Returning integrated genomic risk and clinical recommendations: The eMERGE study.

PURPOSE: Assessing the risk of common, complex diseases requires consideration of clinical risk factors as well as monogenic and polygenic risks, which in turn may be reflected in family history. Returning risks to individuals and providers may influence preventive care or use of prophylactic therapies for those individuals at high genetic risk. METHODS: To enable integrated genetic risk assessment, the eMERGE (electronic MEdical Records and GEnomics) network is enrolling 25,000 diverse individuals in a prospective cohort study across 10 sites. The network developed methods to return cross-ancestry polygenic risk scores, monogenic risks, family history, and clinical risk assessments via a genome-informed risk assessment (GIRA) report and will assess uptake of care recommendations after return of results. RESULTS: GIRAs include summary care recommendations for 11 conditions, education pages, and clinical laboratory reports. The return of high-risk GIRA to individuals and providers includes guidelines for care and lifestyle recommendations. Assembling the GIRA required infrastructure and workflows for ingesting and presenting content from multiple sources. Recruitment began in February 2022. CONCLUSION: Return of a novel report for communicating monogenic, polygenic, and family history-based risk factors will inform the benefits of integrated genetic risk assessment for routine health care.

Beyond the override: Using evidence of previous drug tolerance to suppress drug allergy alerts; a retrospective study of opioid alerts.

OBJECTIVE: Despite the extensive literature exploring alert fatigue, most studies have focused on describing the phenomenon, but not on fixing it. The authors aimed to identify data useful to avert clinically irrelevant alerts to inform future research on clinical decision support (CDS) design. METHODS: We conducted a retrospective observational study of opioid drug allergy alert (DAA) overrides for the calendar year of 2019 at a large academic medical center, to identify data elements useful to find irrelevant alerts to be averted. RESULTS: Overall, 227,815 DAAs were fired in 2019, with an override rate of 91 % (n = 208196). Opioids represented nearly two-thirds of these overrides (n = 129063; 62 %) and were the drug class with the highest override rate (96 %). On average, 29 opioid DAAs were overridden per patient. While most opioid alerts (97.1 %) are fired for a possible match (the drug class of the allergen matches the drug class of the prescribed drug), they are overridden significantly less frequently for definite match (exact match between allergen and prescribed drug) (88 % vs. 95.9 %, p < 0.001). When comparing the triggering drug with previously administered drugs, override rates were equally high for both definite match (95.9 %), no match (95.5 %), and possible match (95.1 %). Likewise, when comparing to home medications, overrides were excessively high for possible match (96.3 %), no match (96 %), and definite match (94.4 %). CONCLUSION: We estimate that 74.5% of opioid DAAs (46.4% of all DAAs) at our institution could be relatively safely averted, since they either have a definite match for previous inpatient administrations suggesting drug tolerance or are fired as possible match with low risk of cross-sensitivity. Future research should focus on identifying other relevant data elements ideally with automated methods and use of emerging standards to empower CDS systems to suppress false-positive alerts while avoiding safety hazards.

PAGER-CoV: a comprehensive collection of pathways, annotated gene-lists and gene signatures for coronavirus disease studies.

PAGER-CoV (http://discovery.informatics.uab.edu/PAGER-CoV/) is a new web-based database that can help biomedical researchers interpret coronavirus-related functional genomic study results in the context of curated knowledge of host viral infection, inflammatory response, organ damage, and tissue repair. The new database consists of 11 835 PAGs (Pathways, Annotated gene-lists, or Gene signatures) from 33 public data sources. Through the web user interface, users can search by a query gene or a query term and retrieve significantly matched PAGs with all the curated information. Users can navigate from a PAG of interest to other related PAGs through either shared PAG-to-PAG co-membership relationships or PAG-to-PAG regulatory relationships, totaling 19 996 993. Users can also retrieve enriched PAGs from an input list of COVID-19 functional study result genes, customize the search data sources, and export all results for subsequent offline data analysis. In a case study, we performed a gene set enrichment analysis (GSEA) of a COVID-19 RNA-seq data set from the Gene Expression Omnibus database. Compared with the results using the standard PAGER database, PAGER-CoV allows for more sensitive matching of known immune-related gene signatures. We expect PAGER-CoV to be invaluable for biomedical researchers to find molecular biology mechanisms and tailored therapeutics to treat COVID-19 patients.

Mapping 2 Decades of Research in Health Services Research, Health Policy, and Health Economics Journals.

