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SAM HARRIS WAKING UP A Guide to Spirituality Without Religion Simon and Schuster. 272 pp. $28.00 ISBN: 978-0231163583 SAM HARRIS IS NOT THE ONLY ATHEIST offering a replacement for something [...]

Objective: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations.Materials and Methods: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements.Results: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback.Discussion: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate.Conclusion: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require. [ABSTRACT FROM AUTHOR]

Introduction: Computable biomedical knowledge artifacts (CBKs) are digital objects conveying biomedical knowledge in machine‐interpretable structures. As more CBKs are produced and their complexity increases, the value obtained from sharing CBKs grows. Mobilizing CBKs and sharing them widely can only be achieved if the CBKs are findable, accessible, interoperable, reusable, and trustable (FAIR+T). To help mobilize CBKs, we describe our efforts to outline metadata categories to make CBKs FAIR+T. Methods: We examined the literature regarding metadata with the potential to make digital artifacts FAIR+T. We also examined metadata available online today for actual CBKs of 12 different types. With iterative refinement, we came to a consensus on key categories of metadata that, when taken together, can make CBKs FAIR+T. We use subject‐predicate‐object triples to more clearly differentiate metadata categories. Results: We defined 13 categories of CBK metadata most relevant to making CBKs FAIR+T. Eleven of these categories (type, domain, purpose, identification, location, CBK‐to‐CBK relationships, technical, authorization and rights management, provenance, evidential basis, and evidence from use metadata) are evident today where CBKs are stored online. Two additional categories (preservation and integrity metadata) were not evident in our examples. We provide a research agenda to guide further study and development of these and other metadata categories. Conclusion: A wide variety of metadata elements in various categories is needed to make CBKs FAIR+T. More work is needed to develop a common framework for CBK metadata that can make CBKs FAIR+T for all stakeholders. [ABSTRACT FROM AUTHOR]

Sharing of individual participant data is encouraged by the International Committee of Medical Journal Editors. We analyzed clinical trial registry data from ClinicalTrials.gov (CTG) and determined the proportion of trials sharing de-identified Individual Participant Data (IPD). We looked at 3,138 medical conditions (as Medical Subject Heading terms). Overall, 10.8% of trials with first registration date after December 1, 2015 answered 'Yes' to plan to share de-identified IPD data. This sharing rate ranges between 0% (biliary tract neoplasms) to 72.2% (meningitis, meningococcal) when analyzed by disease that is focus of a study. Via a predictive model, we found that studies that deposited basic summary results data to CTG results registry, large studies and phase 3 interventional studies are most likely to declare intent to share IPD data. As part of an HIV common data element analysis project, we further compared a body of HIV trials (24% sharing rate) to other diseases., (©2019 AMIA - All rights reserved.)

Value sets are essential in activities such as electronic clinical quality measures (eCQM) and patient cohort definition. Creation and maintenance of value sets is labor intensive and error prone. Our method aims to use existing inter-terminology maps to improve the quality of value sets that are defined in more than one terminology. For 197 eCQM value sets defined in SNOMED CT plus ICD-9-CM and/or ICD-10-CM, the map-generated codes showed good overlap with the value set codes. Manual review showed that some new codes identified by mapping should probably be included in the value sets. This could potentially augment the ICD-9-CM codes by 45% (1.5 codes), ICD-10-CM codes by 25% (1.8 codes) and SNOMED CT codes by up to 42% (4.8 codes) per value set on average. The mapping between SNOMED CT and ICD-10-PCS did not perform as well because of the granularity discrepancy in the map., (©2019 AMIA - All rights reserved.)

This paper focuses on value sets as an essential component in the health analytics ecosystem. We discuss shared repositories of reusable value sets and offer recommendations for their further development and adoption. In order to motivate these contributions, we explain how value sets fit into specific analytic tasks and the health analytics landscape more broadly; their growing importance and ubiquity with the advent of Common Data Models, Distributed Research Networks, and the availability of higher order, reusable analytic resources like electronic phenotypes and electronic clinical quality measures; the formidable barriers to value set reuse; and our introduction of a concept-agnostic orientation to vocabulary collections. The costs of ad hoc value set management and the benefits of value set reuse are described or implied throughout. Our standards, infrastructure, and design recommendations are not systematic or comprehensive but invite further work to support value set reuse for health analytics. The views represented in the paper do not necessarily represent the views of the institutions or of all the co-authors .

We present a method that extracts medication information from discharge summaries. The program relies on parsing rules written as a set of regular expressions and on a user-configurable drug lexicon. Our evaluation shows a precision of 94% and recall of 83% in the extraction of medication information. We use a broader definition of medication information than previous studies, including drug names appearing with and without dosage information, misspelled drug names, and contextual information.