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SITeCard: A human-centric explainable intelligent system to support the cardiovascular rehabilitation process

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives: To report the development and early formative, user-centered evaluation of a human-centric explainable artificial intelligence (AI)-enabled platform for remote and hybrid phase II cardiovascular rehabilitation (CR), and to discuss its policy and technology implications for adoption and governance in health systems facing access constraints. Methods: A four-stage methodology was applied: (1) multidisciplinary needs elicitation with cardiovascular rehabilitation professionals; (2) development of machine-learning models for rehabilitation-related risk assessment with integrated explainability; (3) adaptation of expla-nations to clinicians’ and patients’ mental models; and (4) system implementation followed by early multidisciplinary evaluation focused on usability, perceived clinical utility, and safety positioning as a second-opinion decision support tool. Results: The platform integrates remote patient monitoring, explainable risk assessment, and coordinated multidisciplinary workflows. In early formative evaluation, healthcare professionals reported high acceptance of the explainable second-opinion functionality, highlighting improved interpretability and support for rehabilitation assessment and discharge-related discussions, without replacing clinical judgment. Conclusions: This study provides an early-stage, policy-relevant account of how explainable AI can be operationalized in cardiovascular rehabilitation while remaining aligned with clinical practice and governance expectations. Rather than demonstrating system-level impact, the con-tribution lies in outlining a practical framework for evaluating adoption conditions, governance needs, and future scale-up of AI-enabled rehabilitation technologies. Public interest summary: Cardiovascular rehabilitation helps people recover after a heart event, but many patients face barriers to attending in-person programs, particularly due to distance, mobility, or limited service availability. SITeCard is a digital platform developed to support remote and hybrid cardiovascular rehabilitation by organizing patient data and providing clinicians with explainable AI-based risk assessments to inform multidisciplinary discussions. The system was co-designed with healthcare teams to ensure usability and clinically meaningful explanations, and it can be accessed through standard smartphones, including in low-connectivity settings. This study reports early, user-centered evaluation results and highlights policy and governance considerations relevant to the adoption of explainable AI tools in rehabilitation services.

Original languageEnglish
Article number101168
JournalHealth Policy and Technology
Volume15
Issue number3
DOIs
Publication statusPublished - Apr 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • AI-based systems
  • Cardiovascular Rehabilitation
  • Clinical decision support systems
  • Explainable Artificial Intelligence
  • Human-centric explainable artificial intelligence

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Policy

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