OBJECTIVE: We discuss implications of potential ascertainment biases for studies examining diabetes risk following SARS-CoV-2 infection using electronic health records (EHRs). We quantitatively explore sensitivity of results to misclassification of COVID-19 status using data from the U.S.-based Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network on children (≤17 years) and young adults (18-44 years). MATERIALS AND METHODS: In our retrospective case study from the DiCAYA Network, SARS-CoV-2 was identified using labs and diagnoses from June 1, 2020 to December 31, 2021. Patients were followed through December 31, 2022 for new diabetes diagnoses. Sites examined incident diabetes by COVID-19 status using Cox proportional hazards models. Results were pooled in meta-analyses. A bias analysis examined potential impact of COVID-19 misclassification scenarios on results, guided by hypotheses that sensitivity would be <50% and would be higher among those who developed diabetes. RESULTS: Prevalence of documented COVID-19 was low overall and variable across sites (children: 4.4%-7.7%, young adults: 6.2%-22.7%). Individuals with documented COVID-19 were at higher risk of incident diabetes compared to those with no documented infection, but results were heterogeneous across sites. Findings were highly sensitive to COVID-19 misclassification assumptions. Observed results could be biased away from the null under several differential misclassification scenarios. DISCUSSION: Although EHR-based documentation of COVID-19 was associated with incident diabetes, COVID-19 phenotypes likely had low sensitivity, with considerable variation across sites. Misclassification assumptions strongly impacted interpretation of results. CONCLUSION: Given the potential for low phenotype sensitivity and misclassification, caution is warranted when interpreting analyses of COVID-19 and incident diabetes using clinical or administrative databases.

INTRODUCTION: We created a probabilistic maternal-child electronic health record (EHR) linkage algorithm to promote clinical research in maternal-child health. METHODS: We used EHR data from 1994 to 2024 to create an XGBoost model to predict maternal-child linkages. The model used standard EHR elements as predictor variables, including first name, last name, birthdate, address, phone number, email, and an EHR-embedded maternal-child indicator as the deterministic outcome. RESULTS: From 82 million unique records, 6.2 billion potential pairs met blocking criteria. Of the potential pairs, 33 364 674 contained the deterministic indicator and were used as cases, and an equal number of controls were randomly sampled. The final model obtained an accuracy of 92%, a precision of 98%, a recall of 87%, and an F1-score of 92%. CONCLUSION: We derived and validated a probabilistic maternal-child linkage algorithm using routinely collected EHR data elements that could benefit future observational research in maternal-child health.

OBJECTIVE: The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. MATERIALS AND METHODS: Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. RESULTS: Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. DISCUSSION: Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.

OBJECTIVE: The completion of sequencing the human genome in 2003 has spurred the production and collection of genetic data at ever increasing rates. Genetic data obtained for clinical purposes, as is true for all results of clinical tests, are expected to be included in patients' medical records. With this explosion of information, questions of what, when, where and how to incorporate genetic data into electronic health records (EHRs) have reached a critical point. In order to answer these questions fully, this paper addresses the ethical, logistical and technological issues involved in incorporating these data into EHRs. MATERIALS AND METHODS: This paper reviews journal articles, government documents and websites relevant to the ethics, genetics and informatics domains as they pertain to EHRs. RESULTS AND DISCUSSION: The authors explore concerns and tasks facing health information technology (HIT) developers at the intersection of ethics, genetics, and technology as applied to EHR development. CONCLUSIONS: By ensuring the efficient and effective incorporation of genetic data into EHRs, HIT developers will play a key role in facilitating the delivery of personalized medicine.

Natural Language Processing (NLP) in the medical field has the potential to dramatically influence the way in which everyday clinical care and medical research is conducted. NLP systems provide access to structured content embedded in raw medical texts, therefore enabling automated processing. There are however, several barriers prohibiting wide spread adoption of NLP technology primarily driven by the complexity and cost. This paper describes an approach and implementation which leverages cloud-based deployment and service-based interfaces to extract, process, synthesize, mine, compare/contrast, explore, and manage medical text data in a flexibly secure and scalable architecture. Through a virtual appliance architecture users are able to discover, deploy and utilize NLP engines on demand without requiring knowledge of the underlying, potentially complex, NLP engine. As highlighted in this paper, the system architecture can scale in several configurations: by increasing the number of instances deployed, the number of NLP engines, and the number of databases.

