Towards Exchanging Wearable-PGHD with EHRs: Developing a Standardized Information Model for Wearable-Based Patient Generated Health Data

Wearables have become commonplace for tracking and making sense of patient lifestyle, wellbeing and health data. Most of this tracking is done by individuals outside of clinical settings, however some data from wearables may be useful in a clinical context. As such, wearables may be considered a prominent source of Patient Generated Health Data (PGHD). Studies have attempted to maximize the use of the data from wearables including integrating with Electronic Health Records (EHRs). However, usually a limited number of wearables are considered for integration and, in many cases, only one brand is investigated. In addition, we find limited studies on integration of metadata including data quality and provenance, despite such data being very relevant for clinical decision making. This paper describes a proposed design and development of a generic information model for wearable based PGHD integration with EHRs. We propose a vendor-neutral model that can work with a wider range of wearables and discuss our proposed method to employ an ontology-based approach and provide insights to future work

The application of digital health to the assessment and treatment of substance use disorders: The past, current, and future role of the National Drug Abuse Treatment Clinical Trials Network

The application of digital technologies to better assess, understand, and treat substance use disorders (SUDs) is a particularly promising and vibrant area of scientific research. The National Drug Abuse Treatment Clinical Trials Network (CTN), launched in 1999 by the U.S. National Institute on Drug Abuse, has supported a growing line of research that leverages digital technologies to glean new insights into SUDs and provide science-based therapeutic tools to a diverse array of persons with SUDs. This manuscript provides an overview of the breadth and impact of research conducted in the realm of digital health within the CTN. This work has included the CTN\u27s efforts to systematically embed digital screeners for SUDs into general medical settings to impact care models across the nation. This work has also included a pivotal multi-site clinical trial conducted on the CTN platform, whose data led to the very first “prescription digital therapeutic” authorized by the U.S. Food and Drug Administration (FDA) for the treatment of SUDs. Further CTN research includes the study of telehealth to increase capacity for science-based SUD treatment in rural and under-resourced communities. In addition, the CTN has supported an assessment of the feasibility of detecting cocaine-taking behavior via smartwatch sensing. And, the CTN has supported the conduct of clinical trials entirely online (including the recruitment of national and hard-to-reach/under-served participant samples online, with remote intervention delivery and data collection). Further, the CTN is supporting innovative work focused on the use of digital health technologies and data analytics to identify digital biomarkers and understand the clinical trajectories of individuals receiving medications for opioid use disorder (OUD). This manuscript concludes by outlining the many potential future opportunities to leverage the unique national CTN research network to scale-up the science on digital health to examine optimal strategies to increase the reach of science-based SUD service delivery models both within and outside of healthcare

Enabling Personalized Decision Support with Patient-Generated Data and Attributable Components

Decision-making related to health is complex. Machine learning (ML) and patient generated data can identify patterns and insights at the individual level, where human cognition falls short, but not all ML-generated information is of equal utility for making health-related decisions. We develop and apply attributable components analysis (ACA), a method inspired by optimal transport theory, to type 2 diabetes self-monitoring data to identify patterns of association between nutrition and blood glucose control. In comparison with linear regression, we found that ACA offers a number of characteristics that make it promising for use in decision support applications. For example, ACA was able to identify non-linear relationships, was more robust to outliers, and offered broader and more expressive uncertainty estimates. In addition, our results highlight a tradeoff between model accuracy and interpretability, and we discuss implications for ML-driven decision support systems

Patient generated health data and electronic health record integration, governance and socio-technical issues: A narrative review

Patients’ health records have the potential to include patient generated health data (PGHD), which can aid in the provision of personalized care. Access to these data can allow healthcare professionals to receive additional information that will assist in decision-making and the provision of additional support. Given the diverse sources of PGHD, this review aims to provide evidence on PGHD integration with electronic health records (EHR), models and standards for PGHD exchange with EHR, and PGHD-EHR policy design and development. The review also addresses governance and socio-technical considerations in PGHD management. Databases used for the review include PubMed, Scopus, ScienceDirect, IEEE Xplore, SpringerLink and ACM Digital Library. The review reveals the significance, but current deficiency, of provenance, trust and contextual information as part of PGHD integration with EHR. Also, we find that there is limited work on data quality, and on new data sources and associated data elements, within the design of existing standards developed for PGHD integration. New data sources from emerging technologies like mixed reality, virtual reality, interactive voice response system, and social media are rarely considered. The review recommends the need for well-developed designs and policies for PGHD-EHR integration that promote data quality, patient autonomy, privacy, and enhanced trust

