From Hong Kong Diabetes Register to JADE Program to RAMP-DM for Data-Driven Actions.
Juliana Chung Ngor ChanLee-Ling LimAndrea On Yan LukRisa OzakiAlice Pik-Shan KongRonald Ching-Wan MaWing-Yee SoSu-Vui LoPublished in: Diabetes care (2019)
In 1995, the Hong Kong Diabetes Register (HKDR) was established by a doctor-nurse team at a university-affiliated, publicly funded, hospital-based diabetes center using a structured protocol for gathering data to stratify risk, triage care, empower patients, and individualize treatment. This research-driven quality improvement program has motivated the introduction of a territory-wide diabetes risk assessment and management program provided by 18 hospital-based diabetes centers since 2000. By linking the data-rich HKDR to the territory-wide electronic medical record, risk equations were developed and validated to predict clinical outcomes. In 2007, the HKDR protocol was digitalized to establish the web-based Joint Asia Diabetes Evaluation (JADE) Program complete with risk levels and algorithms for issuance of personalized reports to reduce clinical inertia and empower self-management. Through this technologically assisted, integrated diabetes care program, we have generated big data to track secular trends, identify unmet needs, and verify interventions in a naturalistic environment. In 2009, the JADE Program was adapted to form the Risk Assessment and Management Program for Diabetes Mellitus (RAMP-DM) in the publicly funded primary care clinics, which reduced all major events by 30-60% in patients without complications. Meanwhile, a JADE-assisted assessment and empowerment program provided by a university-affiliated, self-funded, nurse-coordinated diabetes center, aimed at complementing medical care in the community, also reduced all major events by 30-50% in patients with different risk levels. By combining universal health coverage, public-private partnerships, and data-driven integrated care, the Hong Kong experience provides a possible solution than can be adapted elsewhere to make quality diabetes care accessible, affordable, and sustainable.
Keyphrases
- quality improvement
- type diabetes
- glycemic control
- primary care
- patient safety
- cardiovascular disease
- big data
- healthcare
- risk assessment
- end stage renal disease
- machine learning
- randomized controlled trial
- newly diagnosed
- palliative care
- ejection fraction
- chronic kidney disease
- prognostic factors
- artificial intelligence
- insulin resistance
- peritoneal dialysis
- climate change
- deep learning
- adipose tissue
- weight loss
- risk factors
- general practice
- pain management
- social media
- adverse drug