A new joint report from KLAS and CHIME polled some early adopters of artificial intelligence and machine learning tools, and asked how the technology is impacting their clinical, financial and operational goals.
WHY IT MATTERS
The study is based on interviews with IT leaders at 57 organizations – CIOs, CMIOs, data scientists and more – that are using AI across a variety of cases, from clinical decision support to patient engagement to revenue cycle management. It asked them about some tangible gains the technology has helped them achieve. It also gleaned some insights about a handful of leading vendors, and found some common best practices for AI adoption.
KLAS focused on purpose-built AI vendors – those focused primarily on analytics and AI, with dedicated, standalone product – and analytics platforms with AI infrastructure. It did not assess EHR vendors with AI capabilities or other vendors whose IT applications have some AI capabilities but aren’t meant to be standalone AI applications.
The research firm defines AI as software that “provides machine learning or natural language processing capabilities for healthcare-related clinical, operational, or financial areas.”
Specifically, machine learning tools for structured data are able to “study and learn computer systems’ algorithms and statistical models to effectively perform tasks without requiring explicit instructions, relying instead on patterns and inference to determine results.” NLP, meanwhile, for unstructured notes, “enables software solutions to understand, process, and analyze natural language, whether speech or text.”
The report specified some important capabilities AI vendors worth their salt should be able to provide for their healthcare customers, some critical (prebuilt healthcare models, a machine learning platform to create models, NLP and free text functionalities) and others that are “nice to have” (supervised and unsupervised learning, forecasting, modeling multimedia).
Based on its polling of those 57 IT decision-makers, KLAS also rated customer satisfaction for six leading vendors: Jvion, DataRobot, KenSci, Clinithink, IBM Watson Health and Health Catalyst. Researchers noted, however, that “because healthcare AI is such a new market,” only one of those six – Jvion – “has enough evaluations (at least 15) to be considered fully rated. Findings on all other vendors are based on limited data (6–15 evaluations).”