Methodology

There are multiple approaches to collecting data and data are often collected for different purposes. As a result, it is important to understand the methodology behind each dataset and its intended use in order to make valid comparisons. For example, the Bureau of Labor Statistics Occupational Employment Statistics collects data from employment surveys; the data count jobs, not workers, and they count employed, not self-employed positions. Professional association masterfiles (eg, AMA, ADA) are based on membership surveys and other sources, and data may not accurately account for professionals that are licensed and practicing in more than one state. State licensure data are self-reported through license applications and renewals, and hinge on the licensees accuracy and timeliness. The National Provider and Plan Enumeration System (NPPES) is a registry of providers that submit Medicare and Medicaid claims; this is an administrative database where the billing address of the provider may not match the provider’s practice location. Health professionals are mobile, some more than others, and change jobs and locations; these moves may not be reflected accurately or in a timely manner.

While it is important to understand how many health professionals there are and in which professions, specialties, employment settings, and geographic locations they practice, health workforce research is moving beyond understanding supply to better understanding demand for health professionals, how they are training and practicing, how they impact the quadruple aim, and how to more effectively plan for the future. The Global Health Workforce Alliance reports, “The current discourse on HRH is evolving from an exclusive focus on availability of health workers – ie, numbers – towards according equal importance to accessibility, acceptability, quality and performance.”

A special issue of Health Services Research in 2017 provides a summary and examples for how health workforce research is evolving. Washko and Fennell summarize 4 main themes, including “(1) the changing roles of health care providers, (2) the changing combinations of different providers who work together to deliver care, (3) the impact of these workforce changes on quality of care and access to care, and (4) advances in methodological challenges inherent in the study of evolving health workforce changes.”

HRSA has funded 9 Health Workforce Research Centers (HWRCs) to conduct and disseminate “rigorous research that strengthens evidence-based policy and enhances government’s and the public’s understanding of issues and trends in the health workforce” to help inform health workforce planning and policy. The HWRCs’ research focuses on allied health, behavioral health, ”emerging topics”, health equity, long-term care, oral health, public health, and technical assistance. In 2017, the George Washington University HWRC compiled a report, Health Workforce Centers (HWRCs) Key Findings, 2013-2016, that identifies 3 main themes in the HWRCs’ work, including understanding the evolving health workforce configuration; spotlighting job growth and career paths in middle- and low-skilled health professions; and identifying workforce strategies to increase access to high-quality health care.

This depends on many different factors, such as how many health professionals you want to track, the method used to collect data (licensure, survey, continuous monitoring, secondary data), the types of deliverables for which you’re accountable, and organization structure. If the data system is embedded within a larger organization, such as a university or state government office, it is likely that some administration, finance, and infrastructure resources are already available for basic operation. If the data system is a stand-alone organization, you will need to secure funding.

In terms of staff, you may consider having a director to guide the work, make decisions, present results and acquire funding; one or more project managers/researchers to analyze data, write reports and present results; and a data manager to collect, clean and analyze data. Other positions may include communications specialist, visualization specialist, research assistant, administrative assistant, grants manager, and financial manager.

Additional resources needed include computer hardware and software for data management, statistical analysis, GIS, and graphic design.

During the recession, healthcare jobs increased at the same time when many sectors were losing jobs. This trend is continuing to hold.

• The Bureau of Labor Statistics projects that employment of healthcare occupations will grow 18% from 2016 to 2020. This is faster than the average for all occupations and will add about 2.3 million new jobs.
• Altarum produces monthly Health Sector Economic Indicators℠ briefs that monitor trends in health care employment, spending, and prices. Their 2017 employment brief shows continued growth in the number of health care jobs, with the greatest growth found in outpatient care centers.
• A November 2017 Health Affairs Blog discussed projected changes in health care employment under different policy proposals including the current law, H.R. 1628 American Health Care Act (AHCA), and ending cost-sharing reduction (CSR) payments to insurers. The projections and comparisons predict a loss of jobs under the AHCA and CSR payment reduction proposals. It concluded that continued monitoring is needed to ensure there are enough health care workers to meet the population’s needs.

