Addressing Data Quality in the EHR
By Greg Chittim
What if you found out that you might have missed out on seven of your 22 ACO performance measures, not because of your actual clinical and financial performance, but because of the quality of data in your EHRs? It happens, but it’s not an intractable problem if you take a systematic approach to understanding and addressing data quality in all of your different ambulatory EHRs.
In HIStalk’s recent coverage of HIMSS14, an astute reader wrote:
Several vendors were showing off their “big data” but weren’t ready to address the “big questions” that come with it. Having dealt with numerous EHR conversions, I’m keenly aware of the sheer magnitude of bad data out there. Those aggregating it tend to assume that the data they’re getting is good. I really pushed one of the major national vendors on how they handle data integrity and the answers were less than satisfactory. I could tell they understood the problem because they provided the example of allergy data where one vendor has separate fields for the allergy and the reaction and another vendor combines them. The rep wasn’t able to explain how they’re handling it even though they were displaying a patient chart that showed allergy data from both sources. I asked for a follow up contact, but I’m not holding my breath.
All too often as the HIT landscape evolves, vendors and their clients are moving too quickly from EHR implementation to population health to risk-based contracts, glossing over (or skipping entirely) a focus on the quality of the data that serves as the foundation of their strategic initiatives. As more provider organizations adopt population health-based tools and methodologies, a comprehensive, integrated, and validated data asset is critical to driving effective population-based care.
Health IT maturity can be defined as four distinct steps:
- EHR implementation
- Achievement of high data quality
- Reporting on population health
- Transformation into a highly functioning PCMH or ACO.
High-quality data is a key foundational piece that is required to manage a population and drive quality. When the quality of data equals the quality of care physicians are providing, one can leverage that data as an asset across the organization. Quality data can provide detailed insight that allows pinpointing opportunities for intervention — whether it’s around provider workflow, data extraction, or patient follow-up and chart review. Understanding the origins of compromised data quality help recognize how to boost measure performance, maximize reimbursements, and lay the foundation for effective population health reporting.
It goes without saying that reporting health data across an entire organization is not an easy task. However, there are steps that organizations must take to ensure they are extracting sound data from their EHR systems.
Outlined below are the key issues that contribute to poor data quality impacting population health programs, how they are typically resolved, and more optimal ways organizations can resolve them.
Variability across disparate EHRs and other data …read more
