Impact of US state government regulation on patient access to elective surgical care
Bibliographic record
Abstract
PURPOSE: Rising health care costs in the United States have led to government regulation of services via a Certificate of Need (CON) law in many states. Such regulation may decrease access to elective surgical procedures. This study describes the impact of CON laws on elective surgical care. METHODS: This retrospective cohort trial used data from the Health Care Utilization Project, a publicly available, inpatient database. Rates of six elective procedures were compared between 21 CON states and 5 non-CON states (2004-2005). Further, facility type (non-profit versus for-profit), facility teaching status, and median charges were also compared as a function of CON status. Statistical analysis was performed by Student's t-tests (?=0.05). RESULTS: CON laws did not affect procedure rates (P = 0.11-0.97), but lower charges were found for lumbar discectomy ($16,819 versus $13,493 p=0.04), acoustic neuroma resection ($60,993 versus $46,353, P < 0.001), and microvascular decompression (MVD) for trigeminal neuralgia ($37,741 versus $27,729, P < 0.001) in CON states. Various procedures exhibited a shift from for-profit to non-profit facilities including lumbar disectomy (20% versus 9%, P=0.01), acoustic neuroma resection (5.5% versus 0.2%, P=0.03), MVD (20% versus 3%, P=0.02), and rotator cuff repair (23% versus 10%, P=0.01). CON status had no effect on proportion of cases occurring at teaching facilities. CONCLUSIONS: CON laws appear to maintain patient access to elective surgical care while successfully reducing hospital charges. The location of surgery may shift to non-profit centers suggesting preferential certificate distribution, though this only partly explains the decreased charges in states with CON regulation.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".