To say artificial intelligence (AI) is a hot topic is an understatement. An expansive term, AI encompasses a wide range of technologies, including machine learning (ML), natural language processing (NLP), generative AI, computer vision, and robotics. As these technologies continue to develop, leaders across various industries are trying to determine their AI strategies. How do we harness the power of AI effectively? How do we keep up with new capabilities in the rush of progress? How do we avoid pitfalls?
Within the healthcare industry, AI is showing promise in streamlining manual processes and facilitating better patient care. For example, ambient speech technology—which uses healthcare-specific language models to capture patient-provider encounters and contextualize them into structured notes—is successfully streamlining documentation and improving clinician satisfaction (read KLAS’ Ambient Speech 2025 report to learn more about this technology).
Data science solutions—which provide ML capabilities for clinical, operational, and/or financial use cases—have been in the market for years and have been a continued focus for healthcare organizations, who are looking to their vendors for guidance on how further their AI adoption and outcomes. Recently, KLAS published a report that examines customer satisfaction with their data science vendors as well as how they are commonly using the solutions.
Points to Know
- Healthcare AI data science solutions can be divided into EHR-agnostic and EHR-specific solutions. EHR-agnostic solutions cater to organizations not using Epic or Oracle Health, while EHR-specific solutions integrate AI into existing EHR systems. Organizations want both types to incorporate AI into existing products and workflows and to partner with customers to set AI strategies.
- Healthcare organizations frequently leverage machine learning capabilities in their data science solutions for clinical and population health use cases. Many respondents tell KLAS that these solutions help improve patient risk stratification and reduce admissions, though many organizations have not yet extended their use into other areas.
- Customers of EHR-agnostic vendors like ClosedLoop and N1 Health appreciate the partnership and expertise provided. Epic and Oracle Health customers value the strong capabilities of their solutions but seek more training and guidance to expand their adoption.
Read on below for more details.
How Are Organizations Using Healthcare AI Data Science Solutions?
Healthcare AI data science solutions can generally be grouped into two categories: EHR-agnostic solutions and EHR-specific solutions (see next section). EHR-agnostic solutions are typically used by organizations that aren’t already using the enterprise vendors Epic or Oracle Health, since both vendors offer data science solutions. For all solution types, organizations want their vendors to be able to (1) incorporate AI into existing products and workflows and (2) partner with customers around setting AI strategies, guiding implementations, and validating use cases.
Interviewed healthcare organizations report that they most frequently leverage ML capabilities in their data science solutions for clinical and population health use cases. Many respondents say that using data science solutions in these areas has enabled them to improve patient risk stratification and reduce the number of admissions. Despite the fact that organizations have deepened their adoption of these use cases, many have not extended use of their data science solution into other areas.
How Do Customers Feel About Their Data Science Vendors?
EHR-Agnostic Solutions
Among measured vendors in KLAS’ data science solutions report, two vendors provide EHR-agnostic solutions—ClosedLoop and N1 Health. In general, customers highlight the partnership they receive from these third-party vendors, noting that the vendors work closely with customers and provide needed expertise. Interviewed ClosedLoop customers report using the solution for the highest number of use cases, including less common use cases like care management and marketing. N1 Health customers appreciate the solution’s SDOH capabilities.
EHR-Specific Solutions
As mentioned before, enterprise vendors Epic and Oracle Health offer data science solutions to their EHR customers, who value being able to further consolidate their applications. Epic respondents feel the ML capabilities are well developed; they also report achieving a wide variety of outcomes with the solution, including improved sepsis identification. Interviewed Oracle Health customers also feel the solution’s capabilities are strong, though they note the learning curve is difficult. Overall, customers of both Epic and Oracle Health would like more training and guidance to further expand and deepen their adoption.
We encourage everyone to read the full report for more in-depth information about customer satisfaction with these four vendors.
Looking Ahead
One of the common questions that healthcare and vendor organizations ask KLAS is this: what are others doing with AI? With this in mind, we hope that the recent report on healthcare AI data science solutions will help organizations gauge what is possible with AI as they continue to refine their own strategies. KLAS will continue to monitor vendor performance and customer adoption in the data science space. To see the most up-to-date information on data science solutions, please visit the KLAS website. Additionally, we encourage organizations that are using these solutions to share their experience with peers through KLAS’ standard survey.
For additional insights on AI, see KLAS’ recently published Healthcare AI 2025 report.
