Incorporating Evidence: Use of Computer Based Clinical Decision Support System for Health Professionals
Overview
This chapter focuses on defining, applying and understanding of CDSS. It includes its definition, a brief history, types and characteristics, importance and evaluation and as well as the legal issues concerned with CDSS. A CDSS is stressed out to never replace the role of a physician in decision making and is merely a guiding tool to help with the decision making process. Developing a CDSS requires a lot of financial and intellectual investment but has been proven to be of great help especially with avoiding medication prescription errors.
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.
• CDSS – designed to support healthcare providers in making decisions about the delivery and management of patient care.
- the primary goal: is the optimization of both the efficiency and effectiveness with which clinical decisions are made and care is delivered.
- development requires a huge financial and intellectual investment but also represents the potential to reduce care costs through improvement of the decision process at the point of care and to reduce the possibility of costly errors.
• NURSING DECISION SUPPORT SYSTYEMS (NDSS) – tools that help nurses improve their effectiveness, identify appropriate interventions, determine areas in need of policy and protocol development, and support patient safety initiatives and quality improvement activities.
• CDSS – is a “tool” not a “rule” system. In no way does a CDSS usurp the clinician’s decision-making role.
- may be defines as any computer program that helps health professionals make clinical decisions.
- 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 assignments o recommendations are then presented to the clinician or the patient for a decision.”
• 3 main purposes of a DSS:
1. Assist in problem solving with semi-structured problems
2. Support, not replace, the judgement of a manager or clinician
3. Improve the effectiveness of the decision-making process
HISTORY OF CDSS
• Early Systems
de Dombal- developed one of the earliest known CDSS designed to support diagnosis of acute abdominal pain in 1972 at Leeds University
-this system used used Bayesian theory to predict the probability that a given patient, based on symptoms, had one of seven possible conditions.
• 1974 – INTERNIST 1 was developed at the University of Pittsburgh to support the diagnostic process in general internal medicine by linking diseases with symptoms.
-later became the basis for successor systems including Quick Medical Reference (QMR).
• MYCIN – a rule based expert system to diagnose and recommend treatment for certain blood infections in 1976
• “Greek Oracle” approach – DNSS provided a solution from “ on high” and, and the clinician entered information and passively awaited for the solution.
• Miller and Masarie (1990) – called instead for a “catalyst” approach whereby the DSS serves as a vehicle to provide guidance, but the user remains in control.
• 2 early systems developed to assist nurses with care planning and nursing diagnosis:
1. COMMES ( Creighton online multiple modular expert system)
2. CANDI ( Computer-aided nursing diagnosis and intervention)
TYPES AND CHARACTERISTICS OF DSS
• Types of DSS
1. Administrative and Organizational Systems
- these systems encompass decision processes other than direct patient care delivery and are mainly used to strategic planning, budgeting, financial analysis, quality management, continuous process improvement, and clinical benchmarking.
2. Integrated Systems – such systems are able to support outcomes performance management by integrating operational data, budgeting, executive decision making, financial analysis, quality management, and strategic planning data.
Key CDSS Functions (Perreault 1999)
• Administrative – support for clinical coding and documentation
• Management of clinical complexity and details – keeping patients on research and chemotherapy protocols, tracking orders, referrals, follow-ups, and preventive care
• Cost Control – monitoring medication orders and avoiding duplicate or unnecessary tests
• Decision Support – supporting clinical diagnostic and treatment plan processes promotion of best practices, use of condition-specific guidelines, and population-based management.
Classification of CDSS from an ontologic perspective
• data-based (population-based) systems
- capitalize on fundamental input into any intelligent system, data.
- provide decision support with population perspective and use routinely collected longitudinal, cohort, and cross-sectional data bases.
• model-based – driven by access to and manipulation of a statistical, financial, optimization, and/or simulation model.
-data in this instance are compared to various decision-making and analytic models.
• 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.
• 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.
• Decision Tree Logic (DTL) - useful for specific, straightforward tasks.
- often based on probabilities, the correct interpretation of the probabilities can be challenging for both developer and user.
• Sims and Berlin (2003) – created the taxonomy for CDSS
1. Context
2. Knowledge and data Sources
3. Workflow
4. Decision Support
5. Information Delivery
CDSS IMPACT ON CLINICIANS AND CLINICAL DECISIONS
• Need for evidence based practice
-there is growing pressure for clinicians including nurses to use knowledge at the point of care that is based on research evidence.
- applications of CDSS suggests the ability to lessen the incidence of adverse drug events, nosocomial infections, and the inappropriate use of antibiotics.
- prevention of prescription errors is seen as one of the most valuable and widely used function of CDSS
EVALUATION OF CDSS
The effect of CDSS on clinical outcomes currently remains uncertain without valid and generalizable findings.
Increasing pressure to delivery quality care to the lowest possible cost has led to the implementation of CDSS to drive appropriate process improvement activities needed to achieve successful care outcomes.
