scholarly journals EMS Heroin Overdoses with Refusal to Transport & Impacts on ED Overdose Surveillance

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Peter Rock ◽  
Michael Singleton
Keyword(s):  
Emergency Department ◽  
Emergency Medical Services ◽  
Syndromic Surveillance ◽  
Emergency Department Visits ◽  
Medical Services ◽  
Opioid Overdose ◽  
Billing Data ◽  
Heroin Overdose ◽  
Emergency Medical ◽  
Significant Difference

ObjectiveThe aim of this project was to explore changing patterns in patient refusal to transport by emergency medical services for classified heroin overdoses and possible implications on heroin overdose surveillance in Kentucky.IntroductionAs a Centers for Disease Control and Prevention Enhanced State Opioid Overdose Surveillance (ESOOS) funded state, Kentucky started utilizing Emergency Medical Services (EMS) data to increase timeliness of state data on drug overdose events in late 2016. Using developed definitions of heroin overdose for EMS emergency runs, Kentucky analyzed the patterns of refused/transported EMS runs for both statewide and local jurisdictions. Changes in EMS transportation patterns of heroin overdoses can have a dramatic impact on other surveillance systems, such as emergency department (ED) claims data or syndromic surveillance (SyS) data.MethodsAs part of the ESOOS grant, Kentucky receives all emergency-only EMS runs monthly from Kentucky Board for Emergency Medical Services, Kentucky State Ambulance Reporting System data. Heroin cases were classified based on text and medications (Narcan) administered, with comparisons to historic data discussed elsewhere (Rock & Singleton, 2018). Transportation classifications are based on EMS standard elements defining treatment with transportation vs refusal to transport to hospital and canceled runs were excluded. Initial analysis included trend analysis at state and local levels, as well as demographic comparisons of refusal vs transported heroin overdose encounters.ResultsStatewide trends in EMS heroin overdoses with refusal transport significantly increased from 5% (n=42) in 2016 quarter three to 22% (n=290) in 2018 quarter two (Fig 1). Initial demographic analysis does not show any significant difference between refusals/transported for age, gender, or race. However, there are significant differences among geographic regions in Kentucky with heroin encounter refusal proportion ranging from 3%-48% in 2018 quarter two. Specifically, one urban area (Fig 2) shows the change in proportion of refusal increasing from 15% (n=23) in 2016 quarter three to 47% (n=110) in 2018 quarter two. In this geographic area, combined refused/transported EMS heroin overdoses compared to traditional ED data demonstrates opposing heroin overdose patterns for the same local with EMS showing and increasing trend overtime and ED showing a decreasing trend (Fig 3).ConclusionsTraditional public health surveillance for heroin overdose has historically relied on ED billing data, though agencies are starting to use syndromic surveillance, too (Vivolo-Kantor et al., 2016). These systems share similar underlying ED data, albeit with different components, quality, and limitations. However, in terms of the overdose epidemic, both are limited to only heroin overdoses that result in ED hospital encounters. The recent drastic increase in refused transport can have significant impacts on heroin surveillance. Jurisdictions relying on SyS or ED data for monitoring overdose patterns and/or evaluating interventions may be significantly underestimating acute overdose occurrence in the population. This analysis highlights the importance of this preclinical data source in surveillance of the heroin epidemic.ReferencesRock, P. J., & Singleton, M. D. (2018). Assessing Definitions of Heroin Overdose in ED & EMS Data Using Hospital Billing Data, 10(1), 2579.Vivolo-Kantor, A. M., Seth, P., Gladden, ; R Matthew, Mattson, C. L., Baldwin, G. T., Kite-Powell, A., & Coletta, M. A. (2016). Morbidity and Mortality Weekly Report Vital Signs: Trends in Emergency Department Visits for Suspected Opioid Overdoses — United States, 67(9), 279–285. Retrieved from https://www.cdc.gov/mmwr/volumes/67/wr/pdfs/mm6709e1-H.pdf

Emergency Medical Services and Syndromic Surveillance: A Comparison With Traditional Surveillance and Effects on Timeliness

2021 ◽  
Vol 136 (1_suppl) ◽  
pp. 72S-79S
Author(s):  
Peter J. Rock ◽  
Dana Quesinberry ◽  
Michael D. Singleton ◽  
Svetla Slavova
Keyword(s):  
Public Health ◽  
Time Series ◽  
Emergency Medical Services ◽  
Syndromic Surveillance ◽  
Surveillance Data ◽  
Medical Services ◽  
Opioid Overdose ◽  
Billing Data ◽  
Public Health Response ◽  
Emergency Medical

