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Decision Support Systems

Decision support systems (DSS) have been around since the beginning of the era of distributed computing. The first decision support system made its appearance in the mid to late 1960s and can now be found in almost all industries where information systems are used.

Decision support systems are increasingly being used in healthcare, where doctors, for use during their consultations, design some while others are aimed at the wider industry for not only doctors, but also other healthcare professionals and patients.


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These decision support systems generally provide two types of support:

  • Diagnostic Support: - Here systems provide support concerning diagnosis or prognosis. They provide outcomes that reduce the uncertainty concerning the patient’s current or future situation.

  • Management Support: - Systems provide support by providing suggestions on how best to manage a patient’s condition. Some of the suggestions might involve tests that have to be carried out, what medication or treatment should be considered, sometimes with financial and ethical considerations taken into account.

Decision support systems aid clinicians in applying new information to patient care through the analysis of various patient specific data and enhance diagnostic and management outcomes.

Decision support systems operate in three modes – active (systems triggered automatically and make decisions without any intervention), semi active (raise reminders and alarms according to the users input) and passive (where the user must make an explicit request to the system in order to gain advice).

Whatever mode the decision support system operate at, it must provide accurate and reliable data, which is retrieved from a knowledge base. The knowledge base is made of several sources of information from various medical disciplines, which might include patient observations, medical books and journals, and the medical experience of several physicians.

The outcomes that are derived from the knowledge base are usually modelled on the following:

  • Mathematical models: are used to describe complex biological or physiological systems

  • Statistical models: are mainly based on multi-dimensional classification systems. Some of the statistical tools they make use of are multiple regression and discriminate analysis.

  • Bayesian networks: are probability-based models and essentially use Baye’s Theorem, which provides a mathematical model to update probabilities on the basis of new information.

  • Artificial Intelligence (AI): has been used to develop expert systems, which are used mainly in specialized domains to provide functions that would have normally been done by a human expert. The terms "expert systems" and "decision support systems" have often been used interchangeably.

  • Neural networks: are made of rules that are linked by arcs, mimicking the structure and operation of the human brain, hence its name. Information is processed by being propagated from an inner layer, through intermediate layers to an outer layer.

Sample Decision Support Systems

  • DXplain – Decision support system that uses a set of clinical findings to produce a ranked list, which might help explain or be associated with the clinical manifestations. DXplain is owned by Massachusetts General Hospital.

  • QMR (Quick Medical Reference) – Developed by the University of Pittsburgh, Pennsylvania and First DataBank, Inc., QMR is a diagnostics decision support system with a knowledge base of diseases, diagnosis, findings, disease associations and laboratory information.

  • PRODIGY – Developed at the Sowerby Centre for Health Informatics Newcastle (SCHIN) and funded by the National Health Service (NHS) in the United Kingdom. PRODIGY is a computerised prescribing decision support system for General Practice.

  • HELP system – HELP stands for Health Evaluation through Logical Processing. It was developed at the Latter Days Saint (LDS) Hospital in Salt Lake City, Utah, and is integrated into a hospital information system. It takes laboratory and dosage data and generate alerts and warnings, usually when a patient’s records are updated.

 

 

QUICK LINKS
International Medical Informatics Association
American Medical Informatics Association
UK Health Informatics Society
Healthcare Information and Management Systems Society


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Last Updated: 10 August 2006.



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