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proactive risk surveillance
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ISPOR Europe 2024 : Proactive Risk Surveillance for Heart Failure and Stroke: A Quality-by-Design Approach with Multimodal Data Integration

22/08/2024

AUTHORS: Mariam Bibi, Amrit Kaliasethi


OBJECTIVES 

The value of Real-World Evidence (RWE) is widely recognised and is valued by Regulators, HTA bodies and manufacturers. The vast number of available data sources provide opportunities to leverage multiple data sources to support complementary evidence generation, gaining a deeper understanding of the impact of a disease or treatment. RWE can also be used to evaluate the risk of a disease. For example, Heart failure (HF) and stroke are leading causes of morbidity and mortality worldwide. Early detection and intervention are crucial for improved patient outcomes. 

METHODS 

We propose a quality-by-design (QbD) framework for proactive risk surveillance in HF and stroke patients.  It integrates information from various data sources to create a comprehensive picture of each patient’s health status and risk factors. The data sources included Electronic Health Records (EHR), claims data, social media, patient characteristics, comorbidities, chronic complications, and adverse events, stratified by age groups. Anomaly detection and association mining techniques were employed to identify patients at high risk of future events. The quality-by-design emphasises proactive risk management throughout the patient care pathway.

RESULTS 

The framework aims to utilise multimodal data integration:

  • Patient-centric: Focuses on individual patient needs and risk factors
  • Data-driven: Utilises real-world data for risk assessment and intervention strategies
  • Proactive: Aims to prevent future events rather than reacting to crises
  • Continual improvement: Regularly evaluates and refines the risk model based on new data and outcomes

CONCLUSION  

This quality-by-design framework for proactive risk surveillance in HF and stroke patients leverages the power of multi-modal data analysis and advanced statistical techniques. By integrating diverse data sources and employing anomaly detection and association mining, healthcare providers can identify high-risk patients and implement targeted interventions, leading to improved patient outcomes and reduced healthcare costs. 


Read our full research below (available post-conference).

Plus, read our other research abstracts for ISPOR Europe 2024 here.

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