• Sittig (1999) cites the following five elements as necessary, but not sufficient for a real-time clinical decision support system:
1. Integrated real-time patient database which combines patient data from multiple sources, lab, radiology, pharmacy, admissions, nursing notes, and so on. This is needed to provide context for results interpretation.
2. Data-drive mechanism that allows event triggers to go into effect and activate alerts and reminders automatically.
3. Knowledge Engineer who can translate the knowledge representation scheme used in the system
4. Time-driven mechanism to permit automatic execution of programs at a specific time to alert provider to carry out a specific action to 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.
KNOWLEDGE AND COGNITIVE PROCESSES
• Knowledge Engineering – field concerned with knowledge acquisition and the organization and structure of hat knowledge within a computer system.
• Cognitive Task Analysis (CTA) – refers to a set of methods that attempt to capture the skills . Knowledge and processing ability of experts in dealing with complex tasks.
- often beneficial in comparing an “expert performance” with the performance of “less than experts”.
- attempts to identify pitfalls or trouble spots in the reasoning process of the beginner or intermediate level practitioner while comparing the reasoning process with that of an expert.
• Tan and Sheps (1998) describe a 6 step approach to CTA as follows:
1. Identification of the problem to target in the analysis
2. Generation of cases 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. Analysis of expert versus less than expert problem solving.
6. Recommendation of system needs, design specs and knowledge base components.
• Miller and Gardner (1997) recommended adoption of a 0-3 risk-based review system for software where a 0 indicated systems providing factual content, and a 3 was a high risk, patient-specific system that was difficult to override by the clinician.
RESPONSIBILITY OF USER: ETHICAL AND LEGHAL ISSUES
CDSS are considered similar to medical devices but the legal responsibility for treatment and advice given to a patient rests with the clinician regardless of whether a CDSS is used.
Courts seem to believe that cost should have no role in clinical decision-making. Attempts to save money by reducing treatment below an undisputed standard of care are not acceptable.. If a treatment plan is based on anything besides “sound medical consideration” it would most certainly be considered malpractice.
There seems to be no adverse effect with the use of CDSS, however, such systems must be developed with high standards of quality. The provision of erroneous information and/or incorrect guidance does have the potential for harmful impact.
Safe use of CDSS may include such techniques such as limiting access, developing audit trials, monitoring use, and clinical hazard alerts.
IMPLICATIONS OF FUTURE USES OF CDSS IN NURSING
• Increasing Inclusion of Patients
- CDSS systems of the future may allow patient access to the knowledge base of the system.
• Dual purpose of documentation
- we must 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.
Overview
This chapter focuses on defining, applying and understanding of CDSS. It includes its definition, a brief history, types and characteristics, importance and evaluation and as well as the legal issues concerned with CDSS. A CDSS is stressed out to never replace the role of a physician in decision making and is merely a guiding tool to help with the decision making process. Developing a CDSS requires a lot of financial and intellectual investment but has been proven to be of great help especially with avoiding medication prescription errors.
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.
• CDSS – designed to support healthcare providers in making decisions about the delivery and management of patient care.
- the primary goal: is the optimization of both the efficiency and effectiveness with which clinical decisions are made and care is delivered.
- development requires a huge financial and intellectual investment but also represents the potential to reduce care costs through improvement of the decision process at the point of care and to reduce the possibility of costly errors.
• NURSING DECISION SUPPORT SYSTYEMS (NDSS) – tools that help nurses improve their effectiveness, identify appropriate interventions, determine areas in need of policy and protocol development, and support patient safety initiatives and quality improvement activities.
• CDSS – is a “tool” not a “rule” system. In no way does a CDSS usurp the clinician’s decision-making role.
- may be defines as any computer program that helps health professionals make clinical decisions.
- 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 assignments o recommendations are then presented to the clinician or the patient for a decision.”
• 3 main purposes of a DSS:
1. Assist in problem solving with semi-structured problems
2. Support, not replace, the judgement of a manager or clinician
3. Improve the effectiveness of the decision-making process
HISTORY OF CDSS
• Early Systems
de Dombal- developed one of the earliest known CDSS designed to support diagnosis of acute abdominal pain in 1972 at Leeds University
-this system used used Bayesian theory to predict the probability that a given patient, based on symptoms, had one of seven possible conditions.
• 1974 – INTERNIST 1 was developed at the University of Pittsburgh to support the diagnostic process in general internal medicine by linking diseases with symptoms.
-later became the basis for successor systems including Quick Medical Reference (QMR).
• MYCIN – a rule based expert system to diagnose and recommend treatment for certain blood infections in 1976
• “Greek Oracle” approach – DNSS provided a solution from “ on high” and, and the clinician entered information and passively awaited for the solution.
• Miller and Masarie (1990) – called instead for a “catalyst” approach whereby the DSS serves as a vehicle to provide guidance, but the user remains in control.
