A National Assessment of EMS Performance at the Response and Agency Level

Abstract

Background

In 2019, the National EMS Quality Alliance (NEMSQA) established a suite of 11 evidence-based EMS quality measures, yet little is known regarding EMS performance on a national level. Our objective was to describe EMS performance at a response and agency level using the National EMS Information System (NEMSIS) dataset.

Methods

The 2019 NEMSIS research dataset of all EMS 9-1-1 responses in the United States was utilized to calculate 10 of 11 NEMSQA quality measures. Measure criteria and pseudocode was implemented to calculate the proportion meeting measure criteria and 95% confidence intervals across all encounters and for each anonymized agency. We omitted Pediatrics-03b because the NEMSIS national dataset does not report patient weight. Agency level analysis was subsequently stratified by call volume and urbanicity.

Results

Records from 9,679 agencies responding to 26,502,968 9-1-1 events were analyzed. Run-level average performance ranged from 12% for Safety-01 (encounter documented as initial response without the use of lights and siren to 82% for Pediatrics-02 (documented respiratory assessment in pediatric patients with respiratory distress) At the agency level, significant variation in measure performance existed by agency size and by urbanicity. At the individual agency performance analysis, Trauma-04 (trauma patients transported to trauma center) had the lowest agency-level performance with 47% of agencies reporting 0% of eligible runs with documented transport to a trauma center.

Conclusion

There is a wide range of performance in key EMS quality measures across the United States that demonstrate a need to identify strategies to improve quality and equity of care in the prehospital environment, system performance and data collection.

Disclosure Statement

Michael Redlener is the President of the Board of Directors for the National EMS Quality Alliance. Jeffrey Jarvis is a board member of the National EMS Quality Alliance

Additional information

Funding

This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.