Dataset Information

Use of patient-generated health data across healthcare settings: implications for health systems.

ABSTRACT:

Objective

The growing prevalence of chronic conditions requiring changes in lifestyle and at-home self-management has increased interest in and need for supplementing clinic visits with data generated by patients outside the clinic. Patient-generated health data (PGHD) support the ability to diagnose and manage chronic conditions, to improve health outcomes, and have the potential to facilitate more "connected health" between patients and their care teams; however, health systems have been slow to adopt PGHD use in clinical care.

Materials and methods

We surveyed current and potential users of PGHD to catalog how PGHD is integrated into clinical care at an academic health center. The survey included questions about data type, method of collection, and clinical uses of PGHD. Current users were asked to provide detailed case studies of PGHD use in research and care delivery.

Results

Thirty-one respondents completed the survey. Seventeen individuals contributed detailed case studies of PGHD use across diverse areas of care, including behavioral health, metabolic and gastrointestinal conditions, musculoskeletal/progressive functional conditions, cognitive symptoms, and pain management. Sensor devices and mobile technologies were the most commonly reported platforms for collection. Clinicians and researchers involved in PGHD use cited the potential for PGHD to enhance care delivery and outcomes, but also indicated substantial barriers to more widespread PGHD adoption across healthcare systems.

Conclusion

The results of our survey illustrate how PGHD is used in targeted areas of one healthcare system and provide meaningful insights that can guide health systems in supporting the widespread use of PGHD in care delivery.

SUBMITTER: Austin E

PROVIDER: S-EPMC7309248 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Use of patient-generated health data across healthcare settings: implications for health systems.

Austin Elizabeth E Lee Jenney R JR Amtmann Dagmar D Bloch Rich R Lawrence Sarah O SO McCall Debbe D Munson Sean S Lavallee Danielle C DC

JAMIA open 20191129 1

<h4>Objective</h4>The growing prevalence of chronic conditions requiring changes in lifestyle and at-home self-management has increased interest in and need for supplementing clinic visits with data generated by patients outside the clinic. Patient-generated health data (PGHD) support the ability to diagnose and manage chronic conditions, to improve health outcomes, and have the potential to facilitate more "connected health" between patients and their care teams; however, health systems have be ...[more]

PMID: 32607489

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Project description:BackgroundHealthcare systems data (HSD) has the potential to optimise the efficiency of randomised controlled trials (RCTs), by decreasing trial-specific data demands. Therefore, the use of HSD in trials is expected to increase. In 2019, it was estimated that 47% of NIHR-funded trials were planning to use HSD. We aim to understand the extent and nature of its current use and its evolution over time.MethodsWe identified a cohort of RCTs within the NIHR Journals Library that commenced after 2019 and were described as being in progress on 6 June 2022. Details on the source and use of HSD were extracted from eligible RCTs. The use of HSD was categorised according to whether it was used as the sole data source for outcomes and whether the outcomes were primary or secondary. HSD is often insufficient for patient-reported outcomes (PROs). We aimed to determine methods used by trialists for collecting PRO data alongside HSD.ResultsOf the 84 eligible studies, 52 (62%) planned to use HSD and 79 (94%) planned to collect PROs. The number of RCTs planning to use HSD for at least one outcome was 28 (54%) with 24 of these planning to use HSD as the sole data source for at least one outcome. The number of studies planning to use HSD for primary and secondary outcomes was 10 (20%) and 21 (40%) respectively. The sources of HSD were National Health Service (NHS) Digital (n = 37, 79%), patient registries (n = 7, 29%), primary care (n = 5, 21%), The Office for National Statistics (ONS) (n = 3, 13%) and other (n = 2, 8%). PROs were collected for 92% of the trials planning to use HSD. Methods for collection of PROs included in-person (n = 26, 54%), online (n = 22, 46%), postal (n = 18, 38%), phone (n = 14, 29%) and app (n = 2, 4%).ConclusionsHSD is being used in around two thirds of the studies but cannot yet be used to support PRO data collection within the cohort we examined. Comparison with an earlier cohort demonstrates an increase in the number of RCTs planning to use HSD.

Project description:BackgroundPatient-generated health data (PGHD) have been largely collected through mobile health (mHealth) apps and wearable devices. PGHD can be especially helpful in mental health, as patients' illness history and symptom narratives are vital to developing diagnoses and treatment plans. However, the extent to which clinicians use mental health-related PGHD is unknown.ObjectiveA mixed methods study was conducted to understand clinicians' perspectives on PGHD and current mental health apps. This approach uses information gathered from semistructured interviews, workflow analysis, and user-written mental health app reviews to answer the following research questions: (1) What is the current workflow of mental health practice and how are PGHD integrated into this workflow, (2) what are clinicians' perspectives on PGHD and how do they choose mobile apps for their patients, (3) and what are the features of current mobile apps in terms of interpreting and sharing PGHD?MethodsThe study consists of semistructured interviews with 12 psychiatrists and clinical psychologists from a large academic hospital. These interviews were thematically and qualitatively analyzed for common themes and workflow elements. User-posted reviews of 56 sleep and mood tracking apps were analyzed to understand app features in comparison with the information gathered from interviews.ResultsThe results showed that PGHD have been part of the workflow, but its integration and use are not optimized. Mental health clinicians supported the use of PGHD but had concerns regarding data reliability and accuracy. They also identified challenges in selecting suitable apps for their patients. From the app review, it was discovered that mHealth apps had limited features to support personalization and collaborative care as well as data interpretation and sharing.ConclusionsThis study investigates clinicians' perspectives on PGHD use and explored existing app features using the app review data in the mental health setting. A total of 3 design guidelines were generated: (1) improve data interpretation and sharing mechanisms, (2) consider clinical workflow and electronic health record integration, and (3) support personalized and collaborative care. More research is needed to demonstrate the best practices of PGHD use and to evaluate their effectiveness in improving patient outcomes.

