Automated real-time text messaging as a means for rapidly identifying acute stroke patients for clinical trials
© Jegzentis et al.; licensee BioMed Central Ltd. 2014
Received: 10 January 2014
Accepted: 17 July 2014
Published: 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.
KeywordsAcute trials CIS IT-based Notification tool Patient identification Recruitment Selection criteria Stroke
Effective therapy of acute stroke is limited to the first few hours after stroke onset. Upon hospital arrival, the diagnostic workflow is of highest priority and treatment needs to be initiated as soon as possible. Acute treatment trials offer additional therapeutic options for stroke patients. For hospital staff, however, identifying the patients who meet eligibility criteria for acute stroke trials within minutes can be challenging[1, 2]. Even in active academic centers the number of eligible patients is limited[3, 4], and only one third of acute treatment trials recruit the targeted number of patients within the time originally planned. Thus, improving the recruiting process is of supreme importance. Lack of time in the emergency setting is one of the obstacles for the identification of acute trial patients.
Number of studies conducted by the stroke trial team per year
Number of patients recruited by the trial team per year
Number of patients recruited by the trial team classified by time of symptom onset
Acute (<9 h)
Subacute (<36 h)
Subacute (<72 h)
To ensure that the trial physician on duty is rapidly notified when a patient arrives in the emergency department (ED), we implemented an automated screening tool that transmits the relevant data via text messaging to a mobile phone. In contrast to other automated screening systems we wanted to minimize time delay because acute interventional stroke trials are often limited up to 6 hours[7–9] or 9 hours[10, 11] from symptom onset. Even for interventional stroke trials that include patients up to 24 hours from symptom onset or when eligibility is based on imaging criteria[12, 13], an immediate notification is necessary to guarantee that patient treatment is initiated promptly.
Here, we report on the technical development and organizational feasibility of an automated stroke alerting tool for emergency stroke patients.
Information documented in the emergency department (ED)
Demographic and organizational patient data
Information documented by admitting medical staff
Information documented by ED neurologist
- current complaints
- general evaluation incl. neurological examination
- date of birth
- description of symptoms
- tentative diagnosis (ICD-10-Code)
- medical history
- indicated diagnostics (e.g., imaging) and results
- case ID
- risk factors (e.g., smoking behavior, hypertension, diabetes mellitus)
- medication administered
- treating department (e.g., emergency department)
- vital signs
- recommendations, for example admission to stroke unit
- date and time (d&t) of hospital admission
- concomitant medication
- NIHSS incl. single sub-points**
- d&t of first contact with physician
- leading symptoms (selection via radio button):
- information about stroke/TIA (selection via radio button):**
- means of patient transport to hospital (e.g., ambulance)
- no information
- no stroke or TIA
- stomach ache
- stroke with alarm***
- chest ache
- stroke without alarm
- stroke/transient ischemic attack (TIA)*
- d&t alarm**
- d&t symptom onset or d&t last seen well**
- none of these symptoms
- information about thrombolysis:**
if yes: d&t start of thrombolysis;
if no: reason why not: (textfield)
Information on campuses and admitted patients
Middle of Berlin (0 km)
About 3 km to the northwest
About 10 km to the south
Number of patients with suspected stroke per year
Technical and organizational feasibility of the SSA was tested by cohort analysis. For technical stability, we analyzed the number of text messages sent correctly by the system. For the organizational benefit of the system, door-to-message times and the number of correctly identified stroke-patients were examined. To monitor the completeness of the identified patients list, a manual screening of acute stroke patients was conducted by a trial physician. To balance cohort analysis, we compared datasets of consecutive patients during three months with empirically high (November), average (April), and low (June) stroke incidence rates. To check inter-variability between cohorts, patients’ demographic data such as median age, sex, and median NIHSS, were analyzed.
Demographics, NIHSS, and stroke subtype of patients identified by SSA (n = 513)
Text messages sent (n)
Age, median (IQR)
Female sex,% (n)
NIHSS, median (IQR)
Time from admission to triggered text message (TTM), median in min (IQR)
Tentative diagnosis at admission
Ischemic stroke,% (n)
In the period of investigation, a total of 595 patients were diagnosed at admission with suspected stroke (ischemic or hemorrhage) in CIS. For 48 patients, the original diagnosis was refuted by the ED consultant, for 16 patients no standardized stroke sheet was compiled by CIS (patients were admitted to adjacent departments where SSA is not implemented). A text message to a mobile phone was correctly sent in 513 (96.6%) of the remaining 531 patients with suspected stroke. For 15 patients, no text message was generated because of hospital-wide technical modifications in CIS that affected the filter settings. For 3 patients, no text message was triggered.
Median time from admission to text-messaging was 62 minutes (IQR, 32–118) with 48.3% (n = 239) dispatched messages in less than 60 min and 27.3% (n = 135) within 60 to 120 minutes.
