Faculty Bibliography

Many people, especially those with low numeracy, are known to have difficulty interpreting and applying quantitative information to health decisions. These difficulties have resulted in a rich body of research about better ways to communicate numbers. Synthesizing this body of research into evidence-based guidance, however, is complicated by inconsistencies in research terminology and researcher goals. In this article, we introduce three taxonomies intended to systematize terminology in the literature, derived from an ongoing systematic literature review. The first taxonomy provides a systematic nomenclature for the outcome measures assessed in the studies, including perceptions, decisions, and actions. The second taxonomy is a nomenclature for the data formats assessed, including numbers (and different formats for numbers) and graphics. The third taxonomy describes the quantitative concepts being conveyed, from the simplest (a single value at a single point in time) to more complex ones (including a risk-benefit trade-off and a trend over time). Finally, we demonstrate how these three taxonomies can be used to resolve ambiguities and apparent contradictions in the literature.

BACKGROUND:Accurately identifying patients with hypoglycemia is key to preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia-related information when using electronic health record data sources from a large population. OBJECTIVE:The objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from electronic health record clinical notes. METHODS:Literature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, and ACL Anthology. Keywords included hypoglycemia, low blood glucose, NLP, and machine learning. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers. RESULTS:This review (n=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Of the 8 included studies, 4 (50%) reported that the prevalence rate of any level of hypoglycemia was 3.4% to 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia. CONCLUSIONS:The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing.

OBJECTIVE:To develop an annotation model to apply natural language processing (NLP) to device adverse event reports and implement the model to evaluate the most frequently experienced events among women reporting a sterilization device removal. METHODS:score (a combined measure of PPV and sensitivity). Using extracted variables, we summarized the reporting source, the presence of prespecified and other patient and device events, additional sterilizations and other procedures performed, and time from implantation to removal. RESULTS:score was 91.5% for labeled items and 93.9% for distinct events after excluding duplicates. A total of 16 535 reports of device removal were analyzed. The most frequently reported patient and device events were abdominal/pelvic/genital pain (N = 13 166, 79.6%) and device dislocation/migration (N = 3180, 19.2%), respectively. Of those reporting an additional sterilization procedure, the majority had a hysterectomy or salpingectomy (N = 7932). One-fifth of the cases that had device removal timing specified reported a removal after 7 years following implantation (N = 2444/11 293). CONCLUSIONS:We present a roadmap to develop an annotation model for NLP to analyze device adverse event reports. The extracted information is informative and complements findings from previous research using administrative data.

OBJECTIVE:Obtaining electronic patient data, especially from electronic health record (EHR) systems, for clinical and translational research is difficult. Multiple research informatics systems exist but navigating the numerous applications can be challenging for scientists. This article describes Architecture for Research Computing in Health (ARCH), our institution's approach for matching investigators with tools and services for obtaining electronic patient data. MATERIALS AND METHODS/METHODS:Supporting the spectrum of studies from populations to individuals, ARCH delivers a breadth of scientific functions-including but not limited to cohort discovery, electronic data capture, and multi-institutional data sharing-that manifest in specific systems-such as i2b2, REDCap, and PCORnet. Through a consultative process, ARCH staff align investigators with tools with respect to study design, data sources, and cost. Although most ARCH services are available free of charge, advanced engagements require fee for service. RESULTS:Since 2016 at Weill Cornell Medicine, ARCH has supported over 1200 unique investigators through more than 4177 consultations. Notably, ARCH infrastructure enabled critical coronavirus disease 2019 response activities for research and patient care. DISCUSSION/CONCLUSIONS:ARCH has provided a technical, regulatory, financial, and educational framework to support the biomedical research enterprise with electronic patient data. Collaboration among informaticians, biostatisticians, and clinicians has been critical to rapid generation and analysis of EHR data. CONCLUSION/CONCLUSIONS:A suite of tools and services, ARCH helps match investigators with informatics systems to reduce time to science. ARCH has facilitated research at Weill Cornell Medicine and may provide a model for informatics and research leaders to support scientists elsewhere.

Health and biomedical informatics graduate-level degree programs have proliferated across the United States in the last 10 years. To help inform programs on practices in teaching and learning, a survey of master's programs in health and biomedical informatics in the United States was conducted to determine the national landscape of culminating experiences including capstone projects, research theses, internships, and practicums. Almost all respondents reported that their programs required a culminating experience (97%). A paper (not a formal thesis), an oral presentation, a formal course, and an internship were required by ≥50% programs. The most commonly reported purposes for the culminating experience were to help students extend and apply the learning and as a bridge to the workplace. The biggest challenges were students' maturity, difficulty in synthesizing information into a coherent paper, and ability to generate research ideas. The results provide students and program leaders with a summary of pedagogical methods across programs.