A health workforce shortage means that there are not enough health care workers or not enough workers in specific professions, specialties, or settings to adequately serve patients’ needs. Shortage is defined in different ways for different purposes. It is important to understand the difference between “shortage” and “maldistribution”, particularly at the state and national level. Data and models may indicate that the nation or state has a sufficient supply of health professionals. However, this supply may not be evenly distributed across the country or state, creating pockets of shortage, especially in rural areas.

The Shortage Designation Branch at HRSA works with state Primary Care Offices (PCOs) to assign shortage designations to geographic areas, populations, and facilities that have too few providers and services; these are then eligible to receive certain federal resources. Designations include primary care, mental health, and dental Health Professional Shortage Areas (HPSAs) and Medically Underserved Areas and Populations (MUA/P). See https://bhw.hrsa.gov/shortage-designation/types for additional information on shortage areas.

Stakeholders engaged in legislative, education, practice, payment, and regulatory policy discussions need data to help inform their decisions. Data should be presented in different formats (eg, briefs, slides, fact sheets) and at different levels (eg, academic research vs layperson language) depending on the audience. Some health workforce researchers have been advised to cultivate connections with legislative aides and to communicate research findings through social media. Others have been advised to present their data and research on a single page in short, concise bullets and easy-to-read graphics. It is important to highlight key messages and minimize less useful information.

For additional resources on using data and research to inform policy, see:

• Using Evidence to Inform Evolving Telehealth Policies (NASHP 2021)
• The Center for Evidence-Based Policy
• The Value of Workforce Data in Shaping Nursing Workforce Policy: A Case Study From North Carolina (Fraher, Nursing Outlook, 2017)
• A series of briefs on using data to inform policy (UNC Sheps Center, 2017)
• The Rural Health Research Gateway’s toolkit on dissemination of rural health research (Schroeder 2015)

The study’s purpose, design, data, confidentiality considerations, and funder requirements should inform which geographic unit of analysis is most appropriate. For a broader discussion of geographic units, see Chapter 5. Geography and Disparities in Health Care (Ricketts, TC) in Guidance for the National Healthcare Disparities Report.

Studies that work with small cell sizes, especially in small geographic units, should consider the risk of deductive disclosure, where an individual’s identity may be ascertained using known characteristics, such as race and age, even when direct identifiers, such as name and address, are removed.

Studies that use multiple datasets at varying geographic units can aggregate or approximate the data to larger geographic areas. For example, the FutureDocs Forecasting Tool created tertiary service areas by aggregating groups of counties to approximate the Dartmouth Atlas Hospital Referral Regions, which are built on ZIP Codes.

Analyses using sample data should evaluate the data’s sampling unit and frame when determining an appropriate geographic unit of analysis. Data derived from a sample will have some degree of uncertainty associated with the estimates. Generally, the smaller the sample the larger the sampling error. Relying on a small geographic unit may further exacerbate the uncertainty around the estimates and prevent researchers from producing reliable statistics. Therefore one should carefully consider the data-generating process before considering the geographic unit of analysis.

Multiple rural definitions can be used in health workforce analysis. The study’s purpose, unit of analysis, and funder should drive which rural definition is used.

The WWAMI Rural Health Research Center at the University of Washington is a leading resource on analyzing the rural health workforce. See Chapter 3 in their 2003 report, State of the Health Workforce in Rural America: Profiles and Comparisons for guidance on strengths and weaknesses of common rural definitions.

The Rural Health Information Hub, or RHIhub, is another important resource on rural definitions. The RHIhub developed an “Am I Rural?” tool that helps determine if a specific location is considered rural, including definitions used in federal program eligibility criteria. Additionally, states may have their own definitions of rural.

Data systems can be funded through state appropriations, private foundations, grants and contracts, and on a cost-recovery basis. Each funding mechanism has its challenges. State appropriations are tenuous; administrations and priorities change, and budgets get cut. Foundations are often geared to fund initiatives that show more tangible results. Grants are often time-limited. Cost-recovery is subject to demand for data and services, and limits the type of analyses and reports that you can do. Stakeholders who require data may be persuaded to fund the analysis costs to meet their specific needs, but they frequently are not willing or able to fund the fixed infrastructure costs. Consider the appropriate funding source for the specifics of your data collection effort, given the meaning and value of the project.