Over 400 years ago, Sir Francis Bacon correctly declared that knowledge is power. That maxim continues to be true today and is as applicable to healthcare as any other industry. As healthcare providers improve and move ever closer to whole person care, the demand for data-driven community care coordination increases. Health Level 7 (HL7) recently announced Release 4 of the Fast Healthcare Interoperability Resource (FHIR). This new release represents a large step forward in how data is accessed and shared and promises to improve care coordination. While the industry continues to improve and move forward, there are still opportunities for significant improvement.
A recent report from the Office of the National Coordinator for Health IT noted that as of 2015, 96% of non-federal hospitals and 78% of office-based physicians adopted certified health IT standards and that most Americans’ health data is recorded electronically. While that is an incredible achievement, the report also noted that many patients still struggle to access their own health records and that data sharing between providers, patients, and communities is still limited and clumsy. Additionally, while interoperability is a high—and achievable—standard, electronic health records (EHRs) remain a conglomerate of disparate and often incompatible systems and standards that thwart efforts at true interoperability. In fact, a 2017 study noted that only 30% of hospitals were compliant with four key interoperability metrics. Perhaps more concerning to note is that of those numbers only 5.5% improved over 2014.
Yet the implementation of data sharing and data-driven care technologies is a significant opportunity for improved health outcomes, cost savings, and efficiency. The Westchester Center Health Network (WMCHealth) used machine learning and data-driven decision making to develop and implement a predictive model around classifying patients as high-risk and coordinate that information among case managers. Previously, case managers had to manually review patient records in order to identify risks, reducing the time they could spend working with and caring for patients and slowing down processing time. After implementing data technologies, WMCHealth noted a 17% improvement in proper risk classification for patients and a workload reduction of 1,327 hours annually for case managers. Similarly, Grady Health System relied on AI technologies to identify readmission risks and coordinate care visits with similar success.
Implementing closed-loop care coordination processes provides for earlier identification and support of social determinants of health. The Alliance for Better Health relies on a data sharing network to enhance their support of the community. Participating organizations work together to share data and make decisions on healthcare and social supports in order to create a closed-loop referral system that better provides for the needs of the community.
Data integration and interoperability standards will continue to advance and become an increasingly central component of evidence-based, coordinated systems of care for high-risk individuals. Improved data sharing between patients, healthcare professionals, and community providers delivers a comprehensive view of an individual’s needs that result in better care and improved health outcomes. To learn more, see Moving to a Comprehensive Care Plan.
- HL7 Publishes FHIR Release 4
- ONC Report to Congress Shows Flawed Health Data Sharing
- The State of Interoperability in Healthcare
- Machine Learning System Saves Case Managers 1,327 Hours Per Year
- How Grady Health Systems Used AI to Reduce Preventable Readmissions
- How Care Coordination Tech Helped One Health Network Address Social Determinants
- Closed Loop Referral Management and Tracking: Achieving Care Coordination
- CMS Final Rule Incentivizes Interoperability, Health Data Exchange