Decision Support System Framework

1. Decision Support System Framework
2. Decision Support System Framework

Technology Cambridge, Massachusetts

By: David A. Schiiiing Facuity of Management Sciences Ohio State University

Abstract This article examines the implications of utilizing deci- sion support systems (DSS) in the public sector based on a DSS developed and implemented for a community mental health system. The DSS includes a multiple objective (goal programming) allocation model and encompasses a multiple party decision process. The experiences and insights acquired during the develop- ment and implementation of this DSS are relevant to public sector decision support in general. The impor- tance of a DSS as a process-support aid rather than a product-oriented aid (i.e., simply providing answers) and the interaction of system architecture and the chosen design strategy are key insights. In particular, the distinction between model-oriented and data- oriented DSS does not appear to be appropriate. The public sector decision maker’s concern with issues of equity requires the ability to operate in a higher dimen- sional framework than the typical spreadsheet model and there is a critical need for communication support.

Keywords: Goal programming, decision support systems, public sector.

ACM Categories: H.4.2

Introduction

Developing and implementing decision aids in the public sector is a challenging task. As Lamm [14] points out, the political process tends to pro- mote those that survive or win, not those seeking truth. Often, the essential benefit of a decision aid — a valid model — is the very element that most threatens the survival of the public deci- sion maker. It is not surprising that Brill [3] notes, “Designing a solution to a public sector problem is largely an art.”

Hammond [8] suggests that it may not be suffi- cient to provide decision aids unless explicit attention is given to how these aids support effective learning. Without effective learning support dysfunctional consequences are likely to result from policy-making processes. Although Hammond argues a quasi-experimen- tal approach is a necessary condition for learn- ing, he notes that the strong quasi-rational model of inquiry represented by the application of management science techniques has had positive impact on public sector decision mak- ing. For example, management science models can help to externalize multiple objectives and, when combined with the results of quasi-experi- ments, provide an enhanced learning environment.

The need to facilitate access to decision aids as well as to support individual and organizational learning is explicitly addressed in the decision support systems literature [1]. The basic design strategy for DSS begins with an analysis of the decision process and adaptively developing a tool for the user to learn about and cope with semi-structured decisions.

Experience in DSS design has also indicated the Importance of flexibility, ease of use (at least by an intermediary), and adaptability. Design methodologies such as middle-out [16] or proto- typing [12] are explicitly directed towards achieving these characteristics. These design approaches assume there will be significant user and analyst learning in terms of both the technology and the decision process. This learn- ing is enhanced (perhaps even made possible) by developing an initial system with the char- acteristics described above. As both the user and analyst move along a learning curve, the

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system is adapted to support their evolving infor- mation and ieaming needs.

The trend in public sector applications of management science techniques seems consis- tent with this perspective. Public sector planning models have evolved from those that focus on efficiency to those that attempt to describe and account for conflicting objectives [18]. The appii- cation of multi-objective models in areas such as fire station location [22], police patrol scheduling [20], and water resource management [15] are some recent illustrations. Recursive frameworks [4, 11] have been proposed that use a multi- objective planning model to establish system parameters and then disaggregate these solu- tions using heuristic and simulation models in order to evaluate their impact on system opera- tions. This iterative approach is quite consistent with the adaptive design concepts proposed by DSS researchers.

Research on the application of decision support systems in the public sector has emphasized the need to address both the problems of conflicting objectives as well as the need to better support the traditional data analysis efforts of the policy analyst. Hammond notes that both forms of deci- sion aids are necessary. Providing these types of decision aids in a user friendly, adaptive mode is the objective of many current research efforts.

Decision Setting This research will focus on a decision support system designed and Implemented for the Franklin County (Ohio) Mental Health and Retar- dation Board. The Board oversees forty contract agencies which provide required community- wide mental health services. The nature of the decision process for allocation decisions is critical in this environment. There must be opportunities for various constituencies, representing diverse interests, to have an influence on complex programmatic and finan- cial decisions. Unfortunately, within this realm of complexity the decision makers are often untrained. They are chosen based on the con- stituencies and values they represent, rather than on their knowledge of the problem area or

their expertise as planners or decision makers. They serve in a voluntary mode, meeting infre- quently and typically under severe time con- straints. It is little wonder that decisions often reflect the relative power of a special interest group rather than some overall set of community goals and priorities.