OBJECTIVE: To identify major research topics and exhibit trends in these topics in 15 health services research, health policy, and health economics journals over 2 decades. DATA SOURCES: The study sample of 35,159 abstracts (1999-2020) were collected from PubMed for 15 journals. STUDY DESIGN: The study used a 3-phase approach for text analyses: (1) developing the corpus of 40,618 references from PubMed (excluding 5459 of those without abstract or author information); (2) preprocessing and generating the term list using natural language processing to eliminate irrelevant textual data and identify important terms and phrases; (3) analyzing the preprocessed text data using latent semantic analysis, topic analyses, and multiple correspondence analysis. PRINCIPAL FINDINGS: Application of analyses generated 16 major research topics: (1) implementation/intervention science; (2) HIV and women's health; (3) outcomes research and quality; (4) veterans/military studies; (5) provider/primary-care interventions; (6) geriatrics and formal/informal care; (7) policies and health outcomes; (8) medication treatment/therapy; (9) patient interventions; (10) health insurance legislation and policies; (11) public health policies; (12) literature reviews; (13) cost-effectiveness and economic evaluation; (14) cancer care; (15) workforce issues; and (16) socioeconomic status and disparities. The 2-dimensional map revealed that some journals have stronger associations with specific topics. Findings were not consistent with previous studies based on user perceptions. CONCLUSION: Findings of this study can be used by the stakeholders of health services research, policy, and economics to develop future research agendas, target journal submissions, and generate interdisciplinary solutions by examining overlapping journals for particular topics.

The biomedical informatics short course at Woods Hole/Georgia: Training to support institutional change.

The US National Library of Medicine's Biomedical Informatics Short Course ran from 1992 to 2017, most of that time at the Marine Biological Laboratory in Woods Hole, Massachusetts. Its intention was to provide physicians, medical librarians and others engaged in health care with a basic understanding of the major topics in informatics so that they could return to their home institutions as "change agents". Over the years, the course provided week-long, intense, morning-to-night experiences for some 1,350 students, consisting of lectures and hands-on project development, taught by many luminaries in the field, not the least of which was Donald A.B. Lindberg M.D., who spoke on topics ranging from bioinformatics to national policy.

A state-based approach to genomics for rare disease and population screening.

PURPOSE: The Alabama Genomic Health Initiative (AGHI) is a state-funded effort to provide genomic testing. AGHI engages two distinct cohorts across the state of Alabama. One cohort includes children and adults with undiagnosed rare disease; a second includes an unselected adult population. Here we describe findings from the first 176 rare disease and 5369 population cohort AGHI participants. METHODS: AGHI participants enroll in one of two arms of a research protocol that provides access to genomic testing results and biobank participation. Rare disease cohort participants receive genome sequencing to identify primary and secondary findings. Population cohort participants receive genotyping to identify pathogenic and likely pathogenic variants for actionable conditions. RESULTS: Within the rare disease cohort, genome sequencing identified likely pathogenic or pathogenic variation in 20% of affected individuals. Within the population cohort, 1.5% of individuals received a positive genotyping result. The rate of genotyping results corroborated by reported personal or family history varied by gene. CONCLUSIONS: AGHI demonstrates the ability to provide useful health information in two contexts: rare undiagnosed disease and population screening. This utility should motivate continued exploration of ways in which emerging genomic technologies might benefit broad populations.

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

Unintended Consequences of Nationwide Electronic Health Record Adoption: Challenges and Opportunities in the Post-Meaningful Use Era.

The US health system has recently achieved widespread adoption of electronic health record (EHR) systems, primarily driven by financial incentives provided by the Meaningful Use (MU) program. Although successful in promoting EHR adoption and use, the program, and other contributing factors, also produced important unintended consequences (UCs) with far-reaching implications for the US health system. Based on our own experiences from large health information technology (HIT) adoption projects and a collection of key studies in HIT evaluation, we discuss the most prominent UCs of MU: failed expectations, EHR market saturation, innovation vacuum, physician burnout, and data obfuscation. We identify challenges resulting from these UCs and provide recommendations for future research to empower the broader medical and informatics communities to realize the full potential of a now digitized health system. We believe that fixing these unanticipated effects will demand efforts from diverse players such as health care providers, administrators, HIT vendors, policy makers, informatics researchers, funding agencies, and outside developers; promotion of new business models; collaboration between academic medical centers and informatics research departments; and improved methods for evaluations of HIT.

A visual interactive analytic tool for filtering and summarizing large health data sets coded with hierarchical terminologies (VIADS).

