The National Emergency Medical Services Information System (NEMSIS) is the national system used to collect, store, and share data from EMS services in US states and territories. The NEMSIS uniform dataset and database help local, state and national EMS stakeholders more accurately assess EMS needs and performance, as well as support better strategic planning for the EMS systems of tomorrow. Data from NEMSIS are also used to help benchmark performance, determine the effectiveness of clinical interventions, and facilitate cost-benefit analyses. NEMSIS is a program of NHTSA’s Office of EMS and is hosted by the University of Utah.

These data are “event-based” and not “patient-based”. That is, a single patient may be represented in more than one record for a variety of reasons. A patient may request EMS assistance frequently, and therefore, be represented in the dataset more than once. In addition, several agencies may respond to the same event (i.e., one patient) and each submit a patient care record to the National EMS Database. Thus, the dataset is referred to as a registry of “EMS activations.”

The dataset does not contain information that identifies patients, EMS agencies, receiving hospitals, or reporting states. EMS events submitted by states to NEMSIS do not necessarily represent all EMS events occurring within a state. In addition, states may vary in criteria used to determine the types of EMS events submitted to the NEMSIS dataset.

NEMSIS version 3 data are available from 2017 to 2023. Version 2 data are available from 2009 to 2016 but require mapping and translation to version 3 data elements. Users are advised to open a support ticket to discuss their project if they require data from prior to 2017.

Documentation

This dataset was created by CSPH on Wed, 16 Dec 2020 21:57:04 GMT.

Emergency department and hospital discharge status are available for less than 2% of events recorded in the National EMS Information System (NEMSIS) Public Release Research dataset. The purpose of this project was to develop a binary (“dead” vs. “alive”) end-of-event outcome indicator for the NEMSIS dataset. The data dictionary for the Version 3 NEMSIS dataset was evaluated to identify elements and codes providing information about a patient's end-of-event status—defined as the point at which EMS providers stopped providing care for an encountered patient, whether at the scene of the event or the transport destination. Those element and code combinations were then used to test the criteria using the NEMSIS-2017 dataset. After revising the criteria based on the NEMSIS-2017 results, the final criteria were then applied to the 2018 NEMSIS dataset. To assess representativeness, the characteristics of events with a determinable outcome were compared to those of the entire dataset. To assess accuracy, the end-of-event indicator was compared with the final reported outcome for patients with a known emergency department disposition. Eighteen NEMSIS element and code combinations suggest a patient was likely “dead” at the end of EMS care, and 15 combinations suggest a patient was likely “alive” at the end of EMS care. A binary end-of-event outcome indicator could be determined for 13,045,887 (98.6%) of the 13,229,079 NEMSIS-2018 9-1-1 initiated ground EMS responses in which patient contact was established, and for 132,728 (89.1%) of the 148,963 events with documented cardiac arrest. The characteristics of the events with determinable end-of-event outcomes did not differ from those of the full dataset. Among patients with a known outcome, 99.6% of those with an “alive” end-of-event indicator were in fact alive at the time of emergency department disposition. A binary end-of-event outcome indicator can be determined for 98.6% of 9-1-1 initiated ground EMS scene responses and 89.1% of cardiac arrests included in the NEMSIS dataset. The events with a determinable outcome appear representative of the larger dataset and the end-of-event indicators are generally consistent with reported emergency department outcomes.

This dataset originates from the National EMS Information System (NEMSIS) database. This dataset has selected trauma events where ground transport was utilized and includes time and rurality elements.

The National Emergency Medical Services Information System (NEMSIS) is the national database that is used to store EMS data from the U.S. States and Territories. NEMSIS is a universal standard for how patient care information resulting from an emergency 911 call for assistance is collected. NEMSIS is a collaborative system to improve patient care through the standardization, aggregation, and utilization of point of care EMS data at a local, state and national level. NEMSIS is a product of NHTSA’s Office of EMS and in collaboration with the University of Utah is the host of the Technical Assistance Center.

The table FACTPCRARRESTRHYTHMDESTINATION is part of the dataset NEMSIS, available at https://redivis.com/datasets/yqzf-8ex58rrt2. It contains 34206382 rows across 2 variables.

This dataset originates from the National EMS Information System (NEMSIS) database. This dataset has selected severe trauma events where air transport was utilized.

The table FACTPCRRESPONSEDELAY is part of the dataset NEMSIS, available at https://redivis.com/datasets/yqzf-8ex58rrt2. It contains 34454717 rows across 2 variables.