The Franklin County MHR Board, faced with increasing demand and an eroding resource base^ began an effort to improve the quality of their budget planning and allocation process. They identified a need to clarify and link com- munity goals to a comprehensive model for men- tal health delivery. They sought a budget pro- cess that would provide Board members with a better understanding of how specific allocations affected program level and overall community mental health system goals.

As a starting point they chose the Balanced Ser- vice System (BSS) model as the fundamental conceptualization of a mental health service system. The BSS is a model of mental dysfunc- tioning used by the Joint Commission on Accreditation of Hospitals (JCAH) to generate standards for community mental health pro- grams. In its basic form the BSS model consists of two primary service dimensions: the service function (crisis stabilization, growth, and sustenance) and the service environment (pro- tective, supportive, and natural). The function indicates the nature of the service while the environment describes where the service is pro- vided. Each of 200 possible service types are assigned to one of the cells of this two- dimensional matrix. Figure 1 depicts this matrix and includes examples of the types of services in each cell. This model satisfies requirements for a comprehensive mental health framework and also provides the basis for externalizing Board goals. As will be discussed, the goal structure addresses both specific program areas (e.g., a particular cell in the service delivery matrix) and systemwide goals (e.g., the need to balance service delivery across a range of service environments).

‘The Board’s allocations budget Is approximately 20 million dollars, however, projections for budget cutbacks and Inflation are significantly reducing tiiese resources whiie various need assessments indicate increasing demand for service.

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DSS in the Public Sector

Crisis Stabiiization

FUNCTiON

Growth

LU

UJ

Protective

Supportive

Naturai

Sustenance Psychiatric ward of a state hospital

Twenty-four hour community emergency center

Court appointed probate screening

Private psychiatric hospital

Outpatient service at a community mental health center

Direct group counseling at the work place

Long term care in a state institution

Chronic patient deinstitutional- ization

Chronic patient living with foster family

Figure 1. The Baianced Service System Categories

The Board also recognized the need for an ade- quate decision aid. They began an effort to develop a decision support system that would: (1) provide a direct link between Board goals (as formulated using the BSS model) and allocation decisions, (2) provide a means to better under- stand the tradeoffs between goals and the impact of altering goal priorities, (3) provide the means to easily incorporate new restrictions, policies, or cost and service parameters into decisions, and (4) provide training tools for Board members. Given these needs, a DSS design and implementation effort was under- taken. The following sections describe the resulting DSS and its impact.

DSS Framework One of the basic concepts of DSS is the need for flexibility and adaptability. As many public sec- tor researchers note [3], successful public sec- tor decision aids must be able to accommodate unanticipated changes both to the structure of embedded models as well as to the nature of the user interaction. Achieving these system char- acteristics is a fundamental goal of the DSS designer. This flexibility and adaptability can be provided through a modular design. The system framework employed in this study (Figure 2) is consistent with that proposed by Sprague and Carlson [24]. It consists of three basic com- ponents; model management, data manage-

ment, and information management, and it pro- vides a user friendly interface. Each component is decoupled as much as possible and consists of a set of well-defined processing modules. This modularity minimizes the number of system interdependencles, thereby allowing most changes to be relatively localized and straight- forward. Further, various processing modules are written in a high level, analysis-oriented language (SAS). This language provides many data processing-oriented macro statements and parameterized routines which substantially reduce the time required to generate or modify particular system components, in cases where this language did not meet specific needs the module was written in Fortran or a macro com- mand language.

While initial prototyping efforts focused on the development of a mathematical model, the eventual success of the DSS depended on an effective, integrated software environment for each of the component systems. This would suggest appropriate system characteristics for DSS generators as well as give rise to questions of the validity of distinctions made in the DSS literature concerning model-oriented versus data-oriented decision support systems. To pro- vide a background for these remarks, a brief description of each component is provided.

