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Genetic ancestry is a critical co-factor to study phenotype-genotype associations using cohorts of human subjects. Most publicly available molecular datasets are, however, missing this information or only share self-reported race and ethnicity, representing a limitation to identify and repurpose datasets to investigate the contribution of ancestry to diseases and traits. We propose an analytical framework to enrich the metadata from publicly available cohorts with genetic ancestry information and a resulting [...]

Despite the potential values self-tracking data could offer, we have little understanding of how much access people have to "their" data. Our goal of this article is to unveil the current state of the data accessibility-the degree to which people can access their data-of personal health apps in the market.

Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Out-comes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge [...]

Health information technology (HIT) is intended to provide safer and better care to patients. However, poorly designed or implemented HIT poses a key risk to patient safety. It is essential for healthcare providers and researchers to investigate the HIT-related events. Unfortunately, the lack of HIT-related event databases in the community hinders the analysis and management of HIT-related events.

Although electronic health record systems have been implemented in many health settings globally, how organizations can best implement these systems to improve medication safety in mental health contexts is not well documented in the literature. The purpose of this case report is to describe how a mental health hospital in Toronto, Canada, leveraged the process of obtaining Healthcare Information Management Systems Society (HIMSS) Stage 7 on the Electronic Medical Record [...]

Immune checkpoint inhibitors (ICIs) have dramatically improved outcomes in cancer patients. However, ICIs are associated with significant immune-related adverse events (irAEs) and the underlying biological mechanisms are not well-understood. To ensure safe cancer treatment, research efforts are needed to comprehensively detect and understand irAEs.

Integrating patient-reported outcomes (PROs) into electronic health records (EHRs) can improve patient-provider communication and delivery of care. However, new system implementation in health-care institutions is often accompanied by a change in clinical workflow and organizational culture. This study examines how well an EHR-integrated PRO system fits clinical workflows and individual needs of different provider groups within 2 clinics.

Natural language processing (NLP) and machine learning approaches were used to build classifiers to identify genomic-related treatment changes in the free-text visit progress notes of cancer patients.

The study sought to describe patient-entered supplemental information on symptomatic adverse events (AEs) in cancer clinical research reported via a National Cancer Institute software system and examine the feasibility of mapping these entries to established terminologies.