A novel primary-specialist care collaborative demonstration project to improve the access and health care of medically complex patients
Bibliographic record
Abstract
Objective Medically complex patients experience fragmented health care compounded by long wait times. The MedREACH program was developed to improve access and overall system experience for medically complex patients. Program description MedREACH is a novel primary-tertiary care collaborative demonstration program that features community nursing outreach, community specialist outreach, and a multi-specialty consultation clinic. Methods All 179 patients, referring primary care clinicians, and specialists involved were eligible to participate. Patient and clinician feedback were elicited by feedback surveys. Process measures were evaluated by participant retrospective chart reviews. Community nursing outreach patients completed the Goal Attainment Scale. Results Forty-eight patients and 22 clinicians consented to the feedback survey. About 75% of patients were seen within 2 weeks of referral. Patients spent an average of 3, 1.63, and 1.2 visits with the nursing outreach, multi-specialty clinic, and specialist outreach, respectively. Patients indicated a better medical experience, health enablement, and goals attainment. Family physicians felt more supported in the community management of medically complex patients and, overall, physicians felt MedREACH could improve collaborative care for medically complex patients. Qualitative analysis of clinician responses identified the need for increased mental health services. Discussion MedREACH demonstrates a patient-centered link between primary and tertiary care that could improve health care access and overall experience.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".