The table FACTPCRSECONDARYIMPRESSION is part of the dataset NEMSIS, available at https://redivis.com/datasets/yqzf-8ex58rrt2. It contains 33891713 rows across 2 variables.

The National Emergency Medical Services Information System (NEMSIS) provides a robust set of data to evaluate prehospital care. However, a major limitation is that the vast majority of the records lack a definitive outcome. This study aimed to evaluate the performance of a recently proposed method (“MLB” method) to impute missing end-of-EMS-event outcomes (“dead” or “alive”) for patient care reports in the NEMSIS public research dataset. This study reproduced the recently published method for patient outcome imputation in the NEMSIS database and replicated the results for years 2017 through 2022 (n = 686,075). We performed statistical analyses leveraging an array of established performance metrics for binary classification from the machine learning literature. Evaluation metrics included overall accuracy, true positive rate, true negative rate, balanced accuracy, precision, F1 score, Cohen’s Kappa coefficient, Matthews’ coefficient, Hamming loss, the Jaccard similarity score, and the receiver operating characteristic/area under the curve. Extended metrics show consistently good imputation performance from year-to-year but reveal weakness in accurately indicating the minority class: e.g., after adjustments for conflicting labels, “dead” prediction accuracy is 77.7% for 2018 and 61.8% over the six-year NEMSIS sub-sample, even though overall accuracy is 98.8%. Slight over-fitting is also present. This study found that the recently published MLB method produced reasonably good “dead” or “alive” indicators. We recommend reporting of True Positive Rate (“dead” prediction accuracy) and True Negative Rate (“alive” prediction accuracy) when applying the imputation method for analyses of NEMSIS data. More attention by EMS clinicians to complete documentation of target NEMSIS elements can further improve the method’s performance.

Credit report of Nemsis Now contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

Teledyne deployment to test glider

The table FACTPCRPROTOCOL is part of the dataset NEMSIS, available at https://redivis.com/datasets/yqzf-8ex58rrt2. It contains 34823376 rows across 3 variables.

Teledyne deployment to test glider

This study explores the relationship between socioeconomic factors and pediatric opioid-related emergencies requiring naloxone administration in the prehospital setting, an escalating public health concern. A retrospective analysis of the National Emergency Medical Services Information System (NEMSIS) database was conducted, examining data from pediatric opioid-related EMS activations between January 2018 and December 2021. The Social Vulnerability Index (SVI) was used to gauge each incident’s socioeconomic context and assess correlations between SVI scores and the likelihood of opioid-related activations and naloxone interventions. A total of 7,789 pediatric opiate-related EMS activations were identified. Lower socioeconomic status (SES) areas (higher SVI scores) exhibited a decreased rate of opioid-related activations compared to lower SVI-scored areas but an increased frequency of naloxone administration. The analysis demonstrated that as socioeconomic status (SES) improves, the likelihood of opioid-related activations increases significantly supported by a significant negative linear trend (Estimate = −0.2971, SE = 0.1172, z = −2.54, p = 0.0112. On the other hand, naloxone administration was more frequently required in lower SES areas, suggesting an increased emergency response in these (Estimate = 0.05806, SE = 0.2403, z = 0.24, p = 0.8091). The analysis highlights a statistically significant correlation between the SES of an area and pediatric opioid-related EMS activations, yet an inverse correlation with the likelihood of naloxone administration. These findings demonstrate that in lower socioeconomic areas, the total number of opiate-related EMS activations is lower; however, naloxone was more likely to be deployed during those activations. This underscores the need for further research to understand the disparities in opioid crisis management across different socioeconomic landscapes.

Characteristics of EMS encounters involving benzodiazepine-treated seizures in the NEMSIS database, 2010–2014.

Source: Felix Dahn: Gesammelte Werke. Erzählende und poetische Schriften, Zweite Reihe, Band 5: Gedichte und Balladen (Auswahl), Leipzig: Breitkopf und Härtel, 1912.

