How do Hospitals Respond to Payment Incentives?
Bibliographic record
Abstract
Over the past decades, Medicare has developed payment reforms that incentivize quality care, by reimbursing fixed amounts for ex ante similar patients.While these reforms may add value, they require providers to code more information on patient health conditions, which is costly.We evaluate the role of revenues and costs in coding intensity for Medicare hospitalized inpatients.We examine the role of costs by estimating hospitals' changes in coding intensity following a 2007 reform based on whether they had adopted electronic medical records (EMRs).EMR hospitals documented relatively more top billing codes after the reform with the increase occurring only for non-surgical admissions, consistent with the hypotheses that costs became an important determinant of the coding decision and EMRs lower these costs, particularly for medical admissions.We further examine whether increased reimbursements from reporting complex diagnoses led hospitals to report more of these diagnoses.We find evidence in favor of this hypothesis before the reform but not after, suggesting that increased billing complexity postreform made coding costs a more important driver of coding decisions.Our findings suggests that recent payment innovations might add cost to providers, who may want to consider reimbursements in their technology adoption and usage decisions.
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 | 0.012 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".