Quantifying low-value services by using routine data from Austrian primary care
Bibliographic record
Abstract
BACKGROUND: Open debates about the reduction of low-value services, unnecessary diagnostic tests and ineffective therapeutic procedures and initiatives like "Choosing Wisely "in the USA and Canada are still absent in Austria. The objectives of this study are: (i) to establish a list of ineffective or low-value services possibly provided in Austrian primary care, (ii) to explore how many of these services are quantifiable using routine data and (iii) to estimate the number of affected beneficiaries and avoidable costs arising from the provision of these services. METHODS: In May 2014, we identified low-value care services relevant for primary care in Austria. For our analysis we used routine data sets from the Austrian health insurance. All analysis refer to the insured population of the Lower Austrian Sickness Fund (n = 1 168 433) in the year 2013. RESULTS: (i) We found 453 low-value services possibly offered in Austrian primary care. (ii) Only 34 (7.5%) services were quantifiable using routine data. (iii) In the year 2013, these 34 services were provided to at least 246 131 beneficiaries and the estimated avoidable costs arising were at least 11.38 million Euros. This accounts for 1.2% of overall spending of the Lower Austrian Sickness Fund for drugs and services provided by primary care doctors in the year 2013. CONCLUSION: The absence of a homogeneous, transparent and accessible coding system for diagnosis in Austrian primary care restrained our assessment. However, our study findings illustrate the potential utility and limitations of using claims-based measures to identify low-value care.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".