Browsing by Subject "Electronic health record (EHR)"

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    Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning
    (Thieme, 2021) Keswani, Rajesh N.; Byrd, Daniel; Garcia Vicente, Florencia; Heller, J. Alex; Klug, Matthew; Mazumder, Nikhilesh R.; Wood, Jordan; Yang, Anthony D.; Etemadi, Mozziyar; Surgery, School of Medicine
    Background and study aims: Storage of full-length endoscopic procedures is becoming increasingly popular. To facilitate large-scale machine learning (ML) focused on clinical outcomes, these videos must be merged with the patient-level data in the electronic health record (EHR). Our aim was to present a method of accurately linking patient-level EHR data with cloud stored colonoscopy videos. Methods: This study was conducted at a single academic medical center. Most procedure videos are automatically uploaded to the cloud server but are identified only by procedure time and procedure room. We developed and then tested an algorithm to match recorded videos with corresponding exams in the EHR based upon procedure time and room and subsequently extract frames of interest. Results: Among 28,611 total colonoscopies performed over the study period, 21,170 colonoscopy videos in 20,420 unique patients (54.2 % male, median age 58) were matched to EHR data. Of 100 randomly sampled videos, appropriate matching was manually confirmed in all. In total, these videos represented 489,721 minutes of colonoscopy performed by 50 endoscopists (median 214 colonoscopies per endoscopist). The most common procedure indications were polyp screening (47.3 %), surveillance (28.9 %) and inflammatory bowel disease (9.4 %). From these videos, we extracted procedure highlights (identified by image capture; mean 8.5 per colonoscopy) and surrounding frames. Conclusions: We report the successful merging of a large database of endoscopy videos stored with limited identifiers to rich patient-level data in a highly accurate manner. This technique facilitates the development of ML algorithms based upon relevant patient outcomes.
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    Measuring success: perspectives from three optimization programs on assessing impact in the age of burnout
    (Oxford University Press, 2020-12) Lourie, Eli M.; Stevens, Lindsay A.; Webber, Emily C.; Pediatrics, School of Medicine
    Electronic health record (EHR) optimization has been identified as a best practice to reduce burnout and improve user satisfaction; however, measuring success can be challenging. The goal of this manuscript is to describe the limitations of measuring optimizations and opportunities to combine assessments for a more comprehensive evaluation of optimization outcomes. The authors review lessons from 3 U.S. healthcare institutions that presented their experiences and recommendations at the American Medical Informatics Association 2020 Clinical Informatics conference, describing uses and limitations of vendor time-based reports and surveys utilized in optimization programs. Compiling optimization outcomes supports a multi-faceted approach that can produce assessments even as time-based reports and technology change. The authors recommend that objective measures of optimization must be combined with provider and clinician-defined value to provide long term improvements in user satisfaction and reduce EHR-related burnout.