Anjni Joiner

INTRODUCTIONFalls are among the most common reasons for emergency medical services (EMS) activation in older adults, including a substantial proportion of encounters that do not result in transport...

Background: The current landscape of emergency care (EC) is marked by high demand, leading to issues such as emergency department boarding, overcrowding, and subsequent delays that impact the quality and safety of patient care. Integrating data science into EC can enhance decision-making with predictive, preventative, personalized, and participatory approaches. However, gaps in adherence to fairness, accountability, interpretability, and responsibility are evident, particularly due to barriers to data-sharing, which often result in a lack of transparency and robust oversight in these applications. Objective: The FAIR-EC (Fair, Accountable, Interpretable, and Responsible-Emergency Care) collaboration adapts the existing Fair, Accountable, Interpretable, and Responsible principles to address emerging challenges as data science integrates with EC. This initiative aims to transform EC by establishing ethical artificial intelligence standards specifically tailored for this integration. By bridging the gap between EC professionals, data scientists, and other stakeholders, the collaboration promotes international cooperation that leverages advanced data science techniques to enhance EC outcomes across different care settings. Methods: We propose a federated research design to analyze extensive datasets from various global institutions without compromising patient privacy. This approach transforms epidemiological research with advanced data science techniques, emphasizing the harmonization of data for comprehensive analyses across different health care systems. Results: The FAIR-EC initiative has facilitated the identification and harmonization of datasets from diverse geographical regions, enabling the examination of regional variations in EC practices. As of paper submission, participating sites have identified retrospective EC datasets totaling >2 million records (eg, Duke Health >400,000 and Singapore General Hospital >1.7 million records). Initial projects have demonstrated feasibility and operational readiness, including implementation of federated workflows and ongoing development of a federated scoring system, cross-site evaluation, and adaptation of association studies and predictive models across various regions. Cross-site harmonization and pilot analyses are underway (with local ethics approvals in progress), and first multisite results are expected to be submitted in mid-late 2026, with additional project-level publications anticipated in 2027. These efforts highlight the feasibility of leveraging advanced data science techniques to address the complexities of EC while preserving patient privacy without centralizing individual-level data. This project was funded from September 1, 2022, to August 31, 2023. Conclusions: FAIR-EC integrates data science ethically and effectively into EC, addressing challenges such as fragmented data, real-time handoffs, and public health crises. Its federated design harmonizes diverse data streams while preserving privacy, and its emphasis on ethical artificial intelligence aligns with the dynamic nature of EC. Despite challenges in data variability and system complexity, FAIR-EC establishes a strong foundation for innovation in global EC.

INTRODUCTIONPatients treated by emergency medical services (EMS) for opioid overdose face a staggering risk of death, with 10-15% dying within the subsequent year, a rate twice that of patients who...

Prehospital programs for Medications for Opioid Use Disorder (MOUD) have gained significant traction over the past few years. Data describing statewide implementation of MOUD programs are limited. Our objective was to describe the current practices within active prehospital MOUD programs in North Carolina and to compare these data with previously published best practices to assist in standardizing future MOUD programs. This was a cross-sectional study of 21 counties in North Carolina (NC) with an active prehospital MOUD program at the time of the study. Using virtual and phone interviews, counties were asked a set of predefined qualitative and quantitative questions related to their program structure and elements which were then analyzed using descriptive statistics and combined into a comparative format based on current best practice consensus recommendations. A total of 21 counties in NC had an active prehospital MOUD program, defined as having administered at least 1 induction dose. Of these active programs, 19 offered bridge dosing and 18 (86%) had administered at least 1 bridge dose. Inclusion criteria for patients were the same for all 21 active MOUD programs. In 20 counties (95%), only Community Paramedics (CPs) or other specialized personnel like EMS Supervisors were authorized to administer MOUD, while 1 county (5%) allowed any paramedic in the system to administer MOUD. With regards to induction therapy and the consensus recommendation of a 16mg dose, 9 counties (43%) utilized this dose for patients experiencing opiate withdrawal, and 13 (62%) counties used this dosing for patients experiencing withdrawal post overdose reversal with naloxone. Prehospital MOUD programs in North Carolina demonstrated broad consistency with only small variations with respect to dosing, funding sources, hours of available treatment, and team composition. This study demonstrated significant consistency with regards to the approaches of NC EMS systems in the care of OUD, providing replicable models other states can use to drive forward prehospital MOUD programs and create a national standard of care. Further studies are needed to link these efforts to associated patient centered outcomes.

