Implementing Electronic Patient-Reported Outcome Measures in Outpatient Cosmetic Surgery Clinics: An Exploratory Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measure (PROM) data are increasingly being collected over the internet or on a smart device by means of electronic versions (e-PROMs). Limited evidence exists about factors influencing e-PROM implementation in outpatient clinics. OBJECTIVES: The authors sought to identify barriers to collection of PRO data from different locations (home or cosmetic surgery office) by means of different modes (paper vs e-PROM) from the perspective of patients, plastic surgeons, and clinic administrative staff; and to explore patient preferences for the design of e-PROM platforms. METHODS: Semistructured interviews were conducted with 11 patients, 3 cosmetic surgeons, and administrative staff. Patients were shown 1 of the 3 PROMs (ie, the BODY-Q Satisfaction with Body scale, BREAST-Q Augmentation Module Satisfaction with Breast scale, or FACE-Q Satisfaction with Facial Appearance scale). The formats included paper and electronic (REDCap and TickiT) on a tablet and laptop computer. The interviews were audio-recorded and transcribed verbatim. Qualitative descriptive analysis was conducted. RESULTS: Patients and providers preferred electronic over paper format. The flexibility of the hardware, data entry point (remote location vs point-of-care), and the privacy of the data were the most recurring themes from the patient's perspective. The objective of collecting PROM data, role in peer-benchmarking, and return on investment were key to surgeons and administrative staff. CONCLUSIONS: The e-PROMs were well accepted in the community setting by the patients and plastic surgeons alike. The design and interface features of e-PROMs were explored in this study, which may be useful for future, mixed method studies evaluating the implementation of e-PROMs.
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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 | 0.032 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".