BACKGROUND: Vast volumes of data, coded through hierarchical terminologies (e.g., International Classification of Diseases, Tenth Revision-Clinical Modification [ICD10-CM], Medical Subject Headings [MeSH]), are generated routinely in electronic health record systems and medical literature databases. Although graphic representations can help to augment human understanding of such data sets, a graph with hundreds or thousands of nodes challenges human comprehension. To improve comprehension, new tools are needed to extract the overviews of such data sets. We aim to develop a visual interactive analytic tool for filtering and summarizing large health data sets coded with hierarchical terminologies (VIADS) as an online, and publicly accessible tool. The ultimate goals are to filter, summarize the health data sets, extract insights, compare and highlight the differences between various health data sets by using VIADS. The results generated from VIADS can be utilized as data-driven evidence to facilitate clinicians, clinical researchers, and health care administrators to make more informed clinical, research, and administrative decisions. We utilized the following tools and the development environments to develop VIADS: Django, Python, JavaScript, Vis.js, Graph.js, JQuery, Plotly, Chart.js, Unittest, R, and MySQL. RESULTS: VIADS was developed successfully and the beta version is accessible publicly. In this paper, we introduce the architecture design, development, and functionalities of VIADS. VIADS includes six modules: user account management module, data sets validation module, data analytic module, data visualization module, terminology module, dashboard. Currently, VIADS supports health data sets coded by ICD-9, ICD-10, and MeSH. We also present the visualization improvement provided by VIADS in regard to interactive features (e.g., zoom in and out, customization of graph layout, expanded information of nodes, 3D plots) and efficient screen space usage. CONCLUSIONS: VIADS meets the design objectives and can be used to filter, summarize, compare, highlight and visualize large health data sets that coded by hierarchical terminologies, such as ICD-9, ICD-10 and MeSH. Our further usability and utility studies will provide more details about how the end users are using VIADS to facilitate their clinical, research or health administrative decision making.

The UAB Informatics Institute and 2016 CEGS N-GRID de-identification shared task challenge.

Clinical narratives (the text notes found in patients' medical records) are important information sources for secondary use in research. However, in order to protect patient privacy, they must be de-identified prior to use. Manual de-identification is considered to be the gold standard approach but is tedious, expensive, slow, and impractical for use with large-scale clinical data. Automated or semi-automated de-identification using computer algorithms is a potentially promising alternative. The Informatics Institute of the University of Alabama at Birmingham is applying de-identification to clinical data drawn from the UAB hospital's electronic medical records system before releasing them for research. We participated in a shared task challenge by the Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-Scale and RDoC Individualized Domains (N-GRID) at the de-identification regular track to gain experience developing our own automatic de-identification tool. We focused on the popular and successful methods from previous challenges: rule-based, dictionary-matching, and machine-learning approaches. We also explored new techniques such as disambiguation rules, term ambiguity measurement, and used multi-pass sieve framework at a micro level. For the challenge's primary measure (strict entity), our submissions achieved competitive results (f-measures: 87.3%, 87.1%, and 86.7%). For our preferred measure (binary token HIPAA), our submissions achieved superior results (f-measures: 93.7%, 93.6%, and 93%). With those encouraging results, we gain the confidence to improve and use the tool for the real de-identification task at the UAB Informatics Institute.

A Learning Health Care System Using Computer-Aided Diagnosis.

Physicians intuitively apply pattern recognition when evaluating a patient. Rational diagnosis making requires that clinical patterns be put in the context of disease prior probability, yet physicians often exhibit flawed probabilistic reasoning. Difficulties in making a diagnosis are reflected in the high rates of deadly and costly diagnostic errors. Introduced 6 decades ago, computerized diagnosis support systems are still not widely used by internists. These systems cannot efficiently recognize patterns and are unable to consider the base rate of potential diagnoses. We review the limitations of current computer-aided diagnosis support systems. We then portray future diagnosis support systems and provide a conceptual framework for their development. We argue for capturing physician knowledge using a novel knowledge representation model of the clinical picture. This model (based on structured patient presentation patterns) holds not only symptoms and signs but also their temporal and semantic interrelations. We call for the collection of crowdsourced, automatically deidentified, structured patient patterns as means to support distributed knowledge accumulation and maintenance. In this approach, each structured patient pattern adds to a self-growing and -maintaining knowledge base, sharing the experience of physicians worldwide. Besides supporting diagnosis by relating the symptoms and signs with the final diagnosis recorded, the collective pattern map can also provide disease base-rate estimates and real-time surveillance for early detection of outbreaks. We explain how health care in resource-limited settings can benefit from using this approach and how it can be applied to provide feedback-rich medical education for both students and practitioners.

Facilitating biomedical researchers' interrogation of electronic health record data: Ideas from outside of biomedical informatics.

