User Friendliness and Perioperative Guidance Benefits of a Cataract Surgery Education App: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Cataract surgeries are among the most performed surgeries worldwide. A thorough patient education is essential to inform patients about the perioperative process and postoperative target results concerning the intraocular lens and objectives for visual outcomes. However, addressing all relevant aspects and questions is time-consuming. Mobile apps can facilitate this process for both patients and physicians and thus be beneficial. However, the success of such an app depends on its user friendliness and acceptance by patients. OBJECTIVE: This study aimed to evaluate the user friendliness and acceptance of a cataract surgery education app on mobile devices among patients undergoing cataract surgery, the characteristics of patients who benefit the most from app use, and the influence of the app on patient satisfaction with treatment. METHODS: All patients who underwent cataract surgery at an ophthalmological practice from August 2020 to July 2021 were invited to participate in this randomized controlled trial. Out of 493 invited patients, 297 (60.2%) were enrolled in this study. Patients were randomized into 3 different groups. Half of the patients were offered to participate in Group 1 with use of the "Patient Journey" app. However, if they decided not to use the app, they were included in Group 2 (app denial). The other half of the patients were included in Group 3 (control) with no use of the app and with information provided conventionally. The app provided general information on the ophthalmological center, surgeons, cataract, and treatment options. Different questionnaires were used in all 3 groups to evaluate satisfaction with the perioperative process. Group 1 evaluated the app. Demographic characteristics, such as age, gender, and educational degree, were assessed. RESULTS: Group 1 included 77 patients (median age 69 years). Group 2 included 61 patients, and their median age was higher (median age 79 years). Group 3 included 159 patients (median age 74 years). There was no difference in satisfaction with the perioperative process and clinic between the 3 groups. Almost all app users appreciated the digital details provided for the organization and the information on the surgery. Age did not play a major role in appreciation of the app. Female patients tended to appreciate the information provided more than male patients. Patients who did not have a higher university degree experienced more benefits from the informational content of the app and were the most satisfied with the information. However, male patients and academics were in general more aware of technology and handled the app more easily. CONCLUSIONS: The app showed high user friendliness and acceptance, and could particularly benefit specific patient groups. App users demonstrated a noninferior high satisfaction with the treatment in the ophthalmological center in comparison with patients who were informed about the surgery only conventionally.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".