A recent study of skilled nursing facilities (SNFs) in the American Journal of Managed Care finds that using interventional analytics to prevent hospital readmissions can potentially save Medicare nearly $2.8 billion dollars in annual spending.
Hospital readmissions come at a cost to members, health plans, and health systems, representing a significant challenge for SNFs. Of the 1.7 million SNF admissions in the United States annually, more than 20% result in hospital readmissions within 30 days, leading to roughly $5.2 billion dollars in Medicare costs.
Findings from the retrospective analysis of the 30-day hospital readmission rates between 2017 and 2022 indicate that SNFs equipped with interventional analytics consistently achieved lower rates than their state and national counterparts, including other SNFs in the same health plan network without the data technology.
Interventional analytics in action
Conducted by independent researchers, the study set out to demonstrate the impact of interventional analytics on the 30-day readmission rate at Penn Medicine’s Lancaster General Hospital (LGH), a regional hospital within the Pennsylvanian health system with services including routine care and treatment of acute illnesses for SNF members. Developed by Real Time Medical Systems, the analytics platform pulls relevant data from the SNF EHR, using over 300 algorithms to identify clinical changes and concerning trends, and alerting care teams to intervene before a readmission occurs.
According to Kocknes et al., SNFs using interventional analytics saw significantly lower risk-adjusted readmission rates compared to their peers: Over the course of 12 months concluding in the final quarter of 2022, LGH’s SNFs had much lower readmission rates (15%, 12%, and 13% lower) than similar facilities at the national, state, and system levels.
Additionally, analysis of CMS readmission-related claims-based quality measures showed that LGH’s SNFs outperformed national, state, and other facilities within the health system on all four of CMS claims-based quality measures examined:
Risk-standardized potentially preventable readmission rate:
- LGH with interventional analytics: 7.0%
- LGH without interventional analytics: 7.2%
- National: 7.6%
- State: 7.6%
Medicare spending per beneficiary for members in SNF:
- LGH with interventional analytics: 0.8%
- LGH without interventional analytics: 0.9%
- National and State: 1.0%
Percentage of infections members got during their SNF stay that resulted in hospitalizations:
- LGH with interventional analytics: 5.0%
- LGH without interventional analytics: 5.6%
- National: 6.8%
- State: 6.1%
Rate of successful return to home or community from a SNF:
- LGH with interventional analytics: 56.9%
- LGH without interventional analytics: 52.5%
- National: 52.5%
- State: 50.7%
The results indicate the significant impact interventional analytics can have on improving member care and reducing costs.
“The improved quality performance of the cohort with the interventional analytics platform implemented is consistent with findings regarding the value of technology in health care settings,” the authors note. “Timely data on high-risk members also position decision makers with contextual information essential to refine care processes and target quality improvement initiatives.”
Implications for healthcare providers
For interventional analytics to improve care management in a SNF setting, it is crucial for health plans and health systems to work with their network of providers, ensuring adoption and effectiveness. Integration with existing EHR platforms represents a significant challenge, considering that helpful interventional analytics depends on efficiently extracting and processing member data in real-time. It is vital for health plans and health systems to ensure compatibility between their network of providers and the data analytics platform, while investing in adequate training for staff to utilize the technology.
Integrating real-time data analytics with existing EHR platforms is critical—timely interventions prevent unnecessary hospital readmissions and ensure appropriate lengths of stay. However, the fragmented nature of healthcare IT infrastructure hinders provider access and the ability to act on the latest member data. In light of the healthcare system’s continued shift to value-based care, overcoming these challenges marks a key first step in the right direction.
Next steps for healthcare organizations
For interventional analytics to positively impact member care and health outcomes, healthcare organizations and SNFs must undertake a handful of activities enabling effective integration, utilization, and optimization to improve member outcomes and operational efficiency.
1. Invest in interoperability: Work with technology partners to ensure EHR systems can seamlessly communicate with the interventional analytics platform. Adopt industry standards, such as Fast Healthcare Interoperability Resources (FHIR) and participate in health information exchanges for effective data sharing.
2. Prioritize staff training: Establish comprehensive training programs to ensure care teams can utilize the interventional analytics platform effectively. Engage staff early and provide ongoing support to integrate the data analytics platform into their daily workflows.
3. Combine analytics capabilities: Use predictive and interventional analytics to develop proactive care strategies for addressing immediate and long-term member needs.
4. Engage stakeholders: Work with payers, technology partners, and policymakers to tackle challenges in adopting interventional analytics and develop best practices for improving member care.
Conclusion
Interventional analytics utilized by SNF care teams showcases the opportunity to improve member care while reducing costs. Expanding the utilization of data across the entire network means providers must address the technical and operational challenges of integrating interventional analytics with existing EHR platforms, provide comprehensive staff training, and collaborate with care providers.
With the right combination of care staff, processes, and analytics technology, health plans and health systems can turn real-time data into tangible improvements in member outcomes, participate in value-based care initiatives, and contribute to a more efficient, higher quality healthcare network.
