The Role of Measurement Uncertainty in Health Technology Assessments (HTAs) of In Vitro Tests

Behind the scenes of EQA - characteristics, capabilities, benefits and assets of external quality assessment (EQA)

External quality assessment (EQA) enhances patient safety through the evaluation of the quality of laboratory-based and point of care testing. Regulatory agencies and accreditation organizations utilize the results and the laboratory's response to them as part of assessing the laboratory's fitness to practice. In addition, where EQA samples are commutable and the assigned value has been determined using reference measurement procedures (RMPs), EQA data contributes to the verification of metrological traceability of assays as part of the post-market surveillance of in vitro diagnostic (IVD) medical devices (IVD-MDs). More broadly, the scientific and medical communities use EQA data to demonstrate that medical laboratory examination procedures are fit for clinical purposes, to evaluate common reference intervals, and inclusion of data in clinical databases. Scientific groups, the IVD industry, reference laboratories and National Metrology Institutes can work with EQA providers to identify measurands, which should urgently be supported by the development of reference materials or methods. The ability of health systems to respond effectively to fast-evolving medical challenges, such as the Coronavirus Disease-19 (COVID-19) pandemic, is reliant on EQA to demonstrate confidence in the performance of new laboratory methods and testing services. EQA providers are uniquely positioned to assess the performance of IVD-MDs in addition to individual laboratories and testing sites. Although the primary focus of EQA providers remains the improvement of the performance of individual laboratories, there are many stakeholders who benefit from EQA performance data.

Tools for the Economic Evaluation of Precision Medicine: A Scoping Review of Frameworks for Valuing Heterogeneity-Informed Decisions

BACKGROUND AND OBJECTIVE

Precision medicine highlights the importance of exploring heterogeneity in the effectiveness and costs of interventions. Our objective was to identify and compare frameworks for valuing heterogeneity-informed decisions, and consider their strengths and weaknesses for application to precision medicine.

METHODS

We conducted a scoping review to identify papers that proposed an analytical framework to place a value, in terms of costs and health benefits, on using heterogeneity to inform treatment selection. The search included English-language papers indexed in MEDLINE, Embase or EconLit, and a manual review of references and citations. We compared the frameworks qualitatively considering: the purpose and setting of the analysis; the types of precision medicine interventions where the framework could be applied; and the framework's ability to address the methodological challenges of evaluating precision medicine.

RESULTS

Four analytical frameworks were identified: value of stratification, value of heterogeneity, expected value of individualised care and loss with respect to efficient diffusion. Each framework is suited to slightly different settings and research questions. All focus on maximising net benefit, and quantify the opportunity cost of ignoring heterogeneity by comparing individualised or stratified decisions to a means-based population-wide decision. Where the frameworks differ is in their approaches to uncertainty, and in the additional metrics they consider.

CONCLUSIONS

Identifying and utilising heterogeneity is at the core of precision medicine, and the ability to quantify the value of heterogeneity-informed decisions is critical. Using an analytical framework to value heterogeneity will help provide evidence to inform investment in precision medicine interventions, appropriately capturing the value of targeted health interventions.

Health economic evaluations of medical tests: Translating laboratory information into value - A case study example

Health-care providers and funders are focused on identifying value in all their services and that includes laboratories. This means that in order to gain a share of scarce resources, laboratory professionals must also understand and assess the value of tests and that includes their economic impact. This can be assessed using health economic modelling tools which, when used in conjunction with a detailed value proposition for the test, can translate laboratory information into value. While a variety of health economic assessment tools are available, this review will focus on the use of decision analytic models which essentially compare the outcomes from pathways with and without the new test, the value of which is being assessed. A step-by-step framework is provided to guide laboratory professionals through the essential steps of conducting the evaluation. Initial steps include mapping the clinical pathway, understanding the goal of the evaluation, identifying the key stakeholders and their needs and determining a suitable analytical model. Following collection of the actual data, the validity of the model must be checked, and the robustness of the outcomes tested through sensitivity analysis. The last step is to translate the findings into measures of value which can then inform appropriate decisions by the stakeholders. This review of basic health economic modelling should enable laboratory professionals to have an understanding of how modelling can be applied to tests in their own environment and help deliver their potential value.