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    Jan 26, 2021
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    Electronic clinical decision support algorithms incorporating point-of-care diagnostic tests in low-resource settings: a target product profile

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    Pelle et al 2020 Electronic ...
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    Authors
    Pellé, KG
    Rambaud-Althaus, C
    D'Acremont, V
    Moran, G
    Sampath, R
    Katz, Z
    Moussy, FG
    Mehl, GL
    Dittrich, S
    Issue Date
    2020-02-28
    Submitted date
    2020-04-17
    
    Metadata
    Show full item record
    Journal
    BMJ Global Health
    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.
    Publisher
    The British Medical Journal
    URI
    http://hdl.handle.net/10144/619648
    DOI
    10.1136/bmjgh-2019-002067
    PubMed ID
    32181003
    Type
    Article
    Language
    en
    ISSN
    2059-7908
    ae974a485f413a2113503eed53cd6c53
    10.1136/bmjgh-2019-002067
    Scopus Count
    Collections
    Health Politics

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