A. Background
As noted in previous NAMHC reports on parity (National Advisory Mental Health Council 1998), the ability to measure the quality of health and mental health services is crucial for achieving the real intent of parity (i.e., providing the best and most appropriate services for those who need care). Various efforts have been launched, ranging from clinical measures for specific illnesses applied at the individual level to global performance measures applied to the health care system. Although the ability to measure quality has improved in recent years, there is still a long way to go (Dickey et al. 1998). Below we discuss recent developments in this area. Although no current data reveal the impact of parity on quality, these efforts should make it possible as data become available for care under parity.
B. Recent Developments
There are many ways to measure quality, but most are based on a model outlined by Donabedian many decades ago (Donabedian 1966). This model separates quality into three domains: structure, process, and outcome. Most current measures use some aspect of each of these, although the most popular is outcome assessment. Newer measures have expanded to incorporate other aspects, such as access to specific health services, and to include a more comprehensive view of the health system as well as education and caregiver support. Recently there have been efforts to move beyond simply measuring aspects such as structure and process to determine ways to reduce variation in health care by ensuring provider adherence to "evidence-based" guidelines.
Earlier studies showed that the quality of care for mental illnesses is not often good, especially in the general medical sector (Wells 1994; Wells et al. 1994). However, even in the specialty sector there are quality problems, as shown by recent reports from the Schizophrenia Patient Outcomes Research Team (Lehman and Steinwachs 1998). Although some earlier studies raised concerns about the quality of mental health care in managed care settings, there are no consistent data to indicate that managed care settings are any less likely than non-managed care settings to deliver appropriate treatment services (Durham 1998).
No currently available quality measures provide all the answers that consumers, providers, employers, and policymakers want. Some, such as the currently popular Health Plan Employer Data and Information Set (HEDIS) measures are not sufficient in the mental health area (Druss and Rosenheck 1997). In addition, outcomes of health care may be affected by factors that are not under the control of the medical system, or their relevance to a particular treatment may not become apparent for years. Thus, it is crucial to be able to link what is done in a particular patient encounter to the ultimate impact of that intervention. One way to do this is to show through research which interventions work for particular illnesses in specific populations. This is the basis for what is called "evidence-based practice."
The National Institute of Mental Health recently launched several major clinical treatment trials that will determine the effectiveness of treatment interventions in community populations with various mental illnesses. These studies will provide evidence about which interventions are best for the varied populations seen in everyday community practice. The first trials are in bipolar disorder, schizophrenia, depression in adolescents, and treatment-resistant depression in adults. Future studies will focus on other major mental illnesses.
Even with adequate measures, it is essential to have access to data that can be used to assess the quality of care delivered. This requires that a variety of data sources, such as clinical and survey data, be available. For example, information on access to care can be obtained through surveys of consumers, but use of specific services is best determined through administrative databases. The need for these different sources of information presents a multitude of feasibility problems, however. Population surveys are often costly and require a great deal of effort to enlist a representative sample, while medical records and administrative data raise confidentiality concerns.
Because many of the factors that affect health are social and their relationship to health status is unclear, more research is needed at the interface between social sciences and clinical treatment. Acting without such data, or on the bases of imperfect data, could increase costs, disenfranchise groups who have special needs, and even put excellent health plans out of business.
Even with appropriate measures of quality and the ability to put them into place, interventions need to be developed that actually improve the quality of mental health care in places where it is now not optimal. Efforts to improve quality have focused on regulation, competition, quality-improvement programs, and economic incentives (Chassin 1997). Clearly, no single approach is sufficient; multiple approaches are needed. For example, regulation can set the groundwork for what is expected and make it possible to collect data that could be used to formulate risk adjustment. Competition is a powerful tool, but without valid measures of quality, the wrong factors may form the basis for competition among plans. Thus, consumers and health plans may segregate themselves to take advantage of the most positive results that relate to them. This might improve some specific areas of health care but might result in poorer general health care. Financial incentives can certainly influence what care is delivered, but it is unclear how much impact they have on quality and how to use them to improve the quality of care. A number of quality-improvement programs have been tried, and some recent evidence suggests that it is possible to improve the quality of care without major efforts (Wells et al. 2000). However, it still is unclear what programs will work and what other incentives will be needed to ensure that they last.
Current efforts are focused on finding ways to ensure that providers and consumers use the treatments that are considered best. We need to understand how providers, consumers, and systems will change their behavior to ensure that the best care is delivered and used. The NIMH is currently encouraging research that seeks to address these issues.
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This page was last updated: November 1, 2000.
