4medica’s Healthcare Data Quality Platform Uses Artificial Intelligence and Machine Learning to Reduce Costly Patient Matching Errors

4medica, a leader in healthcare data quality and matching technology,announced that its cloud-based Data Quality Platform now deploys artificial intelligence (AI) and machine learning (ML) to improve patient matching.

Utilizing the most technologically advanced Master Patient Index (MPI) process, 4medica has revolutionized how organizations can analyze, improve, and utilize health data. A unique 4-layer MPI process simplifies implementation and facilitates patient identity management and data exchange. 4medica guarantees it can reduce patient duplication rates – which can reach as high as 30% in some health organizations – to less than 1%.

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Duplications and other patient matching errors not only can endanger patient safety, they result in costly inefficiencies (such as duplicate tests being ordered) and dilute the value of a healthcare organization’s data. Duplicate patient records cost healthcare organizations nearly $2,000 per inpatient stay and $800 per emergency department visit. In addition, 33% of claims denials can be traced to inaccurate patient identification or health data, according to a Black Book survey.

By improving patient data quality, hospitals and provider groups, health plans, labs and imaging centers can:

• Reduce patient risk
• Lower costs through greater efficiency
• Increase the value of their data

“Maintaining MPI data management requirements is essential to effective healthcare interoperability and can be a challenge without enduring loss of performance, speed and matching accuracy,” said Jennifer D’Angelo, who oversees the New Jersey Health Information Network (NJHIN) in her role as senior Vice President and General Manager of Healthcare for New Jersey Innovation Institute. “Once we partnered with 4medica and deployed the company’s cloud-based Data Quality Platform with its AI/ML capabilities, we brought our duplication rate under 3%, while maintaining system performance.”

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The first step in 4medica’s MPI-as-a-Service 4-layer process is running an organization’s data through a patented identity resolution engine to deduplicate records. This layer alone can reduce duplications by more than half. Patient records that can’t be reconciled using 4medica’s MPI technology in the past would be sent to a work queue, where data stewards would attempt to resolve patient matching issues.

However, by applying AI and ML technology in the second and third layers to catch more errors and improve eMPI scoring weights, 4medica’s platform can reduce duplication rates to as low as 1% to 3% — and work through high volumes of data much faster. From there, the 4medica data science team can resolve final duplicate candidates, check for overlays (false positives) and, if necessary, rerun layers one through three.

“Poorly identified patients are one of the main causes of safety issues in healthcare,” said Dr. Oleg Bess, 4medica founder and CEO. “Using AI and ML to accelerate and improve the process of reducing patient matching errors leads to more informed clinical decisions and better outcomes.”

Built to process unlimited patient identities, the 4medica Big Data MPI is ideal for health information exchanges (HIEs), large hospital systems, labs, clinics and other healthcare organizations that must manage tens of millions of patient records each year.

“Health data quality should be a priority for all healthcare organizations,” said Gregg Church, president of 4medica. “Harnessing advanced technologies such as AI, ML and automation through the cloud is the most effective and cost-efficient way for an organization to improve the quality – and the value – of its data.”

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