ArticleJournal of medical Internet research2023
The Gap Between AI and Bedside: Participatory Workshop on the Barriers to the Integration, Translation, and Adoption of Digital Health Care and AI Startup Technology Into Clinical Practice.
Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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
15 citing papers in PubMed.
- Advancing Patient-Centred AI With Adaptive Machine Learning.Cancer medicine · 2026Review
- Explaining the Slow Adoption of AI Innovations in Health Care: Network Analysis Approach.JMIR AI · 2026Article
- Review
- Empowering photodynamic therapy with artificial intelligence: current trends and future directions.Frontiers in oncology · 2026Review
- Partners in Practice: Primary Care Physicians Define the Role of Artificial Intelligence.Healthcare (Basel, Switzerland) · 2025Article
- Success factors and measures for scaling patient-facing digital health technologies from leaders' insights.BMC health services research · 2025Article
- Artificial Intelligence in Thoracic Surgery: A Review Bridging Innovation and Clinical Practice for the Next Generation of Surgical Care.Journal of clinical medicine · 2025Review
- Toward Safe and Ethical Implementation of Health Care Artificial Intelligence: Insights From an Academic Medical Center.Mayo Clinic proceedings. Digital health · 2025Review
- Prototype of a multimodal AI system for vitiligo detection and mental health monitoring.Frontiers in medicine · 2025Article
- Leadership for AI Transformation in Health Care Organization: Scoping Review.Journal of medical Internet research · 2024Article
- Human-AI Teaming in Critical Care: A Comparative Analysis of Data Scientists' and Clinicians' Perspectives on AI Augmentation and Automation.Journal of medical Internet research · 2024Article
- Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review.JMIR nursing · 2024Review
- Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions' students in Jordan.BMC medical informatics and decision making · 2023Article
- Quantifying the impact of AI recommendations with explanations on prescription decision making.NPJ digital medicine · 2023Article
- Artificial Intelligence Challenges in the Healthcare Industry: A Systematic Review of Recent Evidence.Healthcare technology lettersReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundArtificial intelligence (AI) and digital health technological innovations from startup companies used in clinical practice can yield better health outcomes, reduce health care costs, and improve patients' experience. However, the integration, translation, and adoption of these technologies into clinical practice are plagued with many challenges and are lagging. Furthermore, explanations of the impediments to clinical translation are largely unknown and have not been systematically studied from the perspective of AI and digital health care startup founders and executives.
objectiveThe aim of this paper is to describe the barriers to integrating early-stage technologies in clinical practice and health care systems from the perspectives of digital health and health care AI founders and executives.
methodsA stakeholder focus group workshop was conducted with a sample of 10 early-stage digital health and health care AI founders and executives. Digital health, health care AI, digital health-focused venture capitalists, and physician executives were represented. Using an inductive thematic analysis approach, transcripts were organized, queried, and analyzed for thematic convergence.
resultsWe identified the following four categories of barriers in the integration of early-stage digital health innovations into clinical practice and health care systems: (1) lack of knowledge of health system technology procurement protocols and best practices, (2) demanding regulatory and validation requirements, (3) challenges within the health system technology procurement process, and (4) disadvantages of early-stage digital health companies compared to large technology conglomerates. Recommendations from the study participants were also synthesized to create a road map to mitigate the barriers to integrating early-stage or novel digital health technologies in clinical practice.
conclusionsEarly-stage digital health and health care AI entrepreneurs identified numerous barriers to integrating digital health solutions into clinical practice. Mitigation initiatives should create opportunities for early-stage digital health technology companies and health care providers to interact, develop relationships, and use evidence-based research and best practices during health care technology procurement and evaluation processes.
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.