A Predictive Risk Index to reduce 30-day Readmissions in NTFGH
Care Process & Redesign
Technology
National Healthcare Innovation and Productivity Medals
National University Health System
31 December 2020
To reduce 30-day readmission rates in NTFGH from 14% to 12% through a Predictive Risk Index model and targeted interventions. Predictive analytics successfully reduced 30-day readmission rates and informed resource allocation for higher-risk patients.
Year Submitted: 2020
Published Date: 31 December 2020
Tags: Technology, Care Process ＆ Redesign, Digital Health, Data Management, Data Analytics, Data Visualization, Access To Care, Readmission Rate, Discharge Planning
About this Content
Aims
To reduce 30-day readmission rates in NTFGH from 14% to 12% through a Predictive Risk Index model and targeted interventions.
Background
Unscheduled readmissions increase healthcare burdens and costs. NTFGH’s 30-day readmission rate was 14%, significantly above the national average (11.3%). One-third of readmissions were preventable.
Methods
Developed a predictive risk tool using machine learning and deployed it in EMR systems. Interventions included risk stratification, discharge calls, home visits, and weekly patient monitoring.
Results
Crude readmission rates reduced from 14.1% to 13.0%. Risk-adjusted rates reduced from 11.4% to 10.1% (p<0.01). High-risk patients were flagged in EMR for targeted care, improving intervention outcomes.
Conclusion
Predictive analytics successfully reduced 30-day readmission rates and informed resource allocation for higher-risk patients. Adopted for NUHS and NHG clusters under NGEMR.
Lessons Learnt
Combining data analytics with structured improvement frameworks drives significant results. Leadership, frontline alignment, and actionable real-time insights are critical for success.
Additional Information
Data-driven care redesign model with machine learning algorithms implemented across clusters.
Keywords
Automation, IT & Robotics, Predictive Analytics, Quality Improvement, Readmission Reduction, EMR, Data Visualization, Machine Learning
Innovators' Details
Innovators' Details
Healthcare Cluster(s) | National University Health System |
Organization(s) Involved | Ng Teng Fong General Hospital |
Platform(s) | National Healthcare Innovation and Productivity Medals |
Healthcare Professional Group(s) | Allied Health, Healthcare Administration, Nursing, Medical |
Applicable Specialty or Discipline | Healthcare Administrators |
Project Lead(s) | Christine Wu Xia |
Project Member(s) | Francis Phng Wei Loong |
Connect with this contributor!
Christine Wu Xia - Christine_WU@nuhs.edu.sg
Project Attachment
36_NTFGH_NHIP_2020_A_Predictive_Risk_Index_to_reduce_30_day_Readmissions_combine.pdf