How to Ease the Pain of Taking a Diagnostic Point of Care Test to the Market: A Framework for Evidence Development

Abstract

Bringing a diagnostic point of care test (POCT) to a healthcare market can be a painful experience as it requires the manufacturer to meet considerable technical, financial, managerial, and regulatory challenges. In this opinion article we propose a framework for developing the evidence needed to support product development, marketing, and adoption. We discuss each step in the evidence development pathway from the invention phase to the implementation of a new POCT in the healthcare system. We highlight the importance of articulating the value propositions and documenting the care pathway. We provide guidance on how to conduct care pathway analysis as little has been published on this. We summarize the clinical, economic and qualitative studies to be considered for developing evidence, and provide useful links to relevant software, on-line applications, websites, and give practical advice. We also provide advice on patient and public involvement and engagement (PPIE), and on product management. Our aim is to help device manufacturers to understand the concepts and terminology used in evaluation of in vitro diagnostics (IVDs) so that they can communicate effectively with evaluation methodologists, statisticians, and health economists. Manufacturers of medical tests and devices can use the proposed framework to plan their evidence development strategy in alignment with device development, applications for regulatory approval, and publication.

Keywords: adoption; care pathway analysis; diagnostic; evidence generation; implementation; medical device; point of care; preparation for marketing; value proposition.

Figure 1

The pathway for developing evidence needed to support the adoption of a new POCT. CEA is Cost-Effectiveness Analysis. The steps are described in more detail in the following sections.

Figure 2

Conventions for symbols in a graphical model of a system.

Figure 3

Care pathways for managing ConditionR in current practice and in proposed new practice with the NewTestRx POCT.

Figure 4

Simple decision tree comparing standard care with standard care plus NewTestRx for ConditionR. TP = number of true positive results, FP = number of false positive results, TN = number of true negative results, FN = number of false negative results, n = total number of patients in that path, PPV = positive predictive value, NPV = negative predictive value. Subscripts (C for clinical diagnosis and R for diagnosis with NewTestRx) identify the relevant branch for TP, FP, FN, TN, PPV, and NPV.

Figure 5

The cost-effectiveness plane with quadrants labelled to show how cost effectiveness study results can inform decision-making.