Big data initiatives hold great promise for healthcare. The ability to collect, analyze and share clinical data between providers can help achieve a multitude of reform goals, from improving patient experiences and outcomes to enabling individuals and families to make better treatment decisions. Healthcare providers also need smart analytics to operate more efficiently, reduce waste and improve margins as value-based care initiatives proliferate.
Why EHR Interoperability Breaks Down
Before any of this can happen, however, providers must tackle the data quality issue that has been brewing for years. This stems from the use of many disparate systems and processes within facilities and within the larger ecosystem of providers. These systems are crucial for managing patient care in a geographic area; yet the lack of integration between them means that patient data is incomplete, inaccurate and misinterpreted. That leads to medical errors and incomplete information—all harmful to a patient’s well-being and recovery.
This is exactly where an interoperability strategy that emphasizes data validation and governance, like the approach behind Emids interoperability capabilities, can help reduce inconsistencies across systems.
Why EHR-Centered Data Repositories Still Fall Short
For years, healthcare providers have attempted to create and maintain rich, centralized repositories for clinical and patient data, with the electronic health record (EHR) system as the linchpin. For those that have successfully implemented an EHR like Epic, the challenges grow over time. There are just too many systems containing data on a single individual—and developing interfaces to all those systems is costly and technically challenging. For example, a system receiving an EHR might not be able to locate data in a specific format or field from the system sending it, even though these missing data fields may be required to integrate information between records.
Common EHR Data Quality Issues That Undermine Interoperability
EHRs also suffer from data quality issues related to human error and process inconsistencies, which include:
- Inaccurate patient identifiers, such as a missing Social Security number or misspelled names, leading to duplicate patient records or the blending of multiple individuals into one record.
- Metrics that don’t end in the appropriate structured fields.
- Incorrect entry of diagnosis codes.
- Missing reports, such as lab and radiology.
A majority of hospitals have an EHR system that has been federally tested and certified for the government’s incentive program, according to the Office of the National Coordinator for Health Information Technology (ONC), but there is still much work to be done. Only 9% of providers were fully compliant with ONC’s 2015 EHR certification with products that enable open APIs, according to an eHealth Initiative survey
Practical Steps to Improve Data Quality
Just like treating an aggressive cancer, tackling the data quality problem requires multiple strategies. Efforts currently underway among organizations include:
- Improving existing standards by implementing a set of national interoperability standards for information systems.
- Working toward developing an enterprise master patient index at the institutional level.
- Participating in public and private healthcare information exchanges, which studies show can facilitate faster service to patients and reduce costs by avoiding preliminary treatments such as tests.
- Implementing data validation technologies into EHR systems to verify the accuracy, meaningfulness and security of data.
- Developing easy-to-use information portals where consumers can access their health records and provide feedback on missing or inaccurate data.
Bringing It All Together: Data Quality as the Foundation for Interoperability
Interoperability is only as strong as the data moving through it. Even with widespread certified EHR adoption, disconnected systems, inconsistent workflows, and manual data entry continue to produce incomplete or inaccurate records—creating downstream risk for care teams and patients. That’s why improving EHR interoperability has to start with data quality: standardizing what “good” data looks like, validating it as it’s created and exchanged, and strengthening identity matching so information reliably ties back to the right person.
A practical path forward combines multiple efforts—advancing standards, building an enterprise master patient index, expanding participation in health information exchanges, embedding data validation into EHR workflows, and giving patients easier ways to spot and correct errors. Taken together, these steps reduce fragmentation, improve trust in shared data, and create the foundation healthcare organizations need to support analytics, value-based care, and better patient experiences at scale.