Virtuelle Behandlernetzwerke
Bibliographic record
Abstract
The analysis of geographic variations has spurred arguments that area of residence determines access to and quality of healthcare. In this paper we argue that unwarranted geographic variations can be traced back to actions of individual patients and their healthcare providers (doctors, hospitals). These actors interact in a complicated web of shared responsibilities. Designing effective interventions to reduce unwarranted geographic variations may therefore depend on methods to identify these interactions and communities of providers with a shared accountability. In the US, Canada, and Germany, routine data have been used to identify self-organized informal or virtual networks of physicians and hospitals, so-called patient-sharing networks (PSNs). This is an emerging field of analysis. We attempt to provide a brief report on the state of work in progress. It can be shown that variation between PSNs in a given area is effectively greater than variation between regions. While this suggests that reducing unwarranted variation needs to start at the level of PSN, methods to identify PSNs still vary widely. We compare epidemiological approaches and approaches based on graph theory and social network analysis. We also present some preliminary findings of exploratory analyses based on comprehensive claims data of physician practices in Germany. Defining PSNs based on usual provider relationships helps to create distinctive patient populations while PSNs may not be mutually exclusive. Social network analysis, on the other hand, appears better equipped to differentiate between provider communities with stronger and weaker ties; it does not yield distinctive patient populations. To achieve accountability and to support change management, analytic methods to describe PSNs still need refinement. There are first projects in Germany which use PSNs as an intervention platform in order to achieve improved cooperation and reduce unwarranted variation in their care processes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.090 | 0.053 |
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".