An Orthopaedic perspective on developing a Patient Experience Survey tool
Bibliographic record
Abstract
Introduction: Our program’s aim is to integrate patients' values and perceptions of quality care into clinical decision-making and integrated orthopaedic care models that are reflective of the community/population that is served. Our institution currently has limited patient experience data or standardized tools that would allow us to comprehensively understand patient perspective across our orthopaedic populations. To that end, we designed a survey to understand patients’ experiences and any gaps that would require actionable change in real-time. Objectives and Methodology: The goal was to develop a survey tool that can meaningfully capture patient needs and preferences that impact their experiences and help identify any gaps or areas of opportunity to address. An environmental scan was completed which identified two validated patient experience surveys used to inform our survey development: CIHI’s CPES-IC (Canadian Institue for Health Information Canadian Patient Experience Survey - Inpatient Care) and OHA (Ontario Hospital Association) Outpatient Experience Survey. The survey was tested across a multidisciplinary team via feedback surveys and focus groups: 6 Patient Partners, 1 Clinical Manager, 3 Advanced Practice Practitioners, 3 Orthopaedic Surgeons, 4 Researchers, and 2 Administrators. Findings were collated and used to iterate the survey content until consensus was reached on a final patient experience survey. Highlights or Results or Key Findings: The focus of the survey is on understanding the patient’s Outpatient experience with their care team in orthopaedic clinics from referral to assessment. The survey was designed to cover multiple orthopaedic populations including patients with Hip and Knee, Foot and Ankle, Shoulder, Elbow and Spine issues. It focuses on two key components of the patient journey: 1) Pre-appointment and 2) Post-appointment. The pre-appointment pathway was deemed to be important as patient experience is often captured once care is delivered and it was important to understand how patients perceive their pre-appointment experience defined as the time from referral through day of appointment up to the time patients are seen (arrival at hospital, checking in to clinic). Specifically, the pre-appointment survey has up to 42 questions that aim to capture the patients’ perspectives on the clinic’s communication practices, wait time to be contacted, wait time for an appointment, how prepared patients felt for their appointment, how they felt about access to the clinic, their experiences with care transitions, their thoughts on transportation and any education that they received, and their overall experience. The post-appointment survey has up to 41 questions focused on understanding the patients’ perspectives on care transitions, the clinic space; wait time to see the practitioner, expectations, engagement in decision making, communication practices, education they received, and any other factors impacting their experience (e.g. confidentiality, empathy, trust, dignity). Conclusion and Next Steps: The survey, reviewed with our patient partners and multidisciplinary team, was found to be comprehensive and have good face validity. The survey will be implemented and the data will be analyzed to understand themes around areas of success and areas of improvement to advance our integrated models of care in order to provide high quality patient-centered 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 | 0.069 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".