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Decision support system:
Decision Support Systems (DSS) are a specific class of computerized information system that
supports business and organizational decision-making activities.
A properly designed DSS is an interactive software-based system intended to help decision
makers compile useful information from raw data, documents, personal knowledge, and/or
business models to identify and solve problems and make decisions.
For example:
A national on-line book seller wants to begin selling its products internationally but first
needs to determine if that will be a wise business decision. The vendor can use a DSS to
gather information from its own resources (using a tool such as OLAP) to determine if
the company has the ability or potential ability to expand its business and also from
external resources, such as industry data, to determine if there is indeed a demand to
meet. The DSS will collect and analyze the data and then present it in a way that can be
interpreted by humans.
One example is the clinical decision support system for medical diagnosis.
Other examples include a bank loan officer verifying the credit of a loan applicant or an
engineering firm that has bids on several projects and wants to know if they can be
competitive with their costs.
Features of DSS:
DSS assists managers in their decision making specifically in semi- structured and
unstructured fields.
DSS supports and enhances, rather than replaces, managerial decisions.
DSS improves the effectiveness of the decision rather than its efficiency.
DSS combines the use of models and analytical techniques with conventional data
access and retrieval functions.
DSS has features which make its use by non-computer people easier.
DSS has enough flexibility to accommodate changes in the environment, the approach
and the needs of the users.
DSS supports managers at all levels that take decisions.
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DSS is user initiated and user controlled.
DSS supports the personal decision making styles of individual managers.
Some Uses:
Typical information that a decision support application might gather and present would be:
Accessing all of your current information assets, including legacy and relational data
sources, cubes, data warehouses, and data marts.
Comparative sales figures between one week and the next.
Projected revenue figures based on new product sales assumptions.
The consequences of different decision alternatives, given past experience in a context
that is described.
DSS PRODUCT:
CLINICAL DECISION SUPPORT SYSTEM:
Clinical Decision Support Systems are "active knowledge systems which use two or more items
of patient data to generate case-specific advice"
Role & Characteristics:
A clinical decision support system has been coined as an “active knowledge systems, which use
two or more items of patient data to generate case-specific advice.”This implies that a CDSS is
simply a DSS that is focused on using knowledge management in such a way to achieve clinical
advice for patient care based on some number of items of patient data.
Purpose/Goal:
The main purpose of modern CDSS is to assist clinicians at the point of care. This means that a
clinician would interact with a CDSS to help determine diagnosis, analysis, etc. of patient data.
The clinician would input the information and wait for the CDSS to output the “right” choice
and the clinician would simply act on that output. The new methodology of using CDSS to assist
forces the clinician to interact with the CDSS utilizing both the clinician’s knowledge and the
CDSS to make a better analysis of the patients data than either human or CDSS could make on
their own. Typically the CDSS would make suggestions of outputs or a set of outputs for the
clinician to look through and the clinician officially picks useful information and removes
erroneous CDSS suggestions.
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Most CDSS consist of three parts, the knowledge base, inference engine, and mechanism to
communicate. The knowledge base contains the rules and associations of compiled data which
most often take the form of IF-THEN rules. If this was a system for determining drug
interactions, then a rule might be that IF drug X is taken AND drug Y is taken THEN alert user.
Using another interface, an advanced user could edit the knowledge base to keep it up to date
with new drugs. The inference engine combines the rules from the knowledge base with the
patient’s data. The communication mechanism will allow the system to show the results to the
user as well as have input into the system.
Effectiveness:
A 2005 systematic review by Garg et al. of 100 studies concluded that CDSS improved
practitioner performance in 64% of the studies.
The CDSs improved patient outcomes in 13% of the studies.
Sustainable CDSs features associated with improved practitioner performance include the 2005
systematic review (quantitative analysis) of 70 studies by Kawamoto et al. found...
"Decision support systems significantly improved clinical practice in 68% of trials."
The CDS features associated with success include the following:
CDSS is integrated into the clinical workflow rather than as a separate log-in or screen.
CDSS is electronic rather than paper-based templates.
CDSS provides decision support at the time and location of care rather than prior to or
after the patient encounter.
CDSS provides (active voice) recommendations for care, not just assessments.
Four key functions of clinical decision support systems are outlined in [Perreault & Metzger,
1999]:
"Administrative: Supporting clinical coding and documentation, authorization of
procedures, and referrals.
"Managing clinical complexity and details: Keeping patients on research and
chemotherapy protocols; tracking orders, referrals follow-up, and preventive care.
"Cost control: Monitoring medication orders; avoiding duplicate or unnecessary tests.
"Decision support: Supporting clinical diagnosis and treatment plan processes; and
promoting use of best practices, condition-specific guidelines, and population-based
management."
