Machine Learning in Optimising Nursing Care Delivery Models: An Empirical Analysis of Hospital Wards.
Objective: This study aims to assess the performance of machine learning (ML) techniques in optimising nurse staffing and evaluating the appropriateness of nursing care delivery models in hospital wards. The primary outcome measures include the adequacy of nurse staffing and the appropriateness of t...
| Publicado en: | Journal of Evaluation in Clinical Practice Vol. 31; no. 1; pp. 1 - 13 |
|---|---|
| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183982926&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183982926 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13561294 EV1 jtl: Journal of Evaluation in Clinical Practice issn: 13561294 maglogo: Y pubinfo: dt: Feb2025 vid: 31 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 183982926 183982926 183982926 10.1111/jep.70001 183982926 ppf: 1 ppct: 12 formats: tig: atl: Machine Learning in Optimising Nursing Care Delivery Models: An Empirical Analysis of Hospital Wards. aug: au: Aslan, Manar Toros, Ergin affil: Department of Nursing, Trakya University Faculty of Health Sciences, Edirne, Turkey sug: subj: Machine Learning Nursing Care Nursing Models, Theoretical Nursing Staff, Hospital Personnel Staffing and Scheduling Hospital Units Human Academic Medical Centers Descriptive Research Support Vector Machine Random Forest Logistic Regression Algorithms Patient Care Descriptive Statistics Scales ab: Objective: This study aims to assess the performance of machine learning (ML) techniques in optimising nurse staffing and evaluating the appropriateness of nursing care delivery models in hospital wards. The primary outcome measures include the adequacy of nurse staffing and the appropriateness of the nursing care delivery system. Background: Historical and current healthcare challenges, such as nurse shortages and increasing patient acuity, necessitate innovative approaches to nursing care delivery. For instance, the COVID‐19 pandemic highlighted the need for flexible and scalable staffing models to manage surges in patient volume and acuity. Materials and Methods: A descriptive study was conducted in 39 inpatient wards across a university hospital and three state hospitals, involving 117 ward‐level observations. Data were collected using the Rush Medicus Patient Classification Scale and analysed using k‐Nearest Neighbour, Support Vector Machine, Random Forest, and Logistic Regression algorithms. Effectiveness was measured by the accuracy of machine learning predictions regarding nurse staffing adequacy, while suitability was determined by the congruence between observed nursing care models and patient needs. Reporting Method: STROBE checklist. Results: The Random Forest algorithm demonstrated the highest accuracy in predicting both nurse staffing adequacy and the appropriateness of nursing care delivery systems. The study found that 68.4% of wards had sufficient nurse staffing and 26.5% of wards used appropriate care delivery models, with functional nursing and total patient care models being the most commonly used. Discussion: The study highlights functional nursing and total patient care models, emphasising the need to consider nurse qualifications and patient needs in selecting care systems. Machine learning, particularly the Random Forest algorithm, proved effective in aligning staffing with patient requirements. Conclusion: Machine learning, particularly the Random Forest algorithm, proves effective in optimising nursing care delivery models, suggesting significant potential for enhancing patient care and nurse satisfaction. Implications: The research underscores machine learning's role in improving nursing care delivery, aligning nurse staffing with patient needs, and advancing healthcare outcomes. Impact: The findings advocate for integrating machine learning in the planning of nursing care delivery models. This study sets a precedent for using data‐driven approaches to improve nurse staffing and care delivery, potentially enhancing global clinical outcomes and operational efficiencies. The global clinical community can learn from this study the value of employing machine learning techniques to make informed, evidence‐based decisions in healthcare management. Patient or Public Contribution: While the study lacked direct patient involvement, its goal was to enhance patient care and healthcare efficiency. Future research will aim to incorporate patient and public insights more directly. Summary: Introduces a novel application of machine learning algorithms in nursing care delivery, showcasing a data‐driven approach to optimise staffing and improve patient care across diverse healthcare settings.Advocates for the integration of technological advancements in healthcare practices, setting a precedent for future research and policy development aimed at enhancing global clinical outcomes and operational efficiencies. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|