Towards Unified Data Exchange Formats for Reporting Molecular Drug Susceptibility Testing.

BACKGROUND: With the rapid development of new advanced molecular detection methods, identification of new genetic mutations conferring pathogen resistance to an ever-growing variety of antimicrobial substances will generate massive genomic datasets for public health and clinical laboratories. Keeping up with specialized standard coding for these immense datasets will be extremely challenging. This challenge prompted our effort to create a common molecular resistance Logical Observation Identifiers Names and Codes (LOINC) panel that can be used to report any identified antimicrobial resistance pattern. OBJECTIVE: To develop and utilize a common molecular resistance LOINC panel for molecular drug susceptibility testing (DST) data exchange in the U.S. National Tuberculosis Surveillance System using California Department of Public Health (CDPH) and New York State Department of Health as pilot sites. METHODS: We developed an interface and mapped incoming molecular DST data to the common molecular resistance LOINC panel using Health Level Seven (HL7) v2.5.1 Electronic Laboratory Reporting (ELR) message specifications through the Orion Health™ Rhapsody Integration Engine v6.3.1. RESULTS: Both pilot sites were able to process and upload/import the standardized HL7 v2.5.1 ELR messages into their respective systems; albeit CDPH identified areas for system improvements and has focused efforts to streamline the message importation process. Specifically, CDPH is enhancing their system to better capture parent-child elements and ensure that the data collected can be accessed seamlessly by the U.S. Centers for Disease Control and Prevention. DISCUSSION: The common molecular resistance LOINC panel is designed to be generalizable across other resistance genes and ideally also applicable to other disease domains. CONCLUSION: The study demonstrates that it is possible to exchange molecular DST data across the continuum of disparate healthcare information systems in integrated public health environments using the common molecular resistance LOINC panel.

COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes.

Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.

The LOINC RSNA radiology playbook - a unified terminology for radiology procedures.

Objective: This paper describes the unified LOINC/RSNA Radiology Playbook and the process by which it was produced. Methods: The Regenstrief Institute and the Radiological Society of North America (RSNA) developed a unification plan consisting of six objectives 1) develop a unified model for radiology procedure names that represents the attributes with an extensible set of values, 2) transform existing LOINC procedure codes into the unified model representation, 3) create a mapping between all the attribute values used in the unified model as coded in LOINC (ie, LOINC Parts) and their equivalent concepts in RadLex, 4) create a mapping between the existing procedure codes in the RadLex Core Playbook and the corresponding codes in LOINC, 5) develop a single integrated governance process for managing the unified terminology, and 6) publicly distribute the terminology artifacts. Results: We developed a unified model and instantiated it in a new LOINC release artifact that contains the LOINC codes and display name (ie LONG_COMMON_NAME) for each procedure, mappings between LOINC and the RSNA Playbook at the procedure code level, and connections between procedure terms and their attribute values that are expressed as LOINC Parts and RadLex IDs. We transformed all the existing LOINC content into the new model and publicly distributed it in standard releases. The organizations have also developed a joint governance process for ongoing maintenance of the terminology. Conclusions: The LOINC/RSNA Radiology Playbook provides a universal terminology standard for radiology orders and results.

Use of Radiology Procedure Codes in Health Care: The Need for Standardization and Structure.

