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Healthcare Simulation, Artificial Intelligence and Interprofessional Collaboration
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Healthcare Simulation, Artificial Intelligence and Interprofessional Collaboration

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

Deadline for manuscript submissions: 30 May 2025 | Viewed by 2848

Special Issue Editors

Dr. Antonio Bonacaro

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Guest Editor
Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
Interests: critical care; clinical simulation; wearable devices; artificial Intelligence; holistic nursing; spirituality
Prof. Dr. Mark I. Johnson

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Guest Editor
Centre for Pain Research, School of Health, Leeds Beckett University, Leeds LS1 3HE, UK
Interests: pain; perception; pain management; education
Special Issues, Collections and Topics in MDPI journals
Dr. Kate Thompson

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Guest Editor
Centre for Pain Research, School of Health, Leeds Beckett University, Leeds LS6 3QL, UK
Interests: pain education; community pain services; pain research
Dr. Manos Georgiadis

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Guest Editor
School of Social Sciences and Humanities, University of Suffolk, Ipswich IP4 1QJ, UK
Interests: health promotion; psychophysiology; positive psychology
Dr. Serena Barello

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Guest Editor
Department of Brain and Behavioural Sciences, University of Pavia, 27100 Pavia, Italy
Interests: qualitative health research; patient engagement; patient centered medicine; psychoncology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapidly evolving landscape of health and social care demands innovative approaches to education, training, and clinical practise. Integrating artificial intelligence with simulation-based training offers a promising pathway to improve interprofessional collaboration among health and social care services providers. This Special Issue will cover various aspects including the development and application of AI tools in simulations, the effectiveness of simulation techniques in fostering teamwork, and case studies illustrating successful interprofessional collaborations driven by AI and simulation in improving patients’ outcomes. We invite empirical research, systematic reviews, and innovative case studies that address these themes.

This Special Issue seeks to explore the convergence of simulation, artificial intelligence (AI), and interprofessional collaboration to enhance patient care, health, and social care outcomes. It aims to bring together multidisciplinary research that demonstrates how AI-driven simulations can support the training and collaboration of diverse healthcare teams. Contributions may include studies on AI algorithms for simulation training, case studies on simulation in interprofessional education, and analyses of AI's role in facilitating teamwork in clinical settings. These contributions are aimed at advancing patient care and optimizing healthcare services.

We look forward to receiving insightful contributions.

Dr. Antonio Bonacaro
Prof. Dr. Mark I. Johnson
Dr. Kate Thompson
Dr. Manos Georgiadis
Dr. Serena Barello
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • simulation
  • artificial intelligence
  • interprofessional collaboration
  • patient outcomes
  • education
  • social care
  • healthcare
  • nursing
  • psychology
  • social work

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

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Research

21 pages, 1892 KiB  
Article
Development, Implementation, and Evaluation of a ‘Virtual Patient’ with Chronic Low Back Pain: An Education Resource for Physiotherapy Students
by Kate Thompson, Steven Bathe, Kate Grafton, Niki Jones, David Spark, Louise Trewern, Thomas van Hille and Mark I. Johnson
Healthcare 2025, 13(7), 750; https://doi.org/10.3390/healthcare13070750 - 27 Mar 2025
Viewed by 740
Abstract
Background: The management of chronic pain is inherently multidisciplinary, requiring collaboration across health and care professions because pain is multidimensional, involving psychological, social, biomedical, cultural, and environmental factors. However, pain education has often focused more on biomedical aspects, limiting the capacity of professionals [...] Read more.
Background: The management of chronic pain is inherently multidisciplinary, requiring collaboration across health and care professions because pain is multidimensional, involving psychological, social, biomedical, cultural, and environmental factors. However, pain education has often focused more on biomedical aspects, limiting the capacity of professionals to deliver integrated, person-centred care. Shifting pain education away from biomedically driven curricula may better prepare graduates for meaningful consultations and biopsychosocial care. Objective: This manuscript reports the development and pilot evaluation of a virtual patient simulation designed to help physiotherapy students develop person-centred pain assessment skills. Methods: We developed and piloted a virtual patient with complex pain scenarios for physiotherapy students. To evaluate the simulation, students completed a self-reported questionnaire assessing their ability, self-confidence in person-centred assessment skills, and their attitudes and beliefs regarding the simulation. Results: Frequency and confidence in person-centred inquiry ranged from 100% to 16.3%, depending on the complexity of information. Inductive thematic analysis revealed four themes: (1) Environmental factors & preferences—students’ preference for the learning environment; (2) Learning experience—including engagement, feedback, discussions, and a ‘safe’ space for building confidence; (3) Professional development—insights into person-centred inquiry, personal biases, and emotional challenges; (4) Limitations—including the desire for more complexity, and technical challenges noted. Conclusions: The development of this virtual patient simulation enabled healthcare students to engage with a multidimensional perspective on pain, fostering skills essential for biopsychosocial pain assessment and patient-centred care. Although designed and piloted with physiotherapy students, this model holds potential for broader application across healthcare disciplines. Full article
(This article belongs to the Special Issue Healthcare Simulation, Artificial Intelligence and Interprofessional Collaboration)
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14 pages, 220 KiB  
Article
Arterial Blood Gas Analysis and Clinical Decision-Making in Emergency and Intensive Care Unit Nurses: A Performance Evaluation
by Arian Zaboli, Chiara Biasi, Gabriele Magnarelli, Barbara Miori, Magdalena Massar, Norbert Pfeifer, Francesco Brigo and Gianni Turcato
Healthcare 2025, 13(3), 261; https://doi.org/10.3390/healthcare13030261 - 28 Jan 2025
Viewed by 1430
Abstract
Background: This study aimed to evaluate Emergency Department and Intensive Care Unit nurses’ skills in interpreting blood gas analysis results and to use those interpretations in clinical decision-making. Methods: In this prospective, multicenter, simulation-based study, nurses from the Emergency Department (ED) of Merano [...] Read more.
Background: This study aimed to evaluate Emergency Department and Intensive Care Unit nurses’ skills in interpreting blood gas analysis results and to use those interpretations in clinical decision-making. Methods: In this prospective, multicenter, simulation-based study, nurses from the Emergency Department (ED) of Merano Hospital and the Intensive Care Unit (ICU) of Bolzano Hospital, Italy, were presented with 16 clinical vignettes based on real patient cases. These vignettes were designed to evaluate the nurses’ ability to identify patients with time-dependent conditions and recommend appropriate therapeutic interventions. Outcomes measured included sensitivity, specificity, and agreement with physician-assigned urgency levels and therapy recommendations. Results: Among the 43 participants (26 ICU and 17 ED nurses), specificity in excluding patients without time-dependent conditions or organ replacement needs was high. However, sensitivity in identifying time-dependent conditions was less than 50%. Agreement with physician-assigned urgency levels was low, with Cohen’s kappa values of 0.139 for ICU nurses and 0.218 for ED nurses. Nurses with lower self-confidence in interpreting BGA results made more errors, while other personal or professional factors did not significantly impact performance. Conclusions: Although critical care nurses can effectively rule out patients without time-dependent conditions, their ability to identify such conditions requires improvement. These findings underscore the need for targeted training programs to enhance nurses’ BGA interpretation skills and clinical decision-making in high-pressure, time-sensitive situations. Full article
(This article belongs to the Special Issue Healthcare Simulation, Artificial Intelligence and Interprofessional Collaboration)
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