Determinants of chronic disease screening and development of implementation strategies to optimise chronic disease screening: an implementation science approach
2022 - 2023
Lead: Lynette Ha
Cardiovascular diseases (CVDs) are the leading causes of disability and mortality globally. Chronic diseases such as diabetes mellitus, hypertension and hypercholesterolemia (“DHL”) are established to be major risk factors of CVDs. To support early detection and management of CVD risk factors, regular health screening has been endorsed by governments around the world.
However, in Singapore, despite national promotion and incentives to participate in DHL screening, the aged-standardized proportion of eligible residents with no previous diagnosis of DHL being screened within the recommended screening frequency remains relatively low in Singapore (60.3%) (Ministry of Health, 2022). The profile of individuals who do not turn up for DHL screening has remained largely unknown, making them harder to reach by mainstream screening efforts. Existing literature that attempted to identify factors associated with CD screening has yet to organise factors influencing DHL screening uptake in a systematic manner, making it difficult to select, in a systematic way, a set of robust, evidence-informed, locally suitable strategies to promote DHL screening uptake. In addition, recent evidence reported that population-level screening programmes for CVD risk and risk factors had no effect on lowering CVD morbidity and mortality. It highlighted the importance of assessing post-screening follow-up to establish a diagnosis.
Using Implementation Science, this study aims to (1) identify factors influencing DHL screening uptake and follow-up, and (2) develop a pragmatic, evidence-informed and tailored toolkit of implementation strategies to promote DHL screening uptake and follow-up.
This work is done in collaboration with National University Health System (NUHS), Regional Health System Office (RHSO).
Chronic Disease Management Platform (CHAMP)
2023 - Present
Leads: Lynette Ha & Dr Laura Martinengo
The use of Artificial Intelligence (AI)-based chatbots in healthcare settings is increasing. AI-based chatbots may support real-time monitoring, self-management of chronic disorders and offer on-demand support and personalized services to patients and healthcare providers (HCPs). In the Western region of Singapore, an AI-based chatbot, the Chronic Disease Management Programme (CHAMP) was recently developed to support continuity of care, promote better use of resources and integrate patient data to national databases (EPIC/EMR) through a population-based approach to provide chronic disease management support to clinicians and residents using digital technology, AI and behavioural science.
This study aims to optimise the deployment of CHAMP across National University Health System (NUHS) polyclinics by developing effective implementation strategies and pilot-testing their effectiveness. Initial work includes steps towards implementation and evaluation of CHAMP for hypertension self-monitoring.
This work is done in collaboration with NUHS.