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In this study, researchers assessed the feasibility of using commercial off-the-shelf mobile technology, including phones and fitness trackers, to collect and report patient-generated health data and patient-reported outcomes from diverse, disadvantaged patients in an urban safety net health care system.
This research used natural language processing and machine learning to develop algorithms to recognize diagnostic criteria in free text for autism spectrum disorder, to increase earlier diagnosis and treatment.
This project developed a natural language processing electronic health record search tool that automatically identifies and ranks relevant clinical information based on a patient’s presenting complaint within the emergency department setting.
This research developed and evaluated a mobile health application to improve screening, intervention, and referrals in the care of pregnant women.
This project developed a patient-centric tool called the Surgical Risk Preoperative Assessment System to estimate the risk of adverse operative outcomes.
This project developed patient-tailored relevant warnings about drug-drug interactions and found that it reduced irrelevant alerts.
This study defined a knowledge base for a clinical decision support (CDS) tool and identified technology requirements for CDS design to optimize adoption of necrotizing enterocolitis prevention practices to support clinician decision making with the overall goal to improve the use of evidence-based practices for prevention and early recognition of NEC among premature infants.
This project developed, implemented, and assessed a patient data collection and clinician feedback system for depression care management in primary care practices, and found improvements in patient medication filling and adherence.
This project evaluated the usability of medication fulfillment data obtained from electronic health records and piloted a clinical decision support tool that alerted physicians to potential hypertensive medication adherence lapses.
This research project explored an innovative method to retrieve clinically relevant images for facilitating timely and accurate evaluation of diabetic retinopathy.