Impact of a New Palliative Care Program on Health System Finances: An Analysis of the Palliative Care Program Inpatient Unit and Consultations at Johns Hopkins Medical Institutions
Bibliographic record
Abstract
PURPOSE: Palliative care inpatient units (PCUs) can improve symptoms, family perception of care, and lower per-diem costs compared with usual care. In March 2013, Johns Hopkins Medical Institutions (JHMI) added a PCU to the palliative care (PC) program. We studied the financial impact of the PC program on JHMI from March 2013 to March 2014. METHODS: This study considered three components of the PC program: PCU, PC consultations, and professional fees. Using 13 months of admissions data, the team calculated the per-day variable cost pre-PCU (ie, in another hospital unit) and after transfer to the PCU. These fees were multiplied by the number of patients transferred to the PCU and by the average length of stay in the PCU. Consultation savings were estimated using established methods. Professional fees assumed a collection rate of 50%. RESULTS: The total positive financial impact of the PC program was $3,488,863.17. There were 153 transfers to the PCU, 60% with cancer, and an average length of stay of 5.11 days. The daily loss pretransfer to the PCU of $1,797.67 was reduced to $1,345.34 in the PCU (-25%). The PCU saved JHMI $353,645.17 in variable costs, or $452.33 per transfer. Cost savings for PC consultations in the hospital, 60% with cancer, were estimated at $2,765,218. $370,000 was collected in professional fees savings. CONCLUSION: The PCU and PC program had a favorable impact on JHMI while providing expert patient-centered care. As JHMI moves to an accountable care organization model, value-based patient-centered care and increased intensive care unit availability are desirable.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".