Objective Traditional public health surveillance of nonfatal opioid overdose relies on emergency department (ED) billing data, which can be delayed substantially. We compared the timeliness of 2 new data sources for rapid drug overdose surveillance—emergency medical services (EMS) and syndromic surveillance—with ED billing data. Methods We used data on nonfatal opioid overdoses in Kentucky captured in EMS, syndromic surveillance, and ED billing systems during 2018-2019. We evaluated the time-series relationships between EMS and ED billing data and syndromic surveillance and ED billing data by calculating cross-correlation functions, controlling for influences of autocorrelations. A case example demonstrates the usefulness of EMS and syndromic surveillance data to monitor rapid changes in opioid overdose encounters in Kentucky during the COVID-19 epidemic. Results EMS and syndromic surveillance data showed moderate-to-strong correlation with ED billing data on a lag of 0 ( r = 0.694; 95% CI, 0.579-0.782; t = 9.73; df = 101; P < .001; and r = 0.656; 95% CI, 0.530-0.754; t = 8.73; df = 101; P < .001; respectively) at the week-aggregated level. After the COVID-19 emergency declaration, EMS and syndromic surveillance time series had steep increases in April and May 2020, followed by declines from June through September 2020. The ED billing data were available for analysis 3 months after the end of a calendar quarter but closely followed the trends identified by the EMS and syndromic surveillance data. Conclusion Data from EMS and syndromic surveillance systems can be reliably used to monitor nonfatal opioid overdose trends in Kentucky in near–real time to inform timely public health response.

scholarly journals Linking Emergency Medical Services and Emergency Department Data to Improve Overdose Surveillance in North Carolina

2021 ◽  
Vol 136 (1_suppl) ◽  
pp. 54S-61S
Author(s):  
Jonathan Fix ◽  
Amy I. Ising ◽  
Scott K. Proescholdbell ◽  
Dennis M. Falls ◽  
Catherine S. Wolff ◽  
...  
Keyword(s):  
Emergency Department ◽  
North Carolina ◽  
Emergency Medical Services ◽  
Data Linkage ◽  
Medical Services ◽  
Opioid Overdose ◽  
Naloxone Administration ◽  
Emergency Medical ◽  
Diagnosis Codes ◽  
Linkage Methods

Introduction Linking emergency medical services (EMS) data to emergency department (ED) data enables assessing the continuum of care and evaluating patient outcomes. We developed novel methods to enhance linkage performance and analysis of EMS and ED data for opioid overdose surveillance in North Carolina. Methods We identified data on all EMS encounters in North Carolina during January 1–November 30, 2017, with documented naloxone administration and transportation to the ED. We linked these data with ED visit data in the North Carolina Disease Event Tracking and Epidemiologic Collection Tool. We manually reviewed a subset of data from 12 counties to create a gold standard that informed developing iterative linkage methods using demographic, time, and destination variables. We calculated the proportion of suspected opioid overdose EMS cases that received International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes for opioid overdose in the ED. Results We identified 12 088 EMS encounters of patients treated with naloxone and transported to the ED. The 12-county subset included 1781 linkage-eligible EMS encounters, with historical linkage of 65.4% (1165 of 1781) and 1.6% false linkages. Through iterative linkage methods, performance improved to 91.0% (1620 of 1781) with 0.1% false linkages. Among statewide EMS encounters with naloxone administration, the linkage improved from 47.1% to 91.1%. We found diagnosis codes for opioid overdose in the ED among 27.2% of statewide linked records. Practice Implications Through an iterative linkage approach, EMS–ED data linkage performance improved greatly while reducing the number of false linkages. Improved EMS–ED data linkage quality can enhance surveillance activities, inform emergency response practices, and improve quality of care through evaluating initial patient presentations, field interventions, and ultimate diagnoses.

Trial to End Ambulance Diversion in Boston: Report from the Conference of the Boston Teaching Hospitals Consortium

2011 ◽  
Vol 26 (2) ◽  
pp. 122-126 ◽  
Author(s):  
Franklin D. Friedman ◽  
Niels K. Rathlev ◽  
Laura White ◽  
Stephen K. Epstein ◽  
Assaad Sayah ◽  
...  
Keyword(s):  
Emergency Department ◽  
Emergency Medical Services ◽  
Online Survey ◽  
Medical Services ◽  
Teaching Hospitals ◽  
Routine Practice ◽  
Ambulance Diversion ◽  
Emergency Medical ◽  
Significant Difference ◽  
The Difference

