Practice Transformation: Early Impact of the Oncology Care Model on Hospital Admissions
Bibliographic record
Abstract
Purpose: The purpose of the Oncology Care Model (OCM) is to improve quality and reduce cost through practice transformation. A foundational tenant is to reduce avoidable emergency room (ER) visits and hospitalizations. In anticipation of being an OCM participant, we instituted a multidimensional campaign designed to meet these objectives. Methods: Prior actions included establishment of phone triage unit, after-hours and weekend calls, and institution of weekend urgent care. Results: On the basis of data from the Chronic Condition Warehouse, as provided by the Centers for Medicare and Medicaid Services, we were successful at reducing the acute care admissions rate by 16%. During the baseline period extending from Jan 2016-Mar 2016, the hospital admission rate was 27 per patient, per quarter, at an average cost per admission event of $11,122, translating to an inpatient cost per patient, per quarter, of $3,003. In the year one reporting period of July 2016-July 2017, the hospital admission rate declined to 22.6 per patient, per quarter, at an average cost per admission event of $11,106, translating to an inpatient cost per patient, per quarter, of $2,505. OCM patient survey scores improved. In addition, at Oncology Hematology Care, we achieved improved results compared with the risk-adjusted national averages for the following measures: readmissions (4.9 v 5.6 per 100 patients, respectively), ER use (17 v 18.6 per 100 patients, respectively), and observation stays (2.7 v 3.6 per 100 patients, respectively). Conclusion: By implementing a cost-efficient, reproducible, and scalable campaign targeting ER avoidance and hospitalizations, we were able to decrease hospital admissions. Reported Medicare savings amounted to nearly $798,000 in inpatient cost per quarter over 1,600 patients.
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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 | 0.012 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".