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When patients develop acute respiratory failure (ARF), accurately identifying the underlying etiology is essential for determining the best treatment. However, differentiating between common medical diagnoses can be challenging in clinical practice. Machine learning models could improve medical diagnosis by aiding in the diagnostic evaluation of these patients.

We describe the Clickbusters initiative implemented at Vanderbilt University Medical Center (VUMC), which was designed to improve safety and quality and reduce burnout through the optimization of clinical decision support (CDS) alerts.

We investigated patient experiences with medication- and test-related cost conversations with healthcare providers to identify their preferences for future informatics tools to facilitate cost-sensitive care decisions.

Partnerships among patients, families, caregivers, and clinicians are critical to helping patients lead their best lives given their specific genetics, conditions, circumstances, and the environments in which they live, work, and play. These partnerships extend to the development of health information technology, including clinical decision support (CDS). Design of these technologies, however, often occurs without a profound understanding of the true needs, wants, and concerns of patients and family members [...]

Falls in community-dwelling older adults are common, and there is a lack of clinical decision support (CDS) to provide health care providers with effective, individualized fall prevention recommendations.

Health information exchange (HIE) allows healthcare providers to access a patient's medical information to improve patient care continuity. The standardized data realize the HIE values. Since the Health Level 7 Clinical Document Architecture (CDA) is flexible, implementation guides (IG) are needed for use cases. Although many CDA IGs have been developed, they did not describe how these CDA IGs were developed. A national CDA IG that meets the local requirements [...]

Over one-third of deaths recorded at health facilities in Zambia are brought in dead (BID) and the causes of death (CODs) are not fully analyzed. The use of automated verbal autopsy (VA) has reportedly determined the CODs of more BID cases than the death notification form issued by the hospital. However, the validity of automated VA is yet to be fully investigated.

Cancer staging information is an essential component of cancer research. However, the information is primarily stored as either a full or semistructured free-text clinical document which is limiting the data use. By transforming the cancer-specific data to the Observational Medical Outcome Partnership Common Data Model (OMOP CDM), the information can contribute to establish multicenter observational cancer studies. To the best of our knowledge, there have been no studies on OMOP [...]

During the coronavirus disease 2019 pandemic, as a safety-net organization with a substantial percentage of patients of color and with limited English proficiency (LEP), we were wary of furthering health disparities in our community. We analyzed gaps in telemedicine (telephone and video) delivery in our communities, quantified the effects of our tests of change, and began the process of accumulating evidence to create a road map for other organizations.