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Uncovering Clinical Insights From Unstructured EMR Data to Improve Patient Outcomes (Cloud Next '19)

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Published on Apr 12, 2019

Rush University Medical Center has identified an opportunity to standardize treatment and improve outcomes for their patients by drawing structured insights (SNOMED codes) from previously-unstructured data. Specifically, the provider, pathology, and lab notes stored in their EMR system contains all of the data necessary to generate a more complete picture of patients' health experience, from complaint and diagnosis to treatment and outcome. Maven Wave is working with Rush to implement a solution to map notes to SNOMED codes, allowing for a more objective approach to clinical care.

Maven Wave, Rush, and Google are working to address this challenge. We are creating a solution that will allow for a more objective approach to clinical care by connecting symptoms, treatments, and outcomes. This broad goal will be achieved by:

1. Standardizing and operationalizing transfer of legacy and ongoing note data into a cloud repository for ease of processing.
2. Creating a scalable architecture that can work for pilots through system-wide implementations.
3. Extracting key medical definitions from unstructured notes and enriching them with treatment and outcome data to create insights for better patient outcomes.
4. Enabling continuous improvement through more advanced AI models (e.g., semantic data layer) and wider availability to the broader Rush ecosystem.

Cloud Healthcare API → https://bit.ly/2UfpYVp

Watch more:
Next '19 Data Analytics Sessions here → https://bit.ly/Next19DataAnalytics
Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions

Subscribe to the GCP Channel → https://bit.ly/GCloudPlatform

Speaker(s): Vasudha Gupta, Jawad Khan, David Patterson

Session ID: DA111
product:Cloud Healthcare,BigQuery,Cloud Dataflow; fullname:Vasudha Gupta;

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