Improving Response to Critical Laboratory Results with Automation
Results of a Randomized Controlled Trial
- Gilad J Kuperman,
- M Jonathan,
- Milenko J Tanasijevic,
- Nell Ma'Luf,
- Eve Rittenberg,
- Ashish Jha,
- Julie Fiskio,
- James Winkelman,
- David W Bates
- Affiliations of the authors: Partners HealthCare System (GJK, JMT, JF, DWB) and Harvard Medical School (GJK, JMT, MJT, NM'L, ER, AJ, JW, DWB), Boston, Massachusetts
- Corresdpondence and reprints: Gilad J. Kuperman, MD, PhD, Partners HealthCare System, Department of Information Systems, 850 Boylston Street, Suite 202, Chestnut Hill, MA 02467. e-mail: 〈gkuperman{at}partners.org〉
- Received 19 April 1999
- Accepted 21 July 1999
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.
Footnotes
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This work was supported in part by research grant RO1-HS08927 from the Agency for Health Care Policy and Research.
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↵* Each alerting rule has an associated list of relevant medications, and the alert review screen displays which of those medications the alerting patient is receiving.
