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Nursing Reports
Special Issues
AI in Nursing: Promoting Patient Safety and Care Quality
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AI in Nursing: Promoting Patient Safety and Care Quality

A special issue of Nursing Reports (ISSN 2039-4403). This special issue belongs to the section "Artificial Intelligence and Digital Innovations in Nursing Care".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1261

Editors

Prof. Dr. Rhanna Emanuela Fontenele Lima de Carvalho

E-Mail Website
Guest Editor
Graduate Program in Clinical Nursing and Health Care, School of Nursing, Universidade Estadual do Ceará, Fortaleza 60714-903, Brazil
Interests: safety patient; technology; machine learning
Dr. Francisco Gilberto Pereira

E-Mail Website
Guest Editor
Graduate Program in Nursing (PPGENF), Federal University of Piauí (UFPI), Teresina 64049-550, Brazil
Interests: educational technologies; nursing education; medical–surgical nursing; patient safety
Dr. Yuliett Mora Pérez

E-Mail Website
Guest Editor
School of Nursing, Universidade Estadual do Ceará, Fortaleza 60714-903, Brazil
Interests: safety patient; technology; machine learning

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is increasingly incorporated into health services to support clinical decision-making, risk prediction, workflow optimization, and quality monitoring. Within this context, nursing practice plays a central role, as nurses are key users of information systems and primary actors in patient safety, care coordination, and quality improvement across healthcare settings. Therefore, understanding how AI technologies interact with nursing work is critical to advancing safe and effective care.

AI-based applications have been developed to assist nurses in the early detection of clinical deterioration, medication safety, workload management, documentation, and surveillance of adverse events. However, their contribution to patient safety and care quality depends on appropriate design, validation, and integration into clinical workflows. Organizational readiness, data quality, ethical governance, accountability, and equity are essential considerations influencing successful implementation, particularly in complex and resource-limited health systems.

This Special Issue invites contributions that examine the use of AI in nursing from a health services research perspective. We welcome evaluative, quantitative, qualitative, mixed-methods, and implementation studies addressing patient safety, care quality, workforce implications, governance, and the real-world application of AI in nursing practice. By bringing together diverse evidence, this Special Issue aims to inform policy, management, and clinical strategies for the responsible use of AI in nursing and the promotion of safer, higher-quality care.

Prof. Dr. Rhanna Emanuela Fontenele Lima de Carvalho
Dr. Francisco Gilberto Pereira
Dr. Yuliett Mora Pérez
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nursing Reports is an international peer-reviewed open access monthly 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 1800 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

  • patient safety
  • health services research
  • quality of care
  • hospital governance
  • risk management
  • patient safety units
  • health system evaluation
  • organizational factors

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

Published Papers (2 papers)

