Estimating the Effect of Intravenous Acetaminophen for Postoperative Pain Management on Length of Stay and Inpatient Hospital Costs
Bibliographic record
Abstract
INTRODUCTION: The provision of safe, effective, cost-efficient perioperative inpatient acute pain management is an important concern among clinicians and administrators within healthcare institutions. Overreliance on opioid monotherapy in this setting continues to present health risks for patients and increase healthcare costs resulting from preventable adverse events. The goal of this study was to model length of stay (LOS), potential opioid-related complications, and costs for patients reducing opioid use and adding intravenous acetaminophen (IV APAP) for management of postoperative pain. METHODS: Data for this study were de-identified inpatient encounters from The Advisory Board Company across 297 hospitals from 2012-2014, containing 2,238,433 encounters (IV APAP used in 12.1%). Encounters for adults ≥18 years of age admitted for cardiovascular, colorectal, general, obstetrics and gynecology, orthopedics, or spine surgery were included. The effects of reducing opioids and adding IV APAP were estimated using hierarchical statistical models. Costs were estimated by multiplying modeled reductions in LOS or complication rates by observed average volumes for medium-sized facilities, and by average cost per day or per complication (LOS: US$2383/day; complications: derived from observed charges). RESULTS: Across all surgery types, LOS showed an average reduction of 18.5% (10.7-32.0%) for the modeled scenario of reducing opioids by one level (high to medium, medium to low, or low to none) and adding IV APAP, with an associated total LOS-related cost savings of $4.5 M. Modeled opioid-related complication rates showed similar improvements, averaging a reduction of 28.7% (5.4-44.0%) with associated cost savings of $0.2 M. In aggregate, costs decreased by an estimated $4.7 M for a medium-sized hospital. The study design demonstrates associations only and cannot establish causal relationships. The cost impact of LOS is modeled based on observed data. CONCLUSIONS: This investigation indicates that reducing opioid use and including IV APAP for postoperative pain management has the potential to decrease LOS, opioid-related complication rates, and costs from a hospital perspective. FUNDING: Mallinckrodt Pharmaceuticals.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".