AbstractIntroduction: Annual ambulance diversion hours in Boston increased more than six-fold from 1997 to 2006. Although interventions and best practices were implemented, there was no reduction in the number of diversion hours.Objectives: A consortium of Boston teaching hospitals instituted a two-week moratorium on citywide diversion from 02 October 2006 to 15 October 2006. The hypothesis was that there would be no significant difference in measures of hospital and emergency medical services (EMS) efficiency compared with the two weeks immediately prior.Methods: A total of nine hospitals and the municipal emergency medical services in Boston submitted data for analysis. The following mean daily hospital measures were studied: (1) emergency department volume; (2) number of emergency department admissions; (3) length of stay (LOS) for all patients; and (4) number of elopements. Mean EMS at-hospital time by destination and the percent of all Boston EMS transports to each hospital destination were calculated. The median differences (MD) were calculated as “before” minus “during” the study period and were compared with paired, Wilcoxon, non-parametric tests. Additional mean EMS measures for all destinations included: (1) to hospital time; (2) number of responses with transport initiated per day; (3) incident entry to arrival; and (4) at-hospital time.Results: The LOS for admitted patients (MD = 0.30 hours; IQR 0.10,1.30; p = 0.03) and number of daily admissions (MD = -1.50 patients; IQR -1.50, -0.10; p = 0.04) were significantly different statistically. The results for LOS for all patients, LOS for discharged patients, ED volume, EMS time at hospital by destination, number of elopements, and percent of Boston EMS transports to each hospital revealed no statistically significant differences. The difference between the study and control periods for mean EMS to hospital time, at-hospital time, and incident entry to arrival was a maximum of 0.6 minutes. The vast majority of EMS respondents to an online survey believed that the “no diversion” policy should be made routine practice.Conclusions: The LOS for admitted patients decreased by 18 minutes, and the number of admissions increased by 1.5 patients per day during the study period. The “no diversion” policy resulted in minimal changes in EMS efficiency and operations. Diversion was temporarily eliminated in a major city without significant detrimental changes in ED, hospital, or EMS efficiency.

Emergency Medical Services for Children: The Report From the Institute of Medicine

PEDIATRICS ◽  
1994 ◽  
Vol 93 (5) ◽  
pp. 821-823 ◽  
Author(s):  
Joseph A. Weinberg ◽  
Donald N. Medearis
Keyword(s):  
United States ◽  
Emergency Department ◽  
Bacterial Meningitis ◽  
Emergency Medical Services ◽  
The United States ◽  
Emergency Department Visits ◽  
Medical Services ◽  
Institute Of Medicine ◽  
Emergency Medical ◽  
Human Cost

The scope and complexity of the problems posed by attempting to provide emergency medical services for children (EMS-C) are great. Of the 90 million emergency department visits in the United States each year, one third, or 30 million, are by children. Many of these are due to injury. In 1990, 266 000 injured children were admitted to hospitals; &gt; 20 000 children died of injuries in 1988 and many more were disabled permanently. In addition to this human cost, the monetary cost of childhood injuries has been estimated to be $7.5 billion annually.1 Most emergency department visits by children, however, are for illnesses, many of which may be serious, such as asthma, pneumonia, or bacterial meningitis.

Feedback to Emergency Medical Services Providers: The Good, the Bad, and the Ignored

1997 ◽  
Vol 12 (2) ◽  
pp. 74-77 ◽  
Author(s):  
Elisabeth F. Mock ◽  
Keith D. Wrenn ◽  
Seth W. Wright ◽  
T. Chadwick Eustis ◽  
Corey M. Slovis
Keyword(s):  
Emergency Department ◽  
Emergency Medical Services ◽  
Emergency Medical Service ◽  
Medical Service ◽  
Service Providers ◽  
Medical Services ◽  
Emergency Medical Service System ◽  
Constructive Feedback ◽  
Emergency Medical ◽  
Emergency Medical Service Provider

AbstractHypothesis:To determine the type and frequency of immediate unsolicited feedback received by emergency medical service (EMS) providers from patients or their family members and emergency department (ED) personnel.Methods:Prospective, observational study of 69 emergency medical services providers in an urban emergency medical service system and 12 metropolitan emergency departments. Feedback was rated by two medical student observers using a prospectively devised original scale.Results:In 295 encounters with patients or family, feedback was rated as follows: 1) none in 224 (76%); 2) positive in 51 (17%); 3) negative in 19 (6%); and 4) mixed in one (<1%). Feedback from 254 encounters with emergency department personnel was rated as: 1) none in 185 (73%); 2) positive in 46 (18%); 3) negative in 21 (8%); and 4) mixed in 2 (1%). Patients who had consumed alcohol were more likely to give negative feedback than were patients who had not consumed alcohol. Feedback from emergency department personnel occurred more often when the emergency medical service provider considered the patient to be critically ill.Conclusion:The two groups provided feedback to emergency medical service providers in approximately one quarter of the calls. When feedback was provided, it was positive more than twice as often as it was negative. Emergency physicians should give regular and constructive feedback to emergency medical services providers more often than currently is the case.

