Alerting automatically provides decision makers with data and information in situations where rapid, sometimes life threatening, decisions are required. Examples are abnormal laboratory values, vital sign trends, failure to perform nursing procedures and medication contraindications. These clinical situations often have episodes of unpredictable random noise data which impair the decision making process leading to errors in patient care.
A system of alerts is used routinely in the HELP (Health Evaluation through Logical Processing)(Bradshaw et al 1989) and Regenstrief Medical Record Systems (RMRS) (McDonald et al 1992a). Established benefits from the use of alerts are a reduction in physician and nursing errors in patient management, increased compliance with predefined standards of care, (McDonald et al 1984) decreased length of stay in hospital and time spent in life-threatening situations (Kuperman et al 1991, Sittig et al 1989).
Using automated alerts in the HELP system during surgery for non-indicated and non- ordered antibiotics Classen demonstrated a fall in the post-operative infection rate from 13% patients per day to 5.5%, and a fall from 35% to 18% in the percentage of patients receiving antibiotics late for surgery. As a consequence there was a reduction in the number of patients receiving antibiotics for an excessive time post-operatively which produced overall savings of
$59,000 in 6 months (Classen 1992). This system for recommending antibiotics has now been extended to primary and ambulatory care (Evans 1991, and 1993).