• 2 early systems developed to assist nurses with care planning and nursing diagnosis:
1. COMMES ( Creighton online multiple modular expert system)
2. CANDI ( Computer-aided nursing diagnosis and intervention)
TYPES AND CHARACTERISTICS OF DSS
• Types of DSS
1. Administrative and Organizational Systems
- these systems encompass decision processes other than direct patient care delivery and are mainly used to strategic planning, budgeting, financial analysis, quality management, continuous process improvement, and clinical benchmarking.
2. Integrated Systems – such systems are able to support outcomes performance management by integrating operational data, budgeting, executive decision making, financial analysis, quality management, and strategic planning data.
Key CDSS Functions (Perreault 1999)
• Administrative – support for clinical coding and documentation
• Management of clinical complexity and details – keeping patients on research and chemotherapy protocols, tracking orders, referrals, follow-ups, and preventive care
• Cost Control – monitoring medication orders and avoiding duplicate or unnecessary tests
• Decision Support – supporting clinical diagnostic and treatment plan processes promotion of best practices, use of condition-specific guidelines, and population-based management.
Classification of CDSS from an ontologic perspective
• data-based (population-based) systems
- capitalize on fundamental input into any intelligent system, data.
- provide decision support with population perspective and use routinely collected longitudinal, cohort, and cross-sectional data bases.
• model-based – driven by access to and manipulation of a statistical, financial, optimization, and/or simulation model.
-data in this instance are compared to various decision-making and analytic models.
• 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.
• 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.
• Decision Tree Logic (DTL) - useful for specific, straightforward tasks.
- often based on probabilities, the correct interpretation of the probabilities can be challenging for both developer and user.
• Sims and Berlin (2003) – created the taxonomy for CDSS
1. Context
2. Knowledge and data Sources
3. Workflow
4. Decision Support
5. Information Delivery
CDSS IMPACT ON CLINICIANS AND CLINICAL DECISIONS
• Need for evidence based practice
-there is growing pressure for clinicians including nurses to use knowledge at the point of care that is based on research evidence.
- applications of CDSS suggests the ability to lessen the incidence of adverse drug events, nosocomial infections, and the inappropriate use of antibiotics.
- prevention of prescription errors is seen as one of the most valuable and widely used function of CDSS
EVALUATION OF CDSS
The effect of CDSS on clinical outcomes currently remains uncertain without valid and generalizable findings.
Increasing pressure to delivery quality care to the lowest possible cost has led to the implementation of CDSS to drive appropriate process improvement activities needed to achieve successful care outcomes.
• Sittig (1999) cites the following five elements as necessary, but not sufficient for a real-time clinical decision support system:
1. Integrated real-time patient database which combines patient data from multiple sources, lab, radiology, pharmacy, admissions, nursing notes, and so on. This is needed to provide context for results interpretation.
2. Data-drive mechanism that allows event triggers to go into effect and activate alerts and reminders automatically.
3. Knowledge Engineer who can translate the knowledge representation scheme used in the system
4. Time-driven mechanism to permit automatic execution of programs at a specific time to alert provider to carry out a specific action to 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.
KNOWLEDGE AND COGNITIVE PROCESSES
• Knowledge Engineering – field concerned with knowledge acquisition and the organization and structure of hat knowledge within a computer system.
• Cognitive Task Analysis (CTA) – refers to a set of methods that attempt to capture the skills . Knowledge and processing ability of experts in dealing with complex tasks.
- often beneficial in comparing an “expert performance” with the performance of “less than experts”.
- attempts to identify pitfalls or trouble spots in the reasoning process of the beginner or intermediate level practitioner while comparing the reasoning process with that of an expert.
• Tan and Sheps (1998) describe a 6 step approach to CTA as follows:
1. Identification of the problem to target in the analysis
2. Generation of cases 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. Analysis of expert versus less than expert problem solving.
6. Recommendation of system needs, design specs and knowledge base components.
• Miller and Gardner (1997) recommended adoption of a 0-3 risk-based review system for software where a 0 indicated systems providing factual content, and a 3 was a high risk, patient-specific system that was difficult to override by the clinician.
RESPONSIBILITY OF USER: ETHICAL AND LEGHAL ISSUES
CDSS are considered similar to medical devices but the legal responsibility for treatment and advice given to a patient rests with the clinician regardless of whether a CDSS is used.
Courts seem to believe that cost should have no role in clinical decision-making. Attempts to save money by reducing treatment below an undisputed standard of care are not acceptable.. If a treatment plan is based on anything besides “sound medical consideration” it would most certainly be considered malpractice.
There seems to be no adverse effect with the use of CDSS, however, such systems must be developed with high standards of quality. The provision of erroneous information and/or incorrect guidance does have the potential for harmful impact.
Safe use of CDSS may include such techniques such as limiting access, developing audit trials, monitoring use, and clinical hazard alerts.
IMPLICATIONS OF FUTURE USES OF CDSS IN NURSING
• Increasing Inclusion of Patients
- CDSS systems of the future may allow patient access to the knowledge base of the system.
• Dual purpose of documentation
- we must 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.
