Mapping Methodologies for Economic Evaluation of Digital Health Technologies: A Scoping Review

Digital health technologies (DHTs) are reshaping healthcare delivery; yet, their diverse functionalities, dynamic nature, and nontraditional impact pathways challenge conventional economic evaluation methods. This scoping review aimed to systematically map existing frameworks for the economic evaluation of DHTs, assess their methodological components, and identify gaps to inform more robust, standardized approaches.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews guidelines, we conducted a comprehensive literature search across academic databases (PubMed, Scopus, Web of Science, and Business Source Complete) and gray literature. Records published since 2015 were included if they described frameworks incorporating economic evaluation methods for DHTs. Data were extracted across the following key methodological dimensions: evaluation type, study design, comparator, time horizon, perspective, effectiveness measures, cost components, and uncertainty analysis.

We identified 26 frameworks, and the analysis revealed pronounced heterogeneity across methodological domains. Although 50% (n = 13) included both full and partial evaluations, core components were often missing: 81% (n = 21) did not define a time horizon, and in 73% (n = 19) the evaluation perspective was absent. Cost-utility analysis and budget impact analysis were the most frequently cited methods; yet, few frameworks justified their choice or linked it to the maturity of the technology. Some addressed adaptive study designs or aligned evaluation strategies with DHTs lifecycle stages. Cost inclusions varied substantially, with limited attention to productivity losses costs.

Current frameworks lack standardization and are not fully adapted to the characteristics of DHTs. Future development should prioritize flexible, lifecycle-aligned evaluation models and standardized guidance to support evidence-based digital health decision making.