Tisch Cancer Institute (TCI) Biostatistics and Clinical Informatics (BCI) Shared Resource Facility (SRF)
Welcome to the TCI-BCI-SRF website. We have wide-ranging expertise in biostatistical and clinical informatics methods and also provide training and educational resources.
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Gathering and synthesizing large amounts of cancer data from multiple sources, as well as efficiently implementing such information into clinical practice has become increasingly important. The multiple domains of data in the cancer control continuum frequently raises complex challenges in data management, integration, and analysis. Data scientists and informaticians of many stripes play a key role in these efforts. Successful internal and external collaboration is often required for high-impact publications, grantsmanship, and advances in clinical care.
Our clinical informatics experts support clinical, translational, and basic science TCI investigators by enhancing and extending Mount Sinai’s informatics infrastructure to make cancer research data and software tools easily accessible. In addition, we aim to establish clinical informatics as an academic discipline at TCI and foster national collaborations to accelerate informatics ideas, best practices, technologies, and standards.
Story 2 - Erin's complicated data analysis
Clinical informatics with MSHS data science team develops a malnutrition screening tool to increase diagnosis rate
The Joint Commission has mandated universal screening and assessment of hospitalized patients for malnutrition because of its association with increased morbidity, mortality, and healthcare costs. Early detection is extremely important for timely intervention but difficult to achieve due to need for specialized skills from registered dieticians (RDs). Machine learning (ML) algorithms, which process massive amounts of data and learn continuously have the potential to identify those at risk for malnutrition more efficiently and effectively than standard screening tools. A clinical decision support tool, MUST-Plus, developed by this team reduced lag between admission and diagnosis, had high usability (> 90%), and increased the rate of malnutrition diagnoses and its documentation.