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Electronic health records (EHRs) have been shown to increase physician workload. One EHR feature that contributes to increased workload is asynchronous alerts (also known as inbox notifications) related to test results, referral responses, medication refill requests, and messages from physicians and other health care professionals. This alert-related workload results in negative cognitive outcomes, but its effect on affective outcomes, such as burnout, has been understudied.

Maternity patients interact with the healthcare system over an approximately ten-month interval, requiring multiple visits, acquiring pregnancy-specific education, and sharing health information among providers. Many features of a web-based patient portal could help pregnant women manage their interactions with the healthcare system; however, it is unclear whether pregnant women in safety-net settings have the resources, skills or interest required for portal adoption.

Large and readily-available clinical datasets combined with improved computational resources have permitted the exploration of many new research and clinical questions. Predictive analytics, especially for adverse events, has surfaced as one promising application of big data, and although statistical results can be highly accurate, little is known about how nurses perceive this new information and how they might act upon it.

Access to patient data within the hospital or between hospitals is still problematic since a variety of information systems is in use applying different vendor specific terminologies and underlying knowledge models. Beyond, the development of electronic health record systems (EHRSs) is time and resource consuming. Thus, there is a substantial need for a development strategy of standardized EHRSs. We are applying a reuse-oriented process model and demonstrate its feasibility and [...]

The rise in the use of electronic health records (EHRs) and associated resources over the last decade is leading to the end of the paper medical record and all the risks associated with the use of a paper chart. However, there has not been a concomitant creation of a systematic oversight body that is specifically charged with ensuring the public's safety through the use of EHR knowledge resource tools or [...]

Unique characteristics of nursing homes (NHs) contribute to high rates of inappropriate antibiotic use for asymptomatic bacteriuria (ASB), a benign condition. A mobile clinical decision support system (CDSS) may support NH staff in differentiating urinary tract infections (UTI) from ASB and reducing antibiotic days.

In the summer of 2016 an international group of biomedical and health informatics faculty and graduate students gathered for the 16th meeting of the International Partnership in Health Informatics Education (IPHIE) masterclass at the University of Utah campus in Salt Lake City, Utah. This international biomedical and health informatics workshop was created to share knowledge and explore issues in biomedical health informatics (BHI).

Blood transfusion is a highly prevalent procedure in hospitalized patients and in some clinical scenarios it has lifesaving potential. However, in most cases transfusion is administered to hemodynamically stable patients with no benefit, but increased odds of adverse patient outcomes and substantial direct and indirect cost. Therefore, the concept of Patient Blood Management has increasingly gained importance to pre-empt and reduce transfusion and to identify the optimal transfusion volume for [...]

Platelet transfusions are a cornerstone of therapy for patients who develop thrombocytopenia while undergoing Hematopoietic Stem Cell Transplantation (HSCT). Many patients who develop Platelet Transfusion Refractoriness (PTR) require HLA-matched platelets. Identifying these patients early could lead to better utilization of platelets as well as increased platelet counts. We built a SMART on FHIR visualization tool to aid the oncology, blood bank, and blood donor center teams in identifying these patients [...]

This study evaluates the accuracy and portability of a natural language processing (NLP) tool for extracting clinical findings of influenza from clinical notes across two large healthcare systems. Effectiveness is evaluated on how well NLP supports downstream influenza case-detection for disease surveillance.