Logical Observation Identifiers Research Articles

BGLM: big data-guided LOINC mapping with multi-language support

Mapping internal, locally used lab test codes to standardized logical observation identifiers names and codes (LOINC) terminology has become an essential step in harmonizing electronic health record (EHR) data across different institutions. However, most existing LOINC code mappers are based on text-mining technology and do not provide robust multi-language support. We introduce a simple, yet effective tool called big data-guided LOINC code mapper (BGLM), which leverages the large amount of patient data stored in EHR systems to perform LOINC coding mapping. Distinguishing from existing methods, BGLM conducts mapping based on distributional similarity. We validated the performance of BGLM with real-world datasets and showed that high mapping precision could be achieved under proper false discovery rate control. In addition, we showed that the mapping results of BGLM could be used to boost the performance of Regenstrief LOINC Mapping Assistant (RELMA), one of the most widely used LOINC code mappers. BGLM paves a new way for LOINC code mapping and therefore could be applied to EHR systems without the restriction of languages. BGLM is freely available at https://github.com/Bin-Chen-Lab/BGLM.

Development and testing of data infrastructure in the American College of Emergency Physicians' Clinical Emergency Data Registry for opioid-related research.

ObjectivePrior research has identified gaps in the capacity of electronic health records (EHRs) to capture the intricacies of opioid‐related conditions. We sought to enhance the opioid data infrastructure within the American College of Emergency Physicians’ Clinical Emergency Data Registry (CEDR), the largest national emergency medicine registry, through data mapping, validity testing, and feasibility assessment.MethodsWe compared the CEDR data dictionary to opioid common data elements identified through prior environmental scans of publicly available data systems and dictionaries used in national informatics and quality measurement of policy initiatives. Validity and feasibility assessments of CEDR opioid‐related data were conducted through the following steps: (1) electronic extraction of CEDR data meeting criteria for an opioid‐related emergency care visit, (2) manual chart review assessing the quality of the extracted data, (3) completion of feasibility scorecards, and (4) qualitative interviews with physician reviewers and informatics personnel.ResultsWe identified several data gaps in the CEDR data dictionary when compared with prior environmental scans including urine drug testing, opioid medication, and social history data elements. Validity testing demonstrated correct or partially correct data for >90% of most extracted CEDR data elements. Factors affecting validity included lack of standardization, data incorrectness, and poor delimitation between emergency department (ED) versus hospital care. Feasibility testing highlighted low‐to‐moderate feasibility of date and social history data elements, significant EHR platform variation, and inconsistency in the extraction of common national data standards (eg, Logical Observation Identifiers Names and Codes, International Classification of Diseases, Tenth Revision codes).ConclusionsWe found that high‐priority data elements needed for opioid‐related research and clinical quality measurement, such as demographics, medications, and diagnoses, are both valid and can be feasibly captured in a national clinical quality registry. Future work should focus on implementing structured data collection tools, such as standardized documentation templates and adhering to data standards within the EHR that would better characterize ED‐specific care for opioid use disorder and related research.

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review.

BackgroundThe standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology. However, its use as a standard for health research is still less prevalent. To use existing data sources more efficiently for health research, data interoperability becomes increasingly important. FHIR provides solutions by offering resource domains such as “Public Health & Research” and “Evidence-Based Medicine” while using already established web technologies. Therefore, FHIR could help standardize data across different data sources and improve interoperability in health research.ObjectiveThe aim of our study was to provide a systematic review of existing literature and determine the current state of FHIR implementations in health research and possible future directions.MethodsWe searched the PubMed/MEDLINE, Embase, Web of Science, IEEE Xplore, and Cochrane Library databases for studies published from 2011 to 2022. Studies investigating the use of FHIR in health research were included. Articles published before 2011, abstracts, reviews, editorials, and expert opinions were excluded. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and registered this study with PROSPERO (CRD42021235393). Data synthesis was done in tables and figures.ResultsWe identified a total of 998 studies, of which 49 studies were eligible for inclusion. Of the 49 studies, most (73%, n=36) covered the domain of clinical research, whereas the remaining studies focused on public health or epidemiology (6%, n=3) or did not specify their research domain (20%, n=10). Studies used FHIR for data capture (29%, n=14), standardization of data (41%, n=20), analysis (12%, n=6), recruitment (14%, n=7), and consent management (4%, n=2). Most (55%, 27/49) of the studies had a generic approach, and 55% (12/22) of the studies focusing on specific medical specialties (infectious disease, genomics, oncology, environmental health, imaging, and pulmonary hypertension) reported their solutions to be conferrable to other use cases. Most (63%, 31/49) of the studies reported using additional data models or terminologies: Systematized Nomenclature of Medicine Clinical Terms (29%, n=14), Logical Observation Identifiers Names and Codes (37%, n=18), International Classification of Diseases 10th Revision (18%, n=9), Observational Medical Outcomes Partnership common data model (12%, n=6), and others (43%, n=21). Only 4 (8%) studies used a FHIR resource from the domain “Public Health & Research.” Limitations using FHIR included the possible change in the content of FHIR resources, safety, legal matters, and the need for a FHIR server.ConclusionsOur review found that FHIR can be implemented in health research, and the areas of application are broad and generalizable in most use cases. The implementation of international terminologies was common, and other standards such as the Observational Medical Outcomes Partnership common data model could be used as a complement to FHIR. Limitations such as the change of FHIR content, lack of FHIR implementation, safety, and legal matters need to be addressed in future releases to expand the use of FHIR and, therefore, interoperability in health research.

