Using Machine Learning to Predict Operating Room Case Duration: A Case Study in Otolaryngology.

Objective: Optimizing operating room (OR) efficiency depends on accurate case duration estimates. Machine learning (ML) methods have been used to predict OR case durations in other subspecialties. We hypothesize that ML methods improve projected case lengths over existing non-ML techniques for otolaryngology-head and neck surgery cases.

Data-driven segmentation of audiometric phenotypes across a large clinical cohort.

Pure tone audiograms are used to assess the degree and underlying source of hearing loss. Audiograms are typically categorized into a few canonical types, each thought to reflect distinct pathologies of the ear. Here, we analyzed 116,400 patient records from our clinic collected over a 24-year period and found that standard categorization left 46% of patient records unclassified.

Audiometric Predictors of Bothersome Tinnitus in a Large Clinical Cohort of Adults With Sensorineural Hearing Loss.

Objective: To identify demographic and audiometric predictors of bothersome tinnitus within a large clinical cohort.

Study Design: Retrospective chart review.

Setting: Tertiary care hospital.

Leveraging Existing Tools in Electronic Health Record Systems to Automate Clinical Registry Compilation.

Clinical registries have proven beneficial by providing a resource to address research questions, monitor care, and identify suitable subjects for clinical studies. Despite a well-organized registry, population is often low because of the human capital required. The increasing prevalence of electronic medical health records provides the opportunity to integrate registry compilation into routine patient encounters.