Maintaining excellence in care coordination during the COVID-19 pandemic and beyond: a survey of multidisciplinary healthcare teams in Ontario, Canada
Bibliographic record
Abstract
Abstract Introduction Patients with comorbidities have been shown to experience increased vulnerability during the COVID-19 pandemic, and to suffer disruption in their care coordination; having a multidisciplinary team is a care coordination strategy that can improve outcomes. The aim of this study was to describe the perspectives of team leads on coordination practices at their multidisciplinary health team (MHT) prior to, and during the COVID-19 pandemic. Methods Using a cross-sectional survey design, the Medical Home Care Coordination Survey for healthcare teams was distributed by email to executive directors or physician leads at all MHTs in Ontario, Canada. The outcome measures included the eight domains of care coordination, and participants’ rating of care coordination in general. Results The response rate was 58/241 (24%); 70% (95% CI: 0.50, 0.76) of teams reported using a validated method to identify complex patients in need of care coordination. High ratings for most items in the domains of care coordination prior to COVID-19 were maintained during the pandemic. Improvements can be made in providing patients with a copy of their care plan, making peer support accessible, and ensuring the timely inclusion of discharge summaries in the primary care record. Most participants (72%) rated care coordination in general at their MHT as very good or excellent prior to the pandemic; this decreased to 59% during the pandemic (p=0.016; 95% CI: −0.048, 0.31). Discussion To improve care coordination beyond the pandemic, providers should consider increasing the use of validated tools to identify patients with complex needs, and incorporating peer support systems to enhance care coordination efforts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".