HIE News Round Up: Health IT Data Standards to Boost Interoperability; DirectTrust Acquisition; CA HIE Expands Data Sharing Through CIE
Welcome to the Health Information Exchange Weekly News Round Up from RosettaHealth. Each week, we will provide you with a summary of all the actionable news that hospitals, HIT vendors, ACOs, and population health providers need to be smart and effective when it comes to health information exchange.
DirectTrust Acquisition Aims to Boost Data Exchange, Interoperability
DirectTrust has acquired the assets of health IT consortium and certification body SAFE Identity, including its Trust Framework, in an effort to expand interoperability use cases for secure health data exchange.
Using Health IT Data Standards to Boost Interoperability for Clinical Research
To leverage EHR data interoperability for clinical research purposes, health IT stakeholders should convert data between FHIR and CDISC data standards, according to an implementation guide from CDISC and HL7.
CA HIE Expands Data Sharing for Community Information Exchange
A California health information exchange (HIE) is expanding its data sharing capabilities to address social determinants of health (SDOH) through community information exchange (CIE).
Access to EHR Clinical Notes Boosts Chronic Condition Medication Adherence
Access to EHR clinical notes may lead to greater patient engagement and medication adherence for older patients with chronic conditions, according to a new study.
How Machine Learning Systems Help EHR Usabilty, Mitigate Burden
Machine learning systems can aid EHR usability and cut burden for disease phenotyping to support clinical research, according to a recent Mount Sinai study published in the journal Patterns.
4 Use Cases for Direct Secure Messaging, Data Exchange in Healthcare
Direct secure messaging supports data exchange for ePrescribing, ADT messaging, electronic case reporting, and COVID-19 vaccination efforts.
Scientists Create a Labor-Saving Automated Method for Studying Electronic Health Records
In an article published in the journal Patterns, scientists at the Icahn School of Medicine at Mount Sinai described the creation of a new, automated, artificial intelligence-based algorithm that can learn to read patient data from electronic health records.
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