BACKGROUND: Emergency medical dispatch systems are used to help categorize and prioritize emergency medical services (EMS) resources for requests for assistance. OBJECTIVE: We examined whether a subset of Medical Priority Dispatch System (MPDS) codes could predict patient outcomes (emergency department [ED] discharge versus hospital admission/ED death). METHODS: This retrospective observational cohort study analyzed requests for EMS through a single public safety answering point (PSAP) serving a mixed urban, suburban, and rural community over one year. Probabilistic matching was used to link subjects. Descriptive statistics, 95% confidence intervals (CIs), and logistic regression were calculated for the 107 codes and code groupings (9E vs. 9E1, 9E2, etc.) that were used 50 or more times during the study period. RESULTS: Ninety percent of PSAP records were matched to EMS records and 84% of EMS records were matched to ED data, resulting in 26,846 subjects with complete records. The average age of the cohort was 46.2 years (standard deviation [SD] 24.8); 54% were female. Of the transported patients, 70% were discharged from the ED, with nine dispatch codes demonstrating a 90% or greater predictive power. Three code groupings had more than 60% predictive power for admission/death. Subjects aged 65 years and older were found to be at increased risk for admission/death in 33 dispatch codes (odds ratio [OR] 2.0 [95% confidence interval 1.3-3.0] to 19.6 [5.3-72.6]). CONCLUSIONS: A small subset (8% of codes; 7% by call volume) of MPDS codes were associated with greater than 90% predictive ability for ED discharge. Older adults are at increased risk for admission/death in a separate subset of MPDS codes, suggesting that age criteria may be useful to identify higher-acuity patients within the MPDS code. These findings could assist in prehospital/hospital resource management; however, future studies are needed to validate these findings for other EMS systems and to investigate possible strategies for improvements of emergency response systems.
BACKGROUND: Emergency medical dispatch systems are used to help categorize and prioritize emergency medical services (EMS) resources for requests for assistance. OBJECTIVE: We examined whether a subset of Medical Priority Dispatch System (MPDS) codes could predict patient outcomes (emergency department [ED] discharge versus hospital admission/ED death). METHODS: This retrospective observational cohort study analyzed requests for EMS through a single public safety answering point (PSAP) serving a mixed urban, suburban, and rural community over one year. Probabilistic matching was used to link subjects. Descriptive statistics, 95% confidence intervals (CIs), and logistic regression were calculated for the 107 codes and code groupings (9E vs. 9E1, 9E2, etc.) that were used 50 or more times during the study period. RESULTS: Ninety percent of PSAP records were matched to EMS records and 84% of EMS records were matched to ED data, resulting in 26,846 subjects with complete records. The average age of the cohort was 46.2 years (standard deviation [SD] 24.8); 54% were female. Of the transported patients, 70% were discharged from the ED, with nine dispatch codes demonstrating a 90% or greater predictive power. Three code groupings had more than 60% predictive power for admission/death. Subjects aged 65 years and older were found to be at increased risk for admission/death in 33 dispatch codes (odds ratio [OR] 2.0 [95% confidence interval 1.3-3.0] to 19.6 [5.3-72.6]). CONCLUSIONS: A small subset (8% of codes; 7% by call volume) of MPDS codes were associated with greater than 90% predictive ability for ED discharge. Older adults are at increased risk for admission/death in a separate subset of MPDS codes, suggesting that age criteria may be useful to identify higher-acuity patients within the MPDS code. These findings could assist in prehospital/hospital resource management; however, future studies are needed to validate these findings for other EMS systems and to investigate possible strategies for improvements of emergency response systems.
Authors: Colby Redfield; Abdulhakim Tlimat; Yoni Halpern; David W Schoenfeld; Edward Ullman; David A Sontag; Larry A Nathanson; Steven Horng Journal: J Am Med Inform Assoc Date: 2020-01-01 Impact factor: 4.497
Authors: Chen Ji; Tom Quinn; Lucia Gavalova; Ranjit Lall; Charlotte Scomparin; Jessica Horton; Charles D Deakin; Helen Pocock; Michael A Smyth; Nigel Rees; Samantha J Brace-McDonnell; Simon Gates; Gavin D Perkins Journal: BMJ Open Date: 2018-07-28 Impact factor: 2.692