Improving response to critical laboratory results with automation: results of a randomized controlled trial

Abstract

Objective: To evaluate the effect of an automatic alerting system on the time until treatment is ordered for patients with critical laboratory results.

Design: Prospective randomized controlled trial.

Intervention: A computer system to detect critical conditions and automatically notify the responsible physician via the hospital's paging system.

Patients: Medical and surgical inpatients at a large academic medical center. One two-month study period for each service.

Main outcomes: Interval from when a critical result was available for review until an appropriate treatment was ordered. Secondary outcomes were the time until the critical condition resolved and the frequency of adverse events.

Methods: The alerting system looked for 12 conditions involving laboratory results and medications. For intervention patients, the covering physician was automatically notified about the presence of the results. For control patients, no automatic notification was made. Chart review was performed to determine the outcomes.

Results: After exclusions, 192 alerting situations (94 interventions, 98 controls) were analyzed. The intervention group had a 38 percent shorter median time interval (1.0 hours vs. 1.6 hours, P = 0.003; mean, 4.1 vs. 4.6 hours, P = 0.003) until an appropriate treatment was ordered. The time until the alerting condition resolved was less in the intervention group (median, 8.4 hours vs. 8.9 hours, P = 0.11; mean, 14.4 hours vs. 20.2 hours, P = 0.11), although these results did not achieve statistical significance. The impact of the intervention was more pronounced for alerts that did not meet the laboratory's critical reporting criteria. There was no significant difference between the two groups in the number of adverse events.

Conclusion: An automatic alerting system reduced the time until an appropriate treatment was ordered for patients who had critical laboratory results. Information technologies that facilitate the transmission of important patient data can potentially improve the quality of care.

Figure 1

Automated alerting system software architecture. New clinical data are sent to the event monitor. The event monitor determines whether the new data warrant an alert. If so, the notification program is called. The notification program queries the coverage list database to determine who is currently covering the patient whose data generated the alert. The notification program then pages the covering physician through an electronic interface to the page computer. The callback number on the physician's digital pager (“8888”) indicates an automated alert on one of the physician's patients. The physician can then log onto any computer workstation to review the alert and take therapeutic action or, if off site, can call the telecommunication office.

Figure 2

Alert review and therapeutic action screen. This is the screen that the physician sees when reviewing the alert. The patient's identifying information is shown at the top, followed by the time of the alert, the alert message, and the details of the alert (in this case, low potassium in a patient receiving digoxin). Relevant medications are shown next. Finally, a list of therapeutic actions is offered.

Figure 3

Notification failsafe sequence. If the alert has not been reviewed within 15 minutes after the physician has been paged, the borders of the computer screens on the patient's floor turn red. The computers on the inpatient floors display new information about patients, and the nurses continually check the screens for new information. When the border of a computer screen turns red, it is an indication to the nurses of an automated alert for one of the floor's patients; a nurse can then review the alert. If, after 30 more minutes, the alert has still not been reviewed, a workstation in the telecommunication office begins beeping. The phone operator then can review the alert and call the floor with the information.

Figure 4

Diagram showing the number of alerts detected, excluded, and eventually analyzed for the current study.