Evidence map›Paper›PMID 37931965›Full record

ArticleBMJ open2023

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

Adeola Bamgboje-Ayodele, Steven M McPhail, David Brain, Richard Taggart, Mitchell Burger, Lenert Bruce, Caroline Holtby, Malcolm Pradhan, Mark Simpson, Tim J Shaw and 1 more

Abstract read
In one paragraph

Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Adeola Bamgboje-AyodeleBiomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia adeola.ba@sydney.edu.au.ORCID 0000-0002-5629-1236
Steven M McPhailAustralian Centre for Health Service Innovation and Centre for Healthcare Transformation, Queensland University of Technology, Brisbane, Queensland, Australia.
David BrainAustralian Centre for Health Service Innovation and Centre for Healthcare Transformation, Queensland University of Technology, Brisbane, Queensland, Australia.ORCID 0000-0002-6612-348X
Richard TaggartSydney Local Health District, NSW Health, Camperdown, New South Wales, Australia.
Mitchell BurgerSydney Local Health District, NSW Health, Camperdown, New South Wales, Australia.
Lenert BruceMurrumbidgee Local Health District, NSW Health, Wagga Wagga, New South Wales, Australia.
Caroline HoltbyMurrumbidgee Local Health District, NSW Health, Wagga Wagga, New South Wales, Australia.
Malcolm PradhanAlcidion Pty Inc, Sydney, New South Wales, Australia.
Mark SimpsoneHealth NSW, Chatswood, New South Wales, Australia.
Tim J ShawBiomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia.
Melissa T BaysariBiomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia.ORCID 0000-0003-1645-9126

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Translational Research, BiomedicalFocus GroupsHumansQualitative ResearchHealth informaticsQUALITATIVE RESEARCHQuality in health care

Identifiers

PMID37931965
PMCPMC10632864

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.