Appl Clin Inform 2011; 02(04): 472-482
DOI: 10.4338/ACI-2011-04-CR-0026
Case Report
Schattauer GmbH

Using real-time alerts for clinical trials

Identifying potential study subjects
E. Chow
1  Centre for Innovation in Complex Care, University Health Network
,
M. Zuberi
2  Investigational Pharmacy Services, Department of Pharmacy Services, University Health Network
3  Leslie Dan Faculty of Pharmacy, University of Toronto
,
R. Seto
2  Investigational Pharmacy Services, Department of Pharmacy Services, University Health Network
,
S. Hota
6  Department of Infection Prevention and Control, University Health Network
7  Division of Infectious Diseases, Department of Medicine, University Health Network
,
E.N. Fish
8  Toronto General Research Institute, University Health Network
9  Department of Immunology, University of Toronto
,
D. Morra
1  Centre for Innovation in Complex Care, University Health Network
4  Department of Medicine, University of Toronto
5  Centre for Interprofessional Education, University of Toronto
› Author Affiliations
Further Information

Publication History

received: 18 April 2011

accepted: 10 October 2011

Publication Date:
16 December 2017 (online)

Summary

Background: Clinical trials are widely accepted as a necessary step in evaluating the safety and efficacy of new pharmaceutical products. In order for a sufficiently powered study, a clinical trial depends on the effective and unbiased recruitment of eligible patients. Trials involving seasonal diseases like influenza pose additional challenges.

Objective: This is a feasibility study of a mobile real-time alerting system to systematically identify potential study subjects for a randomized controlled trial evaluating the safety and efficacy of early intervention with interferon alfacon-1 for patients hospitalized for influenza virus infection. Methods: The alerting system was setup in a 471-bed acute care teaching hospital, enabled with computerized physician order entry (CPOE) and a rules-based alerting system. Patients were identified from the entire hospital using two alerts types: pharmacy prescription records for antiviral drugs, and positive influenza laboratory results. Email alerts were generated and sent to BlackBerry® devices carried by the study personnel for a 6 month period. The alerts were archived automatically on a secure server and were exported for analysis in Microsoft Access.

Results: Over a period of 21 weeks, 779 total alerts were received. The study team was alerted to 241 patients, of whom 85 were potential study subjects. The alert system identified all but one of the patients independently identified by infection control.

Conclusions: Real-time identification of potential study subjects is possible with the integration of computerized physician order entry and BlackBerry® technology. It is a viable method for the systematic identification of patients throughout a hospital, particularly for trials investigating time-sensitive disease progression.