Data missingness in the Michigan NEMSIS (MI-EMSIS) dataset: a mixed-methods study

International Journal of Emergency Medicine

Stuart Hammond¹  Jane Forman²  Jason E. Goldstick³  Mahshid Abir⁴  Rosalie Malsberger⁵  Kathy Wahl⁶  Rekar K. Taymour⁷ 

[1] Acute Care Research Unit, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA;Acute Care Research Unit, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA;Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI, USA;Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA;Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA;Acute Care Research Unit, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA;RAND Corporation, Santa Monica, CA, USA;Mathematica, Boston, MA, USA;Michigan Department of Health and Human Services, Lansing, MI, USA;PRECISIONValue, Detroit, MI, USA;

关键词: Emergency Medical Services;    Quality assurance;    Prehospital health care;    Quality measure;    Data;    Big data;    Data collection;   

DOI  :  10.1186/s12245-021-00343-y

来源: Springer

【 摘 要 】

ObjectiveThe 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.MethodsWe 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.ResultsOnly 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.ConclusionsThe 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.

【 授权许可】

CC BY