Search AMIA.org

To facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects.

To identify chatbot use cases deployed for public health response activities during the Covid-19 pandemic.

After deploying a clinical prediction model, subsequently collected data can be used to fine-tune its predictions and adapt to temporal shifts. Because model updating carries risks of over-updating/fitting, we study online methods with performance guarantees.

Evidence synthesis teams, physicians, policy makers, and patients and their families all have an interest in following the outcomes of clinical trials and would benefit from being able to evaluate both the results posted in trial registries and in the publications that arise from them. Manual searching for publications arising from a given trial is a laborious and uncertain process. We sought to create a statistical model to automatically identify [...]

To evaluate the potential for machine learning to predict medication alerts that might be ignored by a user, and intelligently filter out those alerts from the user's view.

To identify the risk factors home healthcare (HHC) clinicians associate with patient deterioration and understand how clinicians respond to and document these risk factors.

Given that electronic clinical quality measures (eCQMs) are playing a central role in quality improvement applications nationwide, a stronger evidence base demonstrating their reliability is critically needed. To assess the reliability of electronic health record-extracted data elements and measure results for the Elective Delivery and Exclusive Breast Milk Feeding measures (vs manual abstraction) among a national sample of US acute care hospitals, as well as common sources of discrepancies and [...]

The opioid epidemic in the United States has precipitated a need for public health agencies to better understand risk factors associated with fatal overdoses. Matching person-level information stored in public health, medical, and human services datasets can enhance the understanding of opioid overdose risk factors and interventions.

Safe care of central venous access devices (CVAD) requires clinicians be able to identify key CVAD properties from insertion until safe removal. Our objective was to design and evaluate interfaces to improve CVAD documentation quality and information retrieval.

To inform training needs for the revised Certified Clinical Data Manager (CCDMTM) Exam.