DATA-DRIVEN STATE REGULATION OF DIGITAL TRANSFORMATION IN THE REHABILITATION SYSTEM: ARCHITECTURE, INDICATORS AND DECISION-MAKING MODEL
DOI:
https://doi.org/10.67034/2786-8354.2026.20.2.15Keywords:
state regulation, digital transformation, rehabilitation system, data-driven governance, eHealth, indicators, hybrid decision-making, telerehabilitationAbstract
Introduction. The purpose of the article is to develop a conceptual and methodological model of data-driven state regulation of digital transformation in the rehabilitation system by substantiating its regulatory architecture, indicator framework and hybrid decision-making logic.
Materials and Methods. The study is conceptual and methodological in nature and does not involve primary surveys or experimental testing of digital rehabilitation technologies. The methodological basis includes the institutional and systems approaches, structural-functional analysis, conceptual modelling, policy mapping, regulatory mapping and case-based reasoning. The source base consists of international scientific publications, OECD and WHO documents, European approaches to digital health governance, and studies on data-driven governance, health data governance, telerehabilitation, rehabilitation big data and eHealth development.
Results. The article proposes an original conceptual and methodological model of data-driven state regulation of digital transformation in the rehabilitation system. Its core is an adaptive regulatory architecture that integrates the rehabilitation data environment, data governance and interoperability, indicator-based monitoring, regulatory analytics, hybrid decision-making, adaptive regulatory instruments, system performance assessment and a feedback loop. It is substantiated that rehabilitation data acquire regulatory value only when they are transformed into indicators, analytical signals and grounds for public decision-making. An indicator framework has been developed, covering digital infrastructure and interoperability, access to rehabilitation services, quality and rehabilitation outcomes, regulatory capacity, trust, security and system resilience. A hybrid decision-making model has also been formed. In this model, digital analytics, algorithmic support and risk assessment do not replace public authority decisions, but strengthen their evidence base, timeliness and transparency through expert assessment, legal validation and human-in-the-loop oversight.
Conclusions. The proposed model provides a methodological basis for moving from fragmented digitalization of rehabilitation services to adaptive state regulation of digital transformation in the rehabilitation system.
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