ORLANDO, Fla.--(BUSINESS WIRE)--Medal Inc. today announced the debut of its platform that can extract, transform and use the vast and fragmented information that exists about patients – and present it to healthcare providers in a timely and meaningful way.
Health data is only purposeful when it is accessible and contextual. Medal developed a complete system for extracting medical information from every possible source where data is trapped: fax, printed paper, health information exchange data, and from EMRs. Machine learning and Natural Language Processing (NLP) match more than 300 medical attributes to each word of unstructured text, allowing clinicians to more easily gain access to critical data that could save a patient’s life.
Medal’s platform pipeline leverages Fast Healthcare Interoperability Resources (FHIR) standards to unlock medical silos and enable the secure and seamless sharing of a patient’s medical data, including physician notes and care summaries, among patients, providers, payers and researchers. It accounts for the variability of data types with the ability to digest existing and future data formats, and both structured and unstructured data.
Information about the patient is presented in an intuitive and streamlined way, wrapped within a clinical narrative, so that end-users can gain patient insights rapidly and reduce manual labor by up to 20 times.
“To provide quality patient care, quickly and cost-effectively, care teams must not only have access to critical data, they have to understand it,” said Lonnie Rae, Medal founder and CEO. “We founded Medal to solve a massive challenge facing our healthcare industry, which is overrun with fragmented data that often lacks context. The entire healthcare ecosystem will transform when information is easily accessible, easily understood and can be shared seamlessly, rapidly and securely.”
Medal will host platform demonstrations at the NewWave booth (#509) at HIMSS in Orlando, February 11-15.
Medal Inc. delivers a complete platform pipeline to extract health information from fragmented data sources where it is trapped: fax, paper, health information exchanges, legacy databases, and from electronic health records. Medal’s Natural Language Processing and Machine Learning algorithms apply 18 different processing pipelines and query over 300 medical attributes for each word of unstructured text, translating it to FHIR, and making Medal the most comprehensive clinical narrative platform on the market. Medal Inc. is based in San Francisco, CA.