Optimizing Myositis Care with Physiotherapy Integration: A Quality Improvement Project
Bibliographic record
Abstract
Objectives Best practice guidelines recommend that patients with idiopathic inflammatory myopathies (IIM) be assessed by a physiotherapist (PT) with expertise in IIM.[1] However, an audit at a single IIM center revealed that only a limited number of patients were receiving this care. The aim of this quality improvement (QI) project was to achieve a significant increase in IIM patients at the VCH Myositis Clinic that are assessed by the in-house PT. Methods A team consisting of a PT, registered nurse (RN), and physician, all with IIM expertise, investigated the root causes of low PT assessment rates using an Ishikawa fishbone diagram. Process mapping of existing PT assessment steps led to change ideas, which were prioritized through a PICK chart exercise. The following primary interventions were tested through PDSA cycles: 1. Patient education handout on the importance of PT in IIM management. 2. Standardized RN checklist to confirm PT engagement. 3. Same-day PT assessment following a physician visit (either a full 1-hour assessment or a 20-minute intake). 4. Automatic PT referral for patients unable to attend a same-day visit. Improvement measures included the number of patients receiving either a full PT assessment or a 20-minute intake, and the number of automatic referrals leading to a PT visit within 4 months. Patients receiving only a 20-minute intake were not considered to have had a complete PT assessment for the purposes of the study. Data were collected for 4 months pre-intervention and 6 months post-intervention and median rates during these periods were compared using run charts. Results The mean pre-intervention PT assessment rate was 21%. Post-intervention, the rate increased to 68%, as shown by a shift in the run diagram (Figure 1). As well, post-intervention, 28% patients received a 1-hour same-day PT assessment, and 12.5% received a 20-minute intake. 47% of patients received internal referrals and of these, 80% received a PT visit within 4 months. Figure 1. Run chart demonstrating significant shift in rate of IIM patients assessed by a PT with expertise in their disease before and after implementation of interventions. Conclusion Utilizing PDSA cycles and targeted interventions, the project demonstrated a measurable improvement in the rate of PT assessments. The post-intervention data showed a significant and sustained increase in PT engagement, indicating that these changes have positively impacted patient care. Further efforts to refine these interventions will be essential to maintaining care of IIM patients. [1.] Oldroyd A. Rheum 2022;61:1760-68.
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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 | 0.073 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".