The 2009 American Recovery and Reinvestment Act, signed under the Obama administration, mandated physicians to complete certification for electronic medical records (EMRs). Despite these mandates and the increased access to information technology, slow adoption rates persist on the use of EMRs. Guided by the theory of planned behavior and the technology acceptance model, the purpose of this quantitative study was to examine the relationship between the independent variables perceived ease of use, perceived usefulness, perceived behavioral control, perceived social influence, attitudes toward EMR, and the dependent variable user acceptance. This study identified physicians in the United States as end-users of EMRs. In this study, 76 randomly selected physicians in the United States, identified as end-users of EMRs, completed an electronic survey requiring responses to a 5-point Likert Scale model. Standard multiple regression analysis served as the means used to analyze the regression model. Despite the regression model being statistically significant, none of the individual independent variables had statistical significance in predicting user acceptance. Interdependence and homoscedasticity likely contributed to this phenomenon. Social change implications include understanding of physician perceptions and beliefs--how physician perceptions and beliefs affect EMR adoption. Because adoption rates did not achieve 100% certification by end-users, another social change implication includes the necessity of examining how end-user acceptance could decrease medical errors, increase efficiencies in physician workload, and improve communication within the health care industry.

Background: At a primary care center in Brooklyn, New York, approximately 27% of diabetic patients with abnormal Hgb A1C fail to return for follow-up appointments, as recommended by the Centers for Disease Control and Prevention (CDC). According to electronic medical records (EMR), healthcare providers demonstrated inconsistency in ordering and monitoring Hgb A1C and clinic follow-up appointments for patients. Purpose: The purpose of this quality improvement project was to determine retrospectively the healthcare providers’ ordering, monitoring, and follow-up appointments for adult diabetic patients with abnormal Hgb A1Cs; to develop and implement astandardized process for healthcare providers to monitor and follow these patients, especially those with possible nonclinic follow-up compliance and abnormal Hgb A1C; to determine prospectively healthcare providers’ ordering, monitoring, and follow-up appointments; and to evaluate the prospective charts to determine if Hgb AIC results changed from abnormal to normal or elevation over time until the next follow-up appointment.

Indiana University-Purdue University Indianapolis (IUPUI) / With the availability of electronic medical records, medication dispensing data offers an unprecedented opportunity for researchers to explore complex relationships among longterm medication use, disease progression and potential side-effects in large patient populations. However, these data also pose challenges to existing statistical models because both medication exposure status and its intensity vary over time. This dissertation focused on flexible models to investigate the association between time-varying exposures and different types of outcomes. First, a penalized functional regression model was developed to estimate the effect of time-varying exposures on multivariate longitudinal outcomes. Second, for survival outcomes, a regression spline based model was proposed in the Cox proportional hazards (PH) framework to compare disease risk among different types of time-varying exposures. Finally, a penalized spline based Cox PH model with functional interaction terms was developed to estimate interaction effect between multiple medication classes. Data from a primary care patient cohort are used to illustrate the proposed approaches in determining the association between antidepressant use and various outcomes. / NIH grants, R01 AG019181 and P30 AG10133.

Real-world evidence (RWE) plays a crucial role in understanding the impact of medical interventions and uncovering disparities in clinical practice. However, confounding bias, especially unmeasured confounding, poses challenges to inferring causal relationships from observational data, such as estimating treatment effects and treatment responses. Various methods have been developed to reduce confounding bias, including methods specific for detecting and adjusting for unmeasured confounding. However, these methods typically rely on assumptions that are either untestable or too strong to hold in practice. Some methods also require domain knowledge that is rarely available in medicine. Despite recent advances in method development, the challenge of unmeasured confounding in observational studies persists. This dissertation provides insights in adjusting for unmeasured confounding by exploiting correlations within electronic health records (EHRs). In Aim 1, we demonstrate a novel use of probabilistic model for inferring unmeasured confounders from drug co-prescription pattern. In Aim 2, we provide theoretical justifications and empirical evidence that adjusting for all (pre-treatment) covariates without explicitly selecting for confounders, as implemented in the large-scale propensity score (LSPS) method, offers a more robust approach to mitigating unmeasured confounding. In Aim 3, we shift focus to the problem of evaluating fairness of treatment allocation in clinical practice from a causal perspective. We develop a causal fairness algorithm for assessing treatment allocation. By applying this fairness analysis method to a cohort of patients with coronary artery disease from EHR data, we uncover disparities in treatment allocation based on gender and race, highlighting the importance of addressing fairness concerns in clinical practice. Furthermore, we demonstrate that social determinants of health, variables that are often unavailable in EHR databases and are potential unmeasured confounders, do not significantly impact the estimation of treatment responses when conditioned on clinical features from EHR data, shedding light on the intricate relationship between EHR features and social determinants of health. Collectively, this dissertation contributes valuable insights into addressing unmeasured confounding in the context of evidence generation from EHRs. These findings have significant implications for improving the reliability of observational studies and promoting equitable healthcare practices.

