Electronic clinical decision support algorithms incorporating point-of-care diagnostic tests in low-resource settings: a target product profile.

Details

Serval ID
serval:BIB_C606E5769710
Type
Article: article from journal or magazin.
Collection
Publications
Title
Electronic clinical decision support algorithms incorporating point-of-care diagnostic tests in low-resource settings: a target product profile.
Journal
BMJ global health
Author(s)
Pellé K.G., Rambaud-Althaus C., D'Acremont V., Moran G., Sampath R., Katz Z., Moussy F.G., Mehl G.L., Dittrich S.
ISSN
2059-7908 (Print)
ISSN-L
2059-7908
Publication state
Published
Issued date
2020
Peer-reviewed
Oui
Volume
5
Number
2
Pages
e002067
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
Health workers in low-resource settings often lack the support and tools to follow evidence-based clinical recommendations for diagnosing, treating and managing sick patients. Digital technologies, by combining patient health information and point-of-care diagnostics with evidence-based clinical protocols, can help improve the quality of care and the rational use of resources, and save patient lives. A growing number of electronic clinical decision support algorithms (CDSAs) on mobile devices are being developed and piloted without evidence of safety or impact. Here, we present a target product profile (TPP) for CDSAs aimed at guiding preventive or curative consultations in low-resource settings. This document will help align developer and implementer processes and product specifications with the needs of end users, in terms of quality, safety, performance and operational functionality. To identify the characteristics of CDSAs, a multidisciplinary group of experts (academia, industry and policy makers) with expertise in diagnostic and CDSA development and implementation in low-income and middle-income countries were convened to discuss a draft TPP. The TPP was finalised through a Delphi process to facilitate consensus building. An agreement greater than 75% was reached for all 40 TPP characteristics. In general, experts were in overwhelming agreement that, given that CDSAs provide patient management recommendations, the underlying clinical algorithms should be human-interpretable and evidence-based. Whenever possible, the algorithm's patient management output should take into account pretest disease probabilities and likelihood ratios of clinical and diagnostic predictors. In addition, validation processes should at a minimum show that CDSAs are implementing faithfully the evidence they are based on, and ideally the impact on patient health outcomes. In terms of operational needs, CDSAs should be designed to fit within clinic workflows and function in connectivity-challenged and high-volume settings. Data collected through the tool should conform to local patient privacy regulations and international data standards.
Keywords
child health, diagnostics and tools, health systems, public health, treatment
Pubmed
Web of science
Open Access
Yes
Create date
09/09/2020 17:19
Last modification date
10/09/2020 5:26
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