Ambulatory Care

This research develops and evaluates an artificial intelligence-enhanced pretreatment peer-review process in radiation oncology, aiming to improve patient safety by reducing variability among providers in treatment planning, minimizing clinical errors, and enhancing overall treatment outcomes.

This research aims to improve the early detection of venous thromboembolism in primary and urgent care by using mixed methods (stakeholder interviews and surveys, electronic health records, and machine learning) to better understand missed and delayed diagnoses, identify risk factors, and develop tools to enhance patient safety.

This research examines whether remote therapeutic monitoring can improve physical therapy outcomes by increasing patient engagement, adherence to home exercises, and communication with providers, while also assessing its impact on healthcare costs and feasibility for broader implementation.

The study will develop and implement a validated machine learning model to optimize voiding cystourethrogram timing and use for diagnosing vesicoureteral reflux (VUR) in children, aiming to reduce the significant health and economic impacts of VUR and recurrent febrile urinary tract infections (fUTIs) by standardizing practices, minimizing unnecessary procedures, and ensuring timely diagnosis for those at highest risk, ultimately seeking to prevent renal injury from fUTIs.

The study will create, implement, and test a patient-centric web app to support older adults with chronic conditions in comprehending, managing, and acting upon their lab test results.

This research will create clinical decision support artifacts for three patient-centered outcomes research guidelines around advanced diagnostic imaging using standards to allow them to be shareable, interoperable, and scalable; and implement them in different workflows and settings measuring their impact. 

This research will create the Shared Decision Aid Navigator System to improve the implementation, integration, and scaling of patient shared decision aids into health informatics systems, aiding shared decision making and improving patient-centered outcomes.

This research aims to examine changes in nursing clinical documentation from 2019 through the COVID-19 pandemic timeline to observe the impact of electronic health record burden on patient care.

This research will adapt and implement a digital mental health intervention to improve care coordination of mental health services in ambulatory care settings.

The Clinical Decision Support Innovation Collaborative (CDSiC) created a learning community of patients, providers, policymakers, researchers, and developers to produce resources that advance patient-centered CDS, promote its adoption, practice, and measurement, and use standards to enable its implementation at scale.