{'fraction_non_alphanumeric': 0.03844328429046037, 'fraction_numerical': 0.027052681537731372, 'mean_word_length': 5.911475409836066, 'pattern_counts': {'":': 0, '<': 1, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Development and Validation of a Dispatcher Identification Algorithm for Stroke Emergencies

Background and Purpose— Recent innovations such as CT installation in ambulances may lead to earlier start of stroke-specific treatments. However, such technically complex mobile facilities require effective methods of correctly identifying patients before deployment. We aimed to develop and validate a new dispatcher identification algorithm for stroke emergencies. Methods— Dispatcher identification algorithm for stroke emergencies was informed by systematic qualitative analysis of the content of emergency calls to ambulance dispatchers for patients with stroke or transient ischemic attack (N=117) and other neurological (N=39) and nonneurological (N=51) diseases (Part A). After training of dispatchers, sensitivity and predictive values were determined prospectively in patients admitted to Charité hospitals by using the discharge diagnosis as reference standard (Part B). Results— Part A: Dysphasic/dysarthric symptoms (33%), unilateral symptoms (22%) and explicitly stated suspicion of stroke (47%) were typically identified in patients with stroke but infrequently in nonstroke cases (all <10%). Convulsive symptoms (41%) were frequent in other neurological diseases but not strokes (3%). Pain (26%) and breathlessness (31%) were often expressed in nonneurological emergencies (6% and 7% in strokes). Part B: Between October 15 and December 16, 2010, 5774 patients were admitted by ambulance with 246 coded with final stroke diagnoses. Sensitivity of dispatcher identification algorithm for stroke emergencies for detecting stroke was 53.3% and positive predictive value was 47.8% for stroke and 59.1% for stroke and transient ischemic attack. Of all 275 patients with stroke dispatcher codes, 215 (78.5%) were confirmed with neurological diagnosis. Conclusions— Using dispatcher identification algorithm for stroke emergencies, more than half of all patients with stroke admitted by ambulance were correctly identified by dispatchers. Most false-positive stroke codes had other neurological diagnoses.