Are you sitting on dozens of outdated technology systems? How long has it been since you were able to archive data in your current EMR? Do your clinicians complain about not having easy access to longitudinal data? Perhaps it’s time to take a different approach to how your organization manages healthcare data archiving. Continuity of care, emerging data analytics and interoperability can all benefit from taking a strategic approach to your healthcare data archiving practices.
Clinical Data Integration to Support Continuity of Care
Studies show that, on average, physicians see up to 20 patients per day, spend approximately 18 minutes with each patient, and spend an average of 36 minutes in the EHR system per patient visit. Combine this with fragmented care across multiple providers, and it’s no wonder continuity of care remains a persistent challenge. A challenge that, if solved through clinical data integration, could eliminate the need to hunt for a patient’s historical data, and improve development and delivery of patient care plans.
In-House Data Management for Analytics and Intelligence
We’ve seen a noticeable shift in healthcare organizations bringing data management back in-house. Health systems recognize the value in accessible data that can be leveraged for advanced analytics to support clinical outcomes and operational efficiency. To support this, they are building their own data lakes, building data science capabilities, and investing in AI startups.
Interoperability
While not a new challenge, interoperability is still top of mind, especially as healthcare organizations continue to merge. EMR consolidation – and even “Epic-first” initiatives – reflect the industry’s push toward connected datasets and comprehensive patient views.
Looking at legacy data from a new perspective could help health systems address these pervasive challenges. Here are a few strategies to consider:
- Implement a practice of cyclical archiving
Ever had to spend days cleaning out a garage that’s bursting at the seams? Keeping the garage clean and organized all the time is much more efficient than letting things pile up over time. The same goes for legacy data in both active and retired systems. For optimal system performance, health systems should consider cyclical archiving practices on a quarterly or bi-annual basis. This allows providers to balance data accessibility with system efficiency and minimize risk by reducing vulnerable data points. - Establish failover capabilities with near real-time archive
The Change Healthcare incident in spring 2024 exposed single-point vulnerabilities in revenue cycle management. The impact was universal and substantial. Imagine, though, the ability to retrieve a near-real time back-up of all legacy data. The impact of such outages – regardless of the source – could be minimal. - Integrate clinical history
With constant transitions between EMR systems, clinicians should demand preservation of clinical history and seamless data integration, not the “2nd screen” solution that’s typically offered. Integration of this data directly into the EHR is feasible and will eliminate the need for providers to go searching for patient data in other systems. - View legacy data as an asset
Perhaps most importantly, healthcare organizations should shift their perspective on data archiving. De-identified clinical history can power technological advancement and drive innovation in care delivery. For example, rather than synthetic data, consider using real-world evidence in the form of your own historical data to train large language models (LLMs).
The path forward requires careful balance: maintaining comprehensive patient records while optimizing system performance, ensuring data accessibility while protecting security, and leveraging historical data while managing system burden.
Success lies in choosing the right partners who understand this complexity and can transform data archiving from a necessary task into a strategic advantage. Legacy Data Access is here to help. Contact us for a complimentary assessment of your clinical data integration process.