Employer-Sponsored Health Centers Provide Access to Integrated Care via a Hybrid of Virtual and In-Person Visits
Bibliographic record
Abstract
Background: Since the explosion of telemedicine resulting from the SARS-CoV2 pandemic, employers have been particularly interested in virtual primary care as a novel means of expanding primary care services. The purpose of this study is to describe a model of integrated care delivered both in-person and virtually at employer-sponsored health centers nationwide. The key outcomes of this analysis were the proportion of all care delivered in-person and virtually by clinical discipline, the types of care and member satisfaction for care delivered in-person and virtually, and a description of the use of multiple clinical disciplines by the employee population. Methods: Retrospective observational study comparing health services utilization of primary care, behavioral health, and physical medicine services both in-person and virtually in employer-sponsored clinics between January 1, 2020 and June 30, 2021. Results: Of the 331,967 visits with employer-sponsored health center staff, 63% were in-person and 37% were delivered virtually. Most visits were for primary care services (59.5%), with physical medicine visits and behavioral health visits accounting for 25.1% and 15.4%, respectively. Whereas the preponderance of behavioral health visits were virtual visits (72.5%), less than a quarter (18.2%) of physical medicine visits were delivered virtually. 19.6% of patients were seen by more than two clinical disciplines and 2.6% were seen by three different disciplines. Overall, patients were highly likely to recommend the health center across both modalities (Net Promoter Score 89.1 for in-person care and 88.4 for virtual care). Discussion: The future of employer-sponsored integrated team-based care may require a hybrid approach that can lean heavily on virtual visits but requires the infrastructure necessary for in-person 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".