How It Works
Traditionally, care support programmes identify patients who are at risk of multiple hospital readmissions, through their historical data. These risk
scoring methods have limitations in handling patients with multiple co-morbidities.
The Multiple Readmissions Predictive Model is able to analyse multi-dimensional facets of the patient, ranging from multiple co-morbidities, chronic
diseases to end of life conditions, and has an accuracy of seven in 10 patients predicted. Using over a thousand indicators, which include patient
age, the number of inpatient admissions and total length of stay in the past two years, the model automatically identifies patients who have a history
or are at risk of multiple readmissions. The team is looking at moving the Multiple Readmissions Predictive Model further upstream to primary care
settings, to enable early intervention and delay the progression of their conditions.
How We Implemented Predictive Analytics
1. Business Integration Approach
Develop as part of H2H service involving clinicians, care team, policy maker, administrator which give clear value & accelerate the adoption.
2. Existent of Analytics Infrastructure
Business Research Analytics Insights Network (BRAIN) deployed as national/common analytics platform to support Ministry, agencies, Public Health Institutions (PHIs) self-help analytics by:
i. Bring data together
ii. Patient linked
v. Common toolsets and capabilities
vi. Collaborate in secured environment
3. Multi-discipline Skill Sets
• Respiratory and critical care
• Family Medicine and continuing care
• Health economics
• Health delivery
• Health research
• Data science and analytics
4. Agile & Scientific Methodology
Scientific & systematic development approach that engage to gain trust of user.
5. Volume & Veracity Data
1.4 billion data points | 7 million records | 200+ variables | 3 years data
Automated daily generation