Teledyne deployment to test glider acknowledgment=Supported by Teledyne, and Integrated Ocean Observing System (IOOS), CeNCOOS NOAA,Grant NA16N0S0120021 cdm_data_type=TrajectoryProfile cdm_profile_variables=time_uv,lat_uv,lon_uv,u,v,profile_id,time,latitude,longitude cdm_trajectory_variables=trajectory,wmo_id comment=Data have not been reviewed and are provide AS-IS. contributor_name=Lauren Cooney and Francisco Chavez contributor_role=Principal investigator, Principal investigator Conventions=Unidata Dataset Discovery v1.0, COARDS, CF-1.6 Easternmost_Easting=-121.84332095998582 featureType=TrajectoryProfile format_version=IOOS_Glider_NetCDF_v2.0.nc geospatial_lat_max=36.80371263551323 geospatial_lat_min=36.334356030177055 geospatial_lat_units=degrees_north geospatial_lon_max=-121.84332095998582 geospatial_lon_min=-122.71323267505632 geospatial_lon_units=degrees_east geospatial_vertical_max=994.2919 geospatial_vertical_min=0.07941009 geospatial_vertical_positive=down geospatial_vertical_units=m gts_ingest=true history=generated from raw glider files by glider_to_IOOS_webb.m id=nemesis_2017_322_0_4_parsed infoUrl=https://gliders.ioos.us/erddap/ institution=Teledyne and Monterey Bay Aquarium Research Institute ioos_dac_checksum=c13154085e7bf117e1f7194aefc56a1a ioos_dac_completed=True ioos_regional_association=CeNCOOS keywords_vocabulary=GCMD Science Keywords Metadata_Conventions=Unidata Dataset Discovery v1.0, COARDS, CF-1.6 naming_authority=org.mbari Northernmost_Northing=36.80371263551323 platform_type=Teledyne Webb glider processing_level=1.1 project=CANON201704 references=https://data.nodc.noaa.gov/accession/0092291 sea_name=North Pacific Ocean source=Teledyne Webb glider nemesis sourceUrl=(local files) Southernmost_Northing=36.334356030177055 standard_name_vocabulary=CF-1.6 subsetVariables=wmo_id,trajectory,profile_id,time,latitude,longitude time_coverage_end=2018-01-09T16:29:44Z time_coverage_start=2017-11-15T22:40:59Z Westernmost_Easting=-122.71323267505632

Source: Johann Wolfgang von Goethe: Berliner Ausgabe. Herausgegeben von Siegfried Seidel: Poetische Werke [Band 1–16]; Kunsttheoretische Schriften und Übersetzungen [Band 17–22], Berlin: Aufbau, 1960 ff.

Teledyne deployment to test glider acknowledgment=Supported by Teledyne, and Integrated Ocean Observing System (IOOS), CeNCOOS NOAA,Grant NA16N0S0120021 cdm_data_type=TrajectoryProfile cdm_profile_variables=time_uv,lat_uv,lon_uv,u,v,profile_id,time,latitude,longitude cdm_trajectory_variables=trajectory,wmo_id comment=Data have not been reviewed and are provide AS-IS. contributor_name=Lauren Cooney, Francisco Chavez contributor_role=Principal investigator, Principal investigator Conventions=Unidata Dataset Discovery v1.0, COARDS, CF-1.6 Easternmost_Easting=-121.83863314508163 featureType=TrajectoryProfile format_version=IOOS_Glider_NetCDF_v2.0.nc geospatial_lat_max=37.00279157148375 geospatial_lat_min=36.71905946883494 geospatial_lat_units=degrees_north geospatial_lon_max=-121.83863314508163 geospatial_lon_min=-122.70487907632891 geospatial_lon_units=degrees_east geospatial_vertical_max=993.188 geospatial_vertical_min=-0.2283052 geospatial_vertical_positive=down geospatial_vertical_units=m gts_ingest=true history=generated from raw glider files by glider_to_IOOS_webb.m id=nemesis_2018_255_1_2_parsed infoUrl=https://gliders.ioos.us/erddap/ institution=Teledyne, Monterey Bay Aquarium Research Institute ioos_dac_checksum=c269c2567fd6fcdf31dd0191b9ba0451 ioos_dac_completed=True ioos_regional_association=CeNCOOS keywords_vocabulary=GCMD Science Keywords Metadata_Conventions=Unidata Dataset Discovery v1.0, COARDS, CF-1.6 naming_authority=org.mbari Northernmost_Northing=37.00279157148375 platform_type=Teledyne Webb glider processing_level=1.1 project=CANON201808 references=https://data.nodc.noaa.gov/accession/0092291 sea_name=North Pacific Ocean source=Teledyne Webb glider nemesis sourceUrl=(local files) Southernmost_Northing=36.71905946883494 standard_name_vocabulary=CF-1.6 subsetVariables=wmo_id,trajectory,profile_id,time,latitude,longitude time_coverage_end=2018-10-03T12:29:55Z time_coverage_start=2018-08-29T22:05:58Z Westernmost_Easting=-122.70487907632891