Addressing the Gap in Data Communication from Home Health Care to Primary Care during Care Transitions: Completeness of an Interoperability Data Standard.

In a future where home health care is no longer an information silo, patient information will be communicated along transitions in care to improve care. Evidence-based practice in the United States supports home health care patients to see their primary care team within the first two weeks of hospital discharge to reduce rehospitalization risk. We sought to identify a parsimonious set of home health care data to be communicated to primary care for the post-hospitalization visit. Anticipating electronic dataset communication, we investigated the completeness of the international reference terminology, Logical Observation Identifiers Names and Codes (LOINC), for coverage of the data to be communicated. We conducted deductive qualitative analysis in three steps: (1) identify home health care data available for the visit by mapping home health care to the information needed for the visit; (2) reduce the resulting home health care data set to a parsimonious set clinicians wanted for the post-hospitalization visit by eliciting primary care clinician input; and (3) map the parsimonious dataset to LOINC and assess LOINC completeness. Our study reduced the number of standardized home health care assessment questions by 40% to a parsimonious set of 33 concepts that primary care team physicians wanted for the post-hospitalization visit. Findings indicate all home health care concepts in the parsimonious dataset mapped to the information needed for the post-hospitalization visit, and 84% of the home health care concepts mapped to a LOINC term. The results indicate data flow of parsimonious home health care dataset to primary care for the post-hospitalization visit is possible using existing LOINC codes, and would require adding some codes to LOINC for communication of a complete parsimonious data set.

Encoding laboratory testing data: case studies of the national implementation of HHS requirements and related standards in five laboratories.

ObjectiveAssess the effectiveness of providing Logical Observation Identifiers Names and Codes (LOINC®)-to-In Vitro Diagnostic (LIVD) coding specification, required by the United States Department of Health and Human Services for SARS-CoV-2 reporting, in medical center laboratories and utilize findings to inform future United States Food and Drug Administration policy on the use of real-world evidence in regulatory decisions.Materials and MethodsWe compared gaps and similarities between diagnostic test manufacturers’ recommended LOINC® codes and the LOINC® codes used in medical center laboratories for the same tests.ResultsFive medical centers and three test manufacturers extracted data from laboratory information systems (LIS) for prioritized tests of interest. The data submission ranged from 74 to 532 LOINC® codes per site. Three test manufacturers submitted 15 LIVD catalogs representing 26 distinct devices, 6956 tests, and 686 LOINC® codes. We identified mismatches in how medical centers use LOINC® to encode laboratory tests compared to how test manufacturers encode the same laboratory tests. Of 331 tests available in the LIVD files, 136 (41%) were represented by a mismatched LOINC® code by the medical centers (chi-square 45.0, 4 df, P < .0001).DiscussionThe five medical centers and three test manufacturers vary in how they organize, categorize, and store LIS catalog information. This variation impacts data quality and interoperability.ConclusionThe results of the study indicate that providing the LIVD mappings was not sufficient to support laboratory data interoperability. National implementation of LIVD and further efforts to promote laboratory interoperability will require a more comprehensive effort and continuing evaluation and quality control.

ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials

BackgroundScreening for eligible patients continues to pose a great challenge for many clinical trials. This has led to a rapidly growing interest in standardizing computable representations of eligibility criteria (EC) in order to develop tools that leverage data from electronic health record (EHR) systems. Although laboratory procedures (LP) represent a common entity of EC that is readily available and retrievable from EHR systems, there is a lack of interoperable data models for this entity of EC. A public, specialized data model that utilizes international, widely-adopted terminology for LP, e.g. Logical Observation Identifiers Names and Codes (LOINC®), is much needed to support automated screening tools.ObjectiveThe aim of this study is to establish a core dataset for LP most frequently requested to recruit patients for clinical trials using LOINC terminology. Employing such a core dataset could enhance the interface between study feasibility platforms and EHR systems and significantly improve automatic patient recruitment.MethodsWe used a semi-automated approach to analyze 10,516 screening forms from the Medical Data Models (MDM) portal’s data repository that are pre-annotated with Unified Medical Language System (UMLS). An automated semantic analysis based on concept frequency is followed by an extensive manual expert review performed by physicians to analyze complex recruitment-relevant concepts not amenable to automatic approach.ResultsBased on analysis of 138,225 EC from 10,516 screening forms, 55 laboratory procedures represented 77.87% of all UMLS laboratory concept occurrences identified in the selected EC forms. We identified 26,413 unique UMLS concepts from 118 UMLS semantic types and covered the vast majority of Medical Subject Headings (MeSH) disease domains.ConclusionsOnly a small set of common LP covers the majority of laboratory concepts in screening EC forms which supports the feasibility of establishing a focused core dataset for LP. We present ELaPro, a novel, LOINC-mapped, core dataset for the most frequent 55 LP requested in screening for clinical trials. ELaPro is available in multiple machine-readable data formats like CSV, ODM and HL7 FHIR. The extensive manual curation of this large number of free-text EC as well as the combining of UMLS and LOINC terminologies distinguishes this specialized dataset from previous relevant datasets in the literature.

MP26-01 COMPLETENESS OF 24-HOUR URINE TESTING AMONG US VETERANS WITH URINARY STONE DISEASE

You have accessJournal of UrologyCME1 May 2022MP26-01 COMPLETENESS OF 24-HOUR URINE TESTING AMONG US VETERANS WITH URINARY STONE DISEASE Ryan Hsi, Autumn Valicevic, Sanjeevkumar Patel, John Hollingsworth, and Vahakn Shahinian Ryan HsiRyan Hsi More articles by this author , Autumn ValicevicAutumn Valicevic More articles by this author , Sanjeevkumar PatelSanjeevkumar Patel More articles by this author , John HollingsworthJohn Hollingsworth More articles by this author , and Vahakn ShahinianVahakn Shahinian More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002569.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Contemporary guidelines for the prevention of urinary stone disease (USD) recommend that a 24-hour urine collection be analyzed with a minimum set of urinary analytes. However, unlike a comprehensive metabolic panel, there is no approved set of measurements that a provider can easily order, potentially leading to incomplete testing. To examine this possibility, we analyzed data from the Veterans Health Administration (VHA). METHODS: Using files from the VHA Corporate Data Warehouse (2012 to 2019), we identified a cohort of patients, who primarily use the VHA for their healthcare needs, with at least two outpatient or one surgical encounter for USD. Next, we determined the subset who performed a 24-hour urine collection within 12 months of their index USD encounter. Through Logical Observation Identifiers Names and Codes, we evaluated each collection’s completeness, defined as including all of urine volume, calcium, oxalate, citrate, uric acid, and creatinine. We then fit a multilevel logistic regression model with random effects for facility to evaluate factors associated with testing completeness. RESULTS: In total, 15,660 patients performed a 24-hour urine collection, but nearly half of tests (46.1%) were incomplete. The percentage of complete collections increased over time (43.1% in 2012 to 63.2% in 2019). The facility where a patient was seen mattered. The Figure shows wide variation in unadjusted rates of 24-urine completeness among facilities, ranging from as low as 0% to as high as 98.1%. In the adjusted analysis, there was considerable between-facility variation in 24-hour urine completeness (Median Odds Ratio, 4.98; 95% CI, 3.50-5.50). Collectively, individual facilities contributed almost 46% (ICC, 0.46; 95% CI, 0.35-0.49) to the observed variation in 24-hour urine completeness. Older age, Hispanic ethnicity, and presence of conditions associated with high risk of USD recurrence were significantly associated with a lower odds, whereas female sex and more recent calendar year were associated with a higher odds, of complete testing. CONCLUSIONS: Nearly half of 24-hour urine tests obtained for prevention of USD do not have the minimum set of urinary analytes. These data suggest that addressing the facility level variation in testing completeness may improve the quality of USD care within VHA facilities. Source of Funding: NIDDK R01 DK121709 © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e438 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ryan Hsi More articles by this author Autumn Valicevic More articles by this author Sanjeevkumar Patel More articles by this author John Hollingsworth More articles by this author Vahakn Shahinian More articles by this author Expand All Advertisement PDF DownloadLoading ...

A Novel Deep Learning Model for Automated Mapping of Chinese Laboratory Test Terminologies to Logical Observation Identifiers Names and Codes (Loinc)

A Novel Deep Learning Model for Automated Mapping of Chinese Laboratory Test Terminologies to Logical Observation Identifiers Names and Codes (Loinc)

Mapping the Korean National Health Checkup Questionnaire to Standard Terminologies.

