COMPUTATIONAL PHENOTYPING AND DRUG REPURPOSING FROM ELECTRONIC MEDICAL RECORDS

Using electronic medical records (EMR) for research involves selecting cohorts and manipulating data for tasks like predictive analysis. Computational phenotyping for cohort characterization and stratification is becoming increasingly important for researchers to produce clinically relevant findings. There are significant amounts of time and effort devoted to manual chart abstraction by subject matter experts and researchers, which creates a large bottleneck for progress in clinical research. I focus on developing computational phenotyping pipelines, and I also focus on using EMR for drug repurposing in breast cancer. Drug repurposing is defined as the process of applying known drugs that are already on the market to new disease indications. Using EMR data for drug repurposing has the unique advantage of being able to observe a patient cohort over time and see drug effects on outcomes. In this dissertation, I present work on computational phenotyping and EMR-based drug repurposing. First, I use embedding models and foundational natural language processing methods to predict oral cancer risk with pathology notes. Second, I use natural language processing methods and transfer learning for breast cancer cohort selection and information extraction. Third, I present a pipeline for producing drug repurposing candidates from EMR and provide supporting evidence for predictions with biomedical literature and existing clinical trials.Doctor of Philosoph

Evaluating the Telehealth Experience of Patients With COVID-19 Symptoms: Recommendations on Best Practices

Positive patient experiences are associated with illness recovery and adherence to medication. To evaluate the virtual care experience for patients with COVID-19 symptoms as their chief complaints. We conducted a cross-sectional study of the first cohort of patients with COVID-19 symptoms in a virtual clinic. The main end points of this study were visit volume, wait times, visit duration, patient diagnosis, prescriptions received, and satisfaction. Of the 1139 total virtual visits, 212 (24.6%) patients had COVID-19 symptoms. The average wait time (SD) for all visits was 75.5 (121.6) minutes. The average visit duration for visits was 10.5 (4.9) minutes. The highest volume of virtual visits was on Saturdays (39), and the lowest volume was on Friday (19). Patients experienced shorter wait times (SD) on the weekdays 67.1 (106.8) minutes compared to 90.3 (142.6) minutes on the weekends. The most common diagnoses for patients with COVID-19 symptoms were upper respiratory infection. Patient wait times for a telehealth visit varied depending on the time and day of appointment. Long wait times were a major drawback in the patient experience. Based on patient-reported experience, we proposed a list of general, provider, and patient telehealth best practices

Recommendations for design of a mobile application to support management of anxiety and depression among Black American women

Black American women experience adverse health outcomes due to anxiety and depression. They face systemic barriers to accessing culturally appropriate mental health care leading to the underutilization of mental health services and resources. Mobile technology can be leveraged to increase access to culturally relevant resources, however, the specific needs and preferences that Black women feel are useful in an app to support management of anxiety and depression are rarely reflected in existing digital health tools. This study aims to assess what types of content, features, and important considerations should be included in the design of a mobile app tailored to support management of anxiety and depression among Black women. Focus groups were conducted with 20 women (mean age 36.6 years, SD 17.8 years), with 5 participants per group. Focus groups were led by a moderator, with notetaker present, using an interview guide to discuss topics, such as participants' attitudes and perceptions towards mental health and use of mental health services, and content, features, and concerns for design of a mobile app to support management of anxiety and depression. Descriptive qualitative content analysis was conducted. Recommendations for content were either informational (e.g., information to find a Black woman therapist) or inspirational (e.g., encouraging stories about overcoming adversity). Suggested features allow users to monitor their progress, practice healthy coping techniques, and connect with others. The importance of feeling “a sense of community” was emphasized. Transparency about who created and owns the app, and how users' data will be used and protected was recommended to establish trust. The findings from this study were consistent with previous literature which highlighted the need for educational, psychotherapy, and personal development components for mental health apps. There has been exponential growth in the digital mental health space due to the COVID-19 pandemic; however, a one-size-fits-all approach may lead to more options but continued disparity in receiving mental health care. Designing a mental health app for and with Black women may help to advance digital health equity by providing a tool that addresses their specific needs and preferences, and increase engagement

Patient Characteristics Associated With Phone and Video Visits at a Tele-Urgent Care Center During the Initial COVID-19 Response: Cross-Sectional Study

BackgroundHealth systems rapidly adopted telemedicine as an alternative health care delivery modality in response to the COVID-19 pandemic. Demographic factors, such as age and gender, may play a role in patients’ choice of a phone or video visit. However, it is unknown whether there are differences in utilization between phone and video visits. ObjectiveThis study aimed to investigate patients’ characteristics, patient utilization, and service characteristics of a tele-urgent care clinic during the initial response to the pandemic. MethodsWe conducted a cross-sectional study of urgent care patients using a statewide, on-demand telemedicine clinic with board-certified physicians during the initial phases of the pandemic. The study data were collected from March 3, 2020, through May 3, 2020. ResultsOf 1803 telemedicine visits, 1278 (70.9%) patients were women, 730 (40.5%) were aged 18 to 34 years, and 1423 (78.9%) were uninsured. There were significant differences between telemedicine modalities and gender (P<.001), age (P<.001), insurance status (P<.001), prescriptions given (P<.001), and wait times (P<.001). Phone visits provided significantly more access to rural areas than video visits (P<.001). ConclusionsOur findings suggest that offering patients a combination of phone and video options provided additional flexibility for various patient subgroups, particularly patients living in rural regions with limited internet bandwidth. Differences in utilization were significant based on patient gender, age, and insurance status. We also found differences in prescription administration between phone and video visits that require additional investigation

Augmenting Quality Assurance Measures in Treatment Review with Machine Learning in Radiation Oncology

Purpose: Pretreatment quality assurance (QA) of treatment plans often requires a high cognitive workload and considerable time expenditure. This study explores the use of machine learning to classify pretreatment chart check QA for a given radiation plan as difficult or less difficult, thereby alerting the physicists to increase scrutiny on difficult plans. Methods and Materials: Pretreatment QA data were collected for 973 cases between July 2018 and October 2020. The outcome variable, a degree of difficulty, was collected as a subjective rating by physicists who performed the pretreatment chart checks. Potential features were identified based on clinical relevance, contribution to plan complexity, and QA metrics. Five machine learning models were developed: support vector machine, random forest classifier, adaboost classifier, decision tree classifier, and neural network. These were incorporated into a voting classifier, where at least 2 algorithms needed to predict a case as difficult for it to be classified as such. Sensitivity analyses were conducted to evaluate feature importance. Results: The voting classifier achieved an overall accuracy of 77.4% on the test set, with 76.5% accuracy on difficult cases and 78.4% accuracy on less difficult cases. Sensitivity analysis showed features associated with plan complexity (number of fractions, dose per monitor unit, number of planning structures, and number of image sets) and clinical relevance (patient age) were sensitive across at least 3 algorithms. Conclusions: This approach can be used to equitably allocate plans to physicists rather than randomly allocate them, potentially improving pretreatment chart check effectiveness by reducing errors propagating downstream