The impact of big data utilisation on Malaysian government Hospital performance

The Malaysian healthcare systems face incredible challenges as technology is being used more and more widely and citizens' expectations are increasing just as rapidly. Meeting costs and improving health outcomes would also serve as obstacles. In this context, Big Data can help providers achieve these objectives in an unparalleled manner. The Healthcare industry is adopting big data in daily operations to ensure excellent performance. However, the Malaysian government hospitals remain unable to implement Big data. Besides, previous studies relating to use of big data among Malaysian government hospitals and its implication to hospital performance is inadequate. Hence, this study examines the mediating role of use of Big data (UBD) on the relationship between hospitals performance (HP), Data quality (DQ), data integration (DI) and data governance (DG). Study framework is established from theories namely Resource Based View (RBV), extending the DeLone and Mclean IS Success Model (D&M ISSM). Data was collected from Malaysian government hospitals. Total questionnaires of 560 were distributed and 212 were responded. The convenience sampling technique was used. Hypotheses tests were performed via Smart PLS 3.9. Results show DQ and DI have significant direct relationships with the UBD. However, DG is not significant with UBD. Findings on use of big data as a mediating variable reveal DQ and DI have significant direct relationship with UBD except DG. Findings provide important insights to Government, policy-makers and researchers to further understand the use of big data to enhance hospitals performance in Malaysia. Organisations are struggling to fulfill all their expected big data related analysis skills in the workplace. Failure to interpret the produced reports in this respect may lead to serious misjudgements and doubtful decisions. This study focused solely on the performance of Government hospitals in Malaysia. There is a need to investigate the performance of other types of hospitals and clinics (Clinicals and Specialist centers), such as private hospitals, clinics and specialist hospitals. As a result, the analysis is constrained by the fact that hospitals or treatment center characteristics vary depending on the form of facility and funding in the healthcare sector. Future research could look into hospital performance and big data technologies in other parts of the world, as well as other sector activities, which could provide more in-depth information. Besides, Future research can also explore how and why big data capacity contributes towards improvement of some IT-enabled transformation activities by means of thorough single or multiple case studies. This is especially true of the most frequent value chain, which leads to profitability from analytical capacity from concrete evidence medicine and IT infrastructure advantages

Enabling Automated, Conversational Health Coaching with Human-Centered Artificial Intelligence

Health coaching is a promising approach to support self-management of chronic conditions like type 2 diabetes; however, there aren’t enough coaching practitioners to support those in need. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have the potential to enable innovative, automated health coaching interventions, but important gaps remain in applying AI and ML to coaching interventions. This thesis aims to identify computational approaches and interactive technologies that enable automated health coaching systems. First, I utilized computational approaches that leverage individuals’ self-tracking and health data and used an expert system to translate ML inferences into personalized nutrition goal recommendations. The system, GlucoGoalie, was evaluated in multiple studies including a 4-week deployment study which demonstrated the feasibility of the approach. Second, I compared human-powered and automated/chatbot approaches to health coaching in a 3-week study which found that t2.coach — a scripted, theoretically-grounded chatbot designed through an iterative, user-centered process — cultivated a coach-like experience that had many similarities to the experience of messaging with actual health coaches, and outlined directions for automated, conversational coaching interventions. Third, I examined multiple AI approaches to enable micro-coaching dialogs — brief coaching conversations related to specific meals, to support achievement of nutrition goals — including a knowledge-based system for natural language understanding, and a data-driven, reinforcement learning approach for dialog management. Together, the results of these studies contribute methods and insights that take steps towards more intelligent conversational coaching systems, with resonance to research in informatics, human-computer interaction, and health coaching