Radiology procedure codes are a fundamental part of most radiology workflows, such as ordering, scheduling, billing, and image interpretation. Nonstandardized unstructured procedure codes have typically been used in radiology departments. Such codes may be sufficient for specific purposes, but they offer limited support for interoperability. As radiology workflows and the various forms of clinical data exchange have become more sophisticated, the need for more advanced interoperability with use of standardized structured codes has increased. For example, structured codes facilitate the automated identification of relevant prior imaging studies and the collection of data for radiation dose tracking. The authors review the role of imaging procedure codes in radiology departments and across the health care enterprise. Standards for radiology procedure coding are described, and the mechanisms of structured coding systems are reviewed. In particular, the structure of the RadLex™ Playbook coding system and examples of the use of this system are described. Harmonization of the RadLex Playbook system with the Logical Observation Identifiers Names and Codes standard, which is currently in progress, also is described. The benefits and challenges of adopting standardized codes-especially the difficulties in mapping local codes to standardized codes-are reviewed. Tools and strategies for mitigating these challenges, including the use of billing codes as an intermediate step in mapping, also are reviewed. In addition, the authors describe how to use the RadLex Playbook Web service application programming interface for partial automation of code mapping. © RSNA, 2017.

Use of Electronic Health Record Data to Evaluate the Impact of Race on 30-Day Mortality in Patients Admitted to the Intensive Care Unit.

INTRODUCTION: The current body of literature examining the impact of race upon outcomes for patients admitted to the intensive care unit (ICU) is limited. The primary objective of our study was to explore this question using a large cohort drawn from an electronic health record (EHR)-based data source. METHODS: We conducted a retrospective cohort study using Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-II), an EHR-derived database encompassing ICU admissions to an academic medical center in Boston, Massachusetts, between 2001 and 2008. Adults admitted to a medical or surgical ICU were assessed for the primary outcome of 30-day mortality and secondary outcomes of in-hospital mortality and hospital length-of-stay. Multivariate logistic regression was used to determine the association between race and the primary outcome. RESULTS: The study cohort consisted of 14,684 adult ICU patients-10,562 White, 1311 Black, 363 Asian, 868 "Other," and 1580 without known race. Thirty-day mortality rates experienced by Black and Asian individuals were significantly lower than mortality among those identified as White, with odds ratios of 0.62 (95 % CI 0.50-0.77) and 0.64 (95 % CI 0.44-0.93), respectively. Patients without known race experienced the highest crude mortality overall (27.4 %) and twice the adjusted odds of mortality compared with the White group. CONCLUSIONS: In a large, racially diverse cohort of general ICU patients, White patients experienced significantly higher mortality than non-White patients. Our results are consistent with findings from other studies that indicate that the non-White race does not appear to negatively impact short-term survival following ICU admission.

The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.

This paper describes a supervised machine learning approach for identifying heart disease risk factors in clinical text, and assessing the impact of annotation granularity and quality on the system's ability to recognize these risk factors. We utilize a series of support vector machine models in conjunction with manually built lexicons to classify triggers specific to each risk factor. The features used for classification were quite simple, utilizing only lexical information and ignoring higher-level linguistic information such as syntax and semantics. Instead, we incorporated high-quality data to train the models by annotating additional information on top of a standard corpus. Despite the relative simplicity of the system, it achieves the highest scores (micro- and macro-F1, and micro- and macro-recall) out of the 20 participants in the 2014 i2b2/UTHealth Shared Task. This system obtains a micro- (macro-) precision of 0.8951 (0.8965), recall of 0.9625 (0.9611), and F1-measure of 0.9276 (0.9277). Additionally, we perform a series of experiments to assess the value of the annotated data we created. These experiments show how manually-labeled negative annotations can improve information extraction performance, demonstrating the importance of high-quality, fine-grained natural language annotations.

An update on the use of health information technology in newborn screening.

Newborn screening (NBS) has high-stakes health implications and requires rapid and effective communication between many people and organizations. Multiple NBS stakeholders worked together to create national guidance for reporting NBS results with HL7 (Health Level 7) messages that contain LOINC (Logical Observation Identifiers Names and Codes) and SNOMED-CT (Systematized Nomenclature of Medicine-Clinical Terms) codes, report quantitative test results, and use standardized computer-readable UCUM units of measure. This guidance (a LOINC panel and an example annotated HL7 message) enables standard HL7 v2.5.1 laboratory messages to carry the information required for reporting NBS results. Other efforts include HL7 implementation guides for reporting point-of-care (POC) NBS results as well as standardizing follow-up of patients diagnosed with conditions identified through NBS. If the guidance is used nationally, regional and national registries can aggregate results from state programs to facilitate research and quality assurance and help ensure continuity of operations following a disaster situation.

