turk. They were then assigned to participate in one of three groups of letter-writing exercises, a control, one to the self 20 years in the futureand another to the 68-year-old version of the self. Our results suggest that a connection with the future aged self interacts with aging anxiety in ways that decrease the value adecision maker assesses to future quality of life. As we expected, we found that a positive effect of expectations regarding aging, however, this effect is lower for those inthe intervention group who were tasked with writing a letter to a far-off future self. Furthermore, we find that anticipated health utility has a negative effect on the healthdiscount rate. This study provides evidence that there are unique characteristics of aging that may impact future health valuation which should be considered before usingFSI to incentivize future oriented health behavior.