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.

Implementation of Ticagrelor Reduced Mortality in Routine Clinical Care: Evidence From a Natural Experiment Including 109 995 Patients With Myocardial Infarction in Sweden

“…Ticagrelor was approved by the European Medicines Agency in December 2010 18 and recommended by Swedish national treatment guidelines in December 2011 19 for secondary prevention after myocardial infarction. This study took advantage of regional differences in the timing and level of ticagrelor implementation 7 , 20 as a source of random treatment assignment. Patients admitted to the same center but at different points in time faced different likelihoods of ticagrelor treatment, and similarly, individuals who suffered a myocardial infarction on the same day had different probabilities of receiving ticagrelor depending on which treatment center they were admitted to.…”

Regional variation in prescription drug spending: Evidence from regional migrants in Sweden

“…In this study we assess regional variation in total drug spending and find limited scope of a place effect, however, the effects on total drug spending may hide heterogeneity with respect to specific drugs or ATC groups. Studies on implementation of drugs in Sweden have shown a larger variation across regions for example, about a 4‐fold variation in drugs for heart failure and for MI‐prevention (Fu et al., 2020; Johannesen et al., 2020), and it remains to be investigated whether the place‐effect has a more prominent role for certain types of drugs.…”

Cost-Effectiveness Analysis in Implementation Science: a Research Agenda and Call for Wider Application

“…Extensions to the value of a perfect implementation framework have similarly explored the influence of relaxing assumptions of immediate and perfect adoption [4,5] to determine whether it is worthwhile to collect more data to reduce decision uncertainty. Another recent extension to this framework has investigated the influence of heterogeneity in adoption across geographical areas [48]. Overly optimistic expectations of the transportability of findings from one setting to another are one of the major causes of unsuccessful policy implementation [49].…”

“…If available, evidence on the cost and effectiveness of interventions designed to improve implementation could also be incorporated explicitly [5]. Recent retrospective work by Johannesen et al has explored the relative gains of eliminating slow or delayed scaling up of prevention in cardiovascular disease [48]. Furthermore, evidence on the uptake of specific health technologies based on diffusion theory could provide a methodological foundation to prospectively model the impact on value provided by different patterns of adoption (and therefore scale-up) in the implementation of EBIs [60].…”