Health Information Exchanges (HIEs) have been instrumental in advancing the exchange of electronic health information among healthcare organizations with benefits for both patients and providers.[i] However, in the post-HITECH era, sustaining their impact requires innovative strategies beyond traditional revenue models like subscription and transaction fees. This article explores the potential of leveraging data aggregation and analytics, specifically predictive analytics, to not only open new revenue streams but also significantly enhance the value HIEs bring to their communities.
The Evolution of Revenue Models
Traditionally, HIEs have relied on subscription and transaction fees to ensure their financial sustainability. However, the future of HIEs will be impacted by effective management of data aggregation and analytics. By harnessing the vast amount of data collected daily and employing sophisticated interfaces and APIs, HIEs can create valuable datasets for research and analytics, thereby establishing a new business model anchored in a trusted analytics platform. The true game-changer lies in the ability of these platforms to predict patterns that will provide insight to address Social Determinants of Health (SDoH) and address health equity, which will continue to be an industry focus.[ii]
The Power of Predictive Analytics in HIEs
Predictive analytics emerges as a crucial tool that unlocks the full potential of the data within an HIE, driving valuable insights and impactful outcomes. Let’s explore how predictive analytics can revolutionize HIEs:
- Early Disease Detection: Identify patterns and risk factors for early detection of diseases, enabling healthcare providers to intervene proactively and improve patient outcomes.
- Population Health Assessment: Assess the health status of entire populations, facilitating targeted interventions and resource allocation to address specific health challenges within communities.
- Risk Stratification: Detect high-risk individuals earlier, allowing healthcare providers to prioritize intensive care management, thereby improving outcomes for those at greatest risk.
- Care Coordination: Enhance care coordination for patients likely to require additional services or resources, ensuring efficient resource allocation and coordinated care plans across multiple providers.
- Reducing Hospital Readmissions: Provide targeted support to individuals at higher risk of hospital readmission, reducing readmissions and associated costs.
- Personalized Treatment Plans: Create personalized treatment plans based on individual health profiles, leading to more effective and efficient care.
- Medication Adherence: Pinpoint patients at risk of medication non-adherence, allowing providers to implement strategies for improvement through education and support mechanisms.
- Cost Savings: Prevent unnecessary hospitalizations and interventions, resulting in cost savings, optimization of resources and a reduction in overall healthcare costs.
Choosing the Right Partner: The Johns Hopkins ACG® System
Implementing a successful predictive analytics strategy requires choosing the right partner, and the Johns Hopkins ACG System stands out as a sophisticated, data-driven approach. The Patient Need Groups (PNGs) segmentation model, as described in our "Understanding Patient Segmentation and How it Adds Value to Your Population Health Strategy" brochure, with its 11 hierarchical segments, provides a patient-centered approach to multimorbidity, allowing for resource allocation based on the unique needs of the population.
Conclusion
Predictive analytics emerges as the game-changer for HIEs seeking to thrive in the evolving healthcare landscape. With the right healthcare technology partner leveraging the power of data for meaningful insights, HIEs can not only ensure their sustainability but also play a central role in improving the quality of care, enhancing population health outcomes and driving overall efficiency in the healthcare system. The journey towards a healthier community begins by harnessing the predictive power within the vast data networks of HIEs.
Contact us for more information about adding the ACG System to your predictive analytics strategy.
[i] https://dhcf.dc.gov/page/benefits-health-information-exchange
[ii] https://www.ncqa.org/health-equity/data-and-measurement/
