Have you ever started a simple home improvement project that snowballed into several more complex projects? Your goal may have been to only paint the kitchen, but then you discovered termites in the wall, a leak in the plumbing and mold in the cabinets. That sound you heard of plinking tiles was the domino effect kicking in.

A similar effect can be had for achieving healthcare interoperability. After mergers and acquisitions, multiple EMRs and millions of patient records…the roadblocks to interoperability can feel like a domino effect.

A similar progression happened recently to a health system in Pennsylvania. As the organization experienced growth, they sought ways to lower costs while ensuring positive patient outcomes and quality measures that met Medicare Access and CHIP Reauthorization Act requirements.

They launched a population health strategy to better understand and treat their patients. What started as a relatively simple project brought to light problems not uncommon to health systems, such as interoperability and patient record linking. Those issues would need to be solved before the new strategy could be implemented. The domino effect was suddenly in full force.

Mergers Create Redundant Records

In recent years, the Pennsylvania health system acquired more than 50 primary care and specialty practices, adding to its 300-bed hospital. It had also merged the electronic health records (EHRs) from six different health systems. In all, it had 1.3 million patient records but thousands of those were likely multiple files for the same patient.

Bringing together these disparate records into a single data source had created a quality problem with the data. And without data integrity, the health system would be unable to properly manage the care of its patient population, detect trends or make decisions regarding how to allocate resources. However, manually linking and matching patient records and resolving duplicates could literally take years.

Patient Safety at Risk

In addition to the population health initiative, several other critical factors were compelling the Pennsylvania health system to take swift action in resolving patient record issues:

  • Patient Safety – Physicians needed to see complete records to correctly diagnose and treat their patients. Duplicate or unmatched records could mean a patient’s information was spread across multiple records.
  • Future Mergers and Acquisitions – The health system expected to continue growing by acquiring practices in its service area. With each new addition, the patient-matching and duplicate records problem would become more complex.
  • Payment Reform – In the evolving value-based healthcare system, claims reimbursement rates could potentially drop in favor of incentive payments. Provider compensation would increasingly be tied to performance and quality data instead of fee for service. The health system wanted to use its own real-time data to be proactive in assessing its performance. Without accurate patient records data, it would be unable to make informed decisions to improve and reduce costs.
  • Gain-Sharing Risk – The health system intended to increase its use of gain-sharing contracts with physician practices. Performance data was critical to its negotiations. It needed accurate counts for the number of patients and the cost of their care in order to determine fair value.

Linking Patient Records for True Healthcare Interoperability

By automating the matching and linking of patient records, the health system was able to efficiently resolve its data integrity problem by identifying common patients among the converging EHRs and eliminate duplicate records.

The health system also assigned each patient a unique identifier, which helps mitigate the risk of exposing a patient’s personal information.

A Fast, Reliable Process Was Needed

In a matter of weeks, the Pennsylvania health system had whittled its 1.3 million records down to 740,000, which accurately reflected its patient population.

It now had the ability to offer more efficient and safe care across its continuum of services. And it had a repeatable process in place to scrub massive patient datasets to merge or remove duplicate records if additional practices were added to the system.

While the domino effect initiated by the system’s population health strategy seemed to cause a chain reaction of difficult, new problems, in reality it had brought to light important concerns related to data integrity. Resolving those issues was critical to patient care and the future success of the health system.

The starting point for achieving healthcare interoperability is through data integrity of patient records, specifically referential patient record matching. Without the ability to sync patient records across disparate EMR systems specifically during and after M&A activity, true interoperability will not be achievable.

With referential matching, data can be exchanged and reconciled in a faster, more efficient manner to achieve true healthcare interoperability.