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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 create patient-centered, interoperable, shareable clinical decision support tools that will support providers and patients in making patient-centered decisions about management of hypertension.
This research will maintain and continue the work of the CDS Connect platform, including its repository, its public work group, and open-source tools.
This research developed and evaluated mobile application messages that assisted individuals with hypertension to adhere to a low-sodium diet.
This research assessed the utilization of a “smart” pillbox, a prefilled electronic medication tray that sends electronic alerts and reports to patients, caregivers, and primary care providers for patients discharged from the hospital, finding an increased medication adherence among patients on five or more chronic medications.
This project developed, implemented, and evaluated a program that includes clinical decision support to improve diagnosis of hypertension in children.
This research combined the artificial intelligence technology technique Dynamic Logic with natural language processing to create a model to predict risk of death over the next 12 months and found it was better than benchmark statistical and machine learning algorithms.
This project will determine the optimal display of blood pressure data for patients and their physicians in order to facilitate shared decisionmaking about blood pressure control and treatment.
This study found increased rates for some screening and preventative services following adoption of federally-certified electronic health records.
This project tested a reinforcement learning (RL) text messaging system, which tailors and adapts messages to the needs of patients with uncontrolled hypertension to promote medication adherence, and found it to be feasible and adaptive.