New 4medica Big Data MPI Functions Reveal Unprecedented Patient Data Enrichment Improvements

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Rules-engine significantly enhances the ability to avoid overlays, deemed the most unsafe and costliest part of patient ID deduplication

www.4medica.com

4medica Big Data MPI

Historically, healthcare has lacked a horizontally scalable, lightweight inference-based business rules engine for big data processing,” said Gregg Church, president of 4medica.

4medica®, creator of Big Data Management Solutions for healthcare today announced new 4medica Big Data MPI® data enrichment functionalities that significantly minimize the risk of human- and technology-error related patient matching problems. 4medica is the nation’s leading developer of cloud-based clinical data exchange enabling accurate and reliable patient information to flow across disparate care locations.

A 2018 Black Book™ survey of nearly 1,400 health technology managers estimated 33 percent of all denied claims result from incorrect patient data, costing the average hospital $1.5 million. Further findings noted that staff at hospitals with more than 150 beds can spend in excess of five months validating, normalizing and cleansing the data of hundreds of thousands of records.

Automated machine learning and improved rules management are driving decision automation across industries. 4medica’s powerful new rules engine takes patient matching to the next level by automating both critical and high-volume decisions to better define and improve the accuracy of the matching sequence.

“Historically, healthcare has lacked a horizontally scalable, lightweight inference-based business rules engine for big data processing,” said Gregg Church, president of 4medica. “With the release of this innovative functionality built within our enterprise master patient index, clients can boost their patient-matching rates through higher quality data enrichment capabilities, which in turn will impact patient safety and reduce duplicative care costs.”

Muthu Kuttalingam, MBA, 4medica’s senior vice president of Product Development and Technology, explained how the new MPI functions work.

“The 4medica Big Data MPI solution can already ingest additional data and enrich existing data sets with that data by seeding and feeding the information to match what’s missing within the patient record. The technology next populates a patient record by reviewing those traits all at the same time,” he said. “However, in those cases where score alone is not enough, our new rules engine improves upon the machine matching process to yield precise patient matching and fewer overlays.”

Overlays in patient identification occur when the records of one patient are overwritten with data from another patient’s record, resulting in two people erroneously sharing the same identifier—considered the most unsafe and costly part of deduplication.

“Consider the case of identical or fraternal twins,” noted Kuttalingam. “The risk of overlays is highly common because part of the traditional matching process uses ‘sounds like’ or speech sounds since twins often have similar sounding names. Instead of automatically matching those two records, our newly engineered rules-based technology demotes the match to a ‘partial’ match and puts that data on a worklist for staff to review and determine if the right person is matched to the right record.”

Data from the October 2018 Pew Charitable Trust report, “Enhanced Patient Matching Is Critical to Achieving Full Promise of Digital Health Records” cited a loss of $43,000 for a health system because the care for an 11-month old twin was documented in her sister’s record.

Built to perform in transactional mode, the 4medica Big Data MPI couples its millisecond responses with a multi-threaded mode which easily scales for use by any healthcare organization that processes hundreds to millions of identities and transactions daily. Its sophisticated MPI algorithms use a “referential matching” approach to patient matching technology which matches electronic records to a comprehensive database of demographic data that may include name changes, old addresses and other information collected over decades. Additionally, it uses in Inverse Indexes – the technology in Google’s search engines – and utilizes modern non-SQL databases to further simplify the system.

About 4medica
4medica provides the industry's leading SaaS (software-as-a-service) Big Data Management and Clinical Data Exchange platforms to help healthcare organizations of diverse types create a seamless view of the patient care experience and further drive better health outcomes. The 4medica Big Data MPI®, 4medica Big Data CDRTM, 4medica Big Data Identity EnrichmentTM and ClinXdata® Clinical Data Exchange modular solutions integrate with and build upon disparate systems to facilitate patient identity management and interoperable data exchange across various care settings to promote care continuity. The cloud-computing model is scalable, lower cost, maintenance-free, easy to use and deployable in a few months or less, eliminating large capital outlays or resource utilization. 4medica connects hundreds of organizations including ACOs, HIEs, HINs, hospitals, health systems, physicians, laboratories, and radiology imaging centers. The company’s national footprint includes regional offices in Indiana, North Carolina, Boston, South Carolina and Oregon, with headquarters located in Marina Del Rey, California. Learn more at http://www.4medica.com.

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Gregory Church

Angela Jenkins
Angela Jenkins & Associates
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