Scoping Review: The Empowerment of Alzheimer’s Disease Caregivers with mHealth Applications

Alzheimer’s Disease (AD) is one of the most prevalent neurodegenerative chronic diseases. As it progresses, patients become increasingly dependent, and their caregivers are burdened with the increasing demand for managing their care. Mobile health (mHealth) technology, such as smartphone applications, can support the need of these caregivers. This paper examines the published academic literature of mHealth applications that support the caregivers of AD patients. Following the PRISMA for scoping reviews, we searched published literature in five electronic databases between January 2014 and January 2021. Twelve articles were included in the final review. Six themes emerged based on the functionalities provided by the reviewed applications for caregivers. They are tracking, task management, monitoring, caregiver mental support, education, and caregiver communication platform. The review revealed that mHealth applications for AD patients’ caregivers are inadequate. There is an opportunity for industry, government, and academia to fill the unmet need of these caregiver

Operationalizing Healthcare Big Data in the Electronic Health Records using a Heatmap Visualization Technique

Background: The majority of the electronic health record (EHR) contains a wealth of information, including unstructured notes. Healthcare professionals may be missing substantial portions of essential diagnostic and treatment information by not focusing on unstructured texts. The objective of this study is to present progress notes data using heatmap visualization. Methods: In this study, the research team used the unstructured text from the progress notes of deidentified patient data. The research team conducted qualitative content-coding based on the clinical complexity model and developed a heatmap based on the processed frequency data. Result: The researchers developed a color-coded heatmap focusing on the severity and acuity of patients’ status accumulated through multiple previous patient’s visits. Conclusions: Future research into creating an automated process to generate the heatmap from an unstructured dataset can open up opportunities to operationalize big data in healthcare

Pharmacogenomics Cascade Testing (PhaCT): A Novel Approach for Preemptive Pharmacogenomics Testing to Optimize Medication Therapy

The implementation of pharmacogenomics (PGx) has come a long way since the dawn of utilizing pharmacogenomic data in clinical patient care. However, the potential benefits of sharing PGx results have yet to be explored. In this paper, we explore the willingness of patients to share PGx results, as well as the inclusion of family medication history in identifying potential family members for pharmacogenomics cascade testing (PhaCT). The genetic similarities in families allow for identifying potential gene variants prior to official preemptive testing. Once a candidate patient is determined, PhaCT can be initiated. PhaCT recognizes that further cascade testing throughout a family can serve to improve precision medicine. In order to make PhaCT feasible, we propose a novel shareable HIPAA-compliant informatics platform that will enable patients to manage not only their own test results and medications but also those of their family members. The informatics platform will be an external genomics system with capabilities to integrate with patients’ electronic health records. Patients will be given the tools to provide information to and work with clinicians in identifying family members for PhaCT through this platform. Offering patients the tools to share PGx results with their family members for preemptive testing could be the key to empowering patients. Clinicians can utilize PhaCT to potentially improve medication adherence, which may consequently help to distribute the burden of health management between patients, family members, providers, and payers

Artificial Intelligence–Powered Smartphone App to Facilitate Medication Adherence: Protocol for a Human Factors Design Study

Background: Medication Guides consisting of crucial interactions and side effects are extensive and complex. Due to the exhaustive information, patients do not retain the necessary medication information, which can result in hospitalizations and medication nonadherence. A gap exists in understanding patients’ cognition of managing complex medication information. However, advancements in technology and artificial intelligence (AI) allow us to understand patient cognitive processes to design an app to better provide important medication information to patients. Objective: Our objective is to improve the design of an innovative AI- and human factor–based interface that supports patients’ medication information comprehension that could potentially improve medication adherence. Methods: This study has three aims. Aim 1 has three phases: (1) an observational study to understand patient perception of fear and biases regarding medication information, (2) an eye-tracking study to understand the attention locus for medication information, and (3) a psychological refractory period (PRP) paradigm study to understand functionalities. Observational data will be collected, such as audio and video recordings, gaze mapping, and time from PRP. A total of 50 patients, aged 18-65 years, who started at least one new medication, for which we developed visualization information, and who have a cognitive status of 34 during cognitive screening using the TICS-M test and health literacy level will be included in this aim of the study. In Aim 2, we will iteratively design and evaluate an AI-powered medication information visualization interface as a smartphone app with the knowledge gained from each component of Aim 1. The interface will be assessed through two usability surveys. A total of 300 patients, aged 18-65 years, with diabetes, cardiovascular diseases, or mental health disorders, will be recruited for the surveys. Data from the surveys will be analyzed through exploratory factor analysis. In Aim 3, in order to test the prototype, there will be a two-arm study design. This aim will include 900 patients, aged 18-65 years, with internet access, without any cognitive impairment, and with at least two medications. Patients will be sequentially randomized. Three surveys will be used to assess the primary outcome of medication information comprehension and the secondary outcome of medication adherence at 12 weeks. Results: Preliminary data collection will be conducted in 2021, and results are expected to be published in 2022. Conclusions: This study will lead the future of AI-based, innovative, digital interface design and aid in improving medication comprehension, which may improve medication adherence. The results from this study will also open up future research opportunities in understanding how patients manage complex medication information and will inform the format and design for innovative, AI-powered digital interfaces for Medication Guides