Emergency medical services (EMS) clinicians report challenges in providing care to patients with limited English proficiency (LEP); however, associated factors are not well defined. The objective of this study was to examine EMS clinicians' self-reported preparedness to provide emergency care to patients with LEP in the prehospital setting. We conducted a cross-sectional survey between October 10 and December 3, 2024, to assess the education and training received by nationally certified EMS clinicians on providing care to patients with LEP. We analyzed the data using univariate and bivariate analyses, including Pearson's chi-square and analysis of variance tests. We used least absolute shrinkage and selection operator to identify key predictors and logistic regression models (Odds Ratio (OR), 95% Confidence Interval (CI)) to determine associations with preparedness. Of the 986 EMS clinicians who responded to the survey, 58.3% reported providing care to one or more patients with LEP over the past month. Although provision of EMS care to patients with LEP was common, only 28.8% of clinicians reported receiving initial education on delivering care to patients with LEP and 29.5% reported additional education. Among respondents, 44.3% reported access to professional interpreter services. Of these, 64.9% reported that it was easy or very easy to access services, 88.0% reported that the services were effective or very effective, but 64.9% reported using interpreter services less than 25% of the time or never. Respondents with initial (OR 1.96, 95% CI (1.46-2.63)) or additional (OR 1.54, 95% CI (1.15-2.06)) education on the provision of care for patients with LEP reported feeling more prepared when delivering care to this population, compared to clinicians who did not receive education on this topic. Prehospital clinicians commonly provide care to patients with LEP; however, EMS education or training on delivering care to this population is uncommon and interpreter use is rare. Clinicians felt more prepared in delivering care when provided any LEP education. Future efforts in EMS should consider incorporating LEP-related content in initial and additional education efforts and create strategies to overcome the low use of professional interpreter services.

Emergency Medical Services (EMS) agencies are beginning to provide low-barrier access to treatment for opioid use disorder (OUD) through the development of EMS buprenorphine (EMS-Bupe) programs. However, evidence-based practices for these programs are lacking. Our aim was to review the current literature on EMS and emergency department (ED) based buprenorphine treatment programs to provide consensus recommendations on the EMS-Bupe program development. We performed a scoping review of EMS-Bupe programs and ED medication for OUD (MOUD) programs. We searched Ovid MEDLINE(R), Embase.com, Cochrane Central Register of Controlled Trials and Web of Science (Science Citation Index) for English language articles and abstracts. Additional articles/abstracts as identified independently by coauthors were added. Recommendations were generated through consensus based on the findings of the scoping review and other relevant literature. We identified a total of 9 EMS-Bupe articles/abstracts and 21 ED MOUD abstract, representing 5 EMS-Bupe programs in 4 states. There was significant variability between programs, from infrastructure, medication dosing, and retention rates. Results and recommendations were grouped into 8 categories: EMS program infrastructure, withdrawal classification thresholds, EMS protocol inclusion/exclusion criteria, buprenorphine dosing and adjunct medications, EMS disposition and scene times, EMS clinician training, referrals, and EMS data collection and quality management. The EMS-Bupe program data are limited but show important variability. In general, we recommend that programs respond to community needs by establishing relationships with local resources. We also favor protocols that increase patient eligibility and treatment retention. Lastly, programs should consider low-barrier, patient-centered strategies aimed at preventing gaps in treatment.

International Prehospital Emergency Care (PEC) standards have been primarily developed by and for high resource settings. Most PEC systems in Asia, which are still in the early stages of development, struggle to achieve these standards. There is a need for an evaluation tool which can define achievable basic building blocks for PEC systems in low resource settings to improve quality of PEC. We aimed to identify the core, basic elements (building blocks of a PEC system) for a Prehospital Emergency Care Systems Evaluation Tool (PECSET) for low resource settings in Asia. A 4-stage modified Delphi consensus method was used to engage 32 PEC experts from 12 Asian countries. Participants voted on 32 elements identified from a prior scoping review, focus group discussions, and survey. Each round of voting was conducted through an anonymous, web-based application and followed by face-to-face group discussions. The first two rounds aimed to answer, "Is the element important and feasible in a low resource setting?" The last two stages aimed to answer "Should this element be prioritized as core in the tool?" A thematic analysis of the recorded and transcribed discussions was used to identify participants' rationale for prioritization. After four rounds of voting, 12 elements were identified as core elements: 1) dispatch assisted instructions, 2) protocols for screening, triage and destination, (3) medical direction, 4) standardized training programs, 5) minimum ambulance standards, 6) operational metrics, 7) quality assurance, 8) operational safety protools, 9) essential patient care documentation, 10) medical records management, 11) layperson awareness and education and 12) universal access emergency number. However, the participants decided to include all 32 elements in the tool grouped into broader categories by percent agreement for a tiered approach for early, intermediate, and advanced PEC systems. Rationales for prioritization included a need for focus on basic infrastructure and building resilience in resource-stretched systems. Through a Delphi consensus process, stakeholders identified core elements for PEC systems in low resource settings. These findings will inform the development of a tool for quality assurance and monitoring in low resource settings in South and Southeast Asian countries.