Table 2

Minimal and optimal target product profile characteristics focused on the electronic clinical decision support algorithm and POC tools, as defined by expert consensus process

Clinical decision support algorithm
Characteristics	Minimal requirements	Optimal requirements	Comments
Content transparency	The algorithm is ‘human interpretable’. The healthcare programme and end user can comprehend the algorithm decision-making processes	The healthcare programme and end user have access to underlying evidence and methodology used to develop the algorithm	Human-interpretable: a human can understand the choices taken by the model in the decision-making process, that is, how output variables are generated based on input variables. Visual representations (eg, decision trees, principal component analyses, protocol charts and so on) and performance metrics can be used to support content transparency
Quality control	The algorithm has been (1) analytically and (2) semantically tested: Analytical verification: the algorithm output is accurate and reproducible Semantical verification: the algorithm does not deviate from expert content/evidence and there are no interactions or conflicts in the logic		(1) and (2) answer the question ‘did I build the model right?’ (See FDA’s SaMD clinical evaluation for current guidance20)
Algorithm validation	The algorithm has been validated. The level of validation will depend on the CDSS status as an SaMD Refer to upcoming rulings from regulatory bodies such as the FDA or the European Commission		Answers the question ‘did I build the right model?’ (See FDA’s SaMD clinical evaluation for current guidance20)
Machine learning	None, the algorithm is static	ML is applied to generate data on the algorithm performance, improve content, inform healthcare system processes and so on Changes in the algorithm based on ML shall be validated
POC tool
POC data inputs	Any kind of data (qualitative data such as positive/negative/invalid lateral flow test results, and quantitative data such as data provided by haemoglobinometers and glucometers)
Disease likelihood	Based on: Pretest probability (in the absence of POC or POC performance data) Or POC positive/negative likelihood ratio	Based on: Pretest probability And POC positive/negative likelihood ratio	The test performance (eg, likelihood ratio) is known and performance data ideally previously determined through independent studies in relevant settings The test brand should ideally also be considered so as to account for changes between manufacturers
POC training	On-site training performed by local authority or implementer

CDSS, clinical decision support system; FDA, Food and Drug Administration; ML, machine learning; POC, point-of-care; SaMD, software as a medical device.