We demonstrate that a SSA that includes an automated text message to a mobile phone for acute stroke patients is technically feasible, and that the system maintains its stability. Only on two consecutive days were text messages (15 = 3.4%) missed as a result of technical modifications in CIS. As these adaptions were not made known to the users beforehand, no adjustments had been carried out in advance on our filter settings. Immediately after notification of CIS changes, filter settings were modified. No text messages were missed after these modifications.
In contrast to previous investigations[15–17], our approach was to focus on the special requirements for acute trials, which mandate start of treatment within a very limited time window: within 6 or 9 hours of symptom onset. To comply with selection criteria, patients must be screened immediately on arrival in hospital. In addition, relevant information, e.g., NIHSS, is essential for reaching a decision regarding a patient’s trial eligibility.
The SSA was implemented in the screening process of the neurological department and was designed as a technical interface to achieve rapid interaction between the physician on-call – who is responsible for routine diagnostic and treatment – and a specialized trial team physician. The SSA was based on information routinely documented in CIS and thus did not entail a higher workload for the treating physician. We think that this is of utmost relevance for the acceptance and feasibility of our screening tool in the emergency setting.
In contrast to other approaches, where selection criteria had to be defined beforehand to identify patients for specific trials, our method aimed at a more general technique: every single patient with suspected stroke was identified by the system and a text message with the most relevant screening data was generated. Text messages were sent to the trial physician on call, who was responsible for the screening and recruitment process for a multitude of stroke trials. Based on the information provided – patient sex and age, date and time of admission, and NIHSS – the trial physician pre-selected the most likely eligible patients for a variety of stroke trials. Naturally, a detailed screening process followed: the screening information supported the trial physician in deciding which patient to see first, i.e., in which case time was most pressing.
As in previous approaches, some patients were identified by the automated screening system which had not been identified in a manual search[16–18]. However, a further analysis showed that these additional patients were stroke mimics or were not admitted to our department, and were thus not eligible for acute stroke trials.
An advantage of our reporting tool was that absolutely all stroke patients were reported to the trial physician. Even patients who failed to meet inclusion criteria at admission were recorded and surveyed later to catch changes in medical condition, e.g., in NIHSS, and later trial eligibility.
As our alerting system did not depend on specific selection criteria, it was easily expanded for new trials or after modification of selection criteria in ongoing trials; no adaptions to the system were necessary. This was especially advantageous for repeated changes in selection criteria or for high turnover of trials.
The main focus of our approach was not only on capturing absolutely all patients but also generating the most rapid alert possible. However, the time from admission to text-messaging still exceeds 60 minutes in the majority of patients, which is rather long. In the emergency setting, documentation had to take second place to acute diagnosis and treatment. This led to delays in documentation and is in fact the major obstacle to using the automated screening tool for acute stroke intervention trials; for trials with a very limited time window, delays exceeding 60 minutes post-hospital admission can reduce trial eligibility. Nevertheless, in our case, delays were due not to technical flaws but rather to suboptimal organization. We aim to solve this problem with better training for the physicians in ED. Nonetheless, we have to take into account that delays in documentation will continue because therapy and treatment are more relevant for the patient than data documentation. In addition, future adaptions to the standardized stroke sheet will be mainly in the area of supplementary information (e.g., time of symptoms onset) for the screening process and the decision on trial eligibility.
A major benefit of the automated screening tool is its availability outside regular working hours. In our survey, 118 (23.0%) patients were admitted at weekends and a further 127 (24.8%) on working days between 6 pm and 6 am. We estimate that the majority of these 245 (47.8%) patients would not have been screened at all for trial eligibility without the automated notification tool.
Additionally, the notification tool might be of interest as an automated alerting system for patients with suspected stroke in standard care as the tool could inform all relevant services within minutes once an appropriate trigger is determined. The screening tool requires an IT-based ED file and HL7. As HL7 is highly available in most systems, the tool could be used at other hospitals as well. Costs of the notification system depend on the number of sent text messages and the specified charges.
We deliberately did not perform a comparison of total trial recruitment numbers with or without the notification tool, so no quantitative correlation for total recruiting numbers is provided.
Our SSA including an automated text message to a mobile phone for acute stroke patients was technically stable and feasible. Data protection rules and privacy laws were obeyed. Standard procedures in the emergency setting were not affected. Nevertheless, documentation in the ED needs to be sped up to minimize delay between patient admission and text message or real-time screening of trial eligibility. An IT-based ED file and HL7 are required. As both are readily available in the clinical setting, the notification tool can be set up elsewhere. The benefits of the automated alerting tool are that its technical prerequisites are readily available and that it provides full documentation, can be utilized even outside working hours, is low in cost, and can be integrated into routine ED processes.
Clinical information system
Center for Stroke Research, Berlin
National Institute of Health Stroke Scale
Standardized stroke algorithm
Standardized stroke documentation sheet.
The authors received funding from the German Federal Ministry of Education and Research (BMBF) via the grant Center for Stroke Research Berlin (01 EO 0801).
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