Computational models in biomedicine rely on biological and clinical assumptions. The selection of these assumptions contributes substantially to modeling success or failure. Assumptions used by experts at the cutting edge of research, however, are rarely explicitly described in scientific publications. One can directly collect and assess some of these assumptions through interviews and surveys. Here we investigate diversity in expert views about a complex biological phenomenon, the process of cancer metastasis. We harvested individual viewpoints from 28 experts in clinical and molecular aspects of cancer metastasis and summarized them computationally. While experts predominantly agreed on the definition of individual steps involved in metastasis, no two expert scenarios for metastasis were identical. We computed the probability that any two experts would disagree on k or fewer metastatic stages and found that any two randomly selected experts are likely to disagree about several assumptions. Considering the probability that two or more of these experts review an article or a proposal about metastatic cascades, the probability that they will disagree with elements of a proposed model approaches 1. This diversity of conceptions has clear consequences for advance and deadlock in the field. We suggest that strong, incompatible views are common in biomedicine but largely invisible to biomedical experts themselves. We built a formal Markov model of metastasis to encapsulate expert convergence and divergence regarding the entire sequence of metastatic stages. This model revealed stages of greatest disagreement, including the points at which cancer enters and leaves the bloodstream. The model provides a formal probabilistic hypothesis against which researchers can evaluate data on the process of metastasis. This would enable subsequent improvement of the model through Bayesian probabilistic update. Practically, we propose that model assumptions and hunches be harvested systematically and made available for modelers and scientists.

Background The third AMIA Summit on Translational Bioinformatics built on the success of the 2008 and 2009 Summits. The Summit continues to highlight the multidisciplinary nature of this rapidly maturing research field and provides the opportunity to forge new transdisciplinary collaborations as the finest minds of the academia, industry, government and non-profit sector are brought together. The six tracks spanned the range from methods for the analyses of molecular through clinical measurements and informatics methods in genetics discoveries and clinical practice. 1: Informatics Methods for the Integrative Analysis of Molecular and Clinical Measurements 2: Computational Approaches to Finding Molecular Mechanisms and Therapies for Disease 3: Informatics Concepts, Tools, and Techniques to Enable Integrative Translational Bioinformatics Research 4: Relating and Representing Phenotypes and Disease for Translational Bioinformatics Research 5: Informatics Methods Bridging Genetics Discoveries and Clinical Practice 6: Dissecting Disease through the Study of Organisms, Evolution, and Taxonomy

The evolving complexity of genome-scale experiments has increasingly centralized the role of a highly computable, accurate, and comprehensive resource spanning multiple biological scales and viewpoints. To provide a resource to meet this need, we have significantly extended the PhenoGO database with gene-disease specific annotations and included an additional ten species. This a computationally-derived resource is primarily intended to provide phenotypic context (cell type, tissue, organ, and disease) for mining existing associations between gene products and GO terms specified in the Gene Ontology Databases Automated natural language processing (BioMedLEE) and computational ontology (PhenOS) methods were used to derive these relationships from the literature, expanding the database with information from ten additional species to include over 600,000 phenotypic contexts spanning eleven species from five GO annotation databases. A comprehensive evaluation evaluating the mappings (n = 300) found precision (positive predictive value) at 85%, and recall (sensitivity) at 76%. Phenotypes are encoded in general purpose ontologies such as Cell Ontology, the Unified Medical Language System, and in specialized ontologies such as the Mouse Anatomy and the Mammalian Phenotype Ontology. A web portal has also been developed, allowing for advanced filtering and querying of the database as well as download of the entire dataset http://www.phenogo.org.

OBJECTIVE: To identify some of the challenges that medical residents face in addressing their information needs in an inpatient setting, by examining how voice capture in natural language of clinical questions fits into workflow, and by characterizing the focus, format, and semantic content and complexity of their questions. DESIGN: Internal medicine residents captured information needs on a digital recorder while on a hospital inpatient service and then participated in semi-structured interviews. MEASUREMENTS: Interviews were analyzed to identify emergent themes. Recorded questions were analyzed for focus (diagnosis, treatment, or epidemiology) and format, either foreground (specific knowledge relating to an individual patient) or background (general knowledge about a condition). Semantic concepts and types were identified using MetaMap (UMLS - Unified Medical Language System) and manually. RESULTS: Voice recording of questions appeared to unmask residents' latent information needs. Although residents were able to record questions during workflow, there was a delay from the time questions materialized to when they were recorded. Question focus was distributed among diagnosis (32%), treatment (40%), and epidemiology (28%), and the majority of questions were background (69%). Questions were semantically complex; foreground and background questions averaged 12.6 (SD 6.0) and 9.1 (SD 6.0) UMLS concepts, respectively. MetaMap failed to recognize concepts when residents used acronyms or abbreviations or omitted key terms. CONCLUSIONS: We found that it is feasible for residents to capture their clinical questions in natural language during workflow and that recording questions may prompt awareness of previously unrecognized information needs. However, the semantic complexity of typical questions and mapping failures due to residents' use of acronyms and abbreviations present challenges to machine-based extraction of semantic content.