Model Management — The model manage- ment component focuses on the generation and execution of the allocation model. A model gen-

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DSS in the Public Sector

MODEL MANAGEMENT

USER

System Control

Model generation

Model execution

DATA MAN

System Control

Transaction database

r AGEMENT

Database generation

Application database generation

INFORMATION

V System Control

r MANAGEMENT

Information processing

Report generation

f

Management reports

Figure 2. Decision Support System Framework

eration module translates variable definitions, system structure, and parameter estimates into an appropriate format for model execution. This module also provides a means to interface with the system transaction database. Relatively extensive changes to the model can be accom- plished by fairly simple adjustments to the model generation module.

The model execution module utilized IBM’s MPS linear programming package. However, the flexibility of the model generator, combined with the capabilities of the data management component, permit the use of any appropriate linear programming software.

Finally, as with each component of the DSS, the model management component includes pro- cessing modules to interface with the host operating system and provides for interactive dialogue with the user. This aspect, termed system control, enables much of the operation of the model management component to be rela- tively transparent to the user and provides the means to integrate this component with other parts of the system.

Data Management — The purpose of the sec- ond component, data management, is to provide the foundation for a delivery system by merging the solution database with various other data-

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bases (e.g., variable labels, historical data trends, etc.) in order to create an integrated solution database. From this solution database, selected application databases are extracted for use by the application programs. The applica- tion databases create significant efficiency in the subsequent information processing modules. It is important to note that this compo- nent decouples the generation and execution of the model from the generation of management information. It is, in fact, the role of a data management component to isolate changes to application programs from changes to primary data sources (in this case, changes to the alloca- tion model).

The data management component aiso pro- vides the means to access and analyze data stored in the system transaction database. As will be discussed later, this capability proved necessary for the successful implementation of the DSS. A high level language (SAS) provided efficient processing of large files^ as well as the ability to quickly adapt parameter calculations for both changes in problem structure and specific data sources.

Information Management — The information processing component creates a wide range of managerial reports. To achieve adaptability and flexibility, this component consists of a number of applications programs that operate on extrac- ted application databases. This structure per- mits modifications to a particular program or report to be localized and, therefore, greatly simplifies the adaptation of the information generation process. This component uses visual representations such as value paths and bar graphs to augment traditional tabular reports. The system allows easy manipulation of both the representation form as well as the particular for- mat via a user interface environment.

The Model The complex and political nature of the alloca- tions decision highlighted the utility of a model- based decision support system. The complexity arose not only from the great variety of allocation decisions required, but also from their inter-

‘The transaction database contained over 500,000 records.

relationships. These issues were addressed by formulating a linear programming resource allocation model.

The selection of an appropriate model structure was influenced by several considerations. First, the presence of lay decision makers and other nontechnical users favored a model structure which was intuitive and, therefore, easy to understand. Second, due to the prototyp- ing/evolutionary approach used in system development, the model had to be capable of extensive elaboration. Third, the chosen struc- ture should address the multiple objective nature of the decision problem, namely the com- peting Balanced Service System categories. Finally, it was important that the model help strengthen the behavioral link between the newly adopted BSS framework and the decision maker’s existing perceptions of system-wide needs.

In response to these desired characteristics a goal programming model structure was selected. Goal programming has been used and tested in a wide variety of multiple objective decision situations with sophisticated users as well as novices. Such a model structure can respond well to an evolutionary development. In addition, the multiple BSS objectives could be represented in a straightforward fashion using county-wide service needs as goal levels. By directing the Board’s attention towards balanc- ing these services, the behavioral link between the BSS framework and a Board member’s cur- rent cognitive model could be improved.

The model formulation follows a class goal pro- gramming structure and is discussed in detail by Henderson and Schilling [10]. While the details of this model are not germane to this article, a brief overview is provided so that the DSS and the evolving model can be discussed.

The primary decision variables reflected the amount of dollars from each funding source allocated to each service type provided by each agency. There were four different sources of funds to be accounted for, resulting in over 500 variables. Besides the budget constraints, which limited total dollars available from each funding source, restrictions were specified on the percentage increase and decrease that any

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