# 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: 3 months ago
- Net upvotes: 2


## Comments
### Comment 1

&lt;p&gt;&lt;span style=&quot;color: rgb(0, 0, 0);&quot;&gt;Does Tilores&#39; real-time fuzzy matching actually catch a likely duplicate the moment a new record lands, or does that only hold up on cleaner data than most systems have in practice?&lt;/span&gt;&lt;/p&gt;

##### Comment Metadata
- Posted at: 11 days ago
- Author title: Marketing



### Comment 2

&lt;p&gt;Worth naming explicitly why the tuning philosophy inverts in this setting. In customer data, a false merge costs you a wasted email, so teams tune toward merging aggressively and absorb some error as the price of a cleaner list. In a hospital, a false merge puts one person&#39;s allergy list and medication history inside another person&#39;s chart, so you&#39;d rather live with a known duplicate than accept a wrong join. That flips which knob you turn, and it&#39;s why the explainability emphasis in the Senzing reviews reads as the most transferable thing on this list. A match you can inspect and reject is closer to the shape of the problem than a match rate is.&lt;/p&gt;

##### Comment Metadata
- Posted at: 13 days ago
- Author title: Tech Consultant



### Comment 3

&lt;p&gt;For a hospital system, I’d lean toward a purpose-built EMPI rather than a general entity-resolution engine. Clinical matching needs healthcare-specific identifiers, auditability, and workflows for ambiguous records that generic customer-data tools usually aren’t designed to handle.&lt;/p&gt;

##### Comment Metadata
- Posted at: 16 days ago
- Author title: Marketer



### Comment 4

&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: 2 months ago
- Author title: Marketer





## Related discussions
- [How well does Trello scale into a larger team?](https://www.g2.com/discussions/1-how-well-does-trello-scale-into-a-larger-team)
  - Posted at: over 13 years ago
  - Comments: 6
- [Can we please add a new section](https://www.g2.com/discussions/2-can-we-please-add-a-new-section)
  - Posted at: over 13 years ago
  - Comments: 0
- [Quantifiable benefits from implementing your CRM](https://www.g2.com/discussions/quantifiable-benefits-from-implementing-your-crm)
  - Posted at: over 13 years ago
  - Comments: 4