Abstract
Transitioning to value-based care makes new demands on understanding and managing patient risk for a variety of adverse outcomes in multiple conditions. Optimizing use of finite healthcare resources then proves challenging, and would benefit from a data-driven approach. Modelling the "risk triangle" paradigm of disease management as a state diagram within the electronic health record helps bring clinical situational awareness and tailored decision support interventions to individual patients at the point-of-care, while automatically capturing new types of state duration and transition sequence data across the whole population. Such data can iteratively inform improving risk prediction models.
Original language | English (US) |
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Title of host publication | ACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics |
Publisher | Association for Computing Machinery, Inc |
Pages | 551-552 |
Number of pages | 2 |
ISBN (Electronic) | 9781450357944 |
DOIs | |
State | Published - Aug 15 2018 |
Event | 9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2018 - Washington, United States Duration: Aug 29 2018 → Sep 1 2018 |
Other
Other | 9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2018 |
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Country | United States |
City | Washington |
Period | 8/29/18 → 9/1/18 |
Keywords
- Clinical decision support
- Electronic health records
- Population health informatics
- State machine diagrams
ASJC Scopus subject areas
- Computer Science Applications
- Software
- Health Informatics
- Biomedical Engineering