Improving Patients\u27 Understanding of their Electronic Medical Record Data in Order to Improve Self-Management - A Quality Improvement Project

Background: Patients are increasingly given access to their electronic medical records (EMRs) to help them keep track of their care, but many may have a difficult time understanding what is in them. Programs such as NoteAid assist in translating medical records and may increase the number of patients who actively use their EMRs, a development which may improve the management of chronic diseases. Purpose: To work on a translation system developed by the University of Massachusetts Informatics group to make outpatient records more understandable for adult patients with chronic disease by using and testing a machine-learning database (NoteAid). Patients’ self-management of chronic disease may improve, as they increase their understanding of medical terminology. Methods: A test version of NoteAid was used with volunteer adult patients during face-to-face sessions in an outpatient office at a health system in Southeastern Pennsylvania. These sessions were used to test NoteAid’s effectiveness as a tool to improve patients’ understanding of their EMRs. Patients read their own office note from a recent visit without the use of NoteAid, and then interpreted the same note using it. Results: 13 participants participated over a two-month period with 85% reporting they would use the system from a patient portal and 100% answering strongly agree or agree when asked if the NoteAid system helped them comprehend their clinical EMR notes. Conclusions: Machine-learning databases like NoteAid have the potential to improve the management of chronic diseases. By integrating these systems into an informative and user-friendly portal, patients are afforded the opportunity to improve understanding of their EMRs. Keywords: medical terms, patient understanding, health literacy, chronic disease, and electronic health record usabilit

Adverse Drug Event Detection, Causality Inference, Patient Communication and Translational Research

Adverse drug events (ADEs) are injuries resulting from a medical intervention related to a drug. ADEs are responsible for nearly 20% of all the adverse events that occur in hospitalized patients. ADEs have been shown to increase the cost of health care and the length of stays in hospital. Therefore, detecting and preventing ADEs for pharmacovigilance is an important task that can improve the quality of health care and reduce the cost in a hospital setting. In this dissertation, we focus on the development of ADEtector, a system that identifies ADEs and medication information from electronic medical records and the FDA Adverse Event Reporting System reports. The ADEtector system employs novel natural language processing approaches for ADE detection and provides a user interface to display ADE information. The ADEtector employs machine learning techniques to automatically processes the narrative text and identify the adverse event (AE) and medication entities that appear in that narrative text. The system will analyze the entities recognized to infer the causal relation that exists between AEs and medications by automating the elements of Naranjo score using knowledge and rule based approaches. The Naranjo Adverse Drug Reaction Probability Scale is a validated tool for finding the causality of a drug induced adverse event or ADE. The scale calculates the likelihood of an adverse event related to drugs based on a list of weighted questions. The ADEtector also presents the user with evidence for ADEs by extracting figures that contain ADE related information from biomedical literature. A brief summary is generated for each of the figures that are extracted to help users better comprehend the figure. This will further enhance the user experience in understanding the ADE information better. The ADEtector also helps patients better understand the narrative text by recognizing complex medical jargon and abbreviations that appear in the text and providing definitions and explanations for them from external knowledge resources. This system could help clinicians and researchers in discovering novel ADEs and drug relations and also hypothesize new research questions within the ADE domain

Consumer Health Information Needs, Seeking and Searching Behavior By Rural Residents in the Kachia Grazing Reserve, with a Focus on Vector-borne Diseases

Information is considered the basic material for making decisions. People from all walks of life have information needs for business and personal use. Consumer Health Information (CHI) is an emerging form of information made accessible to the layperson. It is a simplified form of information from the types of information available to medical professionals. This study examines the health information behavior of the residents of one region in the Kachia Grazing Reserve (KGR) located in the North West of the six geopolitical zones of Nigeria. This dissertation explores the health information needs, seeking and searching behavior of the residents of selected communities that are affected by two vector-borne fly diseases in Nigeria. Insects such as flies are responsible for the transmission of diseases to humans, including trypanosomiasis, caused by the tsetse fly, and malaria, caused by mosquitos. These flies are commonly found in and affect mostly rural dwellers in Nigeria. This study investigates some of the broader contextual issues that may influence consumer health care needs as well as seeking-searching behavior. It asks participants whether they believe their health information needs are being met or not. The study applied a qualitative approach to sampling 50 adult participants. It relied on a triangulation data collection method using a questionnaire, interview instrument, and focus group discussion. NVivo version 12 was used in the data analysis to create a coding scheme following the stages of open, axial, and selective coding processes to develop a grounded theory of rural residents’ information behaviors. The findings of the research revealed various health information needs and seeking behavior the rural residents engaged in; it also revealed the factors that influenced their seeking and searching activities. Furthermore, the findings highlighted the information sources they used and the problems associated with the information-seeking and searching process. The model that was inductively derived from the grounded theory data analysis explains further in detail the strategies and processes members of the community use in their health information-seeking and health-searching behavior