How digital health translational research is prioritised: a qualitative stakeholder-driven approach to decision support evaluation

• Published in: BMJ open Vol. 13; no. 11; p. e075009
• Main Authors: Bamgboje-Ayodele, Adeola, McPhail, Steven M, Brain, David, Taggart, Richard, Burger, Mitchell, Bruce, Lenert, Holtby, Caroline, Pradhan, Malcolm, Simpson, Mark, Shaw, Tim J, Baysari, Melissa T
• Format: Journal Article
• Language: English
• Published: London British Medical Journal Publishing Group 06.11.2023; BMJ Publishing Group LTD; BMJ Publishing Group
• Series: Original research
• Subjects: Artificial intelligence; Collaboration; COVID-19; Data analysis; Health Informatics; Health services; Listening; Literature reviews; Medical research; Medical supplies; Pandemics; QUALITATIVE RESEARCH; Quality in health care; Stakeholders; Systematic review; Technology
• ISSN: 2044-6055; 2044-6055
• DOI: 10.1136/bmjopen-2023-075009

ObjectivesDigital health is now routinely being applied in clinical care, and with a variety of clinician-facing systems available, healthcare organisations are increasingly required to make decisions about technology implementation and evaluation. However, few studies have examined how digital health research is prioritised, particularly research focused on clinician-facing decision support systems. This study aimed to identify criteria for prioritising digital health research, examine how these differ from criteria for prioritising traditional health research and determine priority decision support use cases for a collaborative implementation research programme.MethodsDrawing on an interpretive listening model for priority setting and a stakeholder-driven approach, our prioritisation process involved stakeholder identification, eliciting decision support use case priorities from stakeholders, generating initial use case priorities and finalising preferred use cases based on consultations. In this qualitative study, online focus group session(s) were held with stakeholders, audiorecorded, transcribed and analysed thematically.ResultsFifteen participants attended the online priority setting sessions. Criteria for prioritising digital health research fell into three themes, namely: public health benefit, health system-level factors and research process and feasibility. We identified criteria unique to digital health research as the availability of suitable governance frameworks, candidate technology’s alignment with other technologies in use,and the possibility of data-driven insights from health technology data. The final selected use cases were remote monitoring of patients with pulmonary conditions, sepsis detection and automated breast screening.ConclusionThe criteria for determining digital health research priority areas are more nuanced than that of traditional health condition focused research and can neither be viewed solely through a clinical lens nor technological lens. As digital health research relies heavily on health technology implementation, digital health prioritisation criteria comprised enablers of successful technology implementation. Our prioritisation process could be applied to other settings and collaborative projects where research institutions partner with healthcare delivery organisations.