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Background 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. Methods 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. Results 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. Conclusions 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.

Although the conceptual underpinnings of needs-based health workforce planning have developed over the last two decades, lingering gaps in empirical models and lack of open access tools have partly constrained its uptake in health workforce planning processes in countries. This paper presents an advanced empirical framework for the need-based approach to health workforce planning with an open-access simulation tool in Microsoft® Excel to facilitate real-life health workforce planning in countries. Two fundamental mathematical models are used to quantify the supply of, and need for, health professionals, respectively. The supply-side model is based on a stock-and-flow process, and the need-side model extents a previously published analytical frameworks using the population health needs-based approach. We integrate the supply and need analyses by comparing them to establish the gaps in both absolute and relative terms, and then explore their cost implications for health workforce policy and strategy. To illustrate its use, the model was used to simulate a real-life example using midwives and obstetricians/gynaecologists in the context of maternal and new-born care in Ghana. Sensitivity analysis showed that if a constant level of health was assumed (as in previous works), the need for health professionals could have been underestimated in the long-term. Towards universal health coverage, the findings reveal a need to adopt the need-based approach for HWF planning and to adjust HWF supply in line with population health needs.

Background Effective healthcare workforce management is critical for ensuring quality care delivery, particularly in resource-constrained settings. The World Health Organization’s (WHO) Workload Indicators of Staffing Need (WISN) methodology provides an evidence-based framework for optimizing staffing levels. However, manual implementation of the WISN methodology is labour-intensive, error-prone, and time-consuming. To address these challenges, the Platform for Resource Allocation and Optimization for Healthcare Facilities (PRAYOJN) platform was developed as a web-based tool to automate WISN calculations, streamline data analysis, and improve workforce planning. Objective To develop and validate a web-based system that automates the WISN methodology for healthcare workforce planning. Methods The PRAYOJN platform was developed using an agile methodology, structured over five iterative sprints. These sprints incorporated stakeholder feedback to refine system functionalities, ensuring adaptability to real-world healthcare needs. The platform integrates data for principal, supporting, and ancillary tasks to calculate staffing requirements. Key functionalities include automated computation of Available Work Time (AWT), Standard Workload (SW), Category Allowance Factor (CAF), and Individual Allowance Factor (IAF). Alpha testing validated usability and accuracy, while beta testing in a clinical phlebotomy department assessed real-world performance. Results The platform calculated an ideal staffing requirement of 15.53 Full-Time Equivalent (FTE) for the phlebotomy department, aligning closely with the current staff strength of 15 FTE. Agile development ensured iterative improvements, enhancing user interface (UI) and user experience (UX). Feedback highlighted the platform’s user-friendly design, with dynamic visualizations such as pie charts and bar graphs aiding workload interpretation. Users praised its efficiency, adaptability, and role in reducing calculation complexity. Conclusion PRAYOJN modernizes and enhances WISN-based workforce planning by automating workload calculations, improving data visualization, and supporting real-time decision-making. Its scalability and intuitive interface position it as a valuable tool for optimizing staffing efficiency across diverse healthcare environments.

Background This article contributes to the health workforce planning literature by exploring the dynamics of health professions in New Zealand’s Primary Care sector and deriving broad lessons for an international audience. Professions tend influence health policy and governance decisions and practices to retain their place, status and influence. Therefore, understanding their power dynamics and the positions that they have on workforce policies and issues assists workforce governance or health system reform plans. Methods Using the infrequently reported health workforce policy tool, actor analysis, a reanalysis of previously collected data is undertaken using an actor-based framework for the study of professionalism. Two models were developed, (1) the framework’s original four-actor model and (2) a five-actor model for the comparison of the Medical and Nurse professions. Existing workforce actor data were reclassified, formatted, and entered into actor analysis software to reveal the professions’ relative power, inter-relationships and strategic workforce issue positions. Results In the four-actor model, the Organised user actor is found to be most influential, while the others are found to be dependent. In the five-actor model, the Medical and Nurse professions are individually more influential than their combined position in the four-actor model. Practicing professionals and Organised user actors have strong converging inter-relationships over workforce issues in both models, though in the five-actor model, the Nurse profession has weaker coherency than the Medical profession. The Medical and Nurse professions are found to be in opposition over the workforce issues labelled divisive. Conclusions These results reflect the professions’ potential to influence New Zealand’s Primary Care sector, indicating their power and influence over a range of policy and reform measures. As such, the four lessons that are derived from the case indicate to policy makers that they should be aware of situational contexts and actor power, take care when encountering divisive issues and try to achieve broad-based support for proposed policies.

