John Smith in your EMR. Jon Smth in your billing system. J. Smith in your insurance database. Jonathan Smith in your lab platform. They are the same patient, but your systems treat them as four different people. MatchLogic finds every variant, links them by confidence score, and gives your team a single, verified identity.
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We were running into patient identity issues every time we onboarded data from a new clinic or acquisition. MatchLogic resolved thousands of duplicate patient records that our existing MPI tools had missed.
When the same patient exists as multiple records, clinicians see incomplete histories. Lab results get filed under the wrong chart. Insurance claims get denied for mismatched identifiers. Compliance teams cannot produce accurate patient counts for regulatory filings. MatchLogic resolves patient identities across EMRs, billing platforms, lab systems, and insurance databases so every record points to one verified person.
A patient arrives at the emergency department. The clinician pulls up their chart and sees a partial history: two past visits, no medication list, no allergy records. But the patient has been seen nine times across three facilities in the same health system. The other seven visits are filed under a different MRN because the patient's name was entered differently at each location. The clinician makes treatment decisions based on incomplete information. MatchLogic links every record for that patient across every facility so the full history is available when it matters.
Two reps call the same account because it exists under two different company names. Territory assignments overlap when the CRM can't tell that 'Johnson & Johnson' and 'J&J Inc.' are the same buyer. Pipeline reports show phantom opportunities because a single deal is logged against duplicate contact records. Revenue forecasting suffers when the data underneath it has no integrity.
When a patient has two active records, a lab order placed under one MRN does not appear under the other. The ordering physician does not see that the test was already done, so they order it again. The patient gets an unnecessary blood draw, an unnecessary imaging study, or an unnecessary procedure. The organization absorbs the cost. The patient absorbs the inconvenience and the clinical risk of repeated exposure. MatchLogic prevents this by resolving the duplicate records before orders are placed.
Regulations like GDPR and CCPA require organizations to honor data subject requests across every system. If a customer exists as three separate records, a deletion request might only remove one. Incomplete compliance exposes the organization to fines and audit findings. Duplicate records are a direct liability when regulators come looking.
The new ERP connects to downstream systems through APIs and data feeds. Those integrations assume clean, deduplicated master data. When a customer record exists three times in the new system, downstream processes break in unpredictable ways: order management routes to the wrong account, billing sends invoices to outdated addresses, support tickets open against phantom customer profiles. Every integration point becomes a potential failure point when the underlying data has duplicates.
Quality measures, cancer registries, immunization reporting, and population health analytics all depend on accurate patient counts. If the same patient appears twice, they get counted twice. Readmission rates look different. Vaccination coverage percentages shift. Quality scores that determine reimbursement rates are calculated on inaccurate denominators. MatchLogic deduplicates patient records before data is submitted to registries and reporting systems, so the numbers reflect the actual patient population.
Patient matching is harder than standard deduplication because the stakes are higher and the data is messier. Names get misspelled during registration. Dates of birth get transposed. Patients change addresses and phone numbers. Married names replace maiden names. And different systems use different identifier formats.
MatchLogic compares multiple fields at once: first name, last name, middle initial, date of birth, SSN (full or last four), MRN, address, phone number, and any other field in your patient record. A weak match on name combined with an exact match on DOB and a strong match on address produces a high-confidence link. The algorithms handle phonetic similarities ('Steven' vs. 'Stephen'), nickname conversions ('Bill' vs. 'William'), transposed digits in DOBs (03/15/1982 vs. 03/51/1982), and formatting differences across systems.
Every match decision shows the full field-by-field breakdown. Your MPI analysts can review, approve, or override any link before it affects production data.
Patient names are misspelled at registration, entered as nicknames, abbreviated, or changed after marriage. MatchLogic's algorithms catch phonetic similarities (Kathy vs. Cathy), nickname-to-formal conversions (Bill vs. William), abbreviation expansion (Robt. vs. Robert), and transliteration differences in multilingual patient populations. Standard MPI tools miss these consistently.
Date of birth is the single most common matching field in patient data, and it is also the most commonly miskeyed. MatchLogic handles format differences (03/15/1982 vs. 1982-03-15), transposed digits (03/15 vs. 03/51), and partial entries. Combined with SSN last-four matching and MRN cross-referencing, you get high-confidence links even when individual fields have errors.
Before matching begins, MatchLogic scans every field for completeness, format consistency, and uniqueness. You see which fields are reliable for matching (DOB is 99% populated) and which are not (middle name is 42% populated). This prevents you from building match rules on fields that will produce unreliable results.
Patient data cannot leave your environment in most healthcare organizations. MatchLogic deploys as a desktop application or on-premises server. Your data never leaves your network. There is no cloud upload required. The desktop license starts at $13K/yr, making it accessible to health systems, clinics, and health information exchanges that cannot justify six-figure platform costs.
Bring a de-identified patient dataset. We will show you how many duplicates MatchLogic finds, which fields drove each match, and how the confidence scoring works. Every demo uses your data, not ours.
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Entity resolution shows exactly how your records connect to real-world entities. You'll see which fragments belong together, where identity variations hide, and how records cluster. Visual entity maps highlight relationships across all your systems before any data changes, giving you full control over identity unification.
matchlogic resolves 10 million records in under 8 minutes, linking fragments and clustering related entities at scale. The engine analyzes every field, calculates match confidence, groups related records, and generates visual entity maps without performance issues.
Most companies discover 30-40% entity fragmentation they never knew existed. Resolution catches nicknames hiding as formal names, typos creating false duplicates, and company abbreviations splitting single entities. These variations cost real money in duplicate processes.
Deduplication removes exact duplicates within one dataset. Entity resolution links related records across multiple systems to real-world entities - even when names, formats, and identifiers vary. You get unified profiles showing the complete picture of each customer, vendor, or contact.
Yes - see exactly how records will cluster before any data changes. Visual previews show entity groups with confidence scores highlighted. Review field-by-field evidence, adjust matching rules, and approve results. Nothing changes until you confirm the resolution output.
Entity resolution creates unified customer identities for GDPR right-to-access requests, KYC verification, and AML screening. Track which records belong to each entity, prove proper identity management for audits, and maintain evidence trails showing how identities were resolved.
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