Chapter 18 Decision Support System

1. Chapter 18Decision Support System

2. Outline • Decision lie at the very core of medicine and healthcare. Attempts to make decisions easier are as old as the profession itself-rules of thumb were once included into verses so they would be easier to memorize. In the modern era, we strive to organize the best available clinical evidence into guideline documents and, increasingly, to produce automated models for decision support. This chapter examines the use of clinical decision support system (CDSS) as a means of achieving this.

3. Introduction • A DSS can be thought of as any computer-based system that supports decision-making. • A DSS has at least two distinct architectural components • Models, using knowledge and/or data about the world to support inference • A query interface, by which a decision-maker asks questions of the model (typically getting results in graphical and/or tabular form). • A narrow conception of the role of a DSS is as an executive information system (EIS) • Where DSS for health become special is in support for clinical decision-making

4. How and when decision support systems are used • DSS can be partitioned into several categories of use • Research/exploratory • Diagnostic/ classification • Constructive • Critiquing • The diagnostic use of CDSS is well known, especially for those familiar with the history of Artificial Intelligence • MYCIN designed at Stanford University to diagnose infectious diseases and recommended antibiotic treatment • The chief barrier to use was a medico-legal one- it was unacceptable for a machine to be ‘the expert’ in a clinical setting • As an education tool is another important use of DSS • MYCIN and Iliad have been proven to be successful educational tools

5. The technology of DSS • DSSs are dependent on their models- abstract representations of clinical knowledge – to provide users with help in decision-making • A great diversity in the types of models, and a resultant diversity in the forms and implementations of DSS

6. Form of DSS • Data presentation/visualization • Problem-solving by search • Case bases reasoning • Symbolic reasoning systems • Artificial neural networks • Simulation modeling tools • Statistics/data-mining

7. Instance of clinical DSS (CDSS) • Most of the popular DSS in existence today have had a long history of development • Many are relatively simple DSS as compared to systems like DxPlain or QMR – the model may be a locally trained ANN, a set of algorithm implemented into a spreadsheet, or a query interface to a local data warehouse • Other systems may be quite advanced and well proven and well proven • Evans et al. 1998

10. Implementing and deploying decision support systems • Kawamoto et al. (2005) found four factors that significantly contribute to DSSs’ improving clinical practice • Providing decision support automatically as part of clinician workflow • Delivering decision support at the time and location of decision-making • Providing actionable recommendation (not just assessments) • Enabling computer-based generation of the decision support

11. Organization challanges

12. Technical Challanges