INCORPORATING EVIDENCE
USE OF COMPUTER –BASED
CLINICAL DECISION
SUPPORT SYSTEMS FOR HEALTH
PROFESSIONALS
Ida Androwich
Margaret Kraft
1. Define computerized clinical decision
support systems (CDSS).
2. Identify types of CDSS, their characteristics,
and the levels of responsibility implicit in the
use of each type.
3. Describe effects of CDSS on clinician
performance and patient outcomes in
healthcare.
4. Understand the features, benefits, and limits
of CDSS.
5. Develop a future vision for CDSS within
nursing.
Clinical decision support
Decision support systems
Information systems
Knowledge and
Cognition
• Decision support systems (DSS) are automated tools designed to
support decision-making activities and improve the decisionmaking process and decision outcomes.
• A CDSS is designed to support healthcare providers in making
decisions about the delivery and management of patient care.
• A CDSS program’s goals may include patients safety and
improved outcomes for specific patient populations as well as
compliance with clinical guidelines, standards of practice , and
regulatory requirements.
• The primary goal of CDSS is the optimization of both the
efficiency and effectiveness with which clinical decisions are
made and care is delivered.
• Nursing decision support systems (NDSS) are tools that help
nurses improve their effectiveness, identify appropriate
interventions, determine areas in need of policy or protocol
development, and support patient safety initiatives and quality
improvement activities.
• CDSS includes a set of knowledge-based tools that can be fully
integrated with the clinical data embedded in the computerized
patient record (Electronic Health Record) to assist providers by
presenting information relevant to the healthcare problems being
faced.
• CDSS may focus on treatment, diagnosis or specific patient
information.
• CDSS may be defined as any computer program that helps health
professionals make clinical decisions. CDSS software has a knowledge base
designed for the clinician involved in patient care to aid in clinical decisionsmaking.
• Johnston et al. (1994) defined CDSS as “computer software employing a
knowledge base designed for use by a clinician involved in patient care, as a
direct aid to clinical decision-making.”
• Sims et al. (2001) broadened the definition to “CDSS are software
designed to be a direct aid to clinical decision –making, in which the
characteristics of an individual patient are matched to a computerized
clinical knowledge base and patient-specific assessments or
recommendations are then to the clinician or the patients decision.”
• Coiera (1994) discussed the role of CDSS as augmenting human
performance and providing assistance for healthcare providers especially
for tasks subject to human error.
• Whatever definition chosen, it seems clear that healthcare is being
transformed through information and knowledge management and
technology is being used to “tame data and transform
information”(Berner,1999).
Randall Tobias – former VP of ATT
- Computer has virtually unlimited capacity for processing
and storage of data
- Human has limited storage9memory and processing
power, but does have judgement, experience and intuition.
Three main purposes of a DSS:
1. Assist in problem solving with semi structured problems
2. Support, not replace, the judgment of a manager or
clinician
3. Improve the effectiveness of the decision-making process
Early Systems
Focus on Diagnosis
 Early known CDSS developed by de Dombal in 1972 at Leeds
University
 Used Bayesian theory to predict the probability that a given
patient, based on symptoms, had one of seven possible
conditions
1974 – INTERNIST – Developed at the University of Pittsburgh to
support the diagnostic process in general internal medicine by
linking diseases with symptoms
- Later became the basis of successor systems including quick
medical reference (QMR)
1976 – MYCIN – rule-based expert system to diagnose
and recommend treatment for certain blood infections
In nursing, TWO early and well known systems
a) COMMES ( Creighton online multiple modular expert
systems)
b) CANDI (Computer aided nursing diagnosis and
intervention
- Were developed to assist nurses with care
planning and nursing diagnosis
 ADMINISTRATIVE and ORGANIZATIONAL
SYSTEMS
INTEGRATED SYSTEMS
• Shortliffe – uses function, mode of advice, consultation style,
underlying decision-science methodology, and user-computer
interactions to categorize systems
