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The aim of this study was to collect and synthesize evidence regarding data quality problems encountered when working with variables related to social determinants of health (SDoH).

The objective is to report on the design and evaluation of the inaugural Women in AMIA Leadership Program. A year-long leadership curriculum was developed. Survey responses were summarized with descriptive statistics and quotes selected. Twenty-four scholars participated in the program. There was a significant increase in perceived achievement of learning objectives after the program (P < .0001). The largest improvement was in leadership confidence and presence in work interactions (modal answer Neutral in presurvey from 21 responses rose to Agree in postsurvey from 24 responses). Most (92% of 13) scholars clarified leadership vision and goals and (83% of 18) would be Very Likely to recommend the program to others. The goals of the program-developing women's leader identity, increasing networks, and accumulating experience for future programs-were achieved. The second leadership program is on its way in the United States and Australia. This study may benefit organizations seeking to develop leadership programs for women in informatics and digital health.

Use of artificial intelligence in healthcare, such as machine learning-based predictive algorithms, holds promise for advancing outcomes, but few systems are used in routine clinical practice. Trust has been cited as an important challenge to meaningful use of artificial intelligence in clinical practice. Artificial intelligence systems often involve automating cognitively challenging tasks. Therefore, previous literature on trust in automation may hold important lessons for artificial intelligence applications in healthcare. In [...]

Over a 31-year span as Director of the US National Library of Medicine (NLM), Donald A.B. Lindberg, MD, and his extraordinary NLM colleagues fundamentally changed the field of biomedical and health informatics-with a resulting impact on biomedicine that is much broader than its influence on any single subfield. This article provides substance to bolster that claim. The review is based in part on the informatics section of a new book [...]

Large amounts of health data are becoming available for biomedical research. Synthesizing information across databases may capture more comprehensive pictures of patient health and enable novel research studies. When no gold standard mappings between patient records are available, researchers may probabilistically link records from separate databases and analyze the linked data. However, previous linked data inference methods are constrained to certain linkage settings and exhibit low power. Here, we present [...]

Neural network deidentification studies have focused on individual datasets. These studies assume the availability of a sufficient amount of human-annotated data to train models that can generalize to corresponding test data. In real-world situations, however, researchers often have limited or no in-house training data. Existing systems and external data can help jump-start deidentification on in-house data; however, the most efficient way of utilizing existing systems and external data is unclear [...]

In many cases, genetic testing labs provide their test reports as portable document format files or scanned images, which limits the availability of the contained information to advanced informatics solutions, such as automated clinical decision support systems. One of the promising standards that aims to address this limitation is Health Level Seven International (HL7) Fast Healthcare Interoperability Resources Clinical Genomics Implementation Guide-Release 1 (FHIR CG IG STU1). This study aims [...]

Qualitative research, the analysis of nonquantitative and nonquantifiable data through methods such as interviews and observation, is integral to the field of biomedical and health informatics. To demonstrate the integrity and quality of their qualitative research, authors should report important elements of their work. This perspective article offers guidance about reporting components of the research, including theory, the research question, sampling, data collection methods, data analysis, results, and discussion. Addressing [...]

Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden.

While the professional version of the Mobile App Rating Scale (MARS) has already been translated, and validated into the Spanish language, its user-centered counterpart has not yet been adapted. Furthermore, no other similar tools exist in the Spanish language. The aim of this paper is to adapt and validate User Version of the MARS (uMARS) into the Spanish language.