Search AMIA.org

Like most real-world data, electronic health record (EHR)-derived data from oncology patients typically exhibits wide interpatient variability in terms of available data elements. This interpatient variability leads to missing data and can present critical challenges in developing and implementing predictive models to underlie clinical decision support for patient-specific oncology care. Here, we sought to develop a novel ensemble approach to addressing missing data that we term the "meta-model" and apply [...]

Countries need to determine their level of digital health capability maturity to assess and mobilize their knowledge, skills, and resources to systematically develop, implement, evaluate, scale up and maintain large-scale implementations of standards-based interoperable digital health tools.

Clinical trials are the gold standard for generating reliable medical evidence. The biggest bottleneck in clinical trials is recruitment. To facilitate recruitment, tools for patient search of relevant clinical trials have been developed, but users often suffer from information overload. With nearly 700 coronavirus disease 2019 (COVID-19) trials conducted in the United States as of August 2020, it is imperative to enable rapid recruitment to these studies. The COVID-19 Trial [...]

To apply natural language processing (NLP) techniques to identify individual events and modes of communication between healthcare professionals and families of critically ill patients from electronic medical records (EMR).

Digital medical records have enabled us to employ clinical data in many new and innovative ways. However, these advances have brought with them a complex set of demands for healthcare institutions regarding data sharing with topics such as data ownership, the loss of privacy, and the protection of the intellectual property. The lack of clear guidance from government entities often creates conflicting messages about data policy, leaving institutions to develop [...]

To present clinicians at the point-of-care with real-world data on the effectiveness of various treatment options in a precision cohort of patients closely matched to the index patient.

The study sought to describe the contributions of clinical informatics (CI) fellows to their institutions' coronavirus disease 2019 (COVID-19) response.

We sought to demonstrate the feasibility of utilizing deep learning models to extract safety signals related to the use of dietary supplements (DSs) in clinical text.

The study sought to test the possibility of differentiating chest x-ray images of coronavirus disease 2019 (COVID-19) against other pneumonia and healthy patients using deep neural networks.

We sought reduce electronic health record (EHR) burden on inpatient clinicians with a 2-week EHR optimization sprint.