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Incorporating AI into cardiovascular diseases prevention-insights from Singapore.
Dalakoti, Mayank; Wong, Scott; Lee, Wayne; Lee, James; Yang, Hayang; Loong, Shaun; Loh, Poay Huan; Tyebally, Sara; Djohan, Andie; Ong, Jeanne; Yip, James; Ngiam, Kee Yuan; Foo, Roger.
Afiliação
  • Dalakoti M; Cardiovascular Research Institute, National University Heart Centre, Singapore.
  • Wong S; Cardiovascular Metabolic Disease Translational Research Programme, NUS Yong Loo Lin School of Medicine, Singapore.
  • Lee W; Ng Teng Fong General Hospital, Singapore.
  • Lee J; Group Chief Technology Office, National University Health System, Singapore.
  • Yang H; Group Chief Technology Office, National University Health System, Singapore.
  • Loong S; Group Chief Technology Office, National University Health System, Singapore.
  • Loh PH; Cardiovascular Research Institute, National University Heart Centre, Singapore.
  • Tyebally S; Cardiovascular Metabolic Disease Translational Research Programme, NUS Yong Loo Lin School of Medicine, Singapore.
  • Djohan A; Cardiovascular Research Institute, National University Heart Centre, Singapore.
  • Ong J; Cardiovascular Metabolic Disease Translational Research Programme, NUS Yong Loo Lin School of Medicine, Singapore.
  • Yip J; Cardiovascular Research Institute, National University Heart Centre, Singapore.
  • Ngiam KY; Cardiovascular Metabolic Disease Translational Research Programme, NUS Yong Loo Lin School of Medicine, Singapore.
  • Foo R; Ng Teng Fong General Hospital, Singapore.
Lancet Reg Health West Pac ; 48: 101102, 2024 Jul.
Article em En | MEDLINE | ID: mdl-38855631
ABSTRACT
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.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Lancet Reg Health West Pac Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Singapura

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Lancet Reg Health West Pac Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Singapura