Background The COVID-19 pandemic involved a rapid change to the working conditions of all healthcare workers (HCW), including those in primary care. Organizational responses to the pandemic, including a shift to virtual care, changes in staffing, and reassignments to testing-related work, may have shifted more burden to these HCWs, increasing their burnout and turnover intent, despite their engagement to their organization. Our objectives were (1) to examine changes in burnout and intent to leave rates in VA primary care from 2017–2020 (before and during the pandemic), and (2) to analyze how individual protective factors and organizational context affected burnout and turnover intent among VA primary care HCWs during the early months of the pandemic. Methods We analyzed individual- and healthcare system-level data from 19,894 primary care HCWs in 139 healthcare systems in 2020. We modeled potential relationships between individual-level burnout and turnover intent as outcomes, and individual-level employee engagement, perceptions of workload, leadership, and workgroups. At healthcare system-level, we assessed prior-year levels of burnout and turnover intent, COVID-19 burden (number of tests and deaths), and the extent of virtual care use as potential determinants. We conducted multivariable analyses using logistic regression with standard errors clustered by healthcare system controlled for individual-level demographics and healthcare system complexity. Results In 2020, 37% of primary care HCWs reported burnout, and 31% reported turnover intent. Highly engaged employees were less burned out (OR = 0.57; 95% CI 0.52–0.63) and had lower turnover intent (OR = 0.62; 95% CI 0.57–0.68). Pre-pandemic healthcare system-level burnout was a major predictor of individual-level pandemic burnout ( p  = 0.014). Perceptions of reasonable workload, trustworthy leadership, and strong workgroups were also related to lower burnout and turnover intent ( p  < 0.05 for all). COVID-19 burden, virtual care use, and prior year turnover were not associated with either outcome. Conclusions Employee engagement was associated with a lower likelihood of primary care HCW burnout and turnover intent during the pandemic, suggesting it may have a protective effect during stressful times. COVID-19 burden and virtual care use were not related to either outcome. Future research should focus on understanding the relationship between engagement and burnout and improving well-being in primary care.

Background Health services cannot be delivered without an adequate, competent health workforce. Evidence suggests a direct relationship between density of health workforce and health outcomes. The Philippines is faced with health workforce challenges including shortages, inequitable distribution and inadequate skill mix which hinder health service delivery. Evidence-based workforce planning is, therefore, critical to achieve universal health care. Methods The Philippines adopted the World Health Organization’s workload indicators of staffing need methodology. Using a multistage sampling method, nine regions with poor health indicators in tuberculosis, family planning, and maternal child health were identified. Physicians, nurses, midwives, and medical technologists were prioritized in the study from 89 primary care health facilities (barangay health stations, rural health units, and city health offices). Data was collected using in-depth interviews, document reviews, observations, and field visits. The workload indicators of staffing need software were used for data analysis to determine staffing requirements and analyse workforce pressure. Results The study showed varied results in terms of staffing requirements and workload pressure across cadres and facility types. Some health facilities exhibited staff shortages and high workload pressure. Out of the 40 rural health units and city health offices, only three had the required physicians needed and 22 facilities had a shortage of physicians working under high workload pressure. Other facilities had excess staff compared to the calculated requirements. Nurses at the rural health units showed high workload pressure. Ten rural health units had no medical technologists. Midwives at barangay health stations exhibited extremely low workload pressures. Conclusion The study identifies the need for the Philippine Health System, both through the Department of Health and the local governments to efficiently optimize the available health workers by revising the services offered at the primary health care facilities. The results provide evidence for staffing requirements at various levels of care based on workloads, scope of practice and time taken to undertake specific tasks at the barangay health stations, rural health units and city health offices to be integrated into the human resources for health management systems.

