Nursing manpower forecast for cancer patients
Abstract
Background and objectives This paper presents a dynamic model aimed at predicting nursing manpower requirements for cancer care over the next ten years. The proposed model, based on the Taiwan Health Insurance Database (2000 to 2010), is meant to serve as a reference in establishing policy for government health units.Methods The proposed prediction model uses fuzzy sets to replace definite values with interval values in order to account for uncertainties in real-world data and enhance the flexibility of prediction results.Results Our results suggest that the demand for nursing manpower for cancer care will grow steadily in the foreseeable future. The gap between the demand for nursing staff and the supply is expected to peak in 2027. By then, the number of oncologists is expected to reach 7,083 (54.32% of the total number of in-hospital physicians), but the number of oncology nurses will be less than 26,297 (56.5% of the total healthcare manpower). It is also expected that there will be fewer than 1,613 outpatient physicians (71.81% of the total number of physicians) and fewer than 4,967 outpatient nurses (68.46% of the total nursing manpower).Conclusions This paper provides a valuable reference for government agencies involved in the nursing manpower planning to improve the quality of nursing care.
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In three studies (15%), nurses were passively involved by being a secondary source of patient or other cancer data used for analysis by AI algorithms.
“In three studies (15%), nurses were passively involved by being a secondary source of patient or other cancer data used for analysis by AI algorithms (Chung et al., 2021;Soltani et al., 2022;Takehira et al., 2011).”
From The application and use of artificial intelligence in cancer nursing: A systematic review (2024) · cites this paper as Chung et al., 2021
Two studies (10%) focused on administration and management to help predict patient demand for oncology services and forecast cancer nursing manpower.
“Two studies (10%) focused on administration and management to help predict patient demand for oncology services (Soltani et al., 2022) and forecast cancer nursing manpower (Chung et al., 2021).”
From The application and use of artificial intelligence in cancer nursing: A systematic review (2024) · cites this paper as Chung et al., 2021
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The application and use of artificial intelligence in cancer nursing: A systematic review
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- Authors
- Chung, M.-H., Hung, K.-C., Chiou, J.-F., Fang, H.-F., & Chiu, C.-H.
- Affiliations
- Taipei Medical University-Shuang Ho Hospital (TW)
- Hungkuang University (TW)
- Taipei Medical University (TW)
- Taipei Medical University Hospital (TW)
- Tunghai University (TW)
- Corresponding author
- Hung, Kuo-Chen
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 201
- Pages
- 105967
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W3127112461
- Cited by
- 5
- References
- 44
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 1
- FWCI
- 0.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Health Promotion Administration, Ministry of Health and Welfare
- Ministry of Health and Welfare (MOHW105-HPA-M-114-000503)
- Topics
- Health Sciences › Nursing › Research and Theory › Nursing education and management
- Health Sciences › Health Professions › Emergency Medical Services › Global Health Workforce Issues
- Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
- Keywords
- cancer care, oncology nurses, nursing workforce demand, fuzzy sets, health workforce planning, health policy
- MeSH terms
- Workforce, Forecasting, Humans, Neoplasms, Nursing Staff, Physicians, Taiwan
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