Tracking and proactively managing public health emergencies, such as a pandemic, has become increasingly important across the globe. One way to do so is with new Health AI applications built upon a nationwide health data fabric.
A health data fabric, like Smile’s Health Data Platform (HDP), combines high transaction throughput, enhanced query capabilities, and seamless interoperability across disparate sources. This creates the backbone and foundation for Health AI companies to perform efficient trend analysis at scale while maintaining security and adherence to health regulations.??
A robust HDF provides Health AI vendors a number of key advantages that enable more value generation from their applications. Some of these key advantages are:?
- Data Access and Real-time Surveillance: The HDP can integrate data across multiple sources—hospitals, clinics, public health databases, and laboratories. This integration allows AI applications to access a broad and diverse dataset that is essential for accurate modeling and analysis of public health trends in real time. Emerging public health threats—sudden increases in hospital admissions, or spikes in certain symptoms reported in specific geographic areas— can be identified by leveraging this real-time data.
- Scalability and Performance: Applications that require a processing speed of hundreds of thousands of transactions per second need a performant platform in order to run queries in near-real time. This is crucial for applications like real-time diagnostic tools and patient monitoring systems. An on-demand enterprise system, capable of scaling to handle increased loads during crises, ensures that the infrastructure remains robust and responsive under various scenarios. Interoperability: A Health Data Platform, like Smile’s, enhances data exchange and interoperability among disparate healthcare systems. This is essential for Health AI applications that rely on diverse data sources from various healthcare providers. Additionally, interoperable architecture across a network can facilitate more effective collaboration and coordination in response to health crises.
- Data Integration and Predictive Analytics: The use of a unified data fabric can facilitate easier access and integration of varied data types and formats, improving the functionality of AI applications in data aggregation, normalization, and utilization. With access to comprehensive real-time data from an HDF, AI applications can perform predictive analytics to forecast potential outbreaks and their trajectories. This can aid in proactive public health planning and resource allocation, potentially reducing the impact of pandemics.
- Advanced Query Capabilities: Access to CQL (Clinical Quality Language) allows Health AI applications to perform complex clinical queries more effectively, maintaining clinical terminology and context. For example, intelligent queries can be run on population segments at a higher risk during a pandemic. This can inform public health policies, tailor advisories and optimize treatment strategies.
- Policy and Decision Support: Nationally scaled HDFs can provide policymakers and health officials with the detailed, data-driven insights needed to make informed decisions. AI can enhance this by identifying trends, simulating the effects of potential policies, and recommending strategies based on outcomes.
- Compliance and Security: Smile’s? HDP has built-in compliance and security measures. It can ensure that Health AI applications exchange data and add value to health organizations while meeting strict regulatory requirements, particularly regarding patient data privacy and security.
In conclusion,? integrating Health AI applications with a nationally scaled HDF? enhances the ability to track, manage, and respond to pandemics more effectively. This supports public health efforts through enhanced surveillance, better predictive capabilities, and informed decision-making.
