Clare Medical has created a new artificial intelligence-based diagnostic tool to accurately predict the likelihood of patients requiring admission to a hospital within 30 days.

The data it generates can be deployed to avoid hospital admissions and reduce hospital and emergency room utilization, which are major costs to the healthcare system. 

The platform was built on internal data derived from the company’s EHR, as well as publicly sourced datasets. It uses an advanced form of artificial intelligence that gathers vital pieces of information from a variety of sources contained within a patient’s chart – including provider notes, vital signs, medications, ICD10 coding and laboratory data – to output a probability score. The algorithm has been tested and validated in a variety of settings.

The 30-day risk of hospital admission and readmission are crucial metrics in determining how healthcare organizations and providers are graded. Because of the high costs of hospital admissions, organizations that manage risk appropriately and succeed in reducing hospitalizations are likely to be well-positioned and have significant value in the current healthcare system.

“Although a variety of risk tools have been constructed as general measures of patient health, we don’t know of any that are focused on this specific critical metric, drawing from such a vast data repository, while also relying on a deep learning-based framework that manages to deliver on this level of well-validated performance measures,” said Elie Donath, M.D., one of Clare’s primary care medical directors and project lead.

This is Clare’s first suite of tools expected to provide accurate forecasts, within an error range of 3%. Others in development will tackle a range of clinically meaningful events whose onset can be halted or delayed by early intervention and providing appropriate corrective measures.