This project reflects a longstanding NLM interest in clinical terminology and message standards. It uses and tunes the message and vocabulary standards that NLM has supported to facilitate interoperability, defined as communicating data from a source to a destination where it can be used in computing HIMSS Dictionary of Healthcare Information Technology Terms, Acronyms and Organizations, 2nd Edition, 2010, Appendix B, p190.. NLM has been at the nexus of standards needed to make clinical data flow from sources for clinical care and epidemiologic databases. We are directly engaged in work on HL7 electronic messaging standards related to Clinical Genomics Coded Reporting, and reporting results of newborn screening. The major code systems required by Meaningful Use regulations LOINC, SNOMED, RxNorm, and UCUM are all developed and/or supported by NLM. We continue to collaborate with other organizations to develop and promote the use of these standards. LOINC-related collaborations during FY2016 with: IEEE (importing the clinical variables that come out of IEEE monitoring, and electrophysiological instruments, e.g. Ventilators, anesthesia monitors, Infusion pumps, EEG, Nerve conduction); RadLex and Radiologic Society of North America (RSNA) to define names and codes for radiologic exams; DICOM and Echocardiographers (measures from Echocardiograms); American Nurses Association (ANA) and others (create nursing assessment variables -- in progress); CMS (to unify all of their assessments as LOINC codes -- in progress); National Eye Institute (to develop variables for all of the variables produced by their exams and instruments -- finished this year); SNOMED/IHTSDO (mappings). We developed a very successful tool to access the RxNorm prescription drug terminology, which has been very popular with more than one billion queries per year from electronic health records, EHR developers, and clinical researchers. NLM is developing standards-based tools and techniques that can be used in EHRs, PHRs, and research. In FY2016 we made these tools open source and freely-available by publishing the source code on GitHub. They are Section 508-compliant (i.e., accessible to screen readers). Groups or institutions can customize many of the features for their particular needs. A) LHC-Forms: Form Rendering Widget LHC-Forms creates input forms for Web-based medical applications, EHRs, PHRs, and mobile health apps. LHC-Forms can render a powerful data entry form for laboratory panels, survey instruments, etc. from any of the 2,000+ panels defined in LOINC. Implementers can also use it to develop forms based on their own arbitrary variables. The Web browser turns form descriptions into live forms via a rendering program (about 300K bytes compressed) that it can load once and use over and over. The forms can be described in a spreadsheet or in JSON the ultimate target format and can be authored with the NLM form builder, described below. LHC-Forms supports many form attributes, including: data type, cardinality, default value, units of measure (if numeric), answer lists and multiple choice/multiple answer variables, relationship (in a nested hierarchy) to other questions, scoring of survey instruments, default value settings, validation checks to ensure quality data collection, skip logic (so questions can dynamically appear/disappear based on value recorded in multiple other questions) and help messages. LHC-Forms uses the NLM-developed autocompleter package (autocomplete-lhc) described below. Data types, data structures and conventions are parallel to the widely-adopted HL7 V2 electronic message standard, and a completed form can be converted to an HL7 v2 message. We are also working on the ability to generate FHIR clinical messages. The demo site is at http://lforms-demo.nlm.nih.gov. Programmers interested in using LHC-Forms in their own EMR, PHR, or other application can download the LHC-Forms software from GitHub at http://lhncbc.github.io/lforms/. Developers can add their own custom solutions for storing data, authentication, and user control. Our related Form Builder tool enables users to build and customize forms using an informative dashboard with real-time updates that display user selections. B) Auto-completers NLM-developed auto-completers aim to improve the user experience for entering information in electronic forms (e.g. easier and faster), and also improve the quality of the data entered. They are written in JavaScript and support