{"id":27190,"date":"2024-03-28T12:46:25","date_gmt":"2024-03-28T16:46:25","guid":{"rendered":"https:\/\/www.3pillarglobal.com\/?p=27190"},"modified":"2025-05-14T14:38:33","modified_gmt":"2025-05-14T14:38:33","slug":"data-quality-control-healthcare-interoperability","status":"publish","type":"blog","link":"https:\/\/www.3pillarglobal.com\/insights\/blog\/data-quality-control-healthcare-interoperability\/","title":{"rendered":"Why Data Quality Control is a Prerequisite for True Interoperability"},"content":{"rendered":"Interoperability is about more than integrations and data exchange. If the technical capabilities are there but one or both parties can\u2019t use the data, there\u2019s no value, no true interoperability.\n\nFor example, both payers and providers have EMR systems, but the way they operate and classify data is different enough to drive misalignment. So when the two systems attempt to integrate, data is often misclassified, resulting in errors, gaps, and poor quality.\n\nTo achieve true interoperability, you need data quality control. Here\u2019s how to build the systems and processes necessary to make that happen.\n

Why data quality control is a prerequisite for true interoperability in healthcare<\/h2>\nLet\u2019s consider a scenario. A provider implements a new clinical system. The goal is to clean up the previous unusable data under the old system, enabling the new system to operate efficiently going forward.\n\nBut even if you clean up your data and have a perfect implementation<\/a>, if your underlying data control processes don\u2019t change, the new data will be as unusable as the old. Between your initial assessment and implementation, you\u2019ll have potentially months of bad data\u2014and that\u2019s before the new system starts to operate.\n\nOr, in other cases, you\u2019ll redefine and reclassify old data based on how you think the new system is supposed to work. As time goes on, however, your processes don\u2019t align with the expectations surrounding the new system, resulting in misaligned data.\n\nFor example, your EMR can send an interoperable medical record<\/a> from a provider to a payer. But if the payer needs to not only receive that record, but also understand what was said and done, code all of these procedures and practices correctly, and ensure they\u2019re charging the correct amount. Which means the systems need to be able to classify and coordinate those specific data.\n\nWhile there are data exchange standards in healthcare<\/a>, there are also nuances within those standards. And while they may seem small, when dealing with massive datasets, these nuances have the potential to add up quickly.\n\nThe result: wasted time and resources, inefficiencies, and dissatisfied patients and healthcare workers alike.\n\nWhether EMR or other systems<\/a>, the goal of true interoperability is to reach a state where you\u2019re constantly comparing apples to apples. This is only possible with rigorous, continuous data quality control.\n

Two-step approach to healthcare data quality control<\/h2>\nTrue data quality control comes in two stages. Unfortunately, most healthcare organizations and their technology partners<\/a> focus on one and not the other. This, as we\u2019ve seen, causes major problems.\n\nHere are the two steps necessary for effective data quality control:\n