13. The impact of government regulation of ambulatory surgical facilities on access to elective surgical procedures
Bibliographic record
Abstract
Advances in medical technology have made free-standing ambulatory surgery centres a cost-effective method of delivering health care in the United States. One: Rapid expansion of such centres and duplication of services have raised concerns over rising health care costs, two: leading to government regulation of facilities via a Certificate of Need (CON) law in many states. Three: Such regulation may decrease access to elective procedures. This study investigates access to elective surgical procedures in selected states with and without CON laws. Results of the Health Care Utilization Project were analyzed. Per capita rates of elective carpal tunnel release (CTR) and lumbar discectomy were evaluated in 16 states with CON laws and 5 states without CON laws over the years 2004-2005. Distribution of CTR and lumbar discectomy were analyzed by facility ownership and teaching status, using rates of emergent procedures as a control. Student’s t-tests compared rates of CTR and discectomy as a function of CON legislation. Two-factor ANOVA extended this analysis to account for teaching environment and facility ownership. Fewer CTR cases were performed in states with CON laws (p=0.014), specifically in government-owned (p=0.012) and non-teaching facilities (p=0.01). No difference was observed in lumbar discectomy rates in states with respect to CON regulation. Distribution of both procedures among teaching and non-teaching centers was independent of CON laws. Facility ownership predicts fraction of these cases performed at an institution,(p < 0.01) and this distribution is influenced by CON regulation, increasing fractions of both types of procedures performed at private, not-for-profit centers (p=0.001, p=0.003 respectively). We conclude that CON laws restrict access to certain procedures, specifically in government-owned and non-teaching facilities. These laws may limit the supply of surgical care, notably by redistributing away from government and for-profit centres. Potential solutions include reinvestigating the need for CON laws, or examining the CON methodology to accurately reflect need. Small NC, Bert JM. Office Ambulatory Surgery Centers: Creation and Management. J Am Acad Orthop Surg 2003; 11:157-62. Casalino LP, Devers KJ, Brewster LR. Focused Factories? Physician-Owned Specialty Facilities. Health Affairs 22(6):56-67. Lanning JA, Morrisey MA, Ohsfeldt RL. “Endogenous hospital regulation and it’s effects on hospital and non-hospital expenditures” Journal of Regulatory Economics1991 (June); 3(2):137-54.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".