ArticleHealthcare (Basel, Switzerland)2025
Integrating Artificial Intelligence, Electronic Health Records, and Wearables for Predictive, Patient-Centered Decision Support in Healthcare.
Article in Healthcare (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From conceptual emergence to multidisciplinary expansion: a bibliometric and knowledge mapping analysis of cardiovascular-kidney-metabolic syndrome research.Frontiers in endocrinology · 2026Pooled it
- Artificial Intelligence for Clinical Decision Support in Rural Spine Care: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Drivers of Artificial Intelligence (AI) Adoption in Supporting Chronic Disease Management in Primary Care: A Scoping Review.Nursing reports (Pavia, Italy) · 2026Review
- The Digital Transformation of Rehabilitation Medicine: A Narrative Review of Artificial Intelligence Innovations, Clinical Integration, and Future Paradigms.Journal of evaluation in clinical practice · 2026Review
- Research Priorities in Perioperative Nursing Across the Lifespan: An e-Delphi Consensus Study.Nursing reports (Pavia, Italy) · 2026Article
- Artificial Intelligence for Personalized Management of Acute Myeloid Leukemia.Journal of personalized medicine · 2026Review
- Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies.World journal of nephrology · 2026Review
- Longitudinal patient-reported outcomes after spinal fusion for adolescent idiopathic scoliosis: recovery trajectories and insights into surgical timing.Spine deformity · 2026Article
- Optimizing Extracorporeal Cardiopulmonary Resuscitation Candidate Selection in out-of-Hospital Cardiac Arrest: A Machine-Learning Individualized Treatment Effect Approach Versus Rule-Based Criteria.Journal of the American Heart Association · 2026Article
- Explainability and Human Oversight for AI-Generated Exercise Guidance in Digital Healthcare: A Governance-Oriented Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Healthcare and Public Health: Emerging Applications, Clinical Integration and Future Directions.Bioengineering (Basel, Switzerland) · 2026Article
- Value, Structure, and Curriculum in US Graduate Health Informatics Programs: Cross-Sectional Study.JMIR medical education · 2026Article
- Healthcare Goes Digital: mHealth, eHealth, Artificial Intelligence, and Emerging Digital Technologies Within Digital Health Transformation.Healthcare (Basel, Switzerland) · 2026Article
- Correction: Fernandes Prabhu et al. Integrating Artificial Intelligence, Electronic Health Records, and Wearables for Predictive, Patient-Centered Decision Support in Healthcare.Healthcare (Basel, Switzerland) · 2026Article
- Administrative Perspectives on Digital Workflow Transformation and Artificial Intelligence Implementation in Dental Clinics.Dentistry journal · 2026Article
- Automating Critical Care EEG: Enhancing Accuracy, Efficiency, and Patient Outcomes in Critical Care.Epilepsy currents · 2026Article
- Global Predictors of Appointment Non-Adherence in Primary Care Settings: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Letter comments on: "Patient and staff experiences with an EHR-integrated symptom management program (eSyM) in oncology".Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Ethical oversight of AI-driven paediatric trials: a proactive, risk-sensitive interim review model.Frontiers in digital health · 2026Article
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026Review
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study explores how patients and stakeholders envision integrated digital health systems. BACKGROUND/
objectivesIntegrating artificial intelligence (AI), wearable data, electronic health records (EHRs), and patient-reported outcomes could enable proactive and personalized healthcare. However, current solutions remain fragmented and poorly aligned with user expectations. This study aimed to explore patient and stakeholder needs for AI-driven integration and propose a conceptual framework to inform future system design.
methodsAs part of the NSF Innovation Corps (I-Corps) program, we conducted semi-structured interviews with 44 participants representing Health Enthusiasts, Chronic Condition Managers, and Low-Engagement Users. Interviews followed the I-Corps customer discovery framework and were thematically analyzed using a hybrid deductive-inductive approach.
resultsParticipants highlighted four priorities: (i) interoperability and unification of data from wearables, EHRs, and self-reports; (ii) actionable personalization with predictive insights; (iii) trust and transparency in AI recommendations, often requiring clinician oversight; and (iv) usability through low-friction, intuitive interfaces. Age- and persona-specific differences emerged: younger participants favoring predictive features and older participants emphasizing safety, reassurance, and clinical integration.
conclusionsThis exploratory qualitative study identified stakeholder needs that informed a conceptual framework for integrated digital health platforms. While preliminary, the framework provides a blueprint for future technical development and validation of patient- and provider-centered systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.