Clinical decision support tools for risk prediction are readily available, but typically require workflow interruptions and manual data entry that limit adoption. Due to new data interoperability standards for electronic health records (EHRs), other options are available.
As a clinical case study, we built a scalable, web-based system to automate calculation of kidney failure risk and display clinical decision support to users in primary care practices.
In addition, this session will review issues and techniques to prepare clinical data from any EHR for use in analytics generally across providers.
In this session, attendees will learn:
- Architectural overview of the web-scalable analytics apps
- Lessons in using clinical data standards
- Challenges in data readiness for analytics and population health
- Effectiveness of the model in predicting kidney disease
- Importance of a technology, physician, operations partnership