Evaluating the Impact of a Novel Mobile Care Team on the Prevalence of Ambulatory Care Sensitive Conditions Presenting to Emergency Medical Services in Nova Scotia
Bibliographic record
Abstract
Introduction: Hospitalization due to ambulatory care sensitive conditions (ACSC) is a proxy measure for access to primary care. Emergency Medical Services (EMS) are increasingly called when primary care cannot be accessed. A novel paramedic-nurse EMS Mobile Care Team (MCT) was implemented in an under-serviced community. The MCT responds in a non-transport unit to referrals from EMS, emergency and primary care, and to low-acuity 911 calls in a defined geographic region. Our objective was to compare the prevalence of ACSC in ground ambulance (GA) responses before and after the introduction of the MCT. Methods: A cross-sectional analysis of GA and MCT patients with ACSC (determined by chief complaint, clinical impression, treatment protocol, and medical history) from one year pre-MCT implementation to one year post-MCT implementation was conducted for the period of October 1, 2012, to September 30, 2014. Demographics were described. ACSC prevalence was compared using the chi-squared test. Results: There were 975 calls pre-MCT and 1208 GA/95 MCT calls post-MCT. ACSC in GA patients pre- and post-MCT was similar: n=122, 12.5% vs. n=185, 15.3%; p=0.06. ACSC in patients seen by EMS (GA plus MCT) increased in the post-MCT period: 122 (12.5%) vs. 204 (15.7%) p=0.04. Pre-MCT implementation vs post-implementation, GA ACSC calls differed significantly by sex with higher female utilization (n=50 vs. n=105; p=0.007), but not age (65.38, ± 15.12 vs. 62.51 ± 20.48; p=0.16). Post-MCT, the prevalence of specific ACSC increased for GA: hypertension (p<0.001) and congestive heart failure (p=0.04). MCT patients with ACSC were less likely to have a primary care provider compared to GA (90.2% and 87.6% vs. 63.2%; p=0.003, p=0.004). Conclusion: The prevalence of ACSC did not decrease for GA with the introduction of the MCT, but ACSC in the overall patient population served by EMS increased. It is possible more patients with ACSC call, or are referred to EMS, for the new MCT service. Given that MCT patients were less likely to have a primary care provider, this may represent an increase in access to care or a shift away from other emergency/episodic care. These associations must be further studied to inform the ideal utility of adding such services to EMS and healthcare systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".