Characterizing Opioid Overdoses Using Emergency Medical Services Data

2021 ◽  
Vol 136 (1_suppl) ◽  
pp. 62S-71S
Author(s):  
Josie J. Sivaraman ◽  
Scott K. Proescholdbell ◽  
David Ezzell ◽  
Meghan E. Shanahan
Keyword(s):  
Machine Learning ◽  
North Carolina ◽  
Emergency Medical Services ◽  
Expert Knowledge ◽  
Case Definition ◽  
Medical Services ◽  
Opioid Overdose ◽  
Machine Learning Classification ◽  
Emergency Medical ◽  
Definition Of

Objectives Tracking nonfatal overdoses in the escalating opioid overdose epidemic is important but challenging. The objective of this study was to create an innovative case definition of opioid overdose in North Carolina emergency medical services (EMS) data, with flexible methodology for application to other states’ data. Methods This study used de-identified North Carolina EMS encounter data from 2010-2015 for patients aged >12 years to develop a case definition of opioid overdose using an expert knowledge, rule-based algorithm reflecting whether key variables identified drug use/poisoning or overdose or whether the patient received naloxone. We text mined EMS narratives and applied a machine-learning classification tree model to the text to predict cases of opioid overdose. We trained models on the basis of whether the chief concern identified opioid overdose. Results Using a random sample from the data, we found the positive predictive value of this case definition to be 90.0%, as compared with 82.7% using a previously published case definition. Using our case definition, the number of unresponsive opioid overdoses increased from 3412 in 2010 to 7194 in 2015. The corresponding monthly rate increased by a factor of 1.7 from January 2010 (3.0 per 1000 encounters; n = 261 encounters) to December 2015 (5.1 per 1000 encounters; n = 622 encounters). Among EMS responses for unresponsive opioid overdose, the prevalence of naloxone use was 83%. Conclusions This study demonstrates the potential for using machine learning in combination with a more traditional substantive knowledge algorithm-based approach to create a case definition for opioid overdose in EMS data.

Abstract WP261: Non-English-preferring Patients Have No Differences in Stroke Symptom Recognition or Emergency Care Time Metrics

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Shaw Natsui ◽  
Khawja A Siddiqui ◽  
Betty L Erfe ◽  
Nicte I Mejia ◽  
Lee H Schwamm ◽  
...  
Keyword(s):  
Emergency Medical Services ◽  
Emergency Care ◽  
Medical Services ◽  
Similar Response ◽  
Language Preference ◽  
Emergency Department Care ◽  
Hospital Arrival ◽  
Stroke Symptom ◽  
Emergency Medical ◽  
Significant Difference

Introduction: The influence of patients’ language preference on the delivery of acute ischemic stroke (AIS) care in the pre-hospital and in-hospital emergency care settings is scarcely known. We hypothesize that stroke knowledge differences may be associated with non-English preferring (NEP) patients having slower time from symptom discovery to hospital presentation and less engagement of emergency medical services (EMS) than English preferring (EP) patients. Language barriers may also interfere with the delivery of time-sensitive emergency department care. Objectives: To identify whether language preference is associated with differences in patients’ time from stroke symptom discovery to hospital arrival, activation of emergency medical services, door-to-imaging time (DIT), and door-to-needle (DTN) time. Methods: We identified consecutive AIS patients presenting to a single urban, tertiary, academic center between 01/2003-04/2014. Data was abstracted from the institution’s Research Patient Data Registry and Get with the Guidelines-Stroke Registry. Bivariate and regression models evaluated the relationship between language preference and: 1) time from symptom onset to hospital arrival, 2) use of EMS, 3) DIT, and 4) DTN time. Results: Of 3,190 AIS patients who met inclusion/exclusion criteria, 9.4% were NEP (n=300). Time from symptom discovery to arrival, and EMS utilization were not significantly different between NEP and EP patients in unadjusted or adjusted analyses (overall median time 157 minutes, IQR 55-420; EMS utilization: 65% vs. 61.3% p=0.21). There was no significant difference between NEP and EP patients in DIT or in likelihood of DIT ≤ 25 minutes in unadjusted or adjusted analyses (overall median 59 minutes, IQR 29-127; DIT ≤ 25 minutes 24.3% vs. 21.3% p=0.29). There was also no significant different in DTN time or in likelihood of DTN ≤ 60 minutes in unadjusted or adjusted analyses (overall median 53 minutes, IQR 36-73; DTN ≤ 60 minutes 62.5% vs. 58.2% p=0.60). Conclusion: Non-English-preferring patients have similar response to stroke symptoms as reflected by EMS utilization and time from symptom discovery to hospital arrival. Similarly, NEP patients have no differences in in-hospital AIS care metrics of DIT and DTN time.

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