Clinical decision support system (CDSS)

Knowledge-based systems Knowledge based systems are artificial intelligent tools working in a narrow domain to provide intelligent decisions with justification. Knowledge is acquired and represented using various knowledge representation techniques rules, frames and scripts. The basic advantages offered by such system are documentation of knowledge, intelligent decision support, self learning, reasoning and explanation.

Clinical decision support system (CDSS or CDS) is an interactive decision support system (DSS) Computer Software, which is designed to assist physicians and other health professionals with decision making tasks, as determining diagnosis of patient data. A working definition has been proposed by Dr. Robert Hayward of the Centre for Health Evidence; "Clinical Decision Support systems link health observations with health knowledge to influence health choices by clinicians for improved health care".

Purpose/Goal The main purpose of modern CDSS is to assist clinicians at the point of care. This means that a clinician would interact with a CDSS to help determine diagnosis, analysis, etc. of patient data. Previous theories of CDSS were to use the CDSS to literally make decisions for the clinician. The clinician would input the information and wait for the CDSS to output the “right” choice and the clinician would simply act on that output. The new methodology of using CDSS to assist forces the clinician to interact with the CDSS utilizing both the clinician’s knowledge and the CDSS to make a better analysis of the patients data than either human or CDSS could make on their own. Typically the CDSS would make suggestions of outputs or a set of outputs for the clinician to look through and the clinician officially picks useful information and removes erroneous CDSS suggestions.

There are two main types of CDSS: Knowledge-Based NonKnowledge-Based

Features of a Knowledge-Based CDSS Most CDSS consist of three parts, the knowledge base, inference engine, and mechanism to communicate. The knowledge base contains the rules and associations of compiled data which most often take the form of IF-THEN rules. If this was a system for determining drug interactions, then a rule might be that IF drug X is taken AND drug Y is taken THEN alert user. Using another interface, an advanced user could edit the knowledge base to keep it up to date with new drugs. The inference engine combines the rules from the knowledge base with the patient’s data. The communication mechanism will allow the system to show the results to the user as well as have input into the system

Features of a non-Knowledge-Based CDSS CDSS’s that do not use a knowledge base use a form of artificial intelligence called machine learning, which allow computers to learn from past experiences and/or find patterns in clinical data. Two types of non-knowledge-based systems are artificial neural networks and genetic algorithms.

Artificial neural networks Artificial neural networks use nodes and weighted connections between them to analyze the patterns found in the patient data to derive the associations between the symptoms and a diagnosis. This eliminates the need for writing rules and for expert input. However since the system cannot explain the reason it uses the data the way it does, most clinicians don’t use them for reliability and accountability reasons

Genetic Algorithms Genetic Algorithms are based on simplified evolutionary processes using directed selection to achieve optimal CDSS results. The selection algorithms evaluate components of random sets of solutions to a problem. The solutions that come out on top are then recombined and mutated and run through the process again. This happens over and over till the proper solution is discovered. They are the same as neural networks in that they derive their knowledge from patient data. Non-knowledge-based networks often focus on a narrow list of symptoms like ones for a single disease as opposed to the knowledge based approach which cover many different diseases to diagnosis

Effectiveness A 2005 systematic review by Garg et al. of 100 studies concluded that CDSs improved practitioner performance in 64% of the studies. The CDSs improved patient outcomes in 13% of the studies. the CDSs is integrated into the clinical workflow rather than as a separate log-in or screen. the CDSs is electronic rather than paper-based templates. the CDSs provides decision support at the time and location of care rather than prior to or after the patient encounter. the CDSs provides (active voice) recommendations for care, not just assessments.

Challenges to Adoption Clinical Challenges complexity of clinical workflows and the demands on staff time high, care must be taken by the institution deploying the support system to ensure that the system becomes a fluid and integral part of the workflow. To this end CDSSs have met with varying amounts of success, while others suffer from common problems preventing or reducing successful adoption and acceptance.

Technical Challenges Clinical decision support systems face steep technical challenges in a number of areas. Biological systems are profoundly complicated, and a clinical decision may utilize an enormous range of potentially relevant data. For example, an electronic evidence-based medicine system may potentially consider a patient’s symptoms, medical history, family history and genetics, as well as historical and geographical trends of disease occurrence

Maintenance One of the core challenges facing CDSS is difficulty in incorporating the extensive quantity of clinical research being published on an ongoing basis. In a given year, tens of thousands of clinical trials are published.[5] Currently, each one of these studies must be manually read, evaluated for scientific legitimacy, and incorporated into the CDSS in an accurate way.

Evaluation In order for a CDSS to offer value, it must demonstrably improve clinical workflow or outcome. Evaluation of CDSS is the process of quantifying its value to improve a system’s quality and measure its effectiveness. The evaluation benchmark for a CDSS depends on the system’s goal: for example, a diagnostic decision support system may be rated based upon the consistency and accuracy of its classification of disease (as compared to physicians or other decision support systems). An evidence- based medicine system might be rated based upon a high incidence of patient improvement, or higher financial reimbursement for care providers.

Examples of CDSS Zynx Health – the most prominent organization in the CDSS marketplace, whose CDSS is linked to a statistically significant percentage of hospital discharges nationwide. Zynx Health MYCIN, one of the first expert systems to be developed in the 1970s, it does ethiological diagnoses of bacterial diseases. MYCIN CADUCEUS, a medical expert system that could diagnose 1000 diseases. CADUCEUS Internist-I, a computer-assisted diagnostic tool. Internist-I