Academic institutions need to maintain publication lists for thousands of faculty and other scholars. Automated tools are essential to minimize the need for direct feedback from the scholars themselves who are practically unable to commit necessary effort to keep the data accurate. In relying exclusively on clustering techniques, author disambiguation applications fail to satisfy key use cases of academic institutions. Algorithms can perfectly group together a set of publications authored by a common individual, but, for them to be useful to an academic institution, they need to programmatically and recurrently map articles to thousands of scholars of interest en masse. Consistent with a savvy librarian's approach for generating a scholar's list of publications, identity-driven authorship prediction is the process of using information about a scholar to quantify the likelihood that person wrote certain articles. ReCiter is an application that attempts to do exactly that. ReCiter uses institutionally-maintained identity data such as name of department and year of terminal degree to predict which articles a given scholar has authored. To compute the overall score for a given candidate article from PubMed (and, optionally, Scopus), ReCiter uses: up to 12 types of commonly available, identity data; whether other members of a cluster have been accepted or rejected by a user; and the average score of a cluster. In addition, ReCiter provides scoring and qualitative evidence supporting why particular articles are suggested. This context and confidence scoring allows curators to more accurately provide feedback on behalf of scholars. To help users to more efficiently curate publication lists, we used a support vector machine analysis to optimize the scoring of the ReCiter algorithm. In our analysis of a diverse test group of 500 scholars at an academic private medical center, ReCiter correctly predicted 98% of their publications in PubMed.

BACKGROUND:There is concern about the potential of an increased risk related to medications that act on the renin-angiotensin-aldosterone system in patients exposed to coronavirus disease 2019 (Covid-19), because the viral receptor is angiotensin-converting enzyme 2 (ACE2). METHODS:We assessed the relation between previous treatment with ACE inhibitors, angiotensin-receptor blockers, beta-blockers, calcium-channel blockers, or thiazide diuretics and the likelihood of a positive or negative result on Covid-19 testing as well as the likelihood of severe illness (defined as intensive care, mechanical ventilation, or death) among patients who tested positive. Using Bayesian methods, we compared outcomes in patients who had been treated with these medications and in untreated patients, overall and in those with hypertension, after propensity-score matching for receipt of each medication class. A difference of at least 10 percentage points was prespecified as a substantial difference. RESULTS:Among 12,594 patients who were tested for Covid-19, a total of 5894 (46.8%) were positive; 1002 of these patients (17.0%) had severe illness. A history of hypertension was present in 4357 patients (34.6%), among whom 2573 (59.1%) had a positive test; 634 of these patients (24.6%) had severe illness. There was no association between any single medication class and an increased likelihood of a positive test. None of the medications examined was associated with a substantial increase in the risk of severe illness among patients who tested positive. CONCLUSIONS:We found no substantial increase in the likelihood of a positive test for Covid-19 or in the risk of severe Covid-19 among patients who tested positive in association with five common classes of antihypertensive medications.

OBJECTIVE:We aimed to address deficiencies in structured electronic health record (EHR) data for race and ethnicity by identifying black and Hispanic patients from unstructured clinical notes and assessing differences between patients with or without structured race/ethnicity data. MATERIALS AND METHODS/METHODS:Using EHR notes for 16 665 patients with encounters at a primary care practice, we developed rule-based natural language processing (NLP) algorithms to classify patients as black/Hispanic. We evaluated performance of the method against an annotated gold standard, compared race and ethnicity between NLP-derived and structured EHR data, and compared characteristics of patients identified as black or Hispanic using only NLP vs patients identified as such only in structured EHR data. RESULTS:For the sample of 16 665 patients, NLP identified 948 additional patients as black, a 26%increase, and 665 additional patients as Hispanic, a 20% increase. Compared with the patients identified as black or Hispanic in structured EHR data, patients identified as black or Hispanic via NLP only were older, more likely to be male, less likely to have commercial insurance, and more likely to have higher comorbidity. DISCUSSION/CONCLUSIONS:Structured EHR data for race and ethnicity are subject to data quality issues. Supplementing structured EHR race data with NLP-derived race and ethnicity may allow researchers to better assess the demographic makeup of populations and draw more accurate conclusions about intergroup differences in health outcomes. CONCLUSIONS:Black or Hispanic patients who are not documented as such in structured EHR race/ethnicity fields differ significantly from those who are. Relatively simple NLP can help address this limitation.

This study yielded a map of the alignment of American Association of Colleges of Nursing Graduate-Level Nursing Informatics Competencies with American Medical Informatics Association Health Informatics Core Competencies in an effort to understand graduate-level accreditation and certification opportunities in nursing informatics. Nursing Informatics Program Directors from the American Medical Informatics Association and a health informatics expert independently mapped the American Association of Colleges of Nursing competencies to the American Medical Informatics Association Health Informatics knowledge, skills, and attitudes. The Nursing Informatics Program Directors' map connected an average of 4.0 American Medical Informatics Association Core Competencies per American Association of Colleges of Nursing competency, whereas the health informatics expert's map connected an average of 5.0 American Medical Informatics Association Core Competencies per American Association of Colleges of Nursing competency. Agreement across the two maps ranged from 14% to 60% per American Association of Colleges of Nursing competency, revealing alignment between the two groups' competencies according to knowledge, skills, and attitudes. These findings suggest that graduates of master's degree programs in nursing, especially those specializing in nursing informatics, will likely be prepared to sit for the proposed Advanced Health Informatics Certification in addition to the American Nurses Credentialing Center bachelor's-level Informatics Nursing Certification. This preliminary map sets the stage for further in-depth mapping of nursing informatics curricula with American Medical Informatics Association Core Competencies and will enable interprofessional conversations around nursing informatics specialty program accreditation, nursing workforce preparation, and nursing informatics advanced certification. Nursing informaticists should examine their need for credentials as key contributors who will address critical health informatics needs.