Hospital based physician advisor program sheds new light on observation rate
Bibliographic record
Abstract
Hospital observation is a topic of interest among patients for whom being classified as observation has negative financial ramifications. Similarly, observation rate is monitored by some hospital administrators because of its potential financial impact on the health system. During the creation of an internal physician advisor program, the new health system physician advisor was asked to investigate causes for a higher than average observation rate for WellSpan Summit Health. Using Lean methodology, standard work was established for the physician advisor observation patient review process when inpatient criteria were not met. Key performance indicators were tracked using production boards and a dashboard that interfaces with the electronic health record. The physician advisor program decreased missed inpatient conversion opportunities, but despite fixing process problems, improving level of care determination accuracy, and seeing outcomes that should have decreased the observation rate, the observation rate paradoxically increased. The cause of the rising observation rate is unknown but is likely multifactorial. Possible causes include changing standards concerning what qualifies as inpatient, Affordable Care Act (ACA) expansion of insured patients presenting to the emergency department (ED) with low acuity conditions, and the safety net function of the hospital for patients living with adverse social determinants of health. The safety net theory is most likely true for “high utilizers” using a greater portion of hospital resources than the rest of the population. This study provides evidence that observation rate is not a useful metric in the absence of a process problem. A more meaningful metric concerning observation patients is observation length of stay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".