Automated real-time text messaging as a means for rapidly identifying acute stroke patients for clinical trials
1 Center for Stroke Research Berlin (CSB) and Department of Neurology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
2 Department of Neurology, Charité University Hospital Berlin, Charitéplatz 1, 10117 Berlin, Germany
3 Department of Neurology, Vivantes Hospital, Berlin, Vivantes Klinikum Neukölln, Rudower Str 48, 12351 Berlin, Germany
4 Department of Neurology, Jewish Hospital Berlin, Heinz-Galinski-Str. 1, 13347 Berlin, Germany
Trials 2014, 15:304 doi:10.1186/1745-6215-15-304Published: 29 July 2014
Recruiting stroke patients into acute treatment trials is challenging because of the urgency of clinical diagnosis, treatment, and trial inclusion. Automated alerts that identify emergency patients promptly may improve trial performance. The main purposes of this project were to develop an automated real-time text messaging system to immediately inform physicians of patients with suspected stroke and to test its feasibility in the emergency setting.
An electronic standardized stroke algorithm (SSA) was implemented in the clinical information system (CIS) and linked to a remote data capture system. Within 10 minutes following the documentation and storage of basic information to CIS, a text message was triggered for patients with suspected stroke and sent to a dedicated trial physician. Each text message provided anonymized information on the exact department and unit, date and time of admission, age, sex, and National Institute of Health Stroke Scale (NIHSS) of the patient. All necessary information needed to generate a text message was already available – routine processes in the emergency department were not affected by the automated real-time text messaging system. The system was tested for three 4-week periods. Feasibility was analyzed based on the number of patients correctly identified by the SSA and the door-to-message time.
In total, 513 text messages were generated for patients with suspected stroke (median age 74 years (19–106); 50.3% female; median NIHSS 4 (0–41)), representing 96.6% of all cases. For 48.3% of these text messages, basic documentation was completed within less than 1 hour and a text message was sent within 60 minutes after patient admission.
The system proved to be stable in generating text messages using IT-based CIS to identify acute stroke trial patients. The system operated on information which is documented routinely and did not result in a higher workload. Delays between patient admission and the text message were caused by delayed completion of basic documentation. To use the automated real-time text messaging system to immediately identify emergency patients suitable for acute stroke trials, further development needs to focus on eliminating delays in documentation for the SSA in the emergency department.