Managing the performance of general practitioners and specialists referral networks: A system for evaluating the heart failure pathway
Bibliographic record
Abstract
High quality chronic disease management requires coordinated care across different healthcare settings, involving multidisciplinary teams of professionals, and performance evaluation systems able to measure this care. Inter-organizational performance should be measured considering the professional relationships between general practitioners (GPs) and specialists, who are usually linked through informal referral networks. The aim of this paper is to identify and evaluate the performance of naturally occurring networks of GPs and hospital-based specialists providing care for congestive heart failure (CHF) patients in Tuscany, Italy. The analysis focuses on the identification and classification of networks, following CHF patients (n = 15,841) through primary care and inpatient care using administrative data, and on the assessment of process and outcome indicators for CHF patients in these referral networks. We demonstrate the existence of informal links between GPs and hospitals based on patterns of patient flow. These networks which are not geographically based vary in the intensity of relationships and quality of care. Such referral networks may represent the most effective accountability level for chronic disease management, since they encompass the multiple care settings experienced by patients. Overall, an integrated approach to evaluation and performance management that considers the naturally occurring links between professionals working in different settings may enable more efficient, integrated care and quality improvements.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".