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GOODNESS
OF FIT IN MANAGED MENTAL HEALTH CARE:
SERVICES, OUTCOMES, AND LOCAL CONTROL
Two
key theoretical premises of managed care are that standard service
protocols, best practices, or benchmarks exist that can 1) guide
quality of care, and 2) be modified to improve quality or control
cost. However, many efforts to define a standard menu of services
to be made available in specified quantities (i.e. best or evidence-based
practices) have been less than enlightening. This is particularly
true in regards to providing services for citizens who are severely
mentally disabled. One reason may be that while, it is generally
acknowledged "...that the population is heterogeneous,
and what works in some instances may not be appropriate in others,"
(1) previous efforts to describe this heterogeneity and to use
this information to plan and manage services have been only
moderately successful.
Traditionally adults with severe mental disabilities have been
described in several ways. The most widely recognized classification
system is the diagnosis-based DSM IV (3). Here individuals have
been described in terms of the characteristics of their illness.
The DSM-IV system provides important guidance for the prescription
of medication and other somatic interventions, however it is
not as helpful for predicting the need for or utilization of
other community mental health services (4,5,6,7,8,9). Another
approach, Diagnostic Related Groups (DRG's), is based on illness-episodes
(10). DRG's were initially developed more than 20 years ago
to help manage inpatient care. They have however, shown limited
ability to provide clinical pictures (11) or predict resource
utilization and cost (12,13,14). DRG's are still not available
for community mental health or community support systems. Approaches,
such as the Level of Need Care Assessment (15) are based on
need profiles. This system has been used to identify need patterns
and gaps in community services.
While the above approaches are valid for specific purposes,
a process that has broader utility has been needed. One barrier
to the development of best practice models and the management
of adequate systems of care (e.g. recovery-oriented community
support systems or capitated managed care systems) has been
the lack of more holistic pictures of the citizens to be served.
This has also limited attempts to assess the effectiveness of
mental health services and policy (16, 17, 18, 19).
To address the above limitations, the Goodness of Fit study
has employed a process known as Cluster-Based Planning and
Outcomes Management (Rubin et al.,1992; NEED NEW REFERENCES).
This approach, derived in part from the cognitive psychology
literature, seeks to describe mental health consumers in terms
of "prototypes " (20) or Clusters that are based on
a multitude of characteristics. This prototype model assumes
that those who work with such special populations identify naturally
occurring clusters whose typical members share common strengths,
problems, treatment need, and prospects for recovery (21). In
contrast to more classical categorization approaches that require
individual cases to meet necessary and sufficient conditions,
clusters are often characterized by a set of correlated or typical
features (22). Descriptions of members of different clusters
can take into account both the strengths and weaknesses of members
of the group, and can consider "whole" people embedded
in history, community, and social contexts. They frequently
describe both common elements and capture the variability among
members of the same cluster (23). Cluster descriptions of adults
with severe mental disabilities would be expected to include
a broad array of information such as: social and living skills,
work history and work skills, family role and support, history
and/or effectiveness of treatment, psychiatric symptomatology,
interference from substance abuse or chronic physical health
problems, housing and living environments, personal strengths,
and integration in the community (24, 25, 26, 27, 28, 29, 30,
31).
Between 1988 and 1996, research based on this conceptual approach,
identified generalizable clusters of adults with SMD. Clusters
were identified in a multi-step process using functional assessment
ratings, statistical clustering procedures, and expert-based
knowledge elicitation and validation techniques involving consumers,
family members and providers. The basic methods were replicated
in 8 different geographic service areas in Ohio. The overall
effort resulted in holistic Prose Cluster Descriptions of individuals
who share common strengths, problems, treatment histories, social
and/or environmental contexts, and life situations (Rubin and
Panzano in review Psychiatric Services).
In each of the eight geographical service areas, the process
also identified targeted treatment goals for each cluster (Rubin
et al., 1994). The pattern of treatment goals suggested that
cluster had considerable utility for differentiating desired
outcomes among clusters. Empirical evidence also indicated that
clusters had utility for predicting costs and the utilization
of presently available resources and services (e.g. case management
and hospitalization) (Rubin et al., 1994; Rubin, Kurth &
Coyne, 1997). However, the question remained as to whether the
present services represented the "best practices"
for members of each cluster.
Study
Methods
The
Goodness of Fit research is being conducted in two urban areas,
both of which participated in the cluster development and validation
efforts described above. Three large mental health centers serving
a total of 5000 to 6000 adults with SMD initially agreed to serve
as research sites. However, over the course of the study, one
center withdrew. The overall research objectives are:
1. To Use A Community-Based, Expert-Driven Planning Process To
Define Best Practice Models For Each Cluster,
2. To Pilot-Test Portions Of These Models In Mental Health Agencies
For A Period Of Two Years,
3. To Assess Whether Clients Who Receive These Model Services
Are Doing Better Than Clients, In The Same Cluster, At The Same
Agency, Who Do Not Receive The Model Services.
4. To Test The Overall "Goodness Of Fit" Hypothesis
Which States That:
There Is A Positive Association Between The Degree Of Fit Between
Prescribed Services And Actual Services, And The Extent Of Progress
Made Toward Targeted Outcomes
Research
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