{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T09:38:44Z","timestamp":1775727524845,"version":"3.50.1"},"reference-count":115,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,18]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>A literature review of capability maturity models (MMs) to inform the conceptualization, development, implementation, evaluation, and mainstreaming of MMs in digital health (DH).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Electronic databases were searched using \u201cdigital health,\u201d \u201cmaturity models,\u201d and related terms based on the Digital Health Profile and Maturity Assessment Toolkit Maturity Model (DHPMAT-MM). Covidence was used to screen, identify, capture, and achieve consensus on data extracted by the authors. Descriptive statistics were generated. A thematic analysis and conceptual synthesis were conducted.<\/jats:p><\/jats:sec><jats:sec><jats:title>Findings<\/jats:title><jats:p>Diverse domain-specific MMs and model development, implementation, and evaluation methods were found. The spread and pattern of different MMs verified the essential DH foundations and five maturity stages of the DHPMAT-MM. An unanticipated finding was the existence of a new category of community-facing MMs. Common characteristics included:<\/jats:p><jats:p>1. A dynamic lifecycle approach to digital capability maturity, which is:<\/jats:p><jats:p>a. responsive to environmental changes and may improve or worsen over time;<\/jats:p><jats:p>b. accumulative, incorporating the attributes of the preceding stage; and<\/jats:p><jats:p>c. sequential, where no maturity stage must be skipped.<\/jats:p><jats:p>2. Sociotechnical quality improvement of the DH ecosystem and MM, which includes:<\/jats:p><jats:p>a. investing in the organization\u2019s human, hardware, and software resources and<\/jats:p><jats:p>b. a need to engage and improve the DH competencies of citizens.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The diversity in MMs and variability in methods and content can create cognitive dissonance. A metamodel like the DHPMAT-MM can logically unify the many domain-specific MMs and guide the overall implementation and evaluation of DH ecosystems and MMs over the maturity lifecycle.<\/jats:p><\/jats:sec>","DOI":"10.1093\/jamia\/ocac228","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T14:06:51Z","timestamp":1668521211000},"page":"393-406","source":"Crossref","is-referenced-by-count":25,"title":["Digital health and capability maturity models\u2014a critical thematic review and conceptual synthesis of the literature"],"prefix":"10.1093","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5989-3614","authenticated-orcid":false,"given":"Siaw-Teng","family":"Liaw","sequence":"first","affiliation":[{"name":"WHO Collaborating Centre for eHealth (AUS-135), School of Population Health, UNSW Sydney , Sydney, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0081-2506","authenticated-orcid":false,"given":"Myron 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