Electronic health records (EHR) are a vital data resource for research uses, including cohort identification, phenotyping, pharmacovigilance, and public health surveillance. To realize the promise of EHR data for accelerating clinical research, it is imperative to enable efficient and autonomous EHR data interrogation by end users such as biomedical researchers. This paper surveys state-of-art approaches and key methodological considerations to this purpose. We adapted a previously published conceptual framework for interactive information retrieval, which defines three entities: user, channel, and source, by elaborating on channels for query formulation in the context of facilitating end users to interrogate EHR data. We show the current progress in biomedical informatics mainly lies in support for query execution and information modeling, primarily due to emphases on infrastructure development for data integration and data access via self-service query tools, but has neglected user support needed during iteratively query formulation processes, which can be costly and error-prone. In contrast, the information science literature has offered elaborate theories and methods for user modeling and query formulation support. The two bodies of literature are complementary, implying opportunities for cross-disciplinary idea exchange. On this basis, we outline the directions for future informatics research to improve our understanding of user needs and requirements for facilitating autonomous interrogation of EHR data by biomedical researchers. We suggest that cross-disciplinary translational research between biomedical informatics and information science can benefit our research in facilitating efficient data access in life sciences.

User-centered design of multi-gene sequencing panel reports for clinicians.

The objective of this study was to develop a high-fidelity prototype for delivering multi-gene sequencing panel (GS) reports to clinicians that simulates the user experience of a final application. The delivery and use of GS reports can occur within complex and high-paced healthcare environments. We employ a user-centered software design approach in a focus group setting in order to facilitate gathering rich contextual information from a diverse group of stakeholders potentially impacted by the delivery of GS reports relevant to two precision medicine programs at the University of Maryland Medical Center. Responses from focus group sessions were transcribed, coded and analyzed by two team members. Notification mechanisms and information resources preferred by participants from our first phase of focus groups were incorporated into scenarios and the design of a software prototype for delivering GS reports. The goal of our second phase of focus group, to gain input on the prototype software design, was accomplished through conducting task walkthroughs with GS reporting scenarios. Preferences for notification, content and consultation from genetics specialists appeared to depend upon familiarity with scenarios for ordering and delivering GS reports. Despite familiarity with some aspects of the scenarios we proposed, many of our participants agreed that they would likely seek consultation from a genetics specialist after viewing the test reports. In addition, participants offered design and content recommendations. Findings illustrated a need to support customized notification approaches, user-specific information, and access to genetics specialists with GS reports. These design principles can be incorporated into software applications that deliver GS reports. Our user-centered approach to conduct this assessment and the specific input we received from clinicians may also be relevant to others working on similar projects.

Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM).

Efficient communication of a clinical study protocol and case report forms during all stages of a human clinical study is important for many stakeholders. An electronic and structured study representation format that can be used throughout the whole study life-span can improve such communication and potentially lower total study costs. The most relevant standard for representing clinical study data, applicable to unregulated as well as regulated studies, is the Operational Data Model (ODM) in development since 1999 by the Clinical Data Interchange Standards Consortium (CDISC). ODM's initial objective was exchange of case report forms data but it is increasingly utilized in other contexts. An ODM extension called Study Design Model, introduced in 2011, provides additional protocol representation elements. Using a case study approach, we evaluated ODM's ability to capture all necessary protocol elements during a complete clinical study lifecycle in the Intramural Research Program of the National Institutes of Health. ODM offers the advantage of a single format for institutions that deal with hundreds or thousands of concurrent clinical studies and maintain a data warehouse for these studies. For each study stage, we present a list of gaps in the ODM standard and identify necessary vendor or institutional extensions that can compensate for such gaps. The current version of ODM (1.3.2) has only partial support for study protocol and study registration data mainly because it is outside the original development goal. ODM provides comprehensive support for representation of case report forms (in both the design stage and with patient level data). Inclusion of requirements of observational, non-regulated or investigator-initiated studies (outside Food and Drug Administration (FDA) regulation) can further improve future revisions of the standard.

Usability and Acceptance of the Librarian Infobutton Tailoring Environment: An Open Access Online Knowledge Capture, Management, and Configuration Tool for OpenInfobutton.

BACKGROUND: The Librarian Infobutton Tailoring Environment (LITE) is a Web-based knowledge capture, management, and configuration tool with which users can build profiles used by OpenInfobutton, an open source infobutton manager, to provide electronic health record users with context-relevant links to online knowledge resources. OBJECTIVE: We conducted a multipart evaluation study to explore users' attitudes and acceptance of LITE and to guide future development. METHODS: The evaluation consisted of an initial online survey to all LITE users, followed by an observational study of a subset of users in which evaluators' sessions were recorded while they conducted assigned tasks. The observational study was followed by administration of a modified System Usability Scale (SUS) survey. RESULTS: Fourteen users responded to the survey and indicated good acceptance of LITE with feedback that was mostly positive. Six users participated in the observational study, demonstrating average task completion time of less than 6 minutes and an average SUS score of 72, which is considered good compared with other SUS scores. CONCLUSIONS: LITE can be used to fulfill its designated tasks quickly and successfully. Evaluators proposed suggestions for improvements in LITE functionality and user interface.

