Healthcare Management Forum

This article showcases the high-quality, standardized, and national labour force and health-related data that can be leveraged for effective health workforce planning. It also underscores the importance of interoperability, the ability to integrate and harmonize data from multiple sources to optimize health workforce analysis. Using three case studies drawing on five Statistics Canada data sources, it examines persistent shortages of nurses and personal support workers and the impact of increased workload on their stress during the COVID-19 pandemic. This article also outlines how Statistics Canada data can inform planning by identifying unmet labour demand, work-related stress, and untapped labour resources, such as internationally educated healthcare professionals. It aims to guide health leaders in accessing and leveraging Statistics Canada data, including but not limited to those outlined here, to strategically address workforce and policy challenges in the health sector using an evidence-based approach.

The use of population-based data sources can offer healthcare planners and organizations insights on whether the health workforce reflects the sociodemographic diversity of the population it is meant to serve. However, sources outside the traditional boundaries of the healthcare system are typically underused in assessing health workforce imbalances to inform planning. This article offers a guide for health leaders on using national census data to monitor health workforce imbalances by geography, gender, and other equity-oriented characteristics. A case study illustrates how census data can provide standardized quantitative information on the oral health workforce, using various dissemination tools (online census tabulators and full census databanks) and methods (including linkages with other sources for enhanced cohort and ecological analyses). We also address practical issues such as the value of fostering academic partnerships to help navigate the analytics competencies and other requirements for using census products to support decisions.

Advancements in health workforce research depend on access to timely, detailed, and practically oriented data. Yet researchers in this field often encounter barriers to data access, which impacts the research that we are able to conduct. In this article, we share insights as researchers who have studied health workforces through a range of data sources accessed across traditional academic and embedded scientist roles. We concentrate on unique opportunities and limitations from two experiences: (1) use of Statistics Canada surveys as part of university-based studies and (2) health organization administrative data as embedded homecare scientists at VHA Home HealthCare. Collectively, these complementary data sources contribute to providing a more comprehensive advancement of our understanding of personal support workers in Canada. We hope that sharing insights from these experiences provides inspiration and guidance to others who want to pursue a similar path in health workforce research.

The Long-Term Care (LTC) sector in Canada faces persistent challenges in staffing, including limited data to support workforce planning, quality improvement, and policy evaluation. The OnSPARK Data Platform was established in 2023 as a sector-governed, province-wide infrastructure to address these challenges. OnSPARK aggregates de-identified electronic health records from over 200 LTC homes in Ontario, representing approximately one-third of the sector. Many homes also submit shift-level payroll and scheduling-based staffing data to be linked to facility unit-level quality metrics. Near real-time, unit-level insights are provided through an interactive portal, while aggregated data support embedded research, performance benchmarking, and policy simulation. This article introduces the structure and functionality of the OnSPARK platform, describes its unique approach to staffing data collection and use, and outlines its potential to generate operational, clinical, and policy-relevant insights. By enabling ongoing access to workforce and care data, OnSPARK supports a learning health system model that strengthens decision-making.

This article describes the development, implementation and first-year findings of the Ontario College of Social Workers and Social Service Workers’ Equity and Inclusion Data Initiative. This data project was developed to help identify and monitor systemic racism and discrimination within the professions of social work and social service work in Ontario. This initiative was based on the fundamental principle that only what is measured can be effectively understood and improved. College registrants were invited to share their demographic information on a voluntary basis. Data collection launched in the 2024 registration renewal period, with 66.5% response rate in its first year. This is an ongoing large-scale change management initiative, requiring strategic engagements with registrants, clients, government, staff, and other key engagement groups. This workforce project is an innovative example of how demographic data collection can advance equity, diversity, inclusion, and anti-racism efforts in provincial regulation, including healthcare.

Indigenous Peoples have unique legislative considerations as employees that may have implications for researchers and health workforce leaders. This article aims to contextualize workforce surveys from an Indigenous employee perspective (What), identify legislation and taxation considerations for health workforce leaders regarding Indigenous employees (So What), and share organizational measurement tools and promising practices (Now What).

To support health system transformation, efforts are underway to implement multi-professional, population needs-based health workforce planning in the province of Nova Scotia. The purpose of this article is to report on these efforts as well as successes and challenges encountered to date. Implementation has progressed furthest in the COVID-19 response and continuing care sectors, with similar efforts underway in primary care and mental health and addictions. Key enablers of needs-based health workforce planning in Nova Scotia include the availability of robust data on workforce supply and collaborative relationships between health system partners. The main challenges with implementing needs-based health workforce planning in Nova Scotia are the absence of provincial-level clinical service planning or standardized levels of service provision, and a lack of systematic collection of data to measure provider workload or health service outcomes.

In Ontario, pregnant people can choose to seek care from an obstetrician, family physician, or midwife. This study aimed to determine whether Ontario’s Champlain Region displayed the levels of access to the full range of maternity care providers required to afford pregnant people the opportunity to exercise choice of provider. Drawing on data from a census survey of midwifery practice groups, the CIHI National Physician Database, and BORN Ontario, the Enhanced Two-Step Floating Catchment Area Method was adapted to calculate provider-specific accessibility scores for communities across the region. The resulting maps revealed inequities in the distribution of access across the region, differences in relative access across provider groups, and underserviced communities with minimal access to any provider group. This study presents a new approach to mapping alignment between maternity care workforce capacity and pregnant people’s needs, and illustrates that additional action is required to equitably support access and choice.

To make good decisions, health leaders need information about their communities and the health workforce available to meet their needs. Raw data and indicators of population needs and workforce capacity must be transformed into usable intelligence that can support decision-making. Using the case study of integrated primary care workforce planning in Toronto, we outline our workforce planning framework, and with a focus on workforce analysis, describe the inputs and outputs that are needed for planning, key steps in the conversion of data to intelligence, and the impact of the approach. Raw data flow from data partners through a planning model into a six-step workforce analysis that renders the results of data analysis, modelling, synthesis and visualization relevant, and accessible to planners and decision-makers. We highlight important challenges and considerations related to data standardization, comprehensiveness, granularity, accessibility, and timeliness, and envision a system that more effectively supports workforce planning and decision-making.

Poised to receive vital administrative personal health information to serve its role as New Brunswick’s seminal research data centre, DataNB (previously NB-IRDT) inadvertently drew attention to a well-established but unsanctioned use of the New Brunswick public health insurance (Medicare) number. DataNB’s 2014 request to access the NB Medicare number for research purposes also highlighted its use as a unique identifier for the provincial Moose Draw for hunting licenses, disclosure not granted in legislation. With newfound scrutiny being exercised on the appropriate disclosure and use of personal health information, DataNB’s need to access the Medicare number for linking personal administrative data was halted by provincial authorities. In response, provincial government officers and DataNB staff collaborated to develop and introduce legislative mechanisms that would create an authorized Medicare number use for data matching to support research on a wide range of subjects including the recruitment and retention of healthcare professionals in NB.