Amir Mirhaghi1, Hojjat Shafaee2, Javad Malekzadeh3, Farzaneh Hasanzadeh3. 1. Evidence-based Caring Research Center, Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran. 2. Emergency Medical Communication Center, Mashhad University of Medical Sciences, Mashhad, Iran. 3. Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran.
Abstract
OBJECTIVE: To develop decision-support tools to identify patients experiencing sudden cardiac arrest (SCA). METHODS: Eighty calls related to SCA were content analyzed, and the contextual patterns that emerged were organized into a checklist. Two researchers independently analyzed the recorded calls and compared their findings. Eighteen dispatchers scored 20 cases (which included SCA and non-SCA cases) both with and without the checklist. Correct responses for each case and agreement among dispatchers have been reported. RESULTS: Eighty audio files (total time, 96 min) were analyzed, and a total of 602 codes were extracted from the text and recordings. The caller's tone of voice and presence or absence of background voices, calling for an ambulance and giving the dispatcher the address promptly, and description of the primary complaint and respirations accounted for 38%, 39%, and 23% of all codes, respectively. A 15-item complementary checklist has been developed. The mean percentages of correct responses were 66.9%+27.96% prior to the use of checklist and 80.05%+10.84% afterwards. Results of the independent t test for checklist scores showed that statistically significant differences were present between the SCA and non-SCA cases (t=5.88, df=18, p=0.000). CONCLUSION: Decision support tools can potentially increase the recognition rate of SCA cases, and therefore produce a higher rate of dispatcher-directed CPR.
OBJECTIVE: To develop decision-support tools to identify patients experiencing sudden cardiac arrest (SCA). METHODS: Eighty calls related to SCA were content analyzed, and the contextual patterns that emerged were organized into a checklist. Two researchers independently analyzed the recorded calls and compared their findings. Eighteen dispatchers scored 20 cases (which included SCA and non-SCA cases) both with and without the checklist. Correct responses for each case and agreement among dispatchers have been reported. RESULTS: Eighty audio files (total time, 96 min) were analyzed, and a total of 602 codes were extracted from the text and recordings. The caller's tone of voice and presence or absence of background voices, calling for an ambulance and giving the dispatcher the address promptly, and description of the primary complaint and respirations accounted for 38%, 39%, and 23% of all codes, respectively. A 15-item complementary checklist has been developed. The mean percentages of correct responses were 66.9%+27.96% prior to the use of checklist and 80.05%+10.84% afterwards. Results of the independent t test for checklist scores showed that statistically significant differences were present between the SCA and non-SCA cases (t=5.88, df=18, p=0.000). CONCLUSION: Decision support tools can potentially increase the recognition rate of SCA cases, and therefore produce a higher rate of dispatcher-directed CPR.
Entities:
Keywords:
Cardiac arrest; Decision support tool; Emergency medical services; Telephone triage
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