A rapid matrix-assisted laser desorption ionization-time of flight mass spectrometry-based method for single-plasmid tracking in an outbreak of carbapenem-resistant Enterobacteriaceae.

Carbapenem-resistant Enterobacteriaceae (CRE) have spread globally and represent a serious and growing threat to public health. Rapid methods for tracking plasmids carrying carbapenemase genes could greatly benefit infection control efforts. Here, we demonstrate that real-time, direct tracking of a single plasmid in a bacterial strain responsible for an outbreak is possible using a commercial matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) system. In this case, we retrospectively tracked the bla(KPC) carbapenemase gene-bearing pKpQIL plasmid responsible for a CRE outbreak that occurred at the NIH Clinical Center in 2011. An ∼ 11,109-Da MS peak corresponding to a gene product of the bla(KPC) pKpQIL plasmid was identified and characterized using a combination of proteomics and molecular techniques. This plasmid peak was present in spectra from retrospectively analyzed K. pneumoniae outbreak isolates, concordant with results from whole-genome sequencing, and absent from a diverse control set of bla(KPC)-negative clinical Enterobacteriaceae isolates. Notably, the gene characterized here is located adjacent to the bla(KPC) Tn4401 transposon on the pKpQIL plasmid. Sequence analysis demonstrates the presence of this gene in other bla(KPC) Tn4401-containing plasmids and suggests that this signature MS peak may be useful in tracking other plasmids conferring carbapenem resistance. Plasmid identification using this MALDI-TOF MS method was accomplished in as little as 10 min from isolated colonies and 30 min from positive (spiked) blood cultures, demonstrating the potential clinical utility for real-time plasmid tracking in an outbreak.

Consumer-mediated health information exchanges: the 2012 ACMI debate.

The American College of Medical Informatics (ACMI) sponsors periodic debates during the American Medical Informatics Fall Symposium to highlight important informatics issues of broad interest. In 2012, a panel debated the following topic: "Resolved: Health Information Exchange Organizations Should Shift Their Principal Focus to Consumer-Mediated Exchange in Order to Facilitate the Rapid Development of Effective, Scalable, and Sustainable Health Information Infrastructure." Those supporting the proposition emphasized the need for consumer-controlled community repositories of electronic health records (health record banks) to address privacy, stakeholder cooperation, scalability, and sustainability. Those opposing the proposition emphasized that the current healthcare environment is so complex that development of consumer control will take time and that even then, consumers may not be able to mediate their information effectively. While privately each discussant recognizes that there are many sides to this complex issue, each followed the debater's tradition of taking an extreme position in order emphasize some of the polarizing aspects in the short time allotted them. In preparing this summary, we sought to convey the substance and spirit of the debate in printed form. Transcripts of the actual debate were edited for clarity, and appropriate supporting citations were added for the further edification of the reader.

The National Institutes of Health's Biomedical Translational Research Information System (BTRIS): design, contents, functionality and experience to date.

The US National Institutes of Health (NIH) has developed the Biomedical Translational Research Information System (BTRIS) to support researchers' access to translational and clinical data. BTRIS includes a data repository, a set of programs for loading data from NIH electronic health records and research data management systems, an ontology for coding the disparate data with a single terminology, and a set of user interface tools that provide access to identified data from individual research studies and data across all studies from which individually identifiable data have been removed. This paper reports on unique design elements of the system, progress to date and user experience after five years of development and operation.

Planning for Actionable Precision Medicine.

Genome-guided precision medicine applies consensus recommendations to the care of patients with particular genetic variants. As electronic health records begin to include patients' genomic data, recommendations will be formulated at an increasing rate. This study examined recommendations related to the current list of 73 actionable genes compiled by the American College of Medical Genetics and Genomics and found that conditions fall generally into five classes (cardiovascular, medication interactions, metabolic, neoplastic, and structural), with recommendations falling into seven categories (actions or circumstances to avoid, evaluation of relatives at risk, pregnancy management, prevention of primary manifestations, prevention of secondary complications, surveillance, and treatment of manifestations). This study represents a first step in facilitating automated, scalable clinical decision support and provides direction on formal representation of the contexts and actions for clinical recommendations derived from genome-informed learning health systems.