2 results match your criteria: "University of Rochester Health Lab[Affiliation]"
Evaluating the impact of patient-staff interactions on sleep opportunities using the electronic health record.
J Hosp Med
December 2024
University of Rochester Health Lab, Rochester, New York, USA.
Article Synopsis
- Hospitalized patients often have disrupted sleep due to nighttime interactions with staff (patient-staff interactions or PSIs), which can affect their health outcomes.
- The study analyzed 3,305 patient nights in a neurology unit, categorizing PSIs into six types and measuring their impact on sleep using metrics like longest uninterrupted sleep opportunity (LUSO) and interruptive episodes.
- Results showed that PSIs were frequent and significantly reduced sleep time, with certain types (like neurological checks and off-unit testing) causing the largest reductions in LUSO, highlighting the need for better management of nighttime care activities.
An Electronic Medical Record-Based Discharge Disposition Tool Gets Bundle Busted: Decaying Relevance of Clinical Data Accuracy in Machine Learning.
Arthroplast Today
December 2020
Division of Adult Reconstruction, Department of Orthopaedics & Rehabilitation, University of Rochester Medical Center, Rochester, NY, USA.
Background: Determining discharge disposition after total joint arthroplasty (TJA) has been a challenge. Advances in machine learning (ML) have produced computer models that learn by example to generate predictions on future events. We hypothesized a trained ML algorithm's diagnostic accuracy will be better than that of current predictive tools to predict discharge disposition after primary TJA.
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