Data quality impacts everything from patient safety to compliance, and even reimbursement. Yet, despite its importance, many organizations have no real plan for addressing data quality on an ongoing basis. That’s why troubles so easily arise during a migration.
When an organization is migrating from one EHR to another – or extending its EHR to a newly acquired entity – the success of the operation depends on the quality of the data. CIOs know this. Buy many healthcare organizations incorrectly assume their new vendors will help with data quality during a migration.
Not so, said a recent panel.
The Four Tenets of Data Quality
Karen “K” Marhefka, deputy CIO at Robert Wood Johnson Barnabas Health, the clinical partner to Rutgers Medical School, recently helped her organization complete a four-and-a-half-year data migration of more than 200-plus disparate systems to a single platform. She said there are four major data quality tenants to ensure a successful migration–accuracy, completeness, consistency and relevance.
Accuracy is paramount, because data must not only be precise, but it also must maintain its integrity over time, Marhefka said. Completeness and consistency “are givens.” Yet the true challenge is ensuring relevance of the data.
“The one ‘gotcha’ for us was the relevance of those data elements that we might have considered extremely important 10 years ago that are not as important today, and vice versa,” Marhefka said. “We had to constantly question ourselves on the relevance of data, both during the migration and certainly through our data governance participation afterwards.”
Relevance requires good governance and having the right people at the table, including physicians, clinicians, revenue cycle, and analytics team members, Marhefka said. In describing RWJBarnabas governance meetings, she added: “These are always great conversations, always very robust, with a lot of debate. But we’re consistent. We try and stay accurate in terms of relevance. That’s actually the fun part.”
Data Quality and Migration
Rich Amelio, vice president of healthcare IT operations and consulting at the data quality management company e4Health, said many CIOs mistakenly think that for the steep price of an EHR, the vendor will help with cleansing data, and they are surprised to find out—too late, and without a budget for it—this isn’t the case.
“The new EHR vendor is focused on implementing their software, and they’ll give you a table to ensure you’re doing the mappings, and they’ll take that extract file from the legacy system,” Amelio said. “But that’s kind of all they’re doing.” They expect the client to have plans in place, the right mappings, validated and cleansed data, and to start with a clean slate. This is something organizations are struggling with, he said.
Bessie Jay, Emory Health’s director of integration and technical application support, said her organization has worked hard to create a culture centered on data quality. This helps to ensure on an ongoing basis that data is clean for today’s purposes and for Emory’s research into the future.
What would Jay advise organizations that are looking at an immediate migration? “Start with data cleansing as Phase Zero of your project,” she said. “Even prior to selection of your new system.”
In addition, plan ahead for use of the data. “Once you start planning around what you’re going to do with your data in the new EHR, don’t put conversations off,” Jay said. “It’s easy to say, ‘Well, I’m going to focus on that later.’ It is absolutely critical that you work on the migration of the data as you are building out the new EHR so that you understand your data today and how it’s going to appear in your new system for your clinicians.”
Amelio said, “I think we’re all in agreement that data quality is an issue in today’s landscape, but why is it difficult to solve?” Because many organizations still treat their systems and hardware as their assets, he added. Now, some realize, it’s the data, and are addressing that change by putting strong governance in place. “But, most organizations aren’t there yet, and I think until they get there, it’s going to remain a difficult problem to solve, and I think compounding that, from an industry standpoint, there is no measure on what clean data looks like.” That’s where measurement standards and governance comes in.
Despite the current challenges of data fragmentation, ultimate success will involve interpreting the data, understanding its historical significance, and determining how it can inform future care, Jay said. Organizations with a focus on research, like Emory, need to ensure that they don’t lose alignment of historic data as they move forward to streamlining data in the future, she said.
To view the archive of this webinar — Strategies for Addressing the Data Quality Component of Your EHR Migration (Sponsored by e4health) — please click here.
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