Answers are grounded in this paper and cite their sources.
Digital medicine requires evaluation of clinical effects, delivery conditions, and the resources needed for use in practice.This narrative review examines how evidence requirements differ across clinical evaluation, regulatory authorization, purchasing, implementation, and postmarket assessment, using digital therapeutics as the principal illustrative application.Targeted searches of public literature and regulatory sources were supplemented with searches for additional studies of digital care delivery, economic outcomes, and implementation.Sources were selected for relevance to a defined evidence question; no systematic study enumeration or quantitative pooling was undertaken.Published examples include aphasia rehabilitation, migraine prevention, major depressive disorder, schizophrenia, digital pain care, insomnia-related primary care costs, and implementation of substance use disorder treatment.The synthesis distinguishes randomized treatment effects from exploratory analyses, engagement from clinical benefit, regulatory status from purchasing evidence, and observed resource use from modeled economic consequences.The examples show why comparator choice, analysis population, human support, software version, and follow-up duration matter when translating findings to a different setting.A nonsignificant primary analysis remains distinct from a favorable supportive analysis, and low uptake in routine care identifies an implementation problem without independently resolving efficacy.Original tables and conceptual figures organize these distinctions using established evidence-planning, implementation, economic, and measurement frameworks.The review does not rank products or establish that an evidence strategy causes commercial success.Evidence generation can inform commercialization when claims remain traceable to the intervention studied, the comparison performed, and the uncertainty that remains for the intended decision maker.
No takes yet. Share an insight, caveat, or question.
Shaheen Emmanuel Lakhan (2026) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: