Abstract
<jats:p>Introduction: Cochlear implant (CI) patients require regular follow-up care over multiple visits to achieve optimal hearing outcomes. In clinical routine, however, visits rarely occur on the exact scheduled dates and cluster around them, reducing reproducibility and comparability of CI analyses. To address these limitations, a data-driven visit windowing (DDW) approach was developed and evaluated against a study-protocol visit window (SPW) scheme. Methods: The distribution of 31,344 speech test visits from 5,264 patients implanted at Hannover Medical School was analyzed to define (1) visit windows with beginning and ending time points, and (2) a rule to select the most representative visit. Results: This approach demonstrates that variable-length, contiguous, data-driven windows are reproducible for future data and capture variability in visit timing. Compared to SPW, DDW covered a higher proportion of visits (+40.3 pp) and achieved slightly lower prediction errors for speech recognition (e.g., MAE 11.9 vs. 13.0 for Polynomial Ridge Regression). Conclusion: The DDW approach provides a reproducible, extensible, and clinically grounded basis for visit definitions, enabling consistent longitudinal CI analyses, improved data quality and predictions, and comparisons across centers and studies.</jats:p>