| Autor: |
Mayank Dalakoti, Scott Wong, Wayne Lee, James Lee, Hayang Yang, Shaun Loong, Poay Huan Loh, Sara Tyebally, Andie Djohan, Jeanne Ong, James Yip, Kee Yuan Ngiam, Roger Foo |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
The Lancet Regional Health. Western Pacific, Vol 48, Iss , Pp 101102- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2666-6065 |
| DOI: |
10.1016/j.lanwpc.2024.101102 |
| Popis: |
Summary: Improved upstream primary prevention of cardiovascular disease (CVD) would enable more individuals to lead lives free of CVD. However, there remain limitations in the current provision of CVD primary prevention, where artificial intelligence (AI) may help to fill the gaps. Using the data informatics capabilities at the National University Health System (NUHS), Singapore, empowered by the Endeavour AI system, and combined large language model (LLM) tools, our team has created a real-time dashboard able to capture and showcase information on cardiovascular risk factors at both individual and geographical level- CardioSight. Further insights such as medication records and data on area-level socioeconomic determinants allow a whole-of-systems approach to promote healthcare delivery, while also allowing for outcomes to be tracked effectively. These are paired with interventions, such as the CHronic diseAse Management Program (CHAMP), to coordinate preventive cardiology care at a pilot stage within our university health system. AI tools in synergy allow the identification of at-risk patients and actionable steps to mitigate their health risks, thereby closing the gap between risk identification and effective patient care management in a novel CVD prevention workflow. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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