Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan.
National Centre for Health Workforce Studies, College of Health and Medicine, Australian National University, Canberra, Australia.
Hum Resour Health. 2024 Apr 17;22(1):25. doi: 10.1186/s12960-024-00895-z.
Health workforce projection models are integral components of a robust healthcare system. This research aims to review recent advancements in methodology and approaches for health workforce projection models and proposes a set of good practice reporting guidelines.
We conducted a systematic review by searching medical and social science databases, including PubMed, EMBASE, Scopus, and EconLit, covering the period from 2010 to 2023. The inclusion criteria encompassed studies projecting the demand for and supply of the health workforce. PROSPERO registration: CRD 42023407858.
Our review identified 40 relevant studies, including 39 single countries analysis (in Australia, Canada, Germany, Ghana, Guinea, Ireland, Jamaica, Japan, Kazakhstan, Korea, Lesotho, Malawi, New Zealand, Portugal, Saudi Arabia, Serbia, Singapore, Spain, Thailand, UK, United States), and one multiple country analysis (in 32 OECD countries). Recent studies have increasingly embraced a complex systems approach in health workforce modelling, incorporating demand, supply, and demand-supply gap analyses. The review identified at least eight distinct types of health workforce projection models commonly used in recent literature: population-to-provider ratio models (n = 7), utilization models (n = 10), needs-based models (n = 25), skill-mixed models (n = 5), stock-and-flow models (n = 40), agent-based simulation models (n = 3), system dynamic models (n = 7), and budgetary models (n = 5). Each model has unique assumptions, strengths, and limitations, with practitioners often combining these models. Furthermore, we found seven statistical approaches used in health workforce projection models: arithmetic calculation, optimization, time-series analysis, econometrics regression modelling, microsimulation, cohort-based simulation, and feedback causal loop analysis. Workforce projection often relies on imperfect data with limited granularity at the local level. Existing studies lack standardization in reporting their methods. In response, we propose a good practice reporting guideline for health workforce projection models designed to accommodate various model types, emerging methodologies, and increased utilization of advanced statistical techniques to address uncertainties and data requirements.
This study underscores the significance of dynamic, multi-professional, team-based, refined demand, supply, and budget impact analyses supported by robust health workforce data intelligence. The suggested best-practice reporting guidelines aim to assist researchers who publish health workforce studies in peer-reviewed journals. Nevertheless, it is expected that these reporting standards will prove valuable for analysts when designing their own analysis, encouraging a more comprehensive and transparent approach to health workforce projection modelling.
卫生人力预测模型是健全医疗体系的重要组成部分。本研究旨在综述卫生人力预测模型方法学和方法的最新进展，并提出一套良好的报告准则。
我们通过检索医学和社会科学数据库（包括 PubMed、EMBASE、Scopus 和 EconLit）进行了系统综述，检索时间范围为 2010 年至 2023 年。纳入标准包括预测卫生人力需求和供应的研究。PROSPERO 注册号：CRD 42023407858。
我们的综述共纳入 40 项相关研究，其中包括 39 项单一国家分析（澳大利亚、加拿大、德国、加纳、几内亚、爱尔兰、牙买加、日本、哈萨克斯坦、韩国、莱索托、马拉维、新西兰、葡萄牙、沙特阿拉伯、塞尔维亚、新加坡、西班牙、泰国、英国、美国）和 1 项多国分析（32 个经合组织国家）。最近的研究越来越多地在卫生人力建模中采用复杂系统方法，纳入需求、供应和供需差距分析。综述确定了最近文献中常用的至少八种不同类型的卫生人力预测模型：人口与提供者比例模型（n=7）、利用模型（n=10）、需求模型（n=25）、技能混合模型（n=5）、存量与流量模型（n=40）、基于主体的仿真模型（n=3）、系统动态模型（n=7）和预算模型（n=5）。每种模型都有独特的假设、优势和局限性，从业者通常会结合这些模型。此外，我们还发现了七种用于卫生人力预测模型的统计方法：算术计算、优化、时间序列分析、计量经济学回归建模、微观模拟、基于队列的模拟和反馈因果循环分析。劳动力预测通常依赖于不完善的数据，并且在地方一级的数据粒度有限。现有研究在报告方法方面缺乏标准化。有鉴于此，我们提出了一套卫生人力预测模型的良好实践报告准则，旨在适应各种模型类型、新兴方法以及更广泛地利用先进的统计技术来应对不确定性和数据需求。
本研究强调了动态、多专业、团队合作、精细化需求、供应和预算影响分析的重要性，这些分析由强大的卫生人力数据智能支持。建议的最佳实践报告准则旨在为在同行评议期刊上发表卫生人力研究的研究人员提供帮助。然而，预计这些报告标准对于分析师在设计自己的分析时也将具有价值，鼓励对卫生人力预测建模采用更全面和透明的方法。
