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Healthcare
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Artificial Intelligence in Health Services Research and Organizations
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Artificial Intelligence in Health Services Research and Organizations

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Healthcare".

Deadline for manuscript submissions: 27 February 2026 | Viewed by 11876

Special Issue Editor

Prof. Dr. Marie-Pierre Gagnon

E-Mail Website
Guest Editor
Faculté des Sciences Infirmières, Université Laval, Québec, QC G1V 0A6, Canada
Interests: information and communication technologies for health; behaviour of individuals and health professionals; introduce innovation in practice and organization; synthesis, dissemination and application of knowledge; evaluation of health technologies and interventions; quantitative, qualitative and mixed methods

Special Issue Information

Dear Colleagues,

Artificial intelligence applications in healthcare have proliferated in recent years. However, AI also holds great promise for supporting health services research and the organization of care and services. Whether through the integration of automated knowledge synthesis tools, scribes for the automatic transcription of clinical notes, or support of clinical work, AI can facilitate research and decision making in the healthcare services sector. This Special Issue of Healthcare is an opportunity to explore the current state of knowledge on the benefits and limitations of AI applications in healthcare research and services. Contributive topics would include AI tools for automated data collection and analysis in knowledge synthesis, the impact of AI scribes on patients, providers, and organizations, AI tools to support integrated patient trajectories, and the impact of AI on clinical decision making.

Prof. Dr. Marie-Pierre Gagnon
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • electronic health record
  • telemedicine
  • disease diagnosis
  • decision making
  • mobile health

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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15 pages, 895 KB  
Article
Diagnostic Accuracy of AI-Assisted Focused Cardiac Ultrasound (FOCUS) in Primary Care
by Mihai-Sorin Iacob, Nilima Rajpal Kundnani, Abhinav Sharma, Andrei Iacob, Anca-Raluca Dinu and Simona Ruxanda Dragan
Healthcare 2025, 13(21), 2726; https://doi.org/10.3390/healthcare13212726 - 29 Oct 2025
Viewed by 2326
Abstract
Background: Focused cardiac ultrasound (FOCUS) can augment the cardiac exam in primary care but is operator-dependent. We evaluated the diagnostic performance of artificial intelligence-assisted FOCUS (AI-FOCUS) performed by family physicians against cardiologist-performed echocardiography. Methods: This research is a prospective cross-sectional study [...] Read more.
Background: Focused cardiac ultrasound (FOCUS) can augment the cardiac exam in primary care but is operator-dependent. We evaluated the diagnostic performance of artificial intelligence-assisted FOCUS (AI-FOCUS) performed by family physicians against cardiologist-performed echocardiography. Methods: This research is a prospective cross-sectional study in primary care; family physicians performed conventional FOCUS and AI-FOCUS, with cardiologist-performed echocardiography within 24 h as the reference standard. The primary outcomes were accuracy, sensitivity/specificity, and agreement (κ). Results: AI-FOCUS achieved 94.33% accuracy (95% CI 93.15–95.35), 89.91% sensitivity, and 96.49% specificity, with excellent agreement compared to cardiologists (κ = 0.88). Among the confirmed abnormalities (32.9% of participants), valvular disease was most frequent (42%), followed by reduced LVEF < 50% (28%) and pericardial effusion (12%). In multivariable analysis, AI-assisted LVEF < 50% (OR = 6.05, p < 0.0001) and valvular abnormalities (OR = 4.05, p < 0.0001) were strong predictors of cardiac pathology. Conclusions: AI-FOCUS performed by trained family physicians showed high diagnostic accuracy and excellent agreement with blinded cardiologist-performed echocardiography for detecting LVEF < 50%, screening-level valvular abnormalities, and pericardial effusion, supporting its use for early detection and triage in primary care. Its ease of use and reproducibility suggest value in settings with limited access to cardiology. Full article
(This article belongs to the Special Issue Artificial Intelligence in Health Services Research and Organizations)
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21 pages, 899 KB  
Systematic Review
The Impact of AI Scribes on Streamlining Clinical Documentation: A Systematic Review
by Maxime Sasseville, Farzaneh Yousefi, Steven Ouellet, Florian Naye, Théo Stefan, Valérie Carnovale, Frédéric Bergeron, Linda Ling, Bobby Gheorghiu, Simon Hagens, Samuel Gareau-Lajoie and Annie LeBlanc
Healthcare 2025, 13(12), 1447; https://doi.org/10.3390/healthcare13121447 - 16 Jun 2025
Cited by 10 | Viewed by 9055
Abstract
Background: Burnout among clinicians, including physicians, is a growing concern in healthcare. An overwhelming burden of clinical documentation is a significant contributor. While medical scribes have been employed to mitigate this burden, they have limitations such as cost, training needs, and high turnover [...] Read more.
Background: Burnout among clinicians, including physicians, is a growing concern in healthcare. An overwhelming burden of clinical documentation is a significant contributor. While medical scribes have been employed to mitigate this burden, they have limitations such as cost, training needs, and high turnover rates. Artificial intelligence (AI) scribe systems can transcribe, summarize, and even interpret clinical conversations, offering a potential solution for improving clinician well-being. We aimed to evaluate the effectiveness of AI scribes in streamlining clinical documentation, with a focus on clinician experience, healthcare system efficiency, and patient engagement. Methods: We conducted a systematic review following Cochrane methods and PRISMA guidelines. Two reviewers conducted the selection process independently. Eligible intervention studies included quantitative and mixed-methods studies evaluating AI scribe systems. We summarized the data narratively. Results: Eight studies were included. AI scribes demonstrated positive effects on healthcare provider engagement, with users reporting increased involvement in their workflows. The documentation burden showed signs of improvement, as AI scribes helped alleviate the workload for some participants. Many clinicians have found AI systems to be user-friendly and intuitive, although some have expressed concerns about scribe training and documentation quality. A limited impact on reducing burnout was found, although documentation time improved in some studies. Conclusions: Most of the studies reported in this review involved small sample sizes and specific healthcare settings, limiting the generalizability of the findings to other contexts. Accuracy and consistency can vary significantly depending on the specific technology, model training data, and implementation approach. AI scribes show promise in improving documentation efficiency and clinician workflow, although the evidence remains limited and heterogeneous. Broader and real-world evaluations are needed to confirm their effectiveness and inform responsible implementations. Full article
(This article belongs to the Special Issue Artificial Intelligence in Health Services Research and Organizations)
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