INFO-I530 (Foundation of Health Informatics)

1. INFO-I530 (Foundation of Health Informatics) Decision Support Systems Lecture #10

2. Lecture in a Nutshell • Introduction • Characteristics of Decision Support Systems • Methodological Basis of DSS • Implementing Decision Support Systems • Sample Decision Support Systems

3. Introduction • The practice of medicine requires the physician to constantly make multiple decisions that are logically related for a given patient. • Computers can provide direct or indirect assistance in making these decisions. • Indirect: Hospital information systems, systems for managing patient records, analytical presentations of data, bibliographical databases or medical or legal knowledge bases. • Direct: Medical decision support systems  apply medical knowledge to the specific problem of a given patient and suggest solutions that offer the best cost-efficiency ratio. • The assistance expected from a decision support system depends on the clinical context and on the type of users: • A prolonged fever is treated differently in an internal medicine service than in an intensive care unit. • A student does not have the same expectations as an experienced physician.

4. Characteristics of Decision Support Systems • Types of SupportMedical decision support systems follow the general nature of medical interventions: predict (predictive medicine), prevent (preventive medicine), heal (curative medicine), or at least comfort (medical assistance). Two types of decision support systems: • Systems to better understand a patient's state (i.e., what is true). They basically concern decisions for diagnosis or prognosis. • Systems that attempt to suggest the best strategy (i.e., what must be done). In practice, systems are usually a mixture of both. It is difficult to separate the therapeutic side of a problem from the diagnosis.

5. Characteristics of Decision Support Systems cont. • Types of InterventionPassive Systems  The physician describes the patient's case and waits for the system's advice. Two types: (1) In a consultant system, the user supplies information on the patient's state, and the system provides diagnostic or therapeutic advice. (2) In a critical system, the user supplies information on the patient and on the physician's planned strategy (therapeutic and/or investigative). The system makes a critique of the physician's proposals.Semiactive Systems  Semiactive decision support systems are invoked automatically (watchdog programs). Two types: Automatic reminder systems which supervise the care provider's actions. Alarm systems signal changes in the patient's state.Active Systems  can automatically make decisions without the intervention of the physician.

6. Characteristics of Decision Support Systems cont. • Types of KnowledgeDecisions require the application of specific knowledge to clinical case. Three types of information are involved in decision making process:Observations  supplied to the decision support systems for inferencesAcademic Knowledge  contained in medical books and journalsExperience  medical behavior and medical know-how Patient Record Academic Knowledge Medical Decisions Experience Basis for medical decisions

7. Methodological Basis of DSS • Decision support systems utilize a broad variety of methods besides simple algorithms. Each of these methods uses particular means of reasoning: Methods for decision support systems

8. Methodological Basis of DSS cont. • Mathematical ModelsMathematical models have been available for several decades for describing complex biological or physiological systems (e.g. hemodynamics, the effect of ionizing radiation on tissue, or pharmacokinetics). These models may directly assist in making decisions, either in a passive mode (analyzing the consequence of the variation of one or several parameters) or in an active mode (automatic controls). • Statistical MethodsThese methods are mainly based on multidimensional classification techniques. A patient is assigned to a therapeutic or diagnostic class according to the values of certain parameters.Examples of statistical tools include multiple regression and discriminant analysis. They may be used for diagnostic (e.g., discriminant analysis), prognostic or therapeutic classifications (e.g., multiple regression).

9. Methodological Basis of DSS cont. • Statistical Model ExampleTake the following model as an example: f(D) = a1x1 + a2x2 + … + anxnfor two medical diagnoses (appendicitis and salpengitis) and three signs and symptoms (abdominal rigidity written AR; pain in the right lower quadrant written PRLQ; and pain in the left lower quadrant, written PLLQ). We shall use the following discriminating equations:f(appendicitis) = (4 · AR) + (10 . PRLQ) – (10 . PLLQ)f(salpingitis) = (3 · AR) + (5 . PRLQ) + (5 . PLLQ)For a patient with pain in both iliac fossa without any guarding symptoms, we would obtain:f(appendicitis) = 0 + 10 - 10 = 0f(salpingitis) = 0 + 5 + 5 = 10Therefore, the proposed diagnosis is salpingitis.

10. Methodological Basis of DSS cont. • Probability-Based SystemsProbability-based systems essentially use Bayes' formula and decision theory. The a posteriori probability of observing a diagnosis Di when a sign S is present P(Di|S) depends on the a priori probability of the diagnosis and that of observing the sign when the diagnosis is present (conditional probability):Bayes' approach accounts for positive and negative signs, and performance is usually acceptable. The concordance with experts often exceeds 70%, and results improve as the number of cases in the learning base increases. Without the case database, the estimation of a priori probabilities and conditional probabilities can initially be provided by experts and then replaced by real probabilities calculated on the case database as it develops.

11. Methodological Basis of DSS cont. • Bayes' method has inherent limitations that are more difficult to overcome: • the exhaustiveness of decisions (the sum of the probabilities of Di equals 1); • the exclusiveness of decisions • the independence of signs and symptoms • Expert SystemsOne of the major repercussions of artificial intelligence has been the development of expert systems - computer programs that use specialized knowledge and inference mechanisms to obtain high performance levels in specialized domains.Knowledge in expert systems is generally supplied as rules or frames. The knowledge base is separated from the base containing the problem-related data or the factual base of the system that creates the inferences, the inference engine.For well-defined problems where no algorithmic solutions are available, the existence of experts makes possible the creation of knowledge bases.

