The ability to collect and use data quickly, securely, and repeatedly is critical to federal healthcare agencies’ success. This article, originally published on Government Technology Insider, outlines potential obstacles to establishing this competency and options for overcoming them.
One thing we’ve learned from the COVID-19 pandemic, is the central importance of federal healthcare agencies. From the ground-breaking research being conducted at the Centers for Disease Control and Prevention (CDC) and the National Institutes of Health (NIH) to the vital work done by the Defense Health Agency (DHA) and the Department of Veterans Affairs (VA) in keeping active duty troops and veterans healthy, healthcare is a priority for the federal government.
At the heart of today’s healthcare mission success is the ability to gather, analyze, move, and access data quickly and securely. As well as being able to apply data to its original purpose, what’s now critical is the ability to use that data time and again to solve other critical issues. For example patient data that’s being gathered by the DHA and VA today could be critical to helping NIH’s Cancer Moonshot researchers find a cure for veterans with cancer related to burn pit smoke exposure.
However, these agencies face some significant data-centric obstacles in order to deliver mission success. The first is ensuring that data is ingested accurately, and the second is ensuring the accuracy of that data as it is used over time so that a single source of truth is retained.
Jon-Michael Smith, an expert in healthcare data management for public sector agencies, sees the Department of Veterans Affairs EHR migration project as an apt encapsulation of some of these problems. “The migration to Cerner’s EHR is a heavy lift for the Department of Veterans Affairs,” explained Smith. “When you migrate data between data-rich platforms like EHRs you quickly find that very little data is normalized so it’s difficult to bring federated data islands together. Both immediately, and long-term, this makes using and moving the data problematic. Moreover, as data is used and stored in different systems for different projects – both inside and outside the agency – data quality becomes compromised resulting in data that is not fit for mission. ”
In fact, the GAO review of the recent Cerner migration identified these issues. The report issued by the oversight agency found “that although VA made plans for migration and performed data testing activities identified in its plans, the department did not ensure that the quality of data migrated to the new EHR system sufficiently met clinicians’ quality needs.” Continuing on, the report documented physicians’ concerns with data quality, “[a]ccording to another clinician, inaccuracies in the data required additional steps to verify and manually enter the data, which had created barriers to patient care, inefficiencies in workflow, and a significantly increased workload.”
While the VA has certainly been under the microscope, Smith sees this experience of a large data migration project as beneficial for healthcare agencies as they prepare for their data-driven futures. “This was one of the most complex and high profile data projects undertaken by a federal agency,” he noted. “Working with the GAO they’ve managed to lay out a remediation plan to address these issues. But agencies need to know that there are solutions out there that address these challenges before a migration so that the burden of moving data is eliminated, and both immediate and long-term data quality issues are managed before they even begin.”
As federal healthcare agencies prepare to solve some of the biggest challenges in public health and look to provide military personnel, veterans, and citizens with the next-generation of healthcare, the importance of being able access, analyze, and apply high-quality data can not be overstated. By starting with the right foundations and building an environment that is intelligent and designed to make data actionable agencies will be on a fast-track path to mission success.
