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Healthcare
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Health Services, Health Literacy and Nursing Quality
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Health Services, Health Literacy and Nursing Quality

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Healthcare Quality, Patient Safety, and Self-Care Management".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1596

Special Issue Editor

Dr. Haneen Ali

E-Mail Website
Guest Editor
1. Department of Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA
2. Health Services Administration Program, Auburn University, Auburn, AL 36849, USA
Interests: health services; healthcare delivery systems; Lean Six Sigma in healthcare

Special Issue Information

Dear Colleagues,

The healthcare landscape is continuously evolving, driven by advances in medical technologies, patient expectations, and policy changes. Central to this evolution is the critical role of health services and nursing quality, which significantly impacts patient outcomes, satisfaction, and the overall effectiveness of healthcare systems.

In recent years, the integration of data analytics and artificial intelligence (AI) into healthcare practices has revolutionized how nursing quality and health services are delivered. From predictive analytics to personalized care, the applications of data and AI are offering unprecedented opportunities to enhance care quality, improve efficiency, and address key challenges in patient safety.

We are pleased to invite you to contribute to this Special Issue, which aims to foster a rich dialogue that not only addresses existing challenges but also presents actionable strategies and innovative, AI-driven solutions to enhance the quality of health services and nursing care worldwide. We particularly welcome submissions from interdisciplinary teams, including healthcare administrators, nursing professionals, AI developers, and academics.

This issue aspires to be a valuable resource for advancing the field, offering key insights into how data analytics and AI can be effectively leveraged to optimize nursing care, improve health service delivery, and meet the evolving needs of modern healthcare systems, all while maintaining the highest standards of patient care.

This Special Issue aims to explore the current trends, challenges, innovations, and the transformative potential of data and AI in health services delivery and nursing care quality. We invite contributions that examine the multifaceted aspects of nursing practices, health services management, and patient-centered care, with a particular focus on improving patient outcomes, enhancing efficiency, and reducing healthcare disparities.

In this Special Issue, research areas may include (but are not limited to) the following:

  1. Original studies and reviews;
  2. Applications of AI and machine learning in predictive analytics for nursing outcomes;
  3. Data-driven approaches and real-time analytics for continuous quality improvement in nursing;
  4. Mental health and its impact on nursing practices;
  5. Policy and regulatory frameworks shaping nursing quality standards;
  6. Cross-disciplinary approaches to integrating data science and nursing expertise;
  7. Patient-centered care models enhanced by AI and data analytics.

We look forward to receiving your contributions.

Dr. Haneen Ali
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 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

  • nursing quality improvement
  • health services management
  • AI in healthcare
  • data-driven nursing practices
  • patient-centered care
  • healthcare policy and regulation
  • mental 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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17 pages, 787 KB  
Article
Assessing Stress and Shift Quality in Nursing Students: A Pre- and Post-Shift Survey Approach
by Haneen Ali and Yasin Fatemi
Healthcare 2025, 13(14), 1741; https://doi.org/10.3390/healthcare13141741 - 18 Jul 2025
Viewed by 616
Abstract
Background: Nursing students often experience heightened levels of stress during clinical training due to the dual demands of academic and clinical responsibilities. These stressors, compounded by environmental and organizational factors, can adversely affect students’ well-being, academic performance, and the quality of patient care [...] Read more.
Background: Nursing students often experience heightened levels of stress during clinical training due to the dual demands of academic and clinical responsibilities. These stressors, compounded by environmental and organizational factors, can adversely affect students’ well-being, academic performance, and the quality of patient care they deliver. Aim: This study aimed to identify the key stressors influencing nursing students’ perceptions of single-shift quality (SSQ) during clinical training and to examine how well students can predict the quality of their shift based on pre-shift expectations. Methodology: A cross-sectional survey design was implemented, collecting pre- and post-shift data from 325 nursing students undergoing clinical training in Alabama. The survey measured 13 domains related to workload, environmental conditions, organizational interactions, coping strategies, and overall satisfaction. Paired t tests and linear regressions were used to assess changes in perception and identify key predictors of SSQ. Results: This study found significant discrepancies between students’ pre- and post-shift evaluations across multiple domains, including internal environment, organizational interaction with clinical faculty/preceptors, and coping strategies (p < 0.001). Students also accurately predicted stable factors such as patient characteristics and external environment. Pre-shift expectations did not significantly predict post-shift experiences. Post-shift perceptions revealed that stress-coping strategies and collegiality were the strongest predictors of shift quality. Conclusion: Students enter clinical shifts with optimistic expectations that often do not align with actual experiences, particularly regarding support and stress management. The SSQ framework offers a valuable tool for identifying gaps in clinical training and guiding interventions that foster resilience and better alignment between expectations and real-world practice. Full article
(This article belongs to the Special Issue Health Services, Health Literacy and Nursing Quality)
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32 pages, 1219 KB  
Systematic Review
Guidelines for Reducing the Adverse Effects of Shift Work on Nursing Staff: A Systematic Review
by Alessio Danilo Inchingolo, Angelo Michele Inchingolo, Maria Celeste Fatone, Laura Ferrante, Lucia Casamassima, Irma Trilli, Francesco Inchingolo, Andrea Palermo, Grazia Marinelli and Gianna Dipalma
Healthcare 2025, 13(17), 2148; https://doi.org/10.3390/healthcare13172148 - 28 Aug 2025
Viewed by 529
Abstract
Background: The increasing demand for care in hospital settings, often at a high intensity, requires organizing work according to 24 h shifts. Nevertheless, shift work (SW), especially at night, alters the circadian rhythm, negatively affecting the psychophysical health of nurses, compromising their quality [...] Read more.
Background: The increasing demand for care in hospital settings, often at a high intensity, requires organizing work according to 24 h shifts. Nevertheless, shift work (SW), especially at night, alters the circadian rhythm, negatively affecting the psychophysical health of nurses, compromising their quality of life, and jeopardizing patient safety. Shift-work-related diseases (SWDs) can arise from these disruptions. Methods: This systematic review aims to evaluate the effects of several types of medical, psychotherapeutic, and educational interventions and strategies on shift-work-related diseases (SWDs). The databases PubMed, Embase, Web of Science, and Cochrane were searched using the MESH terms “shift work” and “nurses” from January 2015 to March 2025. A total of 43 articles were included in the final analysis. Results: Quantitative findings from the studies showed, for example, improvements in sleep quality scores ranging from 15% to 40% with optimized shift planning, reductions in fatigue scores by 20–35% through strategic napping, and moderate effect sizes for light therapy interventions. Physical activity and relaxation techniques were associated with a 10–25% improvement in subjective well-being indices, while meal timing interventions led to reductions in gastrointestinal symptom prevalence by up to 18%. The selected articles were discussed by dividing them according to the type of intervention applied to shift nurses, namely improvement of shift planning, light and temperature modulation, introduction of napping, supplementation, meal management, psychotherapy, sleep education, physical activity, relaxation techniques and yoga, music therapy, and aromatherapy. This categorization was performed to highlight the range of strategies tested and their relative quantitative impact. Conclusions: There is evidence that SWDs can be mitigated through targeted interventions and strategies. The limitations of the studies examined include small sample sizes, extreme heterogeneity of follow-up, the few numbers of randomized controlled trials, and the prevalence of female or Intensive Care Unit nurses in study samples. Further research should focus on large-scale randomized controlled trials, multicenter longitudinal studies, and the evaluation of the most promising interventions—particularly light therapy, optimized shift scheduling, and structured napping protocols—to assess their long-term efficacy and generalizability. Full article
(This article belongs to the Special Issue Health Services, Health Literacy and Nursing Quality)
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