Prehospital Electronic Patient Care Report Systems: Early Experiences from Emergency Medical Services Agency Leaders
Bibliographic record
Abstract
BACKGROUND: As the United States embraces electronic health records (EHRs), improved emergency medical services (EMS) information systems are also a priority; however, little is known about the experiences of EMS agencies as they adopt and implement electronic patient care report (e-PCR) systems. We sought to characterize motivations for adoption of e-PCR systems, challenges associated with adoption and implementation, and emerging implementation strategies. METHODS: We conducted a qualitative study using semi-structured in-depth interviews with EMS agency leaders. Participants were recruited through a web-based survey of National Association of EMS Physicians (NAEMSP) members, a didactic session at the 2010 NAEMSP Annual Meeting, and snowball sampling. Interviews lasted approximately 30 minutes, were recorded and professionally transcribed. Analysis was conducted by a five-person team, employing the constant comparative method to identify recurrent themes. RESULTS: Twenty-three interviewees represented 20 EMS agencies from the United States and Canada; 14 EMS agencies were currently using e-PCR systems. The primary reason for adoption was the potential for e-PCR systems to support quality assurance efforts. Challenges to e-PCR system adoption included those common to any health information technology project, as well as challenges unique to the prehospital setting, including: fear of increased ambulance run times leading to decreased ambulance availability, difficulty integrating with existing hospital information systems, and unfunded mandates requiring adoption of e-PCR systems. Three recurring strategies emerged to improve e-PCR system adoption and implementation: 1) identify creative funding sources; 2) leverage regional health information organizations; and 3) build internal information technology capacity. CONCLUSION: EMS agencies are highly motivated to adopt e-PCR systems to support quality assurance efforts; however, adoption and implementation of e-PCR systems has been challenging for many. Emerging strategies from EMS agencies and others that have successfully implemented EHRs may be useful in expanding e-PCR system use and facilitating this transition for other EMS agencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".