12. Methodological Basis of DSS cont. Knowledge Base (knowledge rules provided by experts) Fact Database (patient data) Inference Engine (software that provides system’s reasoning) User Interface (communication software) General architecture of an expert system

13. Methodological Basis of DSS cont. • Indications • Human expertise might be lost • Human expertise is scarce • Expertise is needed in many locations • Expertise is needed in hostile environments • Task solution has a high payoff (usefulness, efficiency, etc) • Feasibility • Tasks require cognitive skills • Specialized knowledge • No algorithmic solution • Moderately complex tasks • Knowledge relatively static • Genuine experts exist • Experts are better than amateurs • Limitations • Mixed representation schemes • Knowledge is infinite • Knowledge is contradictory or inconsistent • The computer is blind; it needs an intermediary • It is difficult to extend beyond the microworlds of the expert systems • Reasoning is limited (deduction, abduction) • Difficult to validate knowledge • The expert systems are nonetheless limited by the difficulties involved in building and maintaining the knowledge base. Several disciplines contribute to this knowledge, often inconsistent or contradictory. Knowledge is not easily split into the rules or structured objects required by expert systems. Indications, feasibility, and limitations of expert systems

14. Methodological Basis of DSS cont. • Neural Networks (Connectionist Systems)The architecture of connectionist systems is inspired by the structure and operation of the human brain, hence the name neural network. The first practical applications of neural systems were form recognition systems (character, voice, image and etc.). They are well suited to diagnostic classifications. The input layer of the network represents the symptoms and output layer represents the diagnoses. General architecture of a neural network

15. Implementing Decision Support Systems • Despite considerable effort in developing DSS, the actual number of operational systems in clinical practice remain small. Common problems are: • The User Interface: The quality of the user interface is a key factor for the system's acceptance. Physicians are not often attracted by computer terminals. The ergonomics of the interface must be carefully designed so that the system can be used in normal working conditions. The system should be easy to use and the learning curve minimal. • Knowledge Acquisition and Representation: Most decision support systems need a reliable base of medical records. This base is used to create the a priori and a posteriori probabilities of the diagnoses, to record decisions and their results, and to validate the knowledge. The base requires a logistical infrastructure that is difficult to implement: onsite data entry, connection to the management systems of ancillary systems to avoid re-entering information, training personnel to use the software; etc.

16. Implementing Decision Support Systems cont. • Evaluating and Validating Systems: The evaluation of a decision support system presents methodological problems pertaining to the validity of the knowledge base, the methods of reasoning (the inference engine), and the validity of the proposed solutions.Knowledge of a reference decision is necessary to evaluate the proposals of the decision support system. Standard solutions (a gold standard) frequently do not exist in medicine. • Integrating Decision Support Modules in Information Systems: In the past efforts to integrate medical knowledge in the HIS were limited. Traditional systems were usually administrative and logistical tools for managing patients rather than systems for managing medical data.In the future, an HIS must cover more areas, in particular theoretical and practical medical knowledge.

17. Sample Decision Support Systems • Pharmacokinetics and Assistance in Calculating DosagesA pharmacokinetic model represent and quantify a drug’s various metabolism phases. The model’s parameters can be estimated for different target populations that receive the drug. The model is used to adjust the dosage. • Assistance in Diagnosing Acute Abdominal Pain Evaluating the impact of a decision support system on health care

18. Sample Decision Support Systems cont. • Diagnoses in Internal Medicine: INTERNIST and QMRThe system covers approximately 80% of internal medicine and uses a knowledge base of 4,500 signs and symptoms and 600 diseases. Each disease is described by approximately 80 signs. An expert has assigned three numbers to each pertinent sign for a given disease: • a number between 1 and 5 that represents the frequency of the association • a number between 0 and 5 that represents the evocative power of the sign for the given disease • a number between 1 and 5 that represents the need to explain the sign in the final diagnosis The performance of the system, as measured by clinical cases from the New England Journal of Medicine, was comparable to that of an expert. • Assistance in Chemotherapy: ONCOCINIt helps to identify and select the therapeutic protocols that are applied to a patient, determine chemotherapy doses, and manage treatment.

19. Sample Decision Support Systems cont. • Integration in a Hospital Information System: HELPThe HELP system (Health Evaluation through Logical Processing) is a good example of a decision support system integrated into a hospital information system. It operates in semi-active mode, and updating patient records triggers the decision support modules.After installing the program, the number of cases of surgery patients receiving antibiotic treatment too late dropped from 27% to 14%, and the rate of postoperative infections dropped significantly from 1.9% to 0.9%.Prescriptions are entered interactively, and immediately controlled. Warnings are displayed if necessary.Alerts are generated for allergies and interactions between drugs, between drugs and diets, drugs and dosages, drugs and diseases, and between the laboratory dosage and the drug dosage.

20. Sample Decision Support Systems cont. Medical drug order Medical knowledge base Pharmacist enters Nurse enters HELP patient database History, allergies, etc. Data driven HELP system Drug appropriate? Contact the MD no yes Change prescription yes Log compliance and change Dispense and evaluate no Prescription control in the HELP system

21. Summary • Introduction • Characteristics of Decision Support Systems • Methodological Basis of DSS • Implementing Decision Support Systems • Sample Decision Support Systems