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This project will apply machine learning against a large data set to develop a model to both understand and predict surgical cancellations on individual pediatric patients at two pediatric surgical sites.
This study assessed the usability and impact of inpatient portals on patient experience, engagement, and perceptions of care.
This project applied a human factors-based framework to understand factors associated with missed test results and found that health information technology is a key barrier to test followup.
This project sought to reduce the use of emergency department services for non-urgent care by improving access to primary care physicians for Medicaid patients via the electronic medical record.
This project evaluated the impact of an electronic health record on the quality of diabetes care as measured by compliance with recommended processes of care and patient outcome measures.
This project used a mixed-method approach to investigate the validity of using electronic health record data for diabetes performance measures.
The study identified “hidden” costs – resources and staff time – that provider practices and health care organizations must consider when planning for EHR implementation.
This project used health information technology to identify patients for whom a diagnosis of prostate, lung, or colon cancer had been delayed.
The findings of this study demonstrated that electronic health record-based trigger methods can enable more meaningful measurement and surveillance of diagnostic errors in primary care.