Estimation of patient safety culture in private and public hospitals using machine learning methods.
Background: Patient safety is a critical component of health care systems. Large groups of patients, as a result of medical errors, are at risk of harm. OBJECTIVE: This study evaluated the patient safety culture (PSC) between different work groups in both public and private hospitals, using machine...
| Publicado en: | Work Vol. 82; no. 1; pp. 176 - 188 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Sep2025
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| 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=187409770&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187409770 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: Sep2025 vid: 82 iid: 1 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 187409770 185180711 187409770 187409770 10.1177/10519815251337925 187409770 ppf: 176 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Estimation of patient safety culture in private and public hospitals using machine learning methods. aug: au: Abbasi, Soheil Alijanpour, Khalil Samad-Soltani, Taha Abbasi, Sina Mohammadian, Yousef Aslani, Hassan affil: Department of Health, Safety, and Environment Management, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran sug: subj: Hospitals, Private Administration Hospitals, Public Administration Organizational Culture Evaluation Patient Safety Machine Learning Human Iran Funding Source Questionnaires Neural Networks (Computer) Random Forest Linear Regression Algorithms Educational Status Work Experiences Sex Factors Income Employment Status Feedback Communication Health Care Errors Prevention and Control Data Mining Descriptive Research Cross Sectional Studies Random Sample Cluster Sample Descriptive Statistics Kruskal-Wallis Test Data Analysis Software Adult Male Female Physicians Nursing Staff, Hospital Practical Nurses Adult: 19-44 years Male Female ab: Background: Patient safety is a critical component of health care systems. Large groups of patients, as a result of medical errors, are at risk of harm. OBJECTIVE: This study evaluated the patient safety culture (PSC) between different work groups in both public and private hospitals, using machine learning approaches. Methods: The HSOPSC questionnaire was used for evaluating safety culture, and the artificial neural network (ANN), random forest (RF) and linear regression (LR) algorithms were used for data modeling. Orange Data Mining version 3 and SPSS software were used for analysis. Results: The overall PSC score in public and private hospitals was 41.99 and 40.96, respectively. According to the results, the examined hospitals have a weak PSC. The safety culture level was correlated with education level, work experience, gender, income, and organizational position of the workers. The ANN showed that the issues mostly effecting PSC, in order of priority, include the feedback and communication about errors, organizational learning and continuous improvement, and management support for patient safety. Also, based on the findings LR model showed better performance for PSC prediction than RF model. Conclusions: The healthcare experts and policymakers can improve PSC in hospitals through training and allocation of resources. Considering the importance of PSC in preventing accidents and reducing injuries, the results of the present study and the presented models can be used to predict PSC in hospitals. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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