# Which patient identity resolution platforms work best for a hospital system trying to eliminate duplicate patient records and improve matching accuracy?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">I'm researching which patient identity resolution platforms work best for a hospital system trying to eliminate duplicate patient records and improve matching accuracy, and I want to be upfront: I'm approaching this as a category researcher, not from inside a hospital. One honest note before the list, the tools in G2's<a class="a a--md" elv="true" href="https://www.g2.com/categories/identity-resolution"> </a><a class="a a--md" elv="true" href="https://www.g2.com/categories/identity-resolution">identity resolution</a> category are mostly built for customer and marketing data, so the reviews below describe general entity resolution and record matching rather than patient records specifically. That still speaks to matching accuracy and deduplication, but treat it as a starting point, not healthcare-validated proof.</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/senzing/reviews"><strong>Senzing</strong></a>: Reviewers describe entity resolution at scale with an emphasis on explainability and matching without heavy rule configuration, which is the kind of transparency that matters when a match affects a real record.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/informatica-master-data-management-identity-resolution/reviews"><strong>Informatica Master Data Management - Identity Resolution</strong></a>: Reviewers highlight smart, rule-based matching that merges records for the same entity even when the data is incomplete or formatted differently, aimed squarely at eliminating duplicates.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/tilores/reviews"><strong>Tilores</strong></a>: Reviewers point to fuzzy matching and real-time record linkage that removes duplicates and reconciles records across sources.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/salesforce-data-360-formerly-data-cloud/reviews"><strong>Salesforce Data 360</strong></a>:  health care reviewers describe ending up with one unified profile per person and reducing duplicate records, though within a customer-data context.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/prime-360/reviews"><strong>Prime 360</strong></a>: Reviewers mention identity verification and duplicate removal with real-time data cleansing.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">If you've worked on patient matching, does an entity-resolution engine like this actually fit clinical record linkage, or do you need a purpose-built patient matching tool with EMPI features? I'd like to hear where general-purpose matching falls short in a hospital setting.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p>

##### Post Metadata
- Posted at: about 1 month ago
- Net upvotes: 2


## Comments
### Comment 1

&lt;p&gt;For patient matching, I&#39;d be careful using a marketing-oriented tool as your system of record. The matching engine handles name variations and date-of-birth typos well, but hospitals usually need EMPI features and clinical validation that a CDP won&#39;t cover. Better to have general entity resolution support an EMPI than replace it.&lt;/p&gt;

##### Comment Metadata
- Posted at: 20 days ago
- Author title: Marketer





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