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Real-time automated paging and decision support for critical laboratory abnormalities
  1. Edward Etchells1,2,3,
  2. Neill K J Adhikari1,3,
  3. Robert Wu3,4,
  4. Mark Cheung1,3,5,
  5. Sherman Quan4,6,
  6. Richard Mraz1,
  7. Brian Wong1,2,3,
  8. Ruxandra Pinto1,
  9. Rajin Mehta1,3,
  10. Dante Morra3,4,7,
  11. Robert Fowler1,3,
  12. William Sibbald1,3,
  13. Howard Abrams3,4,7,
  14. Peter G Rossos3,4,6
  1. 1Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
  2. 2Centre for Patient Safety, University of Toronto, Toronto, Ontario, Canada
  3. 3Department of Medicine, University of Toronto, Toronto, Ontario, Canada
  4. 4University Health Network, Toronto, Ontario, Canada
  5. 5Department of Laboratory Medicine, University of Toronto, Toronto, Ontario, Canada
  6. 6Centre for Global eHealth Innovation, University of Toronto, Toronto, Ontario, Canada
  7. 7Center for Innovation in Complex Care, University Health Network, Toronto, Ontario, Canada
  1. Correspondence to Dr Edward Etchells, Centre for Patient Safety, University of Toronto, 2075 Bayview Avenue, Room H469, Toronto, Ontario, M4N 3M5, Canada; edward.etchells{at}syunnybrook.ca

Abstract

Background For patients with critical laboratory abnormalities, timely clinical alerts with decision support could improve management and reduce adverse events.

Methods The authors developed a real-time clinical alerting system for critical laboratory abnormalities. The system sent alerts to physicians as text messages to a smartphone or alphanumeric pager. Decision support was available via smartphone or hospital intranet. The authors evaluated the system in a prospective controlled stepped-wedge study with blinded outcome assessment in general internal medicine units at two academic hospitals. The outcomes were the proportion of potential clinical actions that were actually completed in response to the alert, and adverse events (worsening of condition or complications related to treatment of the condition).

Results The authors evaluated 498 laboratory conditions on 271 patients. Overall, only 50% of potential clinical actions were carried out, and there were adverse clinical events within 48 h for 36% of the laboratory conditions. The median (IQR) proportion of potential clinical actions that were actually completed was 50% (33–75%) with alerting system on and 50% (33–100%) with alerting system off (p=0.94, Wilcoxon rank sum test). When the alerting system was on (n=164 alerts) there were 67 adverse events within 48 h of the alerts (42%). When the alerting system was off (n=334 alerts), there were 112 adverse events within 48 h (33%; difference: 9% higher with alerting system on, p=0.06).

Conclusions The provision of real-time clinical alerts and decision support for critical laboratory abnormalities did not improve clinical management or decrease adverse events.

  • Decision support systems
  • clinical
  • therapy
  • computer assisted
  • designs
  • experimental
  • decision-making
  • effectiveness
  • information technology

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Footnotes

  • Funding This study was supported by a research grant from the Canadian Patient Safety Institute. In kind support for the project was provided by Sunnybrook Health Sciences Centre and the Univesity Health Network.

  • Competing interests None.

  • Ethics approval Ethics approval was provided by the Sunnybrook Health Sciences Centre, and University Health Network.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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