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 study showed the feasibility and value of creating a methodology and process for a health information technology black box to inform electronic health record design and usability.
This research assessed the use of a health information exchange system in emergency department settings, finding that although overall usage is relatively low, additional functionalities such as single sign on add value to clinical decision making and enable faster retrieval of patient records from external sources compared to traditional methods when embedded into existing workflows.
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.
