Testing Digital Methods of Patient-Reported Outcomes Data Collection: Prospective Cluster Randomized Trial to Test SMS Text Messaging and Mobile Surveys
Bibliographic record
Abstract
BACKGROUND: Health care delivery continues to evolve, with an effort being made to create patient-centered care models using patient-reported outcomes (PROs) data. Collecting PROs has remained challenging and an expanding landscape of digital health offers a variety of methods to engage patients. OBJECTIVE: The aim of this study is to prospectively investigate two common methods of remote PRO data collection. The study sought to compare response and engagement rates for bidirectional SMS text messaging and mobile surveys following orthopedic surgery. METHODS: The study was a prospective, block randomized trial of adults undergoing elective orthopedic procedures over 6 weeks. The primary objective was to determine if the method of digital patient engagement would impact response and completion rates. The primary outcome was response rate and total completion of PRO questionnaires. RESULTS: A total of 127 participants were block randomized into receiving a mobile survey (n=63) delivered as a hyperlink or responding to the same questions through an automated bidirectional SMS text messaging system (n=64). Gender, age, number of comorbidities, and opioid prescriptions were similar across messaging arms. Patients receiving the mobile survey were more likely to have had a knee-related surgery (n=50, 83.3% vs n=40, 62.5%; P=.02) but less likely to have had an invasive procedure (n=26, 41.3% vs n=39, 60.9%; P=.03). Overall engagement over the immediate postoperative period was similar. Prolonged engagement for patients taking opioids past postoperative day 4 was higher in the mobile survey arm at day 7 (18/19, 94.7% vs 9/16, 56.3%). Patients with more invasive procedures showed a trend toward being responsive at day 4 as compared to not responding (n=41, 59.4% vs n=24, 41.4%; P=.05). CONCLUSIONS: As mobile patient engagement becomes more common in health care, testing the various options to engage patients to gather data is crucial to inform future care and research. We found that bidirectional SMS text messaging and mobile surveys were comparable in response and engagement rates; however, mobile surveys may trend toward higher response rates over longer periods of time. TRIAL REGISTRATION: ClinicalTrials.gov NCT03532256; https://clinicaltrials.gov/ct2/show/NCT03532256.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".