1. Chapter 10: Incorporating Evidence: Use of Computer-based Clinical Decision Support Systems for Health Professionals

2. Introduction Decision Support Systems (DSS)
- automated tools designed to support decision-making activities and improve the decision-making process and decision outcomes
- Intended to use the enormous amounts of data that exist in information systems to facilitate decision processes

3. Clinical Decision Support Systems (CDSS)
- 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
- Only as effective as its underlying knowledge base
- Tool system not a rule system

4. GOALS:
Patient safety
Improved outcomes for specific patient populations
Compliance with clinical guidelines
Standards of practice
Regulatory requirements
Primary Goal: Optimization of both the efficiency and effectiveness with which clinical decisions are made and care is delivered

5. Nursing Decision Support Systems (NDSS)
– 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

6. Definition
CDSS – any computer program that helps health professionals make clinical decisions
Johnston – 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 – software designed to be a direct aid to clinical decision-making , in which characteristics of an individual patient are matched to a computerized clinical knowledge base and patient-specific assessments or recommendations are then presented to the clinician or the patient for a decision

7. Coiera – role of CDSS – augmenting human performance and providing assistance for healthcare providers
Berner - healthcare is being transformed through information and knowledge management and technology is being used to “tame data and transform information

8. Expanded Uses of CDSS
Randall Tobias – former VP of ATT - Computer has virtually unlimited capacity for processing and storage of data - Human has limited storage(memory) and processing power, but does have judgment, experience, and intuition.

9. 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

10. History of CDSS Early Systems
Focus on Diagnosis
Earliest known CDSS developed by de Dombal in 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 I – 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)

11. 1976 – MYCIN – rule-based expert system to diagnose and recommend treatment for certain blood infections
In nursing, TWO early and well known systems
1. COMMES (Creighton online multiple modular expert systems)
2. CANDI (computer aided nursing diagnosis and intervention
- These were developed to assist nurses with care planning and nursing diagnosis

12. Types and Characteristics of DSS Types of DSS
Administrative and Organizational Systems
Administrative Systems (financial or quality monitoring)- generally support the business decision- making process; encompass decision processes
- Tend to be batch-oriented
Focused on:
Real-time decision support
Goal orientation
Intelligence gathering
designed to be used
at the point of
care by clinicians

13. Integrated Systems
– support outcomes performance management by integrating operational data:
Budgeting
Executive decision-making
Financial analysis
Quality management
Strategic planning data

14. Characteristics of DSS
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

15. 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

16. DSSs could be divided into:
a. Data-based(population-based)
b. Model-based(case-based)
c. Knowledge-based(rule-based)
d. Graphics-based systems

17. A. Data-based systems - Provide decision support
- Capitalize on the fundamental input into any intelligent system, data
- Provide decision support
OLAP – on line analytic processing

18. B. Model-based DSSs
– driven by access to and manipulation of a statistical, financial, optimization, and/or simulation 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 evolving as problem solving solutions

19. C. 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

20. D. Graphics-based systems
– take advantage of the user interface to support decisions by providing decision “cues” to the user in the form of color, graphical representation options, and data visualization

21. **Demand management centers use decision tree logic (DTL) or rule-based logic (RBL) for patient management. 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

22. 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

23. Barriers to the Use of CDSS Systems
Bates – practice lags behind knowledge by several years
- This lag could be shortened if not eliminated by the availability of current knowledge to support the decision-making process

24. Henry – identified essential elements needed for an informatics infrastructure:
1. Standardized vocabularies to describe patient diagnoses, interventions, and outcomes
2. Computer-based methods to examine linkages among patient problems and characteristics, healthcare interventions, patient outcomes, and the intensity of care-resources and to examine practice variations
3. An integrated clinical information system where data required for quality improvement are both collected and returned to the provider during routine processes of patient care

25. Evaluation of CDSS
Sittig – cites the following FIVE elements: 1. Integrated real-time patient database – which combines patient data from multiple sources, lab, radiology, pharmacy, admissions, nursing notes, and so on; provide context for results interpretation 2. Data-drive mechanism – allows even triggers to go into effect and activate alerts and reminders automatically 3. Knowledge engineer – translate the knowledge representation scheme used in system so that the clinical knowledge in the system can be extracted and translated into the machine executable logic

26. 4. Time-driven mechanism – to permit automatic execution of programs at a specific time to alert provider to carry out a specific action or insure that the action had been completed 5. Long-term clinical data repository – data collected over time from a variety of sources allowing a longitudinal patient record

27. Knowledge and Cognitive processes
Knowledge engineering – field concerned with knowledge acquisition and the organization and structure of that knowledge within a computer system
Interviews – MOST COMMONLY used method of eliciting knowledge

28. Cognitive task analysis (CTA) – set of methods that attempt to capture the skills, knowledge, and processing ability of experts in dealing with complex tasks
Attempts to identify pitfalls or trouble spots in the reasoning process of the beginner or intermediate level practitioner
GOAL of CTA: Tap into these “higher order” cognitive functions

29. Tan and Sheps – six-step approach to CTA:
1. Identification of the problem to target in the analysis
2. Generation of cases (decision tasks) that vary on key factors
3. Observation of a record of an expert problem solving for the case using think aloud
4. Observation of the novice and the intermediate problem solving
5. Analyses of expert versus less than expert problem solving
6. Recommendation of systems needs, design specs, and knowledge base components

30. Responsibility of User: Ethical and Legal Issues

31. Implications for Future Uses of CDSS in Nursing
Increasing Inclusion of Patients
CDSS allow patient access to the knowledge base of the system
The computer can become a patient health medium with reference databases, library access for healthcare information, drug and disease management information, self-help programs, and advice about prevention available

32. 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 future populations of patients via aggregated information used for clinical decision-making

33. CDSS can:
Improve patient care quality
Reduce medication errors
Minimize variances in care
Improve guideline compliance
Promote cost savings