Studies on using data-driven decision support systems to improve personalized medicine processes
Cameron, Kellas Ross
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This dissertation looks at how new sources of information should be incorporated into medical decision-making processes to improve patient outcomes and reduce costs. There are three fundamental challenges that must be overcome to effectively use personalized medicine, we need to understand: 1) how best to appropriately designate which patients will receive the greatest value from these processes; 2) how physicians and caregivers interpret additional patient-specific information and how that affects their decision-making processes; and finally, (3) how to account for a patient’s ability to engage in their own healthcare decisions. The first study looks at how we can infer which patients will receive the most value from genomic testing. The difficult statistical problem is how to separate the distribution of patients, based on ex-ante factors, to identify the best candidates for personalized testing. A model was constructed to infer a healthcare provider’s decision on whether this test would provide beneficial information in selecting a patient’s medication. Model analysis shows that healthcare providers’ primary focus is to maximize patient health outcomes while considering the impact the patient’s economic welfare. The second study focuses on understanding how technology-enabled continuity of care (TECC) for Chronic Obstructive Pulmonary Disease (COPD) and Congestive Heart Failure (CHF) patients can be utilized to improve patient engagement, measured in terms of patient activation. We shed light on the fact that different types of patients garnered different levels of value from the use of TECC. The third study looks at how data-driven decision support systems can allow physicians to more accurately understand which patients are at high-risk of readmission. We look at how we can use available patient-specific information for patients admitted with CHF to more accurately identify which patients are most likely to be readmitted, and also why – whether for condition-related reasons versus for non- related reasons, allowing physicians to suggest different patient-specific readmission prevention strategies. Taken together, these three studies allow us to build a robust theory to tackle these challenges, both operational and policy-related, that need to be addressed for physicians to take advantage of the growing availability of patient-specific information to improve personalized medication processes.