Background: Patient experience is a crucial measure of patient-centeredness and quality care delivery. Digital health may contribute to patient experience by offering tailored and accessible avenues of care. Purpose: I explored how access to digital health, including telehealth, electronic health records, and online booking, may be associated with improved primary care experience for Ontario adults. Methods: This cross-sectional study included Ontario adults (16 years or older) who responded to waves 27 to 29 of the Health Care Experience Survey (HCES) between May 2019 and February 2020. Adults who did not see their primary care provider within the past 12 months or did not have a primary care provider were excluded. Outcomes included a summed patient experience score derived from five HCES experience-related questions and time to appointment for a health concern. Associations between outcomes and digital health interventions were tested through chi-square tests and logistic regression while adjusting for confounders and stratifying by health care utilization. Results: 3,700 participants met the inclusion criteria, where 2204 remotely communicated with their primary care provider (59.6%), 98 digitally accessed health records (2.6%), and 120 booked an appointment online (3.2%). We observed no significant associations between digital health tools and patient experience or time to appointments through chi-square tests. Participants with over three primary care visits in the past year who accessed online booking were 84% less likely to report poorer experience scores than participants without online booking access [Adjusted OR 0.16, 95% CI 0.02 – 0.56, p < 0.05]. Participants with three or fewer primary care encounters who accessed online booking, compared to the same reference group, were 72% less likely to report having a same or next day appointment with their primary care provider [Adjusted OR 0.25, 95% CI 0.08 – 0.64, p < 0.01]. Significant associations were observed between other sociodemographic factors and patient experience and access to care outcomes. Interpretation: The associations between digital health access and patient experience and access to care were inconsistent across different analyses. Despite experimental studies observing the benefits of digital health adoption in primary care, the effect is unclear in the real-world context. Furthermore, drawing conclusions on the relationship between digital health and quality care outcomes was limited due to the lack of adoption of digital health before the COVID-19 pandemic. As digital health adoption grows, future research should utilize the availability of further data to evaluate the effectiveness of digital health in Ontario primary care. / Thesis / Master of Science (MSc) / Patient outcomes such as experience and timeliness of care are frequently viewed as aims of quality health care. Although past studies indicate digital health supports quality care, the real-world effectiveness of digital health is underexplored in Ontario. This thesis aimed to explore relationships between real-world use of digital health in Ontario and primary care experience and access using survey data. This study found very few survey respondents used digital health before the COVID-19 pandemic. The primary care experience and access to care of adults who did use digital health did not differ very much from adults who did not use the technology. Some outcomes differed in adults who booked their primary care appointment online compared to those who did not; however, the study could not conclude on the relationship. Other personal factors such as age and residence area impacted the quality of primary care. This study was limited due to the lack of digital health users. Future studies should explore digital health's impact on patient outcomes beyond the pandemic.

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<p>In spite of numerous benefits that are suggested for consumers’ utilizing integrated personal health record (PHR) systems, research has shown that these systems are not yet popular or well known to consumers. Therefore, research is needed to understand what would rise adoption rates for these systems. Hence, the main objective of this dissertation is to develop and empirically validate a theoretical model for explaining consumers’ intention to use integrated PHR systems.</p> <p>In developing the theoretical model of this dissertation, theories of information systems adoption were integrated with Self-Determination Theory (SDT), which is a well established theory from the Psychology literature that explains the mechanism through which individuals become more self-determined, i.e., motivated to take more active (rather than passive) roles in undertaking different behaviours. Taking such an active role by consumers, in the context of personal health management, is suggested to be necessary for realizing the full benefits of integrated PHR systems.</p> <p>The proposed theoretical model was validated using the PLS approach to structural equation modeling, on data collected from a cross-sectional survey involving 159 participants with no prior experience in using PHR systems. A stratified random sampling was employed to draw a representative sample of the Canadian population. The results show that consumers with higher levels of self-determination in managing their health are more likely to adopt integrated PHR systems since they have more positive perceptions regarding the use of such systems. Further, such self-determination is fueled by autonomy support from consumers’ physicians as well as consumers’ personality trait of autonomy orientation.</p> <p>This study advances the theoretical understanding of integrated PHR system adoption, and it contributes to practice by providing insightful implications for designing, promotion, and facilitating the use of integrated PHR systems among consumers.</p> / Doctor of Philosophy (PhD)

