Doctor of Philosophy

dissertationElectronic Health Records (EHRs) provide a wealth of information for secondary uses. Methods are developed to improve usefulness of free text query and text processing and demonstrate advantages to using these methods for clinical research, specifically cohort identification and enhancement. Cohort identification is a critical early step in clinical research. Problems may arise when too few patients are identified, or the cohort consists of a nonrepresentative sample. Methods of improving query formation through query expansion are described. Inclusion of free text search in addition to structured data search is investigated to determine the incremental improvement of adding unstructured text search over structured data search alone. Query expansion using topic- and synonym-based expansion improved information retrieval performance. An ensemble method was not successful. The addition of free text search compared to structured data search alone demonstrated increased cohort size in all cases, with dramatic increases in some. Representation of patients in subpopulations that may have been underrepresented otherwise is also shown. We demonstrate clinical impact by showing that a serious clinical condition, scleroderma renal crisis, can be predicted by adding free text search. A novel information extraction algorithm is developed and evaluated (Regular Expression Discovery for Extraction, or REDEx) for cohort enrichment. The REDEx algorithm is demonstrated to accurately extract information from free text clinical iv narratives. Temporal expressions as well as bodyweight-related measures are extracted. Additional patients and additional measurement occurrences are identified using these extracted values that were not identifiable through structured data alone. The REDEx algorithm transfers the burden of machine learning training from annotators to domain experts. We developed automated query expansion methods that greatly improve performance of keyword-based information retrieval. We also developed NLP methods for unstructured data and demonstrate that cohort size can be greatly increased, a more complete population can be identified, and important clinical conditions can be detected that are often missed otherwise. We found a much more complete representation of patients can be obtained. We also developed a novel machine learning algorithm for information extraction, REDEx, that efficiently extracts clinical values from unstructured clinical text, adding additional information and observations over what is available in structured text alone

Effectiveness of Telemedicine in Diabetes Management: A Retrospective Study in an Urban Medically Underserved Population Area (UMUPA).

The purpose of this research is to assess the efficacy of employing telemedicine (TM) technology compared to traditional face-to-face (F2F) visits as an alternative healthcare delivery service for managing diabetes in populations residing in urban medically underserved areas (UMUPA). Researchers investigating public health and healthcare systems fully grasp the enormous challenges encountered by vulnerable populations as a result of healthcare access barriers.1 Prior to the COVID-19 pandemic, F2F visits were most often utilized for healthcare delivery service, which frequently posed barriers for vulnerable populations. When marginalized people, encounter healthcare access barriers, a cascade of events generally occur leading to forestalling or avoiding healthcare services entirely, complicating disease management, resulting in negative health outcomes. This was a novel study examining the hemoglobin A1c (HbA1c) values of 111 patients with uncontrolled type 2 diabetes mellitus (T2DM) and 81 patients with prediabetes. Retrospective electronic patient health records (PHR) from a medical clinic were examined from January 1st, 2019, to June 30th, 2021. The results indicate that lowering HbA1c values for T2DM patients through utilizing TM is similar to outcomes from traditional visits, suggesting that TM may be an alternative mode of healthcare delivery for vulnerable populations. Results for patients with prediabetes were not statistically significant. Patients with uncontrolled diabetes and prediabetes shared a number of similar characteristics; they were predominantly Black, non-Hispanic, females, with a median age of 57 years; and resided in locations with inadequate access to healthcare services in an UMUPA. The majority of patients with uncontrolled diabetes who reside in an UMUPA completed appointments utilized TM technology, lending credence to its potential as an alternative healthcare delivery service for underserved populations. TM technology supports PH and the healthcare system with a viable, alternative strategy for expanding healthcare access where chronic illness and disease pose a significant threat to the health and wellbeing of vulnerable groups. Optimal treatment for patients with diabetes necessitates a proactive, coordinated, systems-thinking team approach. This research supports PH’s endeavors in tackling the long-standing healthcare access barrier challenges in underserved populations

Ecology and emergence: Understanding factors that drive variation in process quality and clinical outcomes in general practice

Clinical practice variation (CPV), where differences in healthcare delivery do not reflect differences in patient preferences or clinical need, is considered a hallmark of poor quality care. 'Unwarranted' variation is the focus of mounting policy attention and a growing body of literature, but remains poorly explained and theorised, with ways of determining when variation is warranted only weakly developed. Many assertions around CPV remain under-explored and untested. Much of the literature operates on the assumption that the legitimacy of variation depends on its source or cause, and that variation in processes of care will lead to related variation in outcomes. This doctoral research focuses on two overarching questions relating to CPV in Australian general practice: (1) what is CPV, and how can it be best conceptualised and understood; and (2) what can routinely-collected clinical data tell us about the phenomenon of CPV in general practice? Accordingly, this thesis explores the operationalisation of CPV as a theoretical construct and also examines variation in a series of clinical performance measures for coronary heart disease (CHD) and diabetes. Together, these lines of inquiry constitute a mixed-methods 'sense-making' exercise that seeks an incremental interplay between literature and data, to shed light on the phenomenon of CPV. Data are drawn from a unique dataset of aggregate reporting metrics, using extracted electronic medical record data, among an affiliated group of 36 general practice clinics serving approximately 189,848 patients over a 5-year period. These data are examined descriptively and ultimately analysed using Qualitative Comparative Analysis (QCA) against an empirically derived explanatory framework. Theory development draws on complexity science, especially complex adaptive systems theory, and the disciplines of social epidemiology and health ecology. Results show that a series of discourses have strongly shaped thinking about CPV, converging around a normative 'bad apples' approach to understanding variation. However, CPV may also contribute to healthcare quality in ways that are not well considered, especially in primary care settings. I demonstrate that there may be unconventional but more illuminating ways to conceptualise variation that enable our collective understanding to progress. These include using an ecological framework to conceive CPV as an emergent property of coupled, complex adaptive systems, and employing an equity lens to distinguish between CPV in processes and outcomes of care. In descriptive analyses, I find that variation frequently behaves differently across different measures, with crucial system information contained in the interstices of the data. Contrary to common assumptions, relationships between processes and outcomes of care are not straightforward. Using a framework of factors associated with CPV in general practice management of diabetes and CHD, I confirm that causality is complex and multifactorial, operating at a number of levels. Employing the case-based configurational method of QCA, I show that there may be no single or primary cause for CPV. Instead, clinics can arrive at a particular outcome via multiple independent causal pathways which are themselves multifactorial. These multi-component causes may be defined as much by the interactions between component elements as by individual elements themselves. The same factor may have differential effects within different combinations, or at different scales. These findings suggest that relying on causal explanations to demarcate unwarranted variation may be insufficient. However, both theory and methods require continued development to ensure an adequate understanding of the role and representation of warranted and unwarranted variation in performance measurement systems. Case-based configurational methods such as QCA may have substantial utility in helping to explain and delineate these phenomena