A Learning Health System for Long Covid Care and Research in British Columbia
Bibliographic record
Abstract
SummaryThere is growing interest in developing outpatient care and research models to support and learn about patients struggling with long-term symptoms following Covid-19 infection (now commonly referred to as long Covid). These patients can experience a debilitating illness that lasts for months. The prognosis is often unclear, and there is no curative treatment. In British Columbia, Canada, the Post–COVID-19 Interdisciplinary Clinical Care Network (PC-ICCN) was established in June 2020 as a partnership among the Provincial Health Services Authority, British Columbia's health authorities, patients, and research organizations (including authors M.M., D.L., C.C., C.J.R., and A.L.) to support these patients throughout the province, which has a population of nearly 5.4 million and covers about 364,000 square miles. The PC-ICCN (the network) was developed based on the learning health system (LHS) model, which emphasizes the integration of research into clinical care to foster discovery, innovation, rapid learning cycles, and knowledge mobilization. The network's clinical program is anchored to Post–COVID-19 Recovery Clinics (PCRCs), which are staffed by an interdisciplinary team of nurses, allied health professionals, and internal medicine physicians. In parallel, the network has a research team that utilizes clinically ordered laboratory and patient-reported outcome measure (PROM) data captured into its central Patient Records and Outcome Management Information System database during clinical care. Over the first 2 years, the network adjusted strategies as its members learned more about long Covid and faced administrative hurdles. The team has used a variety of metrics to monitor trends and inform operational decisions. In the first 2 years, the network had 6,439 referrals to the program, of which 4,014 (62.3%) were accepted. Patients from all five regional health authorities in the province were represented and came from a variety of ethnic backgrounds. In total, there were 7,116 PCRC assessments, of which 59.6% were virtual. The network gradually increased the number of virtual group education sessions per month, and within the first year, there were 803 sessions that were attended by 778 different patients. Among the subset of PC-ICCN patients who completed at least two PROM questionnaires and either completed the group's 18-month pathway or were discharged earlier by a physician, 40% had improvement in their health-related quality of life, with scores beyond the minimum important difference, and 36% had stability. Altogether, the network highlights the value of an LHS model to simultaneously care for and learn from patients who suffer from long Covid.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.004 |
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".