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Development & Deployment of a Real-time Healthcare Predictive Analytics Platform
Aaron Boussina1, Supreeth Shashikumar1, Fatemeh Amrollahi1
1Division of Biomedical Informatics, University of California San Diego, San Diego, USA.
Medrxiv : the Preprint Server for Health Sciences
|April 24, 2023
Summary
A new platform integrates predictive analytics with electronic health records (EHR) for real-time patient data processing. This scalable, cloud-based solution enhances healthcare workflows by enabling seamless data transfer and analysis.
Area of Science:
- Health Informatics
- Clinical Data Analytics
- Healthcare Technology Integration
Background:
- Integrating predictive analytic algorithms into clinical workflows presents significant challenges.
- Existing systems often struggle with real-time data extraction and processing from electronic health records (EHR).
Approach:
- Developed a scalable, cloud-based, fault-tolerant platform for EHR data extraction and processing.
- Ensured the platform can access any patient's data anytime post-admission and return results to the EHR.
- Utilized interoperable data standards for enhanced portability and integration.
Key Points:
- The platform facilitates seamless integration of predictive analytics into healthcare settings.
- Real-time data processing from EHRs is achieved for all patients.
- Successful deployment within UC San Diego Health demonstrates practical application.
Conclusions:
- The presented platform offers a robust solution for overcoming EHR data integration challenges.
- This technology enables efficient and safe deployment of predictive analytics in healthcare.
- The system's design promotes interoperability and scalability for widespread adoption.
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