High vitamin B12 levels are not associated with increased mortality risk for ICU patients after adjusting for liver function: a cohort study.

BACKGROUND AND AIMS: Recent research has suggested that high vitamin B12 levels may be associated with increased mortality after ICU admission. However, it is known that impaired liver function may lead to elevated B12 since B12 is metabolized through the liver, and therefore high B12 levels may serve as a proxy for poor liver function. The aim of this study is to assess the impact that liver function and liver disease have on the relationship between high vitamin B12 levels and mortality in the ICU. METHODS: We performed an observational cohort study using ICU data that were collected from patients admitted to four ICU types (medical, surgical, cardiac care and cardiac surgery recovery) in one large urban hospital from 2001 to 2008. We analyzed the medical records of 1,684 adult patients (age ≥ 18 years) who had vitamin B12 and liver function measurements up to 14 days prior to ICU admission or within 24 hours after admission. RESULTS: While we found an association between high B12 and mortality when we did not control for any potential confounders, after we adjusted for liver function and liver disease, no significant association existed between B12 and mortality using multivariable logistic regression (30-day mortality: OR=1.18, 95% CI 0.81 to 1.72, p=0.3890; 90-day mortality: OR=1.20, 95% CI 0.84 to 1.71, p=0.3077). CONCLUSIONS: Elevated B12 levels are not a significant predictor of mortality after ICU admission when liver function is controlled for, and may instead be a proxy for poor liver function.

Combining structured and unstructured data to identify a cohort of ICU patients who received dialysis.

OBJECTIVE: To develop a generalizable method for identifying patient cohorts from electronic health record (EHR) data-in this case, patients having dialysis-that uses simple information retrieval (IR) tools. METHODS: We used the coded data and clinical notes from the 24,506 adult patients in the Multiparameter Intelligent Monitoring in Intensive Care database to identify patients who had dialysis. We used SQL queries to search the procedure, diagnosis, and coded nursing observations tables based on ICD-9 and local codes. We used a domain-specific search engine to find clinical notes containing terms related to dialysis. We manually validated the available records for a 10% random sample of patients who potentially had dialysis and a random sample of 200 patients who were not identified as having dialysis based on any of the sources. RESULTS: We identified 1844 patients that potentially had dialysis: 1481 from the three coded sources and 1624 from the clinical notes. Precision for identifying dialysis patients based on available data was estimated to be 78.4% (95% CI 71.9% to 84.2%) and recall was 100% (95% CI 86% to 100%). CONCLUSIONS: Combining structured EHR data with information from clinical notes using simple queries increases the utility of both types of data for cohort identification. Patients identified by more than one source are more likely to meet the inclusion criteria; however, including patients found in any of the sources increases recall. This method is attractive because it is available to researchers with access to EHR data and off-the-shelf IR tools.

Comment on "time to integrate clinical and research informatics".

The same code standards should be used in both research and clinical care to facilitate data integration across domains.

A simple method to extract key maternal data from neonatal clinical notes.

Knowledge about maternal history is critical for guiding certain aspects of newborn clinical care as well as for research on neonatal issues. However, often the only maternal history available in the newborn record is in the clinical notes. We are using data from the MIMIC-II database for a clinical study on newborns admitted to the intensive care unit. Important maternal data were only available in the newborn notes, so we developed a simple algorithm to extract those data. We manually derived patterns for maternal age, gravida/para status, and laboratory results by reviewing a small set of notes. Using regular expressions and specific filters for notes and results, we extracted maternal data with recall of 0.91-0.99 and precision of 0.95-1.0 for the 289 infants in our study. Our methods could be used with other research datasets and with clinical documentation systems to extract maternal data into a more useful, structured format.