Indiana University-Purdue University Indianapolis (IUPUI) / Acceptance of PHR by physicians is fundamental as they play important role towards the promotion of PHR adoption by providing the access to the data to be maintained in PHR and also, using the information within the PHR for decision making. Therefore it is important to measure physicians' perspective on usefulness of PHR, and also the value and trust they have in PHR usage. Review of previous researches identifies the lack of availability of a valid survey instrument that can be used to measure physicians' perception on all different aspects of PHR use and acceptance. Using the integrated literature review methodology and Unified Theory of Acceptance and Use of Technology (UTAUT) as a guiding framework, this study was aimed to identify the factors that can be used in the development of comprehensive evaluation instrument to understand physicians' acceptance of PHR. Total 15 articles were selected for literature review and using the content analysis method, 189 undifferentiated data units were extracted from those articles. These data units were then categorized into the four core constructs of UTAUT. ―Other categorization system was also created for the data units that could not be classified into one of the UTAUT core constructs. Among four core UTAUT constructs, Performance Expectancy is found to be the most influential factor in physicians' acceptance of PHR, followed by ―Other factors, Facilitating Condition and Social Influence. Effort expectancy was found to be the least influential. The identified specific factors within each domain can be used to develop a valid survey instrument to measure physicians' perception on PHR.

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Тема ВКР Разработка и внедрение программного модуля "Паспорт здоровья сотрудников" в систему "1С:Предприятие" на производственном предприятии. Целью данной диссертационной работы является разработка и внедрение модуля ведения электронных медицинских карт сотрудников производственного предприятия на платформе «1С Предприятие 8.3». В результате магистерского исследования решены ряд задач: рассмотрены существующие медицинские системы, а также проведен анализ их возможностей, спроектирован и разработан модуль ведения паспортов здоровья на базе 1С: Предприятие, проведено внедрение программного продукта. / Theme Development and implementation of the program module "Passport of health of employees" in the system "1C:Enterprise" in the manufacturing plant. The aim of this thesis is to develop and implement a module of electronic medical records of employees of the production enterprise on the platform "1C Enterprise 8.3". As a result of the master's study, a number of tasks were solved: the existing medical systems were considered, their capabilities were analyzed, a module for maintaining health passports was designed and developed on the basis of 1C: Enterprise, the introduction of the software product was carried out.

Real world data have enhanced healthcare research, improving our understanding of disease progression, aiding in diagnosis, and enabling the development of personalized and targeted treatments. In recent years, multi-omics data and electronic health record (EHR) data have become increasingly available, providing researchers with a wealth of information to analyze. The use of machine learning methods with EHR and multi-omics data has emerged as a promising approach to extract valuable insights from these complex data sources. This dissertation focuses on the development of supervised and unsupervised learning methods, as well as their applications to EHR and multi-omics data, with a particular emphasis on early detection of clinical outcomes and identification of novel cancer subtypes. The first part of the dissertation centers on developing a risk prediction tool using EHR data that enables disease early detection so that preventive treatments can be taken to better manage the disease. For this goal, we developed a similarity-based supervised learning method with two applications to predict end-stage kidney disease (ESKD) and aortic stenosis (AS). In the second part of the dissertation, we expanded our goal to a phenome-wide prediction task and developed a patient representation based deep learning method that is able to predict phenotypes across the phenome. Through a weighting scheme, this approach is conducting tailored disease phenotype prediction computationally efficiently with good prediction performance. In the final part of the dissertation, I shifted the focus with the goal to identify clinical meaningful novel disease subtypes with unsupervised learning methods using multi-omics data. We tackled this goal through integrating multiple patient graphs being generated from multiple omics data with molecular level features for an improved disease subtyping. This dissertation has significantly contributed to the development of data-driven approaches to healthcare and biomedical research using EHR data and multi-omics data. The new methodologies developed with applications in multiple diseases using EHR and multi-omics data advanced our knowledge in disease diagnosis, vulnerable groups identification, and ultimately improve patient care.

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