<p>Pregnancy is one of the most important periods of a woman’s life, during which lots of potentially worrying changes occur in her body. Being aware of the nature of these changes can help her to make informed decisions and decrease her level of uncertainty and anxiety. Delivering information to pregnant women to help understand these changes is not a new idea. Brief searches of the web turned up many related resources and information. One important aspect of pregnancy that was found to be widely used was keeping daily records in a paper-based format. However, to the author’s best knowledge, there is no pregnancy specific electronic personal health record (ePHR) currently being used in Canada. In this study, a preliminary pregnancy specific PHR module was developed, and its usefulness and usability evaluated.</p> / Master of Science (MSc)

Electronic health record (EHR) systems are used by medical providers to streamline the workflow and enable sharing of patient data with different providers. Beyond that primary purpose, EHR data have been used in healthcare research for exploratory and predictive analytics. EHR data are heterogeneous collections of both structured and unstructured information. In order to store data in a structured way, several ontologies have been developed to describe diagnoses and treatments. On the other hand, the unstructured clinical notes contain various more nuanced information about patients. The multidimensionality and complexity of EHR data pose many unique challenges and problems for both data mining and medical communities. In this thesis, we address several important issues and develop novel deep learning approaches in order to extract insightful knowledge from these data. Representing words as low dimensional vectors is very useful in many natural language processing tasks. This idea has been extended to medical domain where medical codes listed in medical claims are represented as vectors to facilitate exploratory analysis and predictive modeling. However, depending on a type of a medical provider, medical claims can use medical codes from different ontologies or from a combination of ontologies, which complicates learning of the representations. To be able to properly utilize such multi-source medical claim data, we propose an approach that represents medical codes from different ontologies in the same vector space. The new approach was evaluated on the code cross-reference problem, which aims at identifying similar codes across different ontologies. In our experiments, we show the proposed approach provide superior cross-referencing when compared to several existing approaches. Furthermore, considering EHR data also contain unstructured clinical notes, we also propose a method that jointly learns medical concept and word representations. The jointly learned representations of medical codes and words can be used to extract phenotypes of different diseases. Various deep learning models have recently been applied to predictive modeling of Electronic Health Records (EHR). In EHR data, each patient is represented as a sequence of temporally ordered irregularly sampled visits to health providers, where each visit is recorded as an unordered set of medical codes specifying patient's diagnosis and treatment provided during the visit. We propose a novel interpretable deep learning model, called Timeline. The main novelty of Timeline is that it has a mechanism that learns time decay factors for every medical code. We evaluated Timeline on two large-scale real world data sets. The specific task was to predict what is the primary diagnosis category for the next hospital visit given previous visits. Our results show that Timeline has higher accuracy than the state of the art deep learning models based on RNN. Clinical notes contain detailed information about health status of patients for each of their encounters with a health system. Developing effective models to automatically assign medical codes to clinical notes has been a long-standing active research area. Considering the large amount of online disease knowledge sources, which contain detailed information about signs and symptoms of different diseases, their risk factors, and epidemiology, we consider Wikipedia as an external knowledge source and propose Knowledge Source Integration (KSI), a novel end-to-end code assignment framework, which can integrate external knowledge during training of any baseline deep learning model. To evaluate KSI, we experimented with automatic assignment of ICD-9 diagnosis codes to clinical notes, aided by Wikipedia documents corresponding to the ICD-9 codes. The results show that KSI consistently improves the baseline models and that it is particularly successful in rare codes prediction. / Computer and Information Science