Lower short- and long-term mortality associated with overweight and obesity in a large cohort study of adult intensive care unit patients.

INTRODUCTION: Two thirds of United States adults are overweight or obese, which puts them at higher risk of developing chronic diseases and of death compared with normal-weight individuals. However, recent studies have found that overweight and obesity by themselves may be protective in some contexts, such as hospitalization in an intensive care unit (ICU). Our objective was to determine the relation between body mass index (BMI) and mortality at 30 days and 1 year after ICU admission. METHODS: We performed a cohort analysis of 16,812 adult patients from MIMIC-II, a large database of ICU patients at a tertiary care hospital in Boston, Massachusetts. The data were originally collected during the course of clinical care, and we subsequently extracted our dataset independent of the study outcome. RESULTS: Compared with normal-weight patients, obese patients had 26% and 43% lower mortality risk at 30 days and 1 year after ICU admission, respectively (odds ratio (OR), 0.74; 95% confidence interval (CI), 0.64 to 0.86) and 0.57 (95% CI, 0.49 to 0.67)); overweight patients had nearly 20% and 30% lower mortality risk (OR, 0.81; 95% CI, 0.70 to 0.93) and OR, 0.68 (95% CI, 0.59 to 0.79)). Severely obese patients (BMI ≥ 40 kg/m2) did not have a significant survival advantage at 30 days (OR, 0.94; 95% CI, 0.74 to 1.20), but did have 30% lower mortality risk at 1 year (OR, 0.70 (95% CI, 0.54 to 0.90)). No significant difference in admission acuity or ICU and hospital length of stay was found across BMI categories. CONCLUSION: Our study supports the hypothesis that patients who are overweight or obese have improved survival both 30 days and 1 year after ICU admission.

Standardizing clinical laboratory data for secondary use.

Clinical databases provide a rich source of data for answering clinical research questions. However, the variables recorded in clinical data systems are often identified by local, idiosyncratic, and sometimes redundant and/or ambiguous names (or codes) rather than unique, well-organized codes from standard code systems. This reality discourages research use of such databases, because researchers must invest considerable time in cleaning up the data before they can ask their first research question. Researchers at MIT developed MIMIC-II, a nearly complete collection of clinical data about intensive care patients. Because its data are drawn from existing clinical systems, it has many of the problems described above. In collaboration with the MIT researchers, we have begun a process of cleaning up the data and mapping the variable names and codes to LOINC codes. Our first step, which we describe here, was to map all of the laboratory test observations to LOINC codes. We were able to map 87% of the unique laboratory tests that cover 94% of the total number of laboratory tests results. Of the 13% of tests that we could not map, nearly 60% were due to test names whose real meaning could not be discerned and 29% represented tests that were not yet included in the LOINC table. These results suggest that LOINC codes cover most of laboratory tests used in critical care. We have delivered this work to the MIMIC-II researchers, who have included it in their standard MIMIC-II database release so that researchers who use this database in the future will not have to do this work.

Standardizing newborn screening results for health information exchange.

Newborn screening (NBS) is a complex process that has high-stakes health implications and requires rapid and effective communication between many people and organizations. Currently, each NBS laboratory has its own method of reporting results to state programs, hospitals and individual providers, with wide variation in content and format. Pediatric care providers receive reports by mail, email, fax or telephone, depending on whether the results are normal or abnormal. This process is slow and prone to errors, which can lead to delays in treatment. Multiple agencies worked together to create national guidance for reporting newborn screening results with HL7 messages that contain a prescribed set of LOINC and SNOMED CT codes, report quantitative test results, and use standardized units of measure. Several states are already implementing this guidance. If the guidance is used nationally, office EHRs could capture NBS results more efficiently, and regional and national registries could better analyze aggregate results to facilitate improvements in NBS and further research for these rare conditions.