Impact of Chest Pain Protocol Targeting Intermediate Cardiac Risk Patients in an Observation Unit of an Academic Tertiary Care Center
Bibliographic record
Abstract
BACKGROUND: Chest pain (CP) is a frequent cause of emergency room visits in United States and adds a huge financial burden to our healthcare cost. With the addition of observation units, standard CP protocols have shown to decrease length of stay (LOS) and cost per discharge (CPD). We report our experience with the development and implementation of "CP protocol for intermediate cardiac risk patients" and its impact on healthcare resource utilization at our medical center. METHODS AND RESULTS: We retrospectively analyzed 30 patients who presented to Advocate Christ Medical Center (ACMC) with CP and were considered to be at intermediate risk for acute coronary syndrome after obtaining IRB approval. Patients were treated with our standardized CP protocol and labeled as "protocol patients". Our control group consisted of patients with similar demographics and diagnosis but not treated with our CP protocol admitted in the same time period and under our own faculty. This helped remove the bias of different treating attending. Our protocol algorithm consisted of medications, an electrocardiogram (EKG), cardiac troponin I level, and a stress test if indicated. Primary clinical endpoints for this study were LOS in hours and CPD for patients in our protocol group compared to control group. LOS in the protocol group was lower compared to the control but the difference was not statistically significant (P = 0.74). The average CPD in the control group (mean = $13,446) was almost $830 more than the protocol group (mean = $14,276, P = 0.827). CONCLUSION: Implementation of standardized protocols for patients with CP has proven to be a cost effective strategy at several institutions across the country. Our study showed a reduction in CPD although not statistically significant. LOS was also reduced but did not meet statistical significance mainly due to our small sample size. Previous studies had demonstrated much larger savings between a protocol-driven group and a non-protocol-driven group. On further analysis of our data, our protocol group contained five patients who underwent invasive diagnostic tests including computed tomography for pulmonary embolism scans which were not present in the control group. This accounted for the small reduction in costs for the protocol group.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".