What You Should Know:
– A new report from KLAS Research examines the use of artificial intelligence (AI) and machine learning (ML) within healthcare data science solutions.
– The KLAS report, which builds upon a previous KLAS analysis, explores how healthcare organizations are utilizing these technologies, the outcomes they are achieving, and the effectiveness of vendor partnerships in driving success.
AI in Healthcare: A Broad Landscape
The report emphasizes that AI encompasses a wide range of technologies. Within the context of healthcare data science solutions, machine learning is frequently used for clinical and population health use cases, such as patient risk stratification and predictive analytics. The report also notes the increasing interest in generative AI, particularly for ambient speech applications.
Vendor Performance and Customer Satisfaction
The report assesses several vendors offering healthcare AI data science solutions, highlighting key findings for each:
ClosedLoop: Customers report high satisfaction with ClosedLoop, citing strong partnerships, responsive support, and deep expertise in incorporating AI into workflows. ClosedLoop’s solution is used across a broad range of AI use cases, including clinical, population health, patient engagement, operational, and financial applications. N1 Health: Customers value N1 Health’s AI capabilities and partnership approach, particularly for Social Determinants of Health (SDOH) applications. However, some customers express uncertainty about their long-term plans with N1 Health due to cost considerations and the evaluation of building in-house AI capabilities. Epic: Epic provides machine learning and generative AI capabilities within its EHR platform. Customers generally find these capabilities well-developed and appreciate the vendor’s responsiveness. Epic’s solutions are used for a wide variety of use cases, with notable success in sepsis identification and risk stratification. However, some customers desire more strategic guidance from Epic on how to effectively utilize the available AI models. Oracle Health: Customer satisfaction with Oracle Health varies. Some customers are satisfied with the solution’s strong AI capabilities and their ability to achieve desired outcomes. Others express frustration with the vendor’s lack of partnership, insufficient resources and training, and perceived increase in “nickel-and-diming” since the Cerner acquisition. Similar to Epic customers, Oracle Health customers indicate a need for more guidance in deepening their use of the solution.Key Considerations for Healthcare Organizations
The KLAS suggests that healthcare organizations should carefully evaluate vendor partnerships, implementation support, and strategic guidance when adopting AI data science solutions. Organizations should also consider their internal data science capabilities and the specific use cases they aim to address.
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