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KLAS: ClosedLoop leads health AI vendors in client satisfaction

In a KLAS report, ClosedLoop clients reported the highest levels of satisfaction across four measured health AI vendors, noting strong partnership.

Hannah Nelson
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Published: 05 Mar 2025

Customers of health AI data science vendor ClosedLoop reported the highest levels of customer satisfaction among four measured vendors, according to a new KLAS report.

KLAS conducted interviews with healthcare professionals over the last 12 months using its standard quantitative evaluation for healthcare software. The report outlines how clients are using machine learning models within their data science tools, the outcomes they are achieving and the level of partnership vendors are providing to help drive success.

Sample sizes of unique respondents for each of the measured vendors are as follows:

  • ClosedLoop- 19
  • N1 Health- 7
  • Epic- 23
  • OracleHealth- 7  

ClosedLoop

Among measured vendors, ClosedLoop ranked highest for almost every standard KLAS metric. Clients emphasized the vendor’s strong partnership, responsive support staff and expertise in AI workflow integration.

Compared to other measured vendors, ClosedLoop clients reported the widest array of use cases, with respondents using the tool for clinical, population health, patient engagement, operational and financial applications.  

While interviewed customers value their relationship with ClosedLoop, some have decreased their use of the tool or are unsure if it is part of their long-term plans due to budget constraints or efforts to build AI models in-house.

Additionally, some payer respondents said that ClosedLoop focuses more on delivering new features for healthcare provider customers.

N1 Health

Based on limited data, N1 Health respondents value the tool’s AI capabilities and their partnership with the vendor. Respondents most commonly use the technology for its SDOH capabilities, which helps target patient outreach based on factors like credit score or food security.

However, only half of the interviewed organizations said that the tool is part of their long-term plans. One customer indicated that their use has declined due to cost and a lack of measurable outcomes. Several payer respondents are assessing the cost of building their own AI models.

Epic

Interviewed customers said that the AI capabilities within Epic’s EHR are well-developed and that the vendor is responsive to their concerns. Respondents also reported achieving outcomes across various use cases, especially sepsis identification and risk stratification.

Some respondents indicated a desire for more strategic guidance from the vendor on using the available AI to optimize outcomes. Still, most of the clients interviewed expanded their use of Epic’s data science capabilities in the last year and plan to continue expanding their usage. 

Oracle Health

Client satisfaction with Oracle Health AI varies based on limited data. Some respondents reported that despite a challenging learning curve, the tool’s AI capabilities are very strong and drive desired outcomes. Other customers are frustrated with Oracle Health, noting that the vendor is difficult to work with and doesn’t provide sufficient resources or keep promises regarding product development.

Additionally, less satisfied customers said that nickel-and-diming has increased since Oracle Health acquired Cerner.

Overall, clients reported that they want the vendor to provide additional guidance to help them deepen their use of the AI tool, as all respondents report it is part of their long-term plans.

Hannah Nelson has been covering news related to health information technology and health data interoperability since 2020.

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