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23 pages, 1517 KB  
Article
Development and Validation of an Interpretable Machine Learning Model for Inpatient Fall Risk Using Electronic Health Record Data
by Siti Zubaidah Mordiffi, Xiujuan Guo, Mien Li Goh, Kee Yuan Ngiam, Neng Wei Wong, Jenny Chua, Mohammad Shaheryar Furqan and Han Shi Jocelyn Chew
Nurs. Rep. 2026, 16(8), 283; https://doi.org/10.3390/nursrep16080283 - 13 Aug 2026
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Abstract
Background: Falls are the most common hospital-acquired adverse event, leading to extended hospitalization, loss of independence, disability, and premature death. Routine fall risk assessments are time-consuming, even with limited factors. An AI-derived fall prediction model can provide more comprehensive and comparably accurate [...] Read more.
Background: Falls are the most common hospital-acquired adverse event, leading to extended hospitalization, loss of independence, disability, and premature death. Routine fall risk assessments are time-consuming, even with limited factors. An AI-derived fall prediction model can provide more comprehensive and comparably accurate risk predictions quickly and as often as needed. Objective: To develop and validate a fall prediction model for fall risk in adult inpatients. Methods: Patient records from 2016 were extracted from the adult inpatient database, including information from the Electronic Inpatient Medication Records, SAP, and Hospital Incident Reporting System. The sample consisted of 1506 cases (1:5 faller to non-faller). The fall prediction model was trained using the following four variables: demographics, diagnosis, medications, and surgery. Data sources included the hospital’s data repository, integrating admission/discharge, pharmacy, laboratory, and incident reports. Results: The support vector machine model performed best among all tested models, achieving an AUC of 0.803, recall of 0.816, and precision of 0.440. In the validation cohort (978 patients: 163 fallers and 815 non-fallers), the fall prediction model demonstrated moderate-to-good discrimination (AUC 0.79), with accuracy of 0.67, sensitivity of 0.46, and specificity of 0.86. Compared with the nursing four-item fall risk assessment, which showed lower discrimination (AUC 0.65, accuracy 0.65, sensitivity 0.58, specificity 0.72), the fall prediction model had better specificity and overall discrimination, though the nursing tool was more sensitive in identifying fallers. Conclusions: The fall prediction model using demographics, diagnoses, medication, and surgery data predicts falls risk effectively. It enables timely, accurate risk assessments and supports preventive interventions, saving nurses’ time for direct patient care. Full article
(This article belongs to the Special Issue AI in Nursing: Promoting Patient Safety and Care Quality)
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17 pages, 1471 KB  
Systematic Review
Virtual Reality to Improve Breastfeeding Outcomes: A Systematic Review and Meta-Analysis
by Alok Raghav, Geetanjali Kalyan, Soumya Jyoti Raha, Jitendra Meena, Jogender Kumar and Praveen Kumar
Nurs. Rep. 2026, 16(6), 209; https://doi.org/10.3390/nursrep16060209 - 22 Jun 2026
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Abstract
Background: Breastfeeding enhances infant and maternal health, but global breastfeeding rates remain suboptimal. Virtual reality (VR) emerges as a promising tool for breastfeeding education. The objective of this review was to assess the effectiveness of VR-based interventions on breastfeeding outcomes in pregnant [...] Read more.
Background: Breastfeeding enhances infant and maternal health, but global breastfeeding rates remain suboptimal. Virtual reality (VR) emerges as a promising tool for breastfeeding education. The objective of this review was to assess the effectiveness of VR-based interventions on breastfeeding outcomes in pregnant and postpartum women. Methods: PubMed, Embase, Web of Science, Scopus, and CENTRAL were searched until 10 January 2026, for randomized controlled trials (RCTs) and quasi-experimental studies comparing VR-based interventions (immersive simulations, 360° videos, or head-mounted displays) with standard care or non-VR comparators in pregnant or postpartum women. Primary outcomes included breastfeeding self-efficacy, motivation, and breastfeeding technique (LATCH score). Secondary outcomes included exclusive breastfeeding rates, milk production, and maternal anxiety. Risk of bias was assessed using the RoB 2.0 and ROBINS-I tools for RCTs and non-RCTs, respectively. A random-effects meta-analysis was conducted, with results reported as mean differences (MD) or risk ratios (RR), along with 95% confidence intervals (CIs). Certainty of the evidence was assessed using the GRADE approach. Results: Five studies (4 RCTs and 1 quasi-experimental; n = 344) were included. VR improved prenatal breastfeeding self-efficacy (2 studies, MD: 13.93; 95% CI: 10.96–16.90), motivation (1 study, MD: 2.88; 95% CI: 1.66–4.10), and LATCH score (1 study, MD: 1.72; 95% CI: 1.37–2.07), and reduced time to breastfeeding initiation (1 study, MD: −22.4 min; 95% CI: −29 to −15.9), the certainty of evidence was low to very low for these outcomes. No significant effects were observed for postnatal self-efficacy, exclusive breastfeeding, or maternal anxiety. Formal assessment of publication bias could not be done. The small sample sizes for most outcomes, heterogeneity, the open-label nature of the trials, and the subjective nature of the outcomes should be considered when interpreting these results. Conclusions: VR-based interventions may improve process outcomes, such as prenatal breastfeeding self-efficacy, motivation, breastfeeding technique, and early breastfeeding initiation; the certainty of evidence is low to very low. Evidence for clinically important outcomes, including exclusive breastfeeding and maternal anxiety, remains inconsistent. Larger, well-designed RCTs are warranted before these interventions can be considered in routine practice. Full article
(This article belongs to the Special Issue AI in Nursing: Promoting Patient Safety and Care Quality)
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