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A Data-Driven Visit Windowing Approach Applied to Cochlear Implant Follow-Up Data.

Annette Günther¹, Oliver J Bott², Eugen Kludt¹

¹Department of Otolaryngology, Hannover Medical School, Hannover, Germany.

Studies in Health Technology and Informatics

|May 23, 2026

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A new data-driven visit windowing (DDW) method improves cochlear implant (CI) follow-up analysis by better accounting for real-world visit timing. This approach enhances data consistency and prediction accuracy for hearing outcomes.

Area of Science:

Audiology
Medical Data Analysis
Biostatistics

Background:

Cochlear implant (CI) patients need consistent follow-up for optimal hearing.
Clinical visits often deviate from schedules, impacting data reliability.
Existing study-protocol visit window (SPW) methods lack flexibility.

Purpose of the Study:

To develop and evaluate a data-driven visit windowing (DDW) approach for CI patient follow-up.
To compare the DDW method against the traditional SPW scheme.
To improve the reproducibility and comparability of longitudinal CI data analysis.

Main Methods:

Analyzed 31,344 speech test visits from 5,264 CI patients.
Defined data-driven visit windows with start/end points.

Keywords:

clinical data cochlear implants data-driven analysis machine learning reproducibility speech recognition time series visit windowing

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Developed a rule to select the most representative visit within each window.

Main Results:

DDW windows are reproducible and capture visit timing variability.
DDW covered 40.3% more visits than SPW.
DDW achieved slightly lower prediction errors for speech recognition.

Conclusions:

DDW offers a reproducible, extensible, and clinically relevant basis for defining visits.
Enables consistent longitudinal CI analyses and improved data quality.
Facilitates better data comparisons across different clinical centers and studies.