Sustainable Implementation of Hybrid Primary Care Models Through Unified Technology Platforms
Bibliographic record
Abstract
The transition from pandemic-era telehealth to permanent hybrid care models requires evidence-based implementation strategies. We conducted a 24-month prospective evaluation of eight primary care systems across five countries using the RE-AIM framework. Three distinct implementation approaches were compared: parallel track (n=2), sequential integration (n=3), and unified platform (n=3) models. Analysis of 147,892 patient encounters and surveys from 4,847 patients and 124 clinicians revealed that unified platform models achieved superior reach (78.3% vs 52.4%), implementation fidelity (91.2% vs 72.4%), and 24-month sustainability (87.5% vs 68.2%) compared to parallel track approaches. Unified platforms demonstrated 23% cost reduction and 94.2% revenue coverage despite requiring higher initial investment. Statistical modeling identified technology integration, training investment, and organizational culture as critical success determinants. These findings establish that sustainable hybrid care requires unified platforms with comprehensive workflow integration rather than incremental telehealth additions.Full Text Available: Sustainable Implementation of Hybrid Primary Care Models Through Unified Technology Platforms
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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 | 0.030 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| 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".