Informing the Design and Deployment of Health Information Technology to Improve Care Coordination

In the United States, the health care sector is 20 years behind in the use of information technology to improve the process of health care delivery as compared to other sectors. Patients have to deliver their data over and over again to every health professional they see. Most health care facilities act as data repositories with limited capabilities of data analysis or data exchange. A remaining challenge is, how do we encourage the use of IT in the health care sector that will improve care coordination, save lives, make patients more involved in decision-making, and save money for the American people? According to Healthy People 2020, several challenges such as making health IT more usable, helping users to adapt to the new uses of health IT, and monitoring the impact of health IT on health care quality, safety, and efficiency, will require multidisciplinary models, new data systems, and abundant research. In this dissertation, I developed and used systems engineering methods to understand the role of new health IT in improving the coordination, safety, and efficiency of health care delivery. It is well known that care coordination issues may result in preventable hospital readmissions. In this dissertation, I identified the status of the care coordination and hospital readmission issues in the United States, and the potential areas where systems engineering would make significant contributions (see Appendix B). This literature review introduced me to a second study (see Appendix C), in which I identified specific patient cohorts, within chronically ill patients, that are at a higher risk of being readmitted within 30 days. Important to note is that the largest volume of preventable hospital readmissions occurs among chronically ill patients. This study was a retrospective data analysis of a representative patient cohort from Tampa, Florida, based on multivariate logistic regression and Cox proportional hazards models. After finishing these two studies, I directed my research efforts to understand and generate evidence on the role of new health IT (i.e., health information exchange, HIE) in improving care coordination, and thereby reducing the chances of a patient to be unnecessarily readmitted to the hospital. HIE is the electronic exchange of patient data among different stakeholders in the health care industry. The exchange of patient data is achieved, for example, by connecting electronic medical records systems between unaffiliated health care providers. It is expected that HIE will allow physicians, nurses, pharmacists, other health care providers and patients to appropriately access and securely share a patient’s vital medical information electronically, and thereby improving the speed, quality, safety and cost of patient care. The federal government, through the 2009 Health Information Technology for Economic and Clinical Health (HITECH) Act, is actively stimulating health care providers to engage in HIE, so that they can freely exchange patient information. Although these networks of information exchange are the promise of a less fragmented and more efficient health care system, there are only a few functional and financially sustainable HIEs across the United States. Current evidence suggests four barriers for HIE: •Usability and interface issues of HIE systems •Privacy and security concerns of patient data •Lack of sustainable business models for HIE organizations •Loss of strategic advantage of owning patient information by joining HIE to freely share data To contribute in reducing usability and interface issues of HIE systems, I performed a user needs assessment for the internal medicine department of Tampa General Hospital in Tampa, Florida. I used qualitative research tools (see Appendix D) and machine learning techniques (see Appendix E) to answer the following fundamental questions: How do clinicians integrate patient information allocated in outside health care facilities? What are the types of information needed the most for efficient and effective medical decision-making? Additionally, I built a strategic gaming model (see Appendix F) to analyze the strategic role of owning patient information that health care providers lose by joining an HIE. Using bilevel mathematical programs, I mimic the hospital decision of joining HIE and the patient decision of switching from one hospital to another one. The fundamental questions I tried to answer were: What is the role of competition in the decision of whether or not hospitals will engage in HIE? Our mathematical framework can also be used by policy makers to answer the following question: What are the optimal levels of monetary incentives that will spur HIE engagement in a specific region? Answering these fundamental questions will support both the development of user-friendly HIE systems and the creation of more effective health IT policy to promote and generate HIE engagement. Through the development of these five studies, I demonstrated how systems engineering tools can be used by policy makers and health care providers to make health IT more useful, and to monitor and support the impact of health IT on health care quality, safety, and efficiency