Search found 40 items
This research will develop and evaluate an artificial intelligence-driven clinical decision support system to detect and manage acute kidney injury in the emergency department.
This research will identify and characterize the factors differentiating patient portal users from non-users and develop guidelines to optimize portal design and development for population subgroups.
This research will develop, implement, disseminate, and evaluate reusable, shareable clinical decision support for both patients and clinicians in the area of chronic pain management.
This research will evaluate the safety, usability, and impact of an e-prescribing standard on adverse drug events related to erroneously dispensed medications.
This research will evaluate the lifecycle of clinical decision support (CDS), as currently implemented at most health systems, against a future CDS state that incorporates the use of shareable CDS resources created using the Agency for Healthcare Research and Quality’s CDS Connect tools.
This study will create an innovative electronic medication administration record prototype and a medication administration workflow risk assessment to improve the medication administration process and the usability and safety of the electronic medication administration records in response to challenges from COVID-19.
This research supported the development of electronic applications that enable the collection of diverse patient-reported outcome measures in a standardized manner across health providers and systems to enable broader data sharing for clinical care and research.
The central goal of the annual Conference on Health IT & Analytics is to develop a health information technology and analytics (HIT+A) research agenda that supports national efforts to create a learning health system that produces evidence to make health care safer, of higher quality, more accessible, equitable, and affordable.
This study showed the feasibility and value of creating a methodology and process for a health information technology black box to inform electronic health record design and usability.
This research study addressed the overuse of blood cultures to diagnose sepsis by developing an electronic health record-embedded clinical decision support tool that draws upon the strengths of analytical and naturalistic decision making.