While many things in healthcare are unpredictable, fortunately, the healthcare data life cycle isn’t one of them. In fact, healthcare data management is relatively straightforward. Providers start to capture healthcare data (e.g., diagnoses, medical history, medication, and more) the moment a patient receives medical care. Next, they store that data in a secure electronic medical record (EMR) where they can analyze and use it for a variety of purposes (e.g., to inform treatment decisions, improve patient outcomes, and reduce costs). Eventually, they archive the data and then ultimately dispose of it once they no longer need it for legal or regulatory requirements.
Going a step further into detail, the Office of the National Coordinator for Health information Technology (ONC) describes the healthcare data life cycle as having seven formal stages:
- Business specification (e.g., data requirements, business terms, metadata)
- Origination (i.e., the point of data creation or acquisition by the organization)
- Development (e.g., architecture and logical design)
- Implementation (i.e., physical design, initial population in data store[s])
- Deployment (i.e., rollout of physical data usage in an operational environment)
- Operations (e.g., data modifications, data transformations, and integration performance monitoring and maintenance)
- Retirement (i.e., retirement, archiving, and destruction)
While each of the ONC’s stages of healthcare data management is important, it’s the retirement stage that often presents the biggest opportunity for cost savings. More specifically, we’re talking about cost savings inherent in data archiving—the process of moving patient records from active storage in the EMR to long-term storage, typically onsite or in a cloud-based solution that decreases overall maintenance costs and security risks. Data archiving occurs when healthcare organizations no longer need patient records for active patient care but must continue to retain them to comply with legal and regulatory requirements. Ironically, archiving is often the most overlooked stage even though it is a critical one.
Creating a cost-effective strategy for healthcare data management
Why is data archiving typically overlooked in the overall healthcare data life cycle? Once the data no longer becomes relevant to clinical care, it’s sometimes easy to forget it even exists. That is, until someone needs to access it (e.g., to respond to a release of information [ROI] request or investigate a negative patient outcome). It’s the whole notion of “out of sight, out of mind.”
Organizations subsequently fall into the trap of continuing to maintain dozens or even hundreds of legacy systems indefinitely. In the 2019 HIMSS Cybersecurity Survey, 69% of healthcare organizations indicated that they had at least some legacy systems in place. Why is this? Organizations falsely assume data archiving requires complex data mapping and a lengthy governance process because that’s what they’re used to – the slow and tedious process of ETL (extract, transform, load). The cost of this assumption: millions of dollars annually.
In addition to unnecessary maintenance costs, there are also other risks associated with not retiring legacy systems. Consider the following:
- Cybersecurity risk. Legacy applications in read-only mode are often housed on outdated servers and equipment, making them vulnerable to cyberattacks.
- Knowledge gaps. As staffing transitions occur, institutional knowledge about how to use the legacy system is lost.
- Costly maintenance. Technical teams must continually monitor and update outdated servers and systems.
- Negative user experience. As legacy systems pile up, health information (HI) professionals must comb through a growing stack to find required ROI content.
- Noncompliance. More applications mean there’s a greater likelihood of accidentally omitting information while fulfilling an ROI request.
- Suboptimal patient care. It takes more time for providers and others to access critical patient information from the legacy system.
Alternatively, when healthcare organizations recognize the importance of data archiving as part of the overall healthcare data management process, they’re better able to talk through options and identify a solution that mitigates all the risks listed above—particularly the risk of incurring long-term costs unnecessarily. Depending on the option, these cost savings can be significant, allowing organizations greater financial flexibility to invest in staffing, patient engagement technology, artificial intelligence, and more.
Tips for archiving healthcare data as part of a strategic effort to contain costs
As healthcare organizations look for ways to move data archiving into a more prominent position in their overall healthcare data life cycle, these four tips can help:
- Establish a data governance framework. This framework should address guiding principles, key data steward roles (e.g., chief data officer, chief analytics officer, data trustee, lead data steward, and others), data steward responsibilities, policies and procedures, and more. Be particularly mindful of who ultimately decides what happens with patient data once it’s no longer relevant.
- Create a policy that activates data archiving. An archive policy based on state-, record type-, and organization-specific requirements and other variables should clearly specify how long the healthcare data must be retained and what should be done with the data after retention requirements have been met. These important guidelines ensure everyone knows when and how to take important final steps in the healthcare data life cycle with confidence.
- Choose the right data archiving partner. While traditional legacy systems rely heavily on the data manipulation process known as ETL, more innovative vendors leverage a cloud-based archiving platform that allows organizations to retire and archive systems with less time, cost, and resources than the traditional ETL-based approach. For example, the Olah Enterprise Archiving Solution™ (EAS) transforms entire legacy databases, documents, and waveforms to a secure and controlled archive that can be seamlessly integrated with any EMR. The complete original databases are available in EAS, giving providers the opportunity to leverage years of valuable data to optimize insights for benchmarking, business planning, and more.
- Don’t forget data disposal. Even once an organization moves to a cloud-based archiving solution, there will come a time when archiving is no longer necessary. At this point, disposal is advised to reduce costs and mitigate risk.
Discover more about data archiving
Acknowledging the vital role of data archiving in the healthcare data life cycle is the first step toward leveraging this phase to reduce costs, increase efficiency, and mitigate risk. A data governance framework and the right data archiving partner can aid organizations on their journey toward cost containment and high-quality patient care. Learn about Olah’s simple, fast, and complete solution to archiving at https://olahht.com/archiving-plan/.