EMS utilization predictors in a Mobile Integrated Health (MIH) program

dc.contributor.authorPinet-Peralta, Luis M.
dc.contributor.authorGlos, Lukas J.
dc.contributor.authorSanna, Evan
dc.contributor.authorFrankel, Brian
dc.contributor.authorLindqvist, Ernest
dc.date.accessioned2021-03-16T15:40:42Z
dc.date.available2021-03-16T15:40:42Z
dc.date.issued2021-02-04
dc.description.abstractBackground The provision of unnecessary Emergency Medical Services care remains a challenge throughout the US and contributes to Emergency Department overcrowding, delayed services and lower quality of care. New EMS models of care have shown promise in improving access to health services for patients who do not need urgent care. The goals of this study were (1) to identify factors associated with EMS utilization (911) and (2) their effects on total EMS calls and transports in an MIH program. Methods The study sample included 110 MIH patients referred to the program or considered high-users of EMS services between November 2016 and September 2018. The study employed descriptive statistics and Poisson regressions to estimate the effects of covariates on total EMS calls and transports. Results The typical enrollee is a 60-year-old single Black male living with two other individuals. He has a PCP, takes 12 medications and is compliant with his treatment. The likelihood of calling and/or being transported by EMS was higher for males, patients at high risk for falls, patients with asthma/COPD, psychiatric or behavioral illnesses, and longer travel times to a PCP. Each prescribed medication increased the risk for EMS calls or transports by 4%. The program achieved clear reductions in 911 calls and transports and savings of more than 140,000 USD in the first month. Conclusions This study shows that age, marital status, high fall risk scores, the number of medications, psychiatric/behavioral illness, asthma/COPD, CHF, CVA/stroke and medication compliance may be good predictors of EMS use in an MIH setting. MIH programs can help control utilization of EMS care and reduce both EMS calls and transports.en
dc.description.sponsorshipWe are grateful to the Prince George’s County Fire and EMS Department for granting access to their data, and for their expertise and time.en
dc.description.urihttps://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01409-wen
dc.format.extent12 pagesen
dc.genrejournal articlesen
dc.identifierdoi:10.13016/m21fva-d1w6
dc.identifier.citationPinet-Peralta, L.M., Glos, L.J., Sanna, E. et al. EMS utilization predictors in a Mobile Integrated Health (MIH) program. BMC Med Inform Decis Mak 21, 40 (2021). https://doi.org/10.1186/s12911-021-01409-wen
dc.identifier.urihttps://doi.org/10.1186/s12911-021-01409-w
dc.identifier.urihttp://hdl.handle.net/11603/21183
dc.language.isoenen
dc.publisherBMCen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC School of Public Policy Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsAttribution 4.0 International*
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.titleEMS utilization predictors in a Mobile Integrated Health (MIH) programen
dc.typeTexten

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