clinical decision support systems dr ebtissam al-madi

Clinical Decision support systems Dr Ebtissam Al-Madi

Clinical Decision support systems Clinical Decision Support Systems are "active knowledge systems which use two or more items of patient data to generate case-specific advice. Clinical DSSs are typically designed to integrate a medical knowledge base, patient data and an inference engine to generate case specific advice.

Functions of CDSS "Administrative: Supporting clinical coding and documentation, authorization of procedures, and. "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. "

Examples of early CDSS INTERNIST-I (1974) It uses patient observations to deduce a list of compatible disease states (based on a tree-structured database that links diseases with symptoms). MYCIN (1976) An expert system designed to diagnose and recommend treatment for certain blood infections and infectious diseases. Clinical knowledge in MYCIN is represented as a set of IF-THEN rules with certainty factors attached to diagnoses.

Dxplain: (mid 1980s) Uses a set of clinical findings (signs, symptoms, laboratory data) to produce a ranked list of diagnoses which might explain (or be associated with) the clinical manifestations. DXplain provides justification for why each of these diseases might be considered, suggests what further clinical information would be useful to collect for each disease, and lists what clinical manifestations, if any, would be unusual or atypical for each of the specific diseases. DXplain includes 2,200 diseases and 5,000 symptoms in its knowledge base.

Main Benefits of CDSS Improved patient safety e.g. through reduced medication errors and adverse events and improved medication and test ordering; Improved quality of care e.g. by increasing clinicians available time for direct patient care, increased application of clinical pathways and guidelines, facilitating the use of up-to-date clinical evidence, improved clinical documentation and patient satisfaction; Improved efficiency in health care delivery e.g. by reducing costs through faster order processing, reductions in test duplication, decreased adverse events, and changed patterns of drug prescribing favouring cheaper but equally effective generic brands.

Other benefits Automatic provision of relevant, personalised expert advice, expertise and recommendations sourced from up-to-date, best practice knowledge Reduce variation in the quality of care Can support medical education and training Can help overcome problems of inefficient coding of data Can be cost-effective after initial capital costs and update and maintenance costs Can provide immediate feedback to patients If integrated with an EMR, can help streamline workflow (history taking, diagnosis, treatment) and encourage more efficient data gathering Can provide an audit trail and support research Can maintain and improve consistency of care Can supply clinical information anytime, anywhere it's needed.

Drawbacks of CDSS Potential 'deskilling' effect Can be perceived as a threat to clinical judgment Can be considered too inflexible (can appear prescriptive, can appear to direct proceedings; can be difficult to depart from ordered, pre-prepared paths) Promote over-reliance on software; limit clinicians' freedom to think ? Difficult to evaluate - lack of accepted evaluation standards Can be time-consuming to use, possibly lead to longer clinical encounters and create extra work Uncertain and untested ethical and legal status Costs: maintenance, support and training required after initial outlay A clinician's experience and imagination cannot be duplicated in a computer application.

Factors that will increase use of CDSS Cost Attitude of targeted users: breadth and depth of commitment Degree of user acceptance prior to and after installation Ease of use - time needed to learn to use and to use Type, timing, length of training to be provided Availablility of support and maintenance Interoperability: ease/extent of integration with legacy systems (hardware, other devices) and existing software programs (integration with patient record and/or any relevant clinical terminologies would avoid need to re-enter patient data)

Factors that will increase use of CDDS Ease of integration within organisational context and routine workflow - degee to which it entails aredesign of clinical processes Legal and ethical issues User interface: design, structure, number of forms Style, manner of presentation of advice/ recommendations/ results to user Patients' attitudes to use Provision of evidence justifying advice and/or recommendations Involvement of local users during development phase The quality and reliability of a system and its knowledge base which should be populated with trusted, up-to-date and maintainable knowledge

Methodologies of CDSS The basic components of a CDSS include; a dynamic (medical) knowledge base and an inferencing mechanism (usually a set of rules derived from the experts and evidence-based medicine)expertsevidence-based medicine They are implemented through medical logic modules based on computer languagesmedical logic modules

Methodologies (cont.) Bayesian Network The Bayesian network is a knowledge-based graphical representation that shows a set of variables and their probabilistic relationships between diseases and symptoms. They are based on conditional probabilities, the probability of an event given the occurrence of another event, such as the interpretation of diagnostic tests.Bayesian networkconditional probabilities Neural Network Artificial Neural Networks (ANN) is a nonknowledge-based adaptive CDSS that uses a form of artificial intelligence, also known as machine learning, that allows the systems to learn from past experiences / examples and recognizes patterns in clinical information. Artificial Neural Networks Genetic Algorithms A Genetic Algorithm (GA) is a nonknowledge-based method. These algorithms rearrange to form different re- combinations that are better than the previous solutions. Similar to neural networks, the genetic algorithms derive their information from patient data.Genetic Algorithm Rule-Based System A rule-based expert system attempts to capture knowledge of domain experts into expressions that can be evaluated known as rules; an example rule might read, "If the patient has high blood pressure, he or she is at risk for a stroke." Once enough of these rules have been compiled into a rule base, the current working knowledge will be evaluated against the rule base by chaining rules together until a conclusion is reached.expert system Logical Condition The methodology behind logical condition is fairly simplistic; given a variable and a bound, check to see if the variable is within or outside of the bounds and take action based on the result. An example statement might be "Is the patient's heart rate less than 50 BPM?" Causal Probabilistic Network The primary basis behind the causal network methodology is cause and effect. In a clinical causal probabilistic network, nodes are used to represent items such as symptoms, patient states or disease categories. Connections between nodes indicate a cause and effect relationship.

Examples of CDSS in Dentistry Medical History Dental treatment plan Endodontics Caries risk Oral Pathology & oral medicine ect

This weeks assignments Log on to http://faculty.ksu.edu.sa/ealmadi/182DEN/default.aspx http://faculty.ksu.edu.sa/ealmadi/182DEN/default.aspx 1.View this lecture online for review. 2.Article this week: Suggested reading only. 3.Participate in discussion. 4.Homework: Please answer survey for your input on course. 5.Quiz: None. ViewReadDiscussHomework Quiz

clinical decision support systems dr ebtissam al-madi

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