Rates of immune-mediated diseases (IMDs) have rapidly increased. Although the exact etiology has not yet been fully elucidated, disruptions to the microbiome has been proposed as a potential mechanism. We conducted a retrospective, longitudinal, birth cohort study utilizing electronic health records (EHR) to investigate the association between early life antibiotic exposure and the risk of developing juvenile idiopathic arthritis (JIA), pediatric psoriasis, or type 1 diabetes. Incident rate ratios (IRR) were estimated using modified Poisson regression models and adjusted for significant confounders. Children exposed to two or more antibiotics prior to 12 months of age had a 69% increased risk of developing JIA (1.69 IRR, 95% CI [1.04-2.73]), which rose to 97% when exposed prior to 6 months (1.97 IRR, 95% CI [1.11-3.49]). Children exposed to a penicillin antibiotic had a 62% increase in risk for psoriasis (1.62 IRR, 95% CI [1.06-2.49]), which rose slightly to 64% when exposure occurred between 6 and 12 months of age [(1.64 IRR, 95% CI [1.04-2.59]). We found a moderate to strong association between early antibiotic exposure and risk for JIA and psoriasis when exposure was examined by age, frequency, and type of antibiotic, but not for type 1 diabetes. Potential interactions effects between infection and antibiotics with an increased susceptibility to early life infections among children with an IMD was also observed. Overall, children exposed to antibiotics at an early age have an increased probability of developing an IMD after 12 months of age. However, alternative explanations for this association should be considered. / Public Health

A new hospital in United Arab Emirates (UAE) plans to adopt health information technology (HIT) and become fully digitalized once operational. The hospital has identified a need to assess basic computer literacy of new employees prior to offering them training on various HIT applications. Lack of research in identifying an accurate assessment method for basic computer literacy among health care professionals led to this explanatory correlational research study, which compared self-assessment scores and a simulated actual computer skills test to find an appropriate tool for assessing computer literacy. The theoretical framework of the study was based on constructivist learning theory and self-efficacy theory. Two sets of data from 182 hospital employees were collected and analyzed. A t test revealed that scores of self-assessment were significantly higher than they were on the actual test, which indicated that hospital employees tend to score higher on self-assessment when compared to actual skills test. A Pearson product moment correlation revealed a statistically weak correlation between the scores, which implied that self-assessment scores were not a reliable indicator of how an individual would perform on the actual test. An actual skill test was found to be the more reliable tool to assess basic computer skills when compared to self-assessment test. The findings of the study also identified areas where employees at the local hospital lacked basic computer skills, which led to the development of the project to fill these gaps by providing training on basic computer skills prior to them getting trained on various HIT applications. The findings of the study will be useful for hospitals in UAE who are in the process of adopting HIT and for health information educators to design appropriate training curricula based on assessment of basic computer literacy.

Before market authorization of new medicines, their efficacy and safety are evaluated using randomized controlled trials. While there is no doubt about the scientific value of randomized trials, they are usually conducted in selected populations with questionable generalizability to routine care.  In the digital data revolution era, with healthcare data growing at an unprecedented rate, drug monitoring in routine care is still highly under-utilized. Although many countries have access to data on prescription drugs at the individual level in ambulatory care, such data are often missing for hospitals. This is a growing problem considering the clear trend towards more new and expensive drugs administered in the hospital setting. The aim of this thesis was therefore to develop methods for extracting data on drug use from a hospital-based electronic health record system and further to build and evaluate models for real-time monitoring of effectiveness and safety of new drugs in routine care using data from electronic health records and regional and national health care registers. Using the developed techniques, we were able to demonstrate drug use and health service utilization for inflammatory bowel disease and to evaluate the comparative effectiveness and safety of antiarrhythmic drugs. With a rapidly evolving drug development, it is important to optimize the evaluation of effectiveness, safety and health economic value of new medicines in routine care. We believe that the models described in this thesis could contribute to fulfil this need.