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 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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".