A table-driven system for representing and executing clinical rules

The need for computerized decision support in medicine becomes more and more critical as both the amount of data available and the complexity of data requirements increase to a point that exceeds the clinician's capacities and/or his time constraints. This work addresses a specific decision support application—the detection of variances from a pathway standard of care. It stemmed from the needs of Clinical Care Managers (CCMs) following patients on a Respiratory Failure Critical Pathway.; The conditions reflecting the variances can be viewed as the antecedents of “IF-THEN” rules that lend themselves to computerization. The Table-driven Clinical Rules (TCR) system was developed to satisfy the needs of the CCMs and to allow rapid development of new rules and reuse of existing rule components. Its design and development are described.; The effect of TCR system-generated reminders on pathway-related charting deficiencies was evaluated. Intensive Care Unit (ICU) nurses caring for a group of 60 study patients received paper reminders reports when charting deficiencies were found at mid-day. Nurses caring for a group of 60 control patients received no reminders. The average number of charting deficiencies at the end of the shift in the study group patients was 1.02 deficiencies per day per patient while the control group average was 1.40 deficiencies per day per patient (p = .001). The average end-of-shift deficiency rate in the pooled prospective (study/control) population was 1.21 deficiencies per day per patient, compared to the average in a retrospective group of 60 patients of 1.56 deficiencies per day per patient (p < .001).; A questionnaire delivered to participating nurses revealed that 40% of the nurses viewed the reminders as “somewhat” or “very” helpful. The participants regarded either general or patient-specific reminders that automatically appear at a computer terminal as being the “best” method of conveying reminders.; The TCR system allows leveraging of data contained in a legacy information system while assisting in the creation of new applications. It holds promise as a means of more easily creating and reusing decision-support applications and of effecting change in the clinical environment.