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Using a community-engaged health informatics approach to develop a web analytics research platform for sharing data with community stakeholders.

ABSTRACT: Marginalized communities are rarely included in the planning of research relevant to their own health or have access to research data. We implemented a community-based participatory research approach in developing a new health informatics system, WARP (Web Analytics Research Platform), to enable stakeholders to access and analyze research data. We leveraged data from a cohort study of 751 patients in the Transitions Clinic Network (TCN), a network of clinical programs serving patients with a history of incarceration. WARP holds de-identified, patient data, streamlines data processing (i.e. transformation, archival, and partitioning), and has a web analytic tool for users to perform statistical analyses. We used feedback from focus groups of patients with a history of incarceration and workshops with TCN research teams, including patients, community health workers and policymakers, to develop WARP. Our approach advances mechanisms to engage stakeholders in research. Future work will evaluate its effect on community-engagement in research.

SUBMITTER: Wang KH

PROVIDER: S-EPMC5977588 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Using a community-engaged health informatics approach to develop a web analytics research platform for sharing data with community stakeholders.

Wang Karen H KH Marenco Luis L Madera Johanna Elumn JE Aminawung Jenerius A JA Wang Emily A EA Cheung Kei-Hoi KH

AMIA ... Annual Symposium proceedings. AMIA Symposium 20170101

Marginalized communities are rarely included in the planning of research relevant to their own health or have access to research data. We implemented a community-based participatory research approach in developing a new health informatics system, WARP (Web Analytics Research Platform), to enable stakeholders to access and analyze research data. We leveraged data from a cohort study of 751 patients in the Transitions Clinic Network (TCN), a network of clinical programs serving patients with a his ...[more]

PMID: 29854242

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Project description:The degree to which artificial intelligence healthcare research is informed by data and stakeholders from community settings has not been previously described. As communities are the principal location of healthcare delivery, engaging them could represent an important opportunity to improve scientific quality. This scoping review systematically maps what is known and unknown about community-engaged artificial intelligence research and identifies opportunities to optimize the generalizability of these applications through involvement of community stakeholders and data throughout model development, validation, and implementation. Embase, PubMed, and MEDLINE databases were searched for articles describing artificial intelligence or machine learning healthcare applications with community involvement in model development, validation, or implementation. Model architecture and performance, the nature of community engagement, and barriers or facilitators to community engagement were reported according to PRISMA extension for Scoping Reviews guidelines. Of approximately 10,880 articles describing artificial intelligence healthcare applications, 21 (0.2%) described community involvement. All articles derived data from community settings, most commonly by leveraging existing datasets and sources that included community subjects, and often bolstered by internet-based data acquisition and subject recruitment. Only one article described inclusion of community stakeholders in designing an application-a natural language processing model that detected cases of likely child abuse with 90% accuracy using harmonized electronic health record notes from both hospital and community practice settings. The primary barrier to including community-derived data was small sample sizes, which may have affected 11 of the 21 studies (53%), introducing substantial risk for overfitting that threatens generalizability. Community engagement in artificial intelligence healthcare application development, validation, or implementation is rare. As healthcare delivery occurs primarily in community settings, investigators should consider engaging community stakeholders in user-centered design, usability, and clinical implementation studies to optimize generalizability.

Project description:BackgroundAlthough rare diseases (RD) are increasingly becoming a priority for healthcare activities and services around the world, developing research policy for investigating RD in public settings proves challenging due to the limited nature of existing evidence. Rare conditions require the involvement of a wide range of stakeholders in order to promote general awareness and garner political support. Consequently, it is critically important to identify trends in the various types of research focusing on rare disease stakeholders, including the specific topics or issues to be included in surveys and studies focused on RD stakeholders. This systematic review and thematic analysis analyses the existing literature based on RD surveys, including the stakeholders involved, and proposes potential research priorities and initiatives for policy-making related to RD.MethodsArticles were downloaded and analyzed from across five electronic databases (PubMed, EMBASE, Cochrane Central, Web of Science, and CINHAL) and 115 studies were included.ResultsAcross 115 studies, the main research participants were patients and/or caregivers (n = 77, 67.0%), health professionals (n = 18, 15.7%), and the public (n = 7, 6.1%). The studies discussed RDs in general (n = 46, 40.0%), endocrine, nutritional, and metabolic diseases (n = 20, 17.4%) and other RDs. Experiences with RD were examined by more than half of the selected studies (n = 74, 64.3%), followed by the opinions of stakeholders (n = 24, 20.9%). Most of the studies used surveys in order to collect relevant data (n = 114, 99.1%). Additionally, the majority of the studies were conducted in high-income countries (n = 92, 80.0%) and rarely in middle and low-income countries (n = 12, 13.8%).ConclusionStakeholder research on RD reveals that there are significant instances of unmet needs and various challenges faced by the medical system in dealing with RDs. Furthermore, public awareness and support is critical to ensuring political feasibility of increasing national-level investments for RDs and development of medical products and treatment.

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Dataset Information

Using a community-engaged health informatics approach to develop a web analytics research platform for sharing data with community stakeholders.

Publications

Using a community-engaged health informatics approach to develop a web analytics research platform for sharing data with community stakeholders.

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