Early assessment of highly innovative medical technology: clinical and economical gains of point-of-care applications for measuring potassium concentrations

OBJECTIVES: Innovative point-of-care diagnostics are likely to be having a strong impact on health care. The aim of this study is to conduct an early assessment of point-of-care chips. These chips can detect many particles and, consequently, many product-market combinations can be developed. This study investigates the impact of potential point-of-care applications in health care and to quantify the impact in terms of their likely cost-effectiveness. METHODS: The Analytic Hierarchy Process (AHP); a technique for multi-criteria analysis, and Markov modeling were applied in this early assessment. The AHP was used to prioritize six potentially attractive product-market combinations (PCM’s). For estimating the future cost-effectiveness of the most attractive PMC’s, Markov health state transition models were developed. Two Markov models were constructed; models for a potassium chip for both stage 5 CKD patients as well as heart failure (HF) patients which suffer from edemas. RESULTS: AHP identified clinical gain as being the most important criterion to assess the attractiveness of a PMC, followed by market potential, attitude of professionals, R&D barriers and implementation barriers. Regarding these criteria, a potassium-chip for Chronic Kidney Disease (CKD) and Heart Failure (HF) patients was calculated to be the most attractive alternative. Markov model cohort simulation yielded incremental cost-effectiveness ratios (ICERs). The point-of-care chip appeared not to be cost-effective for preventing hyperkalemia in stage 5 CKD patients. For HF patients suffering from edemas the chip was estimated to be cost-effective and could provide a valuable asset to current treatment. CONCLUSIONS: The AHP is valuable in supporting the identification of potentially attractive product-market combinations. For the best combinations, Markov modeling can subsequently provide a more in-depth analysis of the future cost-effectiveness.