Redox partners with IMO

Redox, the company setting the stage for composable healthcare, has partnered with Intelligent Medical Objects (IMO), a healthcare data enablement company to standardize clinical data from diverse systems into consistent, structured, clinically validated terminology including comprehensive mapping to major global coding systems (ICD-9-CM, ICD-10-CM, and SNOMED® CT). Now healthcare companies can spend more time building improved patient and clinician experiences by eliminating the lengthy and taxing process of combining multiple point solutions.

This announcement comes directly after last week’s launch of API Actions, a modular way for healthcare companies to build out their ideal workflows. This present partnership between IMO and Redox benefits any healthcare company that receives clinical problem or diagnosis data (ICD-9-CM, ICD-10-CM, and SNOMED® code sets) from multiple organizations. As a result, these are further able to enrich healthcare data using consistent terminology across all data standards, formats, and workflows with a single Redox API.

“Redox is dedicated to making sure that healthcare data can empower builders to create new experiences for patients and providers,” states Redox CEO Luke Bonney. “We want to be clear that composable healthcare is a practice of bringing together the best technologies to produce the best solutions, and we’re glad to be doing so with IMO.”

“Clinical patient data is a powerful resource in healthcare. IMO partnering with Redox will accelerate the ability for healthcare companies to leverage that data to create better outcomes for doctors and patients,” said IMO CEO Ann Barnes.

Redox will leverage IMO’s technology to standardize healthcare data for its customers. Using IMO Precision Normalize will allow Redox to accelerate data normalization for its hospital and software vendor clients. This lets healthcare technology companies spend more time building powerful solutions by eliminating the lengthy and costly process of cleaning and standardizing messy clinical data.
MB Bureau