A data-driven multicriteria decision model for healthcare workforce retention strategies
The retention of nurses and physicians in Hospitals is a global problem affecting the healthcare system worldwide. This study focuses on the healthcare workforce retention problem considering the current situation in Taiwan. Healthcare staff in Taiwan are undergoing a critical phase, with an increas...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Healthcare Analytics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277244252500022X |
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| author | Debora Di Caprio Sofia Sironi Fan-Yun Lan Ramin Rostamkhani |
| author_facet | Debora Di Caprio Sofia Sironi Fan-Yun Lan Ramin Rostamkhani |
| author_sort | Debora Di Caprio |
| collection | DOAJ |
| description | The retention of nurses and physicians in Hospitals is a global problem affecting the healthcare system worldwide. This study focuses on the healthcare workforce retention problem considering the current situation in Taiwan. Healthcare staff in Taiwan are undergoing a critical phase, with an increasing number of experienced workers leaving their job to go to work for private organizations or as freelancers. We develop a data-driven four-phase methodology based on the design of a satisfaction index that allows to rank different groups of employees against a given set of criteria. First, criteria are identified and clustered to describe different job dimensions (phase 1). Hence, subjective evaluations of the criteria are collected from healthcare workers while experts provide pairwise comparisons among them (phase 2). An adjusted analytic hierarchy process (AHP) is used to weight the job dimensions and the criteria within each job dimension (phase 3). Finally, the satisfaction index is formalized and computed for different groups of employees (phase 4). The methodology has been implemented with data collected from healthcare workers employed in three healthcare institutions in Northern Taiwan. The proposed index represents a novel decision support tool for managers and policy makers in designing intervention strategies able to address different needs of different groups of employees. Besides, it allows for innovative applications to quality management (QM) by extending the standard QM approach to hospitals and healthcare centers far beyond the common focus on patients' satisfaction. Finally, the mathematical formulation of the index is very flexible and allows for applications to any employment sector through a variety of analyses based on different categorizations of the workers. |
| format | Article |
| id | doaj-art-25efdf7d59214f76bfb999f835a2e15f |
| institution | DOAJ |
| issn | 2772-4425 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj-art-25efdf7d59214f76bfb999f835a2e15f2025-08-20T03:21:38ZengElsevierHealthcare Analytics2772-44252025-12-01810040310.1016/j.health.2025.100403A data-driven multicriteria decision model for healthcare workforce retention strategiesDebora Di Caprio0Sofia Sironi1Fan-Yun Lan2Ramin Rostamkhani3Department of Economics and Management, University of Trento, Trento, Italy; Corresponding author. Department of Economics and Management, University of Trento, Via Inama, 5, 38122, Trento, Italy.Department of Economics and Management, University of Trento, Trento, ItalyInstitute of Health and Welfare Policy, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USASchool of Management, Universiti Sains Malaysia, Penang, MalaysiaThe retention of nurses and physicians in Hospitals is a global problem affecting the healthcare system worldwide. This study focuses on the healthcare workforce retention problem considering the current situation in Taiwan. Healthcare staff in Taiwan are undergoing a critical phase, with an increasing number of experienced workers leaving their job to go to work for private organizations or as freelancers. We develop a data-driven four-phase methodology based on the design of a satisfaction index that allows to rank different groups of employees against a given set of criteria. First, criteria are identified and clustered to describe different job dimensions (phase 1). Hence, subjective evaluations of the criteria are collected from healthcare workers while experts provide pairwise comparisons among them (phase 2). An adjusted analytic hierarchy process (AHP) is used to weight the job dimensions and the criteria within each job dimension (phase 3). Finally, the satisfaction index is formalized and computed for different groups of employees (phase 4). The methodology has been implemented with data collected from healthcare workers employed in three healthcare institutions in Northern Taiwan. The proposed index represents a novel decision support tool for managers and policy makers in designing intervention strategies able to address different needs of different groups of employees. Besides, it allows for innovative applications to quality management (QM) by extending the standard QM approach to hospitals and healthcare centers far beyond the common focus on patients' satisfaction. Finally, the mathematical formulation of the index is very flexible and allows for applications to any employment sector through a variety of analyses based on different categorizations of the workers.http://www.sciencedirect.com/science/article/pii/S277244252500022XData-driven analyticsHealthcare workforce retentionMulticriteria modelingSatisfaction indexWorkforce planning |
| spellingShingle | Debora Di Caprio Sofia Sironi Fan-Yun Lan Ramin Rostamkhani A data-driven multicriteria decision model for healthcare workforce retention strategies Healthcare Analytics Data-driven analytics Healthcare workforce retention Multicriteria modeling Satisfaction index Workforce planning |
| title | A data-driven multicriteria decision model for healthcare workforce retention strategies |
| title_full | A data-driven multicriteria decision model for healthcare workforce retention strategies |
| title_fullStr | A data-driven multicriteria decision model for healthcare workforce retention strategies |
| title_full_unstemmed | A data-driven multicriteria decision model for healthcare workforce retention strategies |
| title_short | A data-driven multicriteria decision model for healthcare workforce retention strategies |
| title_sort | data driven multicriteria decision model for healthcare workforce retention strategies |
| topic | Data-driven analytics Healthcare workforce retention Multicriteria modeling Satisfaction index Workforce planning |
| url | http://www.sciencedirect.com/science/article/pii/S277244252500022X |
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