• Teich and Wrinn – examine DSS from the aspects of functional
and logical classes and structural elements
•
•
•
•
•
Feedback provided
Organizational of the data
The extent of proactive information provided
Intelligent actions of the system
Communication method
•
•
•
•
•
•
•
•
•
•
•
•
Substitute therapy alerts
Drug family checking
Structure entry
Consequent actions
Parameter checking
Redundant utilization checking
Relevant information display
Time-based checks
Templates and order sets
Profile display and analysis
Rule-based event detection
Aggregate data trending
•
•
•
•
•
•
•
•
Triggering
Dispatching
Rule logic
Process control
Notification / acknowledgements
Action choices
Action execution
Rule editor
Perreault – organized key CDSS functions as:
1) Administrative – support for clinical coding and
documentation
2) Management of clinical complexity and details –
keeping patients on research and chemotherapy
protocols, tracking orders, referrals, follow-up, and
preventive care
3) Cost control – monitoring medication orders and
avoiding duplicate or unnecessary tests
4) Decision support – supporting clinical diagnostic and
treatment plan processes promotion of best practices, use
of condition-specific guidelines, and population-based
management
a. Data-based (population-based)
b. Model-based (case-based)
c. Knowledge-based (rule-based)
d. Graphics-based systems
A. Data-based systems – capitalized on the
fundamental input into any intelligent system, data
- Provide decision support
OLAP – on line analytic processing
B. Model-based DSSs – driven by access to and
manipulation of a statistical, optimization, and/or
stimulation model
Model – generalization that can be used to describe the
relationships among a number of observations to
represent a perception of how things fit together
Genetic algorithms (Gas) and neural networks (NNs) –
newer computation techniques that are involving
problems solving solutions
Knowledge-based systems – rely on expert
knowledge that is either embedded in the system
or accessible from another source and uses some
type of knowledge acquisition process to
understand and capture the cognitive processes
of healthcare providers
EBP – evidence based practice
D. Graphics-based systems – take advantage of
the user interface to support the decisions by
providing decision “cues” to the user in the form
of color, graphical presentation options, and data
visualization
C.
DTL – useful for specific straightforward tasks
RBL – allows for complex decision capacities
- More flexible with answers. Provides consistent
outcomes and is adaptable to change
- Also tends to have rigid solutions and allows
little or no clinician autonomy
Taxonomy for CDSS:
1. Context
2. Knowledge and data sources
3. Workflow
4. Decision support
5. Information delivery
Institute of Medicine (IOM) – human error as a major source of
patient care morbidity and mortality
• Bates – practice lags behind knowledge by
several years
- This lag could be shortened if not eliminated by
the availability of current knowledge to support
decision-making process
• Henry – identified essential elements needed
for informatics infrastructure:
Sittig – cites the following FIVE Elements:
1.
2.
3.
4.
5.
Integrated real-time patient database
Data-drive mechanism
Knowledge engineer
Time-driven mechanism
Long-term clinical data repository
Knowledge engineering – field concerned with
knowledge acquisition and the organization and structure of
that knowledge within a computer system
Interviews – MOST COMMON used method of eliciting
knowledge
Cognitive Task Analysis (CTA) – set of methods that
attempt to capture the skills, knowledge, and processing ability
of experts in dealing with complex tasks
• GOAL of CTA: Tap into these
“higher order” cognitive functions
• Tan and Sheps – six-step
approach to CTA
• Increasing Inclusion of Patients – CDSS allow
patient access to the knowledge base of the
system
• Dual Purpose of Documentation – balance the
use of poorly designed or inadequately tested
systems with individual clinicians being forced to
make patient care decision-making without
existing evidence at the point of care
• Dual Purpose:
1. Improving care for the individual patient
2. Improving care for the future populations of patients
via aggregated information used for clinical
decision-making
• CDSS can:
o Improve patient care quality
o Reduce medication errors
o Minimize variances in care
o Improve guideline compliance
o Promote cost savings