Nursing Aides, Home Health Aides, and Related Health Care Occupations -- National and Local Workforce Shortages and Associated Data Needs

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Chapter 1. Project Overview | Chapter 2. Paraprofessional Workforce Supply and Demand | Chapter 3. Important Data Issues | Chapter 4. Existing National Data Sources | Chapter 5. State-Level Data Issues | Chapter 6. Occupation and Industry Classification Systems | Chapter 7. Current Data Collection Practice: CNA Registries | Chapter 8. Conclusions | Appendix A. Project Advisory Committee | Appendix B. Proposed State Data Collection Instrument | Appendix C. Occupational and Industry Definitions | Appendix D. Sample Data | Appendix E. Issues from Four States | Appendix F. CNA Registry Details | Appendix G. Annotated Bibliography | Appendix H. References

Chapter 3. Important Data Issues

This chapter reviews state-level issues related to data on the paraprofessional health workforce. The chapter includes the following sections:

• Introduction
• Reasons for Collecting Data
• Criteria for Assessing Data Systems
• Conclusions

Introduction
Collection of accurate and timely data is an often under-attended item on the agendas of planners and policymakers. Sometimes this is simply a matter of limited resources. At other times policymakers may decide to skip data collection because the problem is so severe or widespread that data are not needed to trigger action.

In arenas like the long-term care system that are expected to continue in the future, good data are an essential element of a comprehensive, long-term management strategy. Accurate and timely data can:

• Define the scope and scale of problems and issues
• Permit evaluation for programs and initiatives to correct problems
• Facilitate comparisons that can help to identify appropriate interventions
• Support assessments of cost effectiveness and outcomes

Reasons for Collecting Workforce Data
Workforce planners and policy analysts need data systems that provide clear, accurate, concise, and timely information, using standard terminology and definitions to describe current workforce trends and emerging situations. Few existing systems meet these criteria, and some of these are based on employer samples that must be aggregated over several years to obtain reliable estimates for small areas, and sometimes even for states.

Inadequate information systems severely handicap managers, planners, and policymakers. Without accurate and timely counts of workers, it is impossible to understand the relative roles of different types of workers in the long-term care system. It is also impossible to monitor and track changes in the direct care workforce, let alone develop reliable forecasts on which to base plans and programs. Perhaps even more important, existing systems do not support reliable assessments of the impacts and effectiveness of programs and initiatives designed to address workforce issues.

When designing data systems to support planning and policymaking related to the long-term care workforce, it is important to have a clear idea about the intended use of the data. Data on the long-term care workforce is needed for several important purposes:

Consumer Protection: Many of the patients in the long-term care system are frail and dependent on others for their health and well-being. One of the key reasons for the CNA Registries is to help ensure that the individual workers are properly trained and will not harm or take advantage of the patients they are serving.

Operational Review: Just as the registries collect data on individuals to help protect consumers, Online Survey Certification and Reporting (OSCAR) and other monitoring systems collect data on the facilities and organizations that serve these people. These data systems also hold the promise, not often fulfilled, of helping administrators allocate and use their scarce resources more effectively by pointing out especially effective facilities and programs.

Program Evaluation: Over the past decade national, state, and local initiatives have been taken to address problems of substandard care and worker shortages. Unfortunately, careful evaluations of these programs have been possible in only a handful of cases where outside funding has been available to support systematic assessments of outcomes and costs. This study shows clearly that existing State and Federal data systems are not up to this task.

Program Planning and Budgeting. Program and facility managers need accurate timely data on the health workforce to be able to develop realistic plans and budgets for future operations. It is especially important to have information about possible shortages of different types of workers and about strategies for addressing and/or circumventing such shortages.

Workforce Planning: Careful planning and forecasting provide essential road maps to policymakers about the goals and objectives of the system, the obstacles that may be in the way, and the strategic and tactical options available to move forward. It is especially important to alert education programs about future trends so they can prepare appropriately.

