Integration of Patient-reported Outcomes in a Total Joint Arthroplasty Program at a High-volume Academic Medical Center
Bibliographic record
Abstract
INTRODUCTION: Despite widely appreciated barriers to successful clinical implementation, the literature regarding how to operationalize electronic health record-integrated patient-reported outcomes (PROs) remains sparse. We offer a detailed summary of the implementation of PROs into the standard of care at a major tertiary academic medical center. METHODS: Collection of four Patient-Reported Outcomes Measurement Information System computer adaptive tests was piloted in a large academic orthopaedic surgery ambulatory clinic starting in October 2016. The Patient-Reported Outcomes Measurement Information System computer adaptive tests (Physical Function, Pain Intensity, Pain Interference, and Ability to Return to Social Roles and Activities) were initially implemented as manual order sets to be administered before surgery through 2 years after surgery. Completion rate over time, mean time to completion for all PRO domains, and the overall distribution of symptom severity were used to evaluate the success of the pilot. A subsequent optimization and redesign of the pilot was conducted using tablets, automation of questionnaire deployment, and improved results review to address obstacles encountered during the pilot phase. RESULTS: Two thousand nine distinct joint arthroplasty patients (mean age = 65) completed at least one set of PRO assessments, with overall completion rates reaching 68% and mean completion time of 3 minutes. Focal points during the implementation process included engagement and training of staff, selection of an appropriate patient population and outcome measures, and user friendly data displays for patients and providers. CONCLUSION: Our pilot program successfully demonstrated that PROs can be administered, scored, and made immediately available within the electronic health record to patients and their providers with minimal disruption of clinical workflows. Although considerable operational and technological challenges remain, we found that the implementation of PROs in clinical care within an ambulatory practice at an academic medical center can be achieved through a constellation of several key factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".