two types of lists: Prefetch (small enough to be included when the page loads) and Search (lists are shown in small, AJAX-retrieved chunks that match the user's input). Software download and demos at: http://lhncbc.github.io/autocomplete-lhc/ C) Clinical Table Search Service The Clinical Table Search Service provides look up functions to tables such as master files and coding systems, in order to provide answer lists for fields in many forms. The service connects tables to fields in the form by URLs whose parameters control what table to search, which fields to return to the choice menu grid, and which fields of the selected item to store as hidden content in the input fields. Our implementation provides preconfigured API access to many clinical tables: LOINC, RxTerms, ICD-10-CM, many NCBI genomics tables, COSMIC and others. Try it at: https://clin-tablesearch. lhc.nlm.nih.gov. D) Validator and converter for UCUM units of measure. Standard units of measure are essential to the use of numeric variables in clinical care and research. Currently, they are not standardized and use varying strings. In a large sample of HL7 messages we found 60 different ways to express the units for a red blood cell count. The Universal Code for Reporting Units of Measure (UCUM) was developed to solve this problem. It accommodates all metric units and every kind of conventional unit, plus those that are not so conventional (http://unitsofmeasure.org/). It also includes a formal definition of the syntax and tables with coefficients and other attributes for converting between different units of measure. Standard units are needed to exchange and aggregate numeric values (e.g. laboratory test results and vital signs), or to utilize them for clinical decision support. UCUM has been adopted by most clinical standards organizations, including ANSI approved standards development orgs (SDOs): HL7, IEEE (instrument measurements), DICOM (radiology measurement), and ISO-11240 for development of medicinal products. It is required by meaningful use for HL7 for Public Health laboratory reporting, and for most measurements in HL7s CDA reporting We developed an open source Web-based JavaScript UCUM validator function that will verify that units strings claiming to be UCUM units are valid UCUM units, can batch validate the units in a table submitted as a CSV file, and can also convert values reported as one specific UCUM unit to another commensurate UCUM unit (e.g. ounces to kilograms), which could help aggregate or analyze data from multiple sources. Try it: https://ucum-validator.lhc.nlm.nih.gov.
| Vreeman, Daniel J; Abhyankar, Swapna; McDonald, Clement J (2018) Response to Unit conversions between LOINC codes. J Am Med Inform Assoc 25:614-615 |
| Baik, Seo Hyon; Kury, Fabricio Sampaio Peres; McDonald, Clement Joseph (2017) Risk of Alzheimer's Disease Among Senior Medicare Beneficiaries Treated With Androgen Deprivation Therapy for Prostate Cancer. J Clin Oncol 35:3401-3409 |
| Mundkur, Mallika L; Callaghan, Fiona M; Abhyankar, Swapna et al. (2016) Use of Electronic Health Record Data to Evaluate the Impact of Race on 30-Day Mortality in Patients Admitted to the Intensive Care Unit. J Racial Ethn Health Disparities : |
| Drawz, Paul E; Archdeacon, Patrick; McDonald, Clement J et al. (2015) CKD as a Model for Improving Chronic Disease Care through Electronic Health Records. Clin J Am Soc Nephrol 10:1488-99 |
| Demner-Fushman, Dina; Kohli, Marc D; Rosenman, Marc B et al. (2015) Preparing a collection of radiology examinations for distribution and retrieval. J Am Med Inform Assoc : |
| Fung, Kin Wah; Xu, Julia (2015) An exploration of the properties of the CORE problem list subset and how it facilitates the implementation of SNOMED CT. J Am Med Inform Assoc 22:649-58 |
| Peters, Lee B; Bahr, Nathan; Bodenreider, Olivier (2015) Evaluating drug-drug interaction information in NDF-RT and DrugBank. J Biomed Semantics 6:19 |
| Fontelo, Paul; Liu, Fang; Yagi, Yukako (2015) Evaluation of a smartphone for telepathology: Lessons learned. J Pathol Inform 6:35 |
| Abhyankar, Swapna; Goodwin, Rebecca M; Sontag, Marci et al. (2015) An update on the use of health information technology in newborn screening. Semin Perinatol 39:188-93 |
| Ayvaz, Serkan; Horn, John; Hassanzadeh, Oktie et al. (2015) Toward a complete dataset of drug-drug interaction information from publicly available sources. J Biomed Inform 55:206-17 |
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