Criteria for Assessing Data Systems
Anecdotes abound that current data systems do not provide information sufficient to meet the workforce planning needs of federal, state, and institutional planners and policymakers. Federal systems do not provide the accuracy, consistency over time, or timeliness necessary to monitor or plan for provider organizational needs. In addition, they do not provide State and local detail sufficient to support effective planning and policymaking.

Although issues relating to paraprofessional workforce data have received local and national publicity in recent months, they are not well documented. In fact, the general sense of study informants is that the existing national and State data systems fall far short of what workforce planners and policymakers need. The data problems they cite are generally related to one or more of six broad criteria:

• Nomenclature, definitions, and taxonomies
• Accuracy
• Comparability over time
• Geographic detail
• Timeliness
• Access to data

Nomenclature, Definitions, and Taxonomies
One need only compare the estimates of the numbers of direct care paraprofessional workers from different sources to understand this concern. Due to different labels, definitions, categories, and collection processes, different information systems often provide markedly different estimates for what are nominally the same categories of workers. Unfortunately, it is often not possible to reconcile the differences or even to select the best estimate from among the alternatives.

Accuracy
One of the facts of life in developing and maintaining data systems is that not all figures in a database are necessarily100% accurate. The press of time or lack of resources on occasion leads a person completing a data questionnaire not to check a figure, or to omit a figure altogether. In cases where sampling is done, as in BLS/Occupational Employment Statistics (OES), the estimates are also subject to random error.

Comparability Over Time
A related problem involves discontinuities in data series within the same data sources. In recent years, there have been several changes in the category definitions of health care workers in Federal data systems. While these changes may improve the quality of data in the future, in the short run they make careful tracking of changes in the supply of workers over time impossible.

Geographic Detail
Labor markets for direct care paraprofessionals are generally local (i.e., in the local communities where facilities are located) or in some cases regional. State-level aggregations are too broad to be useful to most employers and policymakers. Current Federal and State data systems do not reflect the local patterns and trends critical for understanding the workforce environments in which employers actually operate.

Timeliness
By the time the data Federal agencies collect are available to planners and policymakers, they are often out of date. Time lags between data collection and availability of two or three years are not uncommon. During the lag time, major changes in supply and/or demand may have taken place.

Access to Data
Another criteria for evaluating data systems is access to the data. An otherwise effective system that no one can retrieve data from is not going to help planners and policymakers. Restricted access to data is generally related to issues of privacy and confidentiality. This means that appropriate aggregations of data by facility or geographic unit must be developed to make it possible to share the data without breaching any privacy requirements.

Conclusions
Fieldwork conducted as apart of this study has confirmed numerous anecdotes that, while there are a number of sources of data on the long-term care paraprofessional workforce, there are major gaps and shortcomings in the available data. Current data collection does not provide sufficient data to track the supply, demand, or use of the direct care workforce. Furthermore, the available data do not provide sufficient data to support assessments of the effectiveness of policies and programs intended to address or prevent workforce shortages or to assess the relationship between the workforce and outcomes of care.

The lack of good data on the workforce reflects a number of factors, some related to the nature of paraprofessional work and some related to a lack of resources to collect detailed data. One of the fundamental problems of data collection on direct care paraprofessionals is the lack of a clear definition of the workforce. In occupations with clear and specific educational requirements for entry and a clear scope of service, such as medicine or dentistry, it is relatively easy to define and measure the workforce. On the other hand, for most types of aides and assistants, there are few if any entry requirements and individuals can flow in and out of jobs relatively easily. Furthermore, because of the overlap in activities performed by personal care aides, health aides, and similar paraprofessionals, getting accurate and consistent counts are problematic.

The next two chapters examine Federal and State data systems in this general context. They clarify the nature and extent of the shortcomings of the various data collection and reporting systems, and they identify steps that could be taken to improve data systems to support workforce planning and policymaking.