RT Journal Article
SR Electronic
T1 Electronic clinical decision support algorithms incorporating point-of-care diagnostic tests in low-resource settings: a target product profile
JF BMJ Global Health
JO BMJ Global Health
FD BMJ Publishing Group Ltd
SP e002067
DO 10.1136/bmjgh-2019-002067
VO 5
IS 2
A1 Karell G Pellé
A1 Clotilde Rambaud-Althaus
A1 Valérie D'Acremont
A1 Gretchen Moran
A1 Rangarajan Sampath
A1 Zachary Katz
A1 Francis G Moussy
A1 Garrett Livingston Mehl
A1 Sabine Dittrich
YR 2020
UL http://gh.bmj.com/content/5/2/e002067.abstract
AB 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.