Subcategorizing the Expected Value of Perfect Implementation to Identify When and Where to Invest in Implementation Initiatives

Abstract

Purpose. Clinical practice variations and low implementation of effective and cost-effective health care technologies are a key challenge for health care systems and may lead to suboptimal treatment and health loss for patients. The purpose of this work was to subcategorize the expected value of perfect implementation (EVPIM) to enable estimation of the absolute and relative value of eliminating slow, low, and delayed implementation. Methods. Building on the EVPIM framework, this work defines EVPIM subcategories to estimate the expected value of eliminating slow, low, or delayed implementation. The work also shows how information on regional implementation patterns can be used to estimate the value of eliminating regional implementation variation. The application of this subcategorization is illustrated by a case study of the implementation of an antiplatelet therapy for the secondary prevention after myocardial infarction in Sweden. Incremental net benefit (INB) estimates are based on published cost-effectiveness assessments and a threshold of SEK 250,000 (£22,300) per quality-adjusted life year (QALY). Results. In the case study, slow, low, and delayed implementation was estimated to represent 22%, 34%, and 44% of the total population EVPIM (2941 QALYs or SEK 735 million), respectively. The value of eliminating implementation variation across health care regions was estimated to 39% of total EVPIM (1138 QALYs). Conclusion. Subcategorizing EVPIM estimates the absolute and relative value of eliminating different parts of suboptimal implementation. By doing so, this approach could help decision makers to identify which parts of suboptimal implementation are contributing most to total EVPIM and provide the basis for assessing the cost and benefit of implementation activities that may address these in future implementation of health care interventions.

Keywords: health care decision making; implementation strategies; value of implementation.

Figure 1

Stylised example illustrating (a) the population expected value of perfect implementation (pEVPIM); (b) the expected value of eliminating slow (A), low (B) and delayed (C = C1 + C2) implementation; and (c) the expected value of eliminating regional implementation variation (D). ρ is the average level of implementation. ρ^high is the level of implementation in the highest implementing region. max(ρ_t) is the highest average level of implementation. max(${\rho }_t^{\mathrm{high}}$) is the highest level of implementation achieved in the highest implementing region. t₀ is the time when the technology becomes available for use. t_implement is the time when implementation starts.

Figure 2

(a) Number of MI patients (age<80) with and without P2Y12 inhibitor in Sweden; and (b) Proportion of dual antoplatelet treated MI patients (age<80) receiving ticagrelor per health care region in Sweden.

Figure 3

The incremental net health benefit from (a) eliminating slow (A), low (B) and delayed (C) implementation; and (b) eliminating regional implementation variation (D), based on the ticagrelor case study.