OBJECTIVE: To develop an electronic health record that facilitates rapid capture of detailed narrative observations from clinicians, with partial structuring of narrative information for integration and reuse. DESIGN: We propose a design in which unstructured text and coded data are fused into a single model called structured narrative. Each major clinical event (e.g., encounter or procedure) is represented as a document that is marked up to identify gross structure (sections, fields, paragraphs, lists) as well as fine structure within sentences (concepts, modifiers, relationships). Marked up items are associated with standardized codes that enable linkage to other events, as well as efficient reuse of information, which can speed up data entry by clinicians. Natural language processing is used to identify fine structure, which can reduce the need for form-based entry. VALIDATION: The model is validated through an example of use by a clinician, with discussion of relevant aspects of the user interface, data structures and processing rules. DISCUSSION: The proposed model represents all patient information as documents with standardized gross structure (templates). Clinicians enter their data as free text, which is coded by natural language processing in real time making it immediately usable for other computation, such as alerts or critiques. In addition, the narrative data annotates and augments structured data with temporal relations, severity and degree modifiers, causal connections, clinical explanations and rationale. CONCLUSION: Structured narrative has potential to facilitate capture of data directly from clinicians by allowing freedom of expression, giving immediate feedback, supporting reuse of clinical information and structuring data for subsequent processing, such as quality assurance and clinical research.

MOTIVATION: The ambiguity of biomedical entities, particularly of gene symbols, is a big challenge for text-mining systems in the biomedical domain. Existing knowledge sources, such as Entrez Gene and the MEDLINE database, contain information concerning the characteristics of a particular gene that could be used to disambiguate gene symbols. RESULTS: For each gene, we create a profile with different types of information automatically extracted from related MEDLINE abstracts and readily available annotated knowledge sources. We apply the gene profiles to the disambiguation task via an information retrieval method, which ranks the similarity scores between the context where the ambiguous gene is mentioned, and candidate gene profiles. The gene profile with the highest similarity score is then chosen as the correct sense. We evaluated the method on three automatically generated testing sets of mouse, fly and yeast organisms, respectively. The method achieved the highest precision of 93.9% for the mouse, 77.8% for the fly and 89.5% for the yeast. AVAILABILITY: The testing data sets and disambiguation programs are available at http://www.dbmi.columbia.edu/~hux7002/gsd2006

Wireless handheld technology provides new ways to deliver and present information. As with any technology, its unique features must be taken into consideration and its applications designed accordingly. In the clinical setting, availability of needed information can be crucial during the decision-making process. Preliminary studies performed at New York Presbyterian Hospital (NYPH) determined that there are inadequate access to information and ineffective communication among clinicians (potential proximal causes of medical errors). In response to these findings, the authors have been developing extensions to their Web-based clinical information system including PalmCIS, an application that provides access to needed patient information via a wireless personal digital assistant (PDA). The focus was on achieving end-to-end security and developing a highly usable system. This report discusses the motivation behind PalmCIS, design and development of the system, and future directions.

OBJECTIVE: A type definition, as a component of the categorical structures of a concept-oriented terminology, must support nonambiguous concept representations and, consequently, comparisons of data that are represented using different terminologies. The purpose of the study was to evaluate the adequacy and utility of a proposed type definition for nursing activity concepts. DESIGN: Nursing activity terms (n = 1039) from patient charts and intervention terms from two nursing terminologies (Home Health Care Classification and Omaha System) were decomposed into the attributes of the proposed type definition-Delivery Mode, Activity Focus, and Recipient. MEASUREMENTS: Attributes of the type definition were coded as present or absent for each term by multiple raters. In addition, Delivery Mode was rated as Explicit or Implicit and Recipient was rated as Explicit, Implicit, or Ambiguous. The data were summarized using descriptive statistics. Inter-rater reliabilities were calculated for each attribute of the type definition. RESULTS: All attributes of the type definition were present in 73.9 percent of the chart terms, 91.3 percent of Home Health Care Classification intervention terms, and 63.5 percent of Omaha System intervention terms. While Delivery Mode and Activity Focus were almost universally present, Recipient was problematic. It was rated as ambiguous in 4.8 percent of the chart terms, 8.7 percent of Home Health Care Classification intervention terms, and 36.5 percent of Omaha System intervention terms. CONCLUSIONS: The study findings supported the adequacy and utility of the type definition. Further research is needed to refine the type definition and its use for representing nursing activity concepts within a concept-oriented terminological system.