The Inclusion of Health Data Standards in the Implementation of Pharmacogenomics Systems: A Scoping Review

Background: Despite potential benefits, the practice of incorporating pharmacogenomics (PGx) results in clinical decisions has yet to diffusewidely. In this study,we conducted a review of recent discussions on data standards and interoperability with a focus on sharing PGx test results among health systems. Materials & methods:We conducted a literature search for PGx clinical decision support systems between 1 January 2012 and 31 January 2020. Thirty-two out of 727 articles were included for the final review. Results: Nine of the 32 articles mentioned data standards and only four of the 32 articles provided solutions for the lack of interoperability. Discussions: Although PGx interoperability is essential for widespread implementation, a lack of focus on standardized data creates a formidable challenge for health information exchange. Conclusion: Standardization of PGx data is essential to improve health information exchange and the sharing of PGx results between disparate systems. However, PGx data standards and interoperability are often not addressed in the system-level implementation

Improving Medication Information Presentation Through Interactive Visualization in Mobile Apps: Human Factors Design

Background: Despite the detailed patient package inserts (PPIs) with prescription drugs that communicate crucial information about safety, there is a critical gap between patient understanding and the knowledge presented. As a result, patients may suffer from adverse events. We propose using human factors design methodologies such as hierarchical task analysis (HTA) and interactive visualization to bridge this gap. We hypothesize that an innovative mobile app employing human factors design with an interactive visualization can deliver PPI information aligned with patients’ information processing heuristics. Such an app may help patients gain an improved overall knowledge of medications. Objective: The objective of this study was to explore the feasibility of designing an interactive visualization-based mobile app using an HTA approach through a mobile prototype. Methods: Two pharmacists constructed the HTA for the drug risperidone. Later, the specific requirements of the design were translated using infographics. We transferred the wireframes of the prototype into an interactive user interface. Finally, a usability evaluation of the mobile health app was conducted. Results: A mobile app prototype using HTA and infographics was successfully created. We reiterated the design based on the specific recommendations from the usability evaluations. Conclusions: Using HTA methodology, we successfully created a mobile prototype for delivering PPI on the drug risperidone to patients. The hierarchical goals and subgoals were translated into a mobile prototype

Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design

Background: According to the September 2015 Institute of Medicine report, Improving Diagnosis in Health Care, each of us is likely to experience one diagnostic error in our lifetime, often with devastating consequences. Traditionally, diagnostic decision making has been the sole responsibility of an individual clinician. However, diagnosis involves an interaction among interprofessional team members with different training, skills, cultures, knowledge, and backgrounds. Moreover, diagnostic error is prevalent in the interruption-prone environment, such as the emergency department, where the loss of information may hinder a correct diagnosis. Objective: The overall purpose of this protocol is to improve team-based diagnostic decision making by focusing on data analytics and informatics tools that improve collective information management. Methods: To achieve this goal, we will identify the factors contributing to failures in team-based diagnostic decision making (aim 1), understand the barriers of using current health information technology tools for team collaboration (aim 2), and develop and evaluate a collaborative decision-making prototype that can improve team-based diagnostic decision making (aim 3). Results: Between 2019 to 2020, we are collecting data for this study. The results are anticipated to be published between 2020 and 2021. Conclusions: The results from this study can shed light on improving diagnostic decision making by incorporating diagnostics rationale from team members. We believe a positive direction to move forward in solving diagnostic errors is by incorporating all team members, and using informatics