A CDSS based Integrated Pathway for Influenza Vaccination Across Primary and Secondary Care
Bibliographic record
Abstract
Abstract Background In Italy, influenza vaccination coverage in people ≥ 65 years is unsatisfactory (56.7%), underlining the need to improve current vaccination strategies. The objectives of this study have been: to define a novel pathway to increase influenza vaccination adherence among ≥ 65 years population in the Lazio region; to provide a predictive estimate of the epidemiological and economic pathway’s potential impact. Methods A multidisciplinary working group (WG) featuring cross-sectoral expertise was created and WG periodic meetings were held to define the patient journey and its flow-chart representation. The pathway’s potential impact was assessed through epidemiological and economic indicators and scenario analyses. Results An integrated pathway across primary and secondary care was defined, based on the active patient in-Hospital recruitment and vaccination and enhanced by a Clinical Decision Support System relying on a digital algorithm to identify eligible patients. Assuming an increase of influenza vaccination coverage from the current rate of 60% (scenario 1) to 65% (scenario 2) in ≥ 65 years population in the Lazio region thanks to the pathway implementation, an increase of 8% in avoided influenza cases, influenza- or pneumonia-related hospitalizations and influenza-related outpatient visits was estimated with a relative increase in savings for hospitalizations and outpatients visits of up 2,367,310 euros. Setting the vaccination coverage at 70% (scenario 3), an increase of 16% in avoided influenza cases, hospitalizations and outpatient visits was estimated with a relative increase in savings for hospitalizations and outpatients’ visits of up to 4,833,259 euros. Conclusions Alongside offering a predictive estimate of the relevant pathway’s potential impact, both epidemiological and economic, this project, with its robust methodology, may serve as a scalable and transferable model for enhancing vaccination coverage at national and international level. Key messages • The proposed pathway, offering the option of receiving flu vaccination within the Hospital, supports the paradigm shift towards primary prevention pathways in secondary care settings. • Another relevant aspect of the integrated pathway is the adoption of an Artificial Intelligence tool to identify suitable patients and improve their recruitment and adherence to vaccination.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".