Data resources for evaluating the economic and financial consequences of surgical care in the United States
Bibliographic record
Abstract
ABSTRACT: Evaluating the relationship between health care costs and quality is paramount in the current health care economic climate, as an understanding of value is needed to drive policy decisions. While many policy analyses are focused on the larger health care system, there is a pressing need for surgically focused economic analyses. Surgical care is costly, and innovative technology is constantly introduced into the operating room, and surgical care impacts patients' short- and long-term physical and economic well-being. Unfortunately, significant knowledge gaps exist regarding the relationship between cost, value, and economic impact of surgical interventions. Despite the plethora of health care data available in the forms of claims databases, discharge databases, and national surveys, no single source of data contains all the information needed for every policy-relevant analysis of surgical care. For this reason, it is important to understand which data are available and what can be accomplished with each of the data sets. In this article, we provide an overview of databases commonly used in surgical health services research. We focus our review on the following five categories of data: governmental claims databases, commercial claims databases, hospital-based clinical databases, state and national discharge databases, and national surveys. For each, we present a summary of the database sampling frame, clinically relevant variables, variables relevant to economic analyses, strengths, weaknesses, and examples of surgically relevant analyses. This review is intended to improve understanding of the current landscape of data available, as well as stimulate novel analyses among surgical populations. Ongoing debates over national health policy reforms may shape the delivery of surgical care for decades to come. Appropriate use of available data resources can improve our understanding of the economic impact of surgical care on our health care system and our patients. LEVEL OF EVIDENCE: Regular Review, Level V.
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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 | 0.074 | 0.315 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.030 | 0.044 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".