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 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.
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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.
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:
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.
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 views and opinions expressed in this blog or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.
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