This research will study how a safety-net hospital responds to a pandemic, specifically COVID-19, to identify how information needs are met and how decisions are made and communicated to other individuals internal and external to the institution.
This study will integrate a previously piloted risk tool and clinical decision aid, MammoScreen, with an EHR to provide personalized decision support for both patients and clinicians to improve quality of care for women who are at increased risk for breast cancer.
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 implement a personalized and electronically integrated shared clinical decision support system for left ventricular assist devices in patients with advanced heart failure.
This research aims to develop and evaluate a clinical decision support strategy to promote influenza vaccination among children who are hospitalized with the goal to identify insights that broadly apply to clinical decision support for health maintenance interventions in pediatric acute care settings.
The purpose of this research is to develop a standards-based, interoperable, and publicly available clinical decision support resource to aid primary care practices in instituting routine fall risk assessment and prevention care plans.
This research aims to enhance and implement a clinical decision support tool that will support providers and underinsured patients in making personalized decisions regarding diet goal setting.
The aim of this research is to implement a clinical decision support tool to provide clinicians patient-specific and evidence-based treatment recommendations regarding the recognition and management of high blood pressure and hypertension in children and adolescents.
This research will maintain and continue the work of the CDS Connect platform, including its repository, its public work group, and open-source tools.
The overall goal of the research is to advance the integration and use of patient-generated digital health data in ambulatory care settings.