The ambition of universal health coverage entails estimation of the number, type and distribution of health workers required to meet the population need for health services. The demography of the population, including anticipated or estimated changes, is a factor in determining the ‘universal’ needs for health and well-being. Demography is concerned with the size, breakdown, age and gender structure and dynamics of a population. The same science, and its robust methodologies, is equally applicable to the demography of the health workforce itself. For example, a large percentage of the workforce close to retirement will impact availability, a geographically mobile workforce has implications for health coverage, and gender distribution in occupations may have implications for workforce acceptability and equity of opportunity. In a world with an overall shortage of health workers, and the expectation of increasing need as a result of both population growth in the global south and population ageing in the global north, studying and understanding demographic characteristics of the workforce can help with future planning. This paper discusses the dimensions of health worker demography and considers how demographic tools and techniques can be applied to the analysis of the health labour market. A conceptual framework is introduced as a step towards the application of demographic principles and techniques to health workforce analysis and planning exercises as countries work towards universal health coverage, the reduction of inequities and national development targets. Some illustrative data from Nepal and Finland are shown to illustrate the potential of this framework as a simple and effective contribution to health workforce planning.

Background Research and monitoring of human resources available for vision rehabilitation services has been a neglected area of work in the past. This study aims to offer an overview of the vision rehabilitation workforce available in Italy, in order to profile the distribution and number of human resources for vision rehabilitation. Methods Data on the available vision rehabilitation professionals were collected from the yearly report on the state of implementation of policies relating to the prevention of blindness, education and vision rehabilitation, according to a law which was passed by the Italian Ministry of Health, Department of Health Prevention. The report presents a review of all professional workers dealing with low vision rehabilitation centers in Italy between January 2005 and December 2019. Data on the distribution and type of services of government-supported low vision centers across the country were also obtained and examined. Results Of the 289 low vision rehabilitation workers in 2019, 28% were ophthalmologists, 31% orthoptists, 19% psychologists, 17% nurses and 5% social workers. The health workforce densities across the Italian regions ranged from 1.62 to 0.12 per 100.000. The density of vision rehabilitation workers showed a no growing trend from 2006 to 2015. During the study period, it was found a weak but statistically significant association of workforce density with the number of government-supported low vision centers across the Italian territory (r 2  = 0.3, p  < 0.05). The vision rehabilitation workforce was not associated with the number of low vision patients who accessed to a vision rehabilitation center (r 2  = 0.05, p  < 0.0001). Discussion A critical review has identified the following national situation: need-based shortages of workers in the vision rehabilitation service sector, as well as deficiencies in data sources. Based on our results, we would recommend increasing the development of human resources trained and dedicated to vision rehabilitation and improve data collection and analysis; provide structural enhancements, across all service levels. These considerations may contribute to the enhancement of policy decisions in order to guarantee an adequate vision rehabilitation workforce and meet national rehabilitation needs. Furthermore, this analysis should be used as a lesson learned by other countries, as low-income ones, in order to develop vision rehabilitation services.

Purpose The purpose of this study is use a bibliometric analysis to explore the relational nature of knowledge creation in WFM in operations. Companies live under constant pressure to find the best ways to plan their workforce, and the workforce emangement (WFM) is one of the biggest challenges faced by managers. Relevant research on WFM in operations has been published in a several range of journals that vary in their scope and readership, and thus the academic contribution to the topic remains largely fragmented. Design/methodology/approach To address this gap, this review aims to map research on WFM in operations to understand where it comes from and where it is going and, therefore, provides opportunities for future work. This study combined two bibliometric approaches with manual document coding to examine the literature corpus of WFM in operations to draw a holistic picture of its different aspects. Findings Content and thematic analysis of the seminal studies resulted in the extraction of three key research themes: workforce cross-training, planning workforce mixed methods and individual workforce characteristics. The findings of this study further highlight the gaps in the WFM in operations literature and raise some research questions that warrant further academic investigation in the future. Originality/value Likewise, this study has important implications for practitioners who are likely to benefit from a holistic understanding of the different aspects of WFM in operations.

Health workforce planning provides a crucial evidence-base for decision-makers in the development and deployment of a fit-for-purpose workforce. Although less common, health workforce planning at the regional level helps to ground planning in the unique realities of local health systems. This commentary provides an overview of the process by which an integrated primary healthcare workforce planning toolkit was co-developed by university-based researchers with the Canadian Health Workforce Network and partners within a major urban regional health authority. The co-development process was guided by a conceptual framework emphasizing the key principles of sound health workforce planning: that it (1) be informed by evidence both quantitative and qualitative in nature; (2) be driven by population health needs and achieve population, worker and system outcomes; (3) recognize that deployment is geographically based and interprofessionally bound within a complex adaptive system; and (4) be embedded in a cyclical process of aligning evolving population health needs and workforce capacity.