Project description:BackgroundAn established and well-known method for usability assessment of various human-computer interaction technologies is called heuristic evaluation (HE). HE has been adopted for evaluations in a wide variety of specialized contexts and with objectives that go beyond usability. A set of heuristics to evaluate how health information technologies (HITs) incorporate features that enable effective patient use of person-generated health data (PGHD) is needed in an era where there is a growing demand and variety of PGHD-enabled technologies in health care and where a number of remote patient-monitoring technologies do not yet enable patient use of PGHD. Such a set of heuristics would improve the likelihood of positive effects from patients' use of PGHD and lower the risk of negative effects.ObjectiveThis study aims to describe the development of a set of heuristics for the design and evaluation of how well remote patient therapeutic technologies enable patients to use PGHD (PGHD enablement). We used the case of Kinect-based stroke rehabilitation systems (K-SRS) in this study.MethodsThe development of a set of heuristics to enable better use of PGHD was primarily guided by the R3C methodology. Closer inspection of the methodology reveals that neither its development nor its application to a case study were described in detail. Thus, where relevant, each step was grounded through best practice activities in the literature and by using Nielsen's heuristics as a basis for determining the new set of heuristics. As such, this study builds on the R3C methodology, and the implementation of a mixed process is intended to result in a robust and credible set of heuristics.ResultsA total of 8 new heuristics for PGHD enablement in K-SRS were created. A systematic and detailed process was applied in each step of heuristic development, which bridged the gaps described earlier. It is hoped that this would aid future developers of specialized heuristics, who could apply the detailed process of heuristic development for other domains of technology, and additionally for the case of PGHD enablement for other health conditions. The R3C methodology was also augmented through the use of qualitative studies with target users and domain experts, and it is intended to result in a robust and credible set of heuristics, before validation and refinement.ConclusionsThis study is the first to develop a new set of specialized heuristics to evaluate how HITs incorporate features that enable effective patient use of PGHD, with K-SRS as a key case study. In addition, it is the first to describe how the identification of initial HIT features and concepts to enable PGHD could lead to the development of a specialized set of heuristics.

Project description:BACKGROUND:Digital innovations continue to shape health and health care. As technology socially integrates into daily living, the lives of health care consumers are transformed into a key source of health information, commonly referred to as patient-generated health data (PGHD). With chronic disease prevalence signaling the need for a refocus on primary prevention, electronic PGHD might be essential in strengthening proactive and person-centered health care. OBJECTIVE:This study aimed to review and synthesize the existing literature on the utilization and implications of electronic PGHD for primary disease prevention and health promotion purposes. METHODS:Guided by a well-accepted methodological framework for scoping studies, we screened MEDLINE, CINAHL, PsycINFO, Scopus, Web of Science, EMBASE, and IEEE Digital Library. We hand-searched 5 electronic journals and 4 gray literature sources, additionally conducted Web searches, reviewed relevant Web pages, manually screened reference lists, and consulted authors. Screening was based on predefined eligibility criteria. Data extraction and synthesis were guided by an adapted PGHD-flow framework. Beyond initial quantitative synthesis, we reported narratively, following an iterative thematic approach. Raw data were coded, thematically clustered, and mapped, allowing for the identification of patterns. RESULTS:Of 183 eligible studies, targeting knowledge and self-awareness, behavior change, healthy environments, and remote monitoring, most literature (125/183, 68.3%) addressed weight reduction, either through physical activity or nutrition, applying a range of electronic tools from socially integrated to full medical devices. Participants generated their data actively (100/183, 54.6%), in combination with passive sensor-based trackers (63/183, 34.4%) or entirely passively (20/183, 10.9%). The proportions of active and passive data generation varied strongly across prevention areas. Most studies (172/183, 93.9%) combined electronic PGHD with reflective, process guiding, motivational and educational elements, highlighting the role of PGHD in multicomponent digital prevention approaches. Most of these interventions (110/183, 60.1%) were fully automatized, underlining broader trends toward low-resource and efficiency-driven care. Only a fraction (47/183, 25.6%) of studies provided indications on the impact of PGHD on prevention-relevant outcomes, suggesting overall positive trends, especially on vitals (eg, blood pressure) and body composition measures (eg, body mass index). In contrast, the impact of PGHD on health equity remained largely unexplored. Finally, our analysis identified a list of barriers and facilitators clustered around data collection and use, technical and design considerations, ethics, user characteristics, and intervention context and content, aiming to guide future PGHD research. CONCLUSIONS:The large, heterogeneous volume of the PGHD literature underlines the topic's emerging nature. Utilizing electronic PGHD to prevent diseases and promote health is a complex matter owing to mostly being integrated within automatized and multicomponent interventions. This underlines trends toward stronger digitalization and weaker provider involvement. A PGHD use that is sensitive to identified barriers, facilitators, consumer roles, and equity considerations is needed to ensure effectiveness.

Dataset Information

Use of patient-generated health data across healthcare settings: implications for health systems.

Objective

Materials and methods

Results

Conclusion

Publications

Use of patient-generated health data across healthcare settings: implications for health systems.

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