The crucial role of data quality during EHR transitions
To make an effective switch to a new records system, IT leaders must prevent a cascade of IT issues that can result in additional costs later.
As healthcare becomes increasingly digital, ensuring the highest levels of data quality during changes in electronic health records systems has never been more crucial. Accurate and complete patient data is the backbone of these systems, and it’s essential for patient safety, informed clinical decision-making, end-user satisfaction and staff productivity.
In an era in which the U.S. is projected to spend nearly $19.9 billion on EHRs by the end of 2024, health systems cannot afford to overlook the importance of data quality. There are five key areas in which healthcare provider organizations must invest to enhance data quality and ensure a smoother transition during their EHR implementations.
The importance of patient data quality
Recent studies highlight ongoing concerns about the quality of patient data. A 2022 National Library of Medicine Health Services Insights article cited data accuracy issues as a significant finding in a qualitative analysis of EHR impacts. Many health systems are unprepared and under budgeted for the extensive work required to achieve data accuracy.
High-quality patient data is essential for the following reasons.
EHR accuracy and patient safety. Inaccurate or incomplete data can lead to medical errors, affecting patient safety and care outcomes.
Informed clinical decision-making. Clinicians rely on precise data to make informed decisions about patient care.
End-user satisfaction and staff productivity. Quality data reduces the time spent by healthcare providers on data verification, which increases productivity and satisfaction.
Mitigating revenue leakage and patient frustration. Correct data ensures accurate billing and reduces patient frustration caused by repeated information requests.
A quality-first approach ensures the success of new EHRs and other IT systems by helping IT leaders maintain authority over vendors and applications. A firm focus on data quality also establishes new systems as accurate sources of truth for clinical care and reimbursement.
By addressing data quality up front during EHR planning and implementation, health IT leaders prevent a cascade of IT issues and additional costs later.
Five crucial components of a plan
The journey to better patient data quality begins with migrating accurate and complete historical data from legacy systems into new EHRs and IT platforms. There are five components for health IT leaders to consider when building a comprehensive patient data quality management plan.
Data strategy and planning. Develop a comprehensive data quality plan that extends beyond the EHR to address areas such as patient identity management and data migration approaches including manual data abstraction where systemic data migration is not a viable option. EHR vendors are a supporting contributor and advisor to the data quality plan, but ownership of data quality remains with the provider organization. Assuming the EHR vendor will “take care of it all” is an unrealistic expectation. The EHR is likely only one of multiple platforms that contain elements of the required legal health record.
Patient identity management. Ensure an accurate and complete electronic master patient index (eMPI) to avoid negative impacts on clinician workflow and patient safety. Patients must be correctly identified and established in go-forward systems. This ensures physicians can easily find correct patient information every time they log in to a new EHR.
Data migration and validation. Conduct thorough data migration and validation to maintain data integrity and clinician trust in the new platforms. Each separate data type and destination requires a workplan and stakeholder involvement throughout the entire extract, transform, load, and validate process.
Document management. Ensure all relevant and required patient information, regardless of format, is transferred to the new system, recognizing that acquired physician practices still may have paper charts.
Manual data abstraction. Plan for manual abstraction where automated migration is not feasible, ensuring completeness of important patient information such as problems, allergies, medications, and immunizations (PAMI data), and patient histories in the go-forward EHR. Manual data abstraction is usually a last-minute realization requiring a rushed effort to assemble an expert abstraction team.
Failure to consider any of the above five steps can lead to long-term dissatisfaction with the new EHR. In our work with health systems over the past several years, the task of cleaning up MPI duplicates across legacy systems prior to data upload into new Epic EHRs was overlooked during the planning phases, which led to unexpected downstream costs and clinician frustration.
Data quality requires continual focus
Data quality is not a one-time effort; it requires sustained executive commitment. Health systems invest millions of dollars in system changes, but if the transfer of patient data from old to new systems is not accurate and complete, the organization’s IT investment will not be optimized. And, if data quality is not properly ensured at every step in the patient journey, the long-term benefits of IT transitions may not be realized.
Adhering to recommended best practices during EHR transitions is essential to safeguarding patient data quality and ultimately improving healthcare outcomes. IT leaders who prioritize patient data quality at every step enhance healthcare delivery, support informed decision-making and foster trustworthy patient-provider relationships.
The journey to better patient data quality starts now, and with the right strategies and investments, the benefits will be substantial and long-lasting.
Jim Hennessy is the President of e4health, a consulting firm specializing in healthcare IT. With over 16 years at e4health, Jim has also served as CEO.