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Data missingness in the Michigan NEMSIS (MI-EMSIS) dataset: a mixed-methods study
OBJECTIVE: The study was done to evaluate levels of missing and invalid values in the Michigan (MI) National Emergency Medical Services Information System (NEMSIS) (MI-EMSIS) and explore possible causes to inform improvement in data reporting and prehospital care quality. METHODS: We used a mixed-me...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8045182/ https://www.ncbi.nlm.nih.gov/pubmed/33853518 http://dx.doi.org/10.1186/s12245-021-00343-y |
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author | Abir, Mahshid Taymour, Rekar K. Goldstick, Jason E. Malsberger, Rosalie Forman, Jane Hammond, Stuart Wahl, Kathy |
author_facet | Abir, Mahshid Taymour, Rekar K. Goldstick, Jason E. Malsberger, Rosalie Forman, Jane Hammond, Stuart Wahl, Kathy |
author_sort | Abir, Mahshid |
collection | PubMed |
description | OBJECTIVE: The study was done to evaluate levels of missing and invalid values in the Michigan (MI) National Emergency Medical Services Information System (NEMSIS) (MI-EMSIS) and explore possible causes to inform improvement in data reporting and prehospital care quality. METHODS: We used a mixed-methods approach to study trends in data reporting. The proportion of missing or invalid values for 18 key reported variables in the MI-EMSIS (2010–2015) dataset was assessed overall, then stratified by EMS agency, software platform, and Medical Control Authorities (MCA)—regional EMS oversight entities in MI. We also conducted 4 focus groups and 10 key-informant interviews with EMS participants to understand the root causes of data missingness in MI-EMSIS. RESULTS: Only five variables of the 18 studied exhibited less than 10% missingness, and there was apparent variation in the rate of missingness across all stratifying variables under study. No consistent trends over time regarding the levels of missing or invalid values from 2010 to 2015 were identified. Qualitative findings indicated possible causes for this missingness including data-mapping issues, unclear variable definitions, and lack of infrastructure or training for data collection. CONCLUSIONS: The adoption of electronic data collection in the prehospital setting can only support quality improvement if its entry is complete. The data suggest that there are many EMS agencies and MCAs with very high levels of missingness, and they do not appear to be improving over time, demonstrating a need for investment in efforts in improving data collection and reporting. |
format | Online Article Text |
id | pubmed-8045182 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-80451822021-04-14 Data missingness in the Michigan NEMSIS (MI-EMSIS) dataset: a mixed-methods study Abir, Mahshid Taymour, Rekar K. Goldstick, Jason E. Malsberger, Rosalie Forman, Jane Hammond, Stuart Wahl, Kathy Int J Emerg Med Original Research OBJECTIVE: The study was done to evaluate levels of missing and invalid values in the Michigan (MI) National Emergency Medical Services Information System (NEMSIS) (MI-EMSIS) and explore possible causes to inform improvement in data reporting and prehospital care quality. METHODS: We used a mixed-methods approach to study trends in data reporting. The proportion of missing or invalid values for 18 key reported variables in the MI-EMSIS (2010–2015) dataset was assessed overall, then stratified by EMS agency, software platform, and Medical Control Authorities (MCA)—regional EMS oversight entities in MI. We also conducted 4 focus groups and 10 key-informant interviews with EMS participants to understand the root causes of data missingness in MI-EMSIS. RESULTS: Only five variables of the 18 studied exhibited less than 10% missingness, and there was apparent variation in the rate of missingness across all stratifying variables under study. No consistent trends over time regarding the levels of missing or invalid values from 2010 to 2015 were identified. Qualitative findings indicated possible causes for this missingness including data-mapping issues, unclear variable definitions, and lack of infrastructure or training for data collection. CONCLUSIONS: The adoption of electronic data collection in the prehospital setting can only support quality improvement if its entry is complete. The data suggest that there are many EMS agencies and MCAs with very high levels of missingness, and they do not appear to be improving over time, demonstrating a need for investment in efforts in improving data collection and reporting. Springer Berlin Heidelberg 2021-04-14 /pmc/articles/PMC8045182/ /pubmed/33853518 http://dx.doi.org/10.1186/s12245-021-00343-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Original Research Abir, Mahshid Taymour, Rekar K. Goldstick, Jason E. Malsberger, Rosalie Forman, Jane Hammond, Stuart Wahl, Kathy Data missingness in the Michigan NEMSIS (MI-EMSIS) dataset: a mixed-methods study |
title | Data missingness in the Michigan NEMSIS (MI-EMSIS) dataset: a mixed-methods study |
title_full | Data missingness in the Michigan NEMSIS (MI-EMSIS) dataset: a mixed-methods study |
title_fullStr | Data missingness in the Michigan NEMSIS (MI-EMSIS) dataset: a mixed-methods study |
title_full_unstemmed | Data missingness in the Michigan NEMSIS (MI-EMSIS) dataset: a mixed-methods study |
title_short | Data missingness in the Michigan NEMSIS (MI-EMSIS) dataset: a mixed-methods study |
title_sort | data missingness in the michigan nemsis (mi-emsis) dataset: a mixed-methods study |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8045182/ https://www.ncbi.nlm.nih.gov/pubmed/33853518 http://dx.doi.org/10.1186/s12245-021-00343-y |
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