ObjectivesAn increasing emphasis has been placed on the integration of clinical data and patient-generated health data (PGHD), which are generated outside of hospitals. This study explored the possibility of using standard terminologies to represent PGHD for data integration.MethodsWe chose the 2020 general health checkup questionnaire of the Korean Health Screening Program as a resource. We divided every component of the questionnaire into entities and values, which were mapped to standard terminologies—Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) version 2020-07-31 and Logical Observation Identifiers Names and Codes (LOINC) version 2.68.ResultsEighty-nine items were derived from the 17 questions of the 2020 health examination questionnaire, of which 76 (85.4%) were mapped to standard terms. Fifty-two items were mapped to SNOMED CT and 24 items were mapped to LOINC. Among the items mapped to SNOMED CT, 35 were mapped to pre-coordinated expressions and 17 to post-coordinated expressions. Forty items had one-to-one relationships, and 17 items had one-to-many relationships.ConclusionsWe achieved a high mapping rate (85.4%) by using both SNOMED CT and LOINC. However, we noticed some issues while mapping the Korean general health checkup questionnaire (i.e., lack of explanations, vague questions, and overly narrow concepts). In particular, items combining two or more concepts into a single item were not appropriate for mapping using standard terminologies. Although it is not the case that all items need to be expressed in standard terminology, essential items should be presented in a way suitable for mapping to standard terminology by revising the questionnaire in the future.

Logical Observation Identifiers Names and Codes (LOINC®) Applied to Microbiology: A National Laboratory Mapping Experience in Taiwan.

Background and Objective: Logical Observation Identifiers Names and Codes (LOINC) is a universal standard for identifying laboratory tests and clinical observations. It facilitates a smooth information exchange between hospitals, locally and internationally. Although it offers immense benefits for patient care, LOINC coding is complex, resource-intensive, and requires substantial domain expertise. Our objective was to provide training and evaluate the performance of LOINC mapping of 20 pathogens from 53 hospitals participating in the National Notifiable Disease Surveillance System (NNDSS). Methods: Complete mapping codes for 20 pathogens (nine bacteria and 11 viruses) were requested from all participating hospitals to review between January 2014 and December 2016. Participating hospitals mapped those pathogens to LOINC terminology, utilizing the Regenstrief LOINC mapping assistant (RELMA) and reported to the NNDSS, beginning in January 2014. The mapping problems were identified by expert panels that classified frequently asked questionnaires (FAQs) into seven LOINC categories. Finally, proper and meaningful suggestions were provided based on the error pattern in the FAQs. A general meeting was organized if the error pattern proved to be difficult to resolve. If the experts did not conclude the local issue’s error pattern, a request was sent to the LOINC committee for resolution. Results: A total of 53 hospitals participated in our study. Of these, 26 (49.05%) used homegrown and 27 (50.95%) used outsourced LOINC mapping. Hospitals who participated in 2015 had a greater improvement in LOINC mapping than those of 2016 (26.5% vs. 3.9%). Most FAQs were related to notification principles (47%), LOINC system (42%), and LOINC property (26%) in 2014, 2015, and 2016, respectively. Conclusions: The findings of our study show that multiple stage approaches improved LOINC mapping by up to 26.5%.

Preparing Laboratories for Interconnected Health Care.

In an increasingly interconnected health care system, laboratory medicine can facilitate diagnosis and treatment of patients effectively. This article describes necessary changes and points to potential challenges on a technical, content, and organizational level. As a technical precondition, electronic laboratory reports have to become machine-readable and interpretable. Terminologies such as Logical Observation Identifiers Names and Codes (LOINC), Nomenclature for Properties and Units (NPU), Unified Code for Units of Measure (UCUM), and SNOMED-CT can lead to the necessary semantic interoperability. Even if only single “atomized” results of the whole report are extracted, the necessary information for correct interpretation must be available. Therefore, interpretive comments, e.g., concerns about an increased measurement uncertainty must be electronically attached to every affected measurement result. Standardization of laboratory analyses with traceable standards and reference materials will enable knowledge transfer and safe interpretation of laboratory analyses from multiple laboratories. In an interconnected health care system, laboratories should strive to transform themselves into a data hub that not only receives samples but also extensive information about the patient. On that basis, they can return measurement results enriched with high-quality interpretive comments tailored to the individual patient and unlock the full potential of laboratory medicine.

A Worker-Centered Personal Health Record App for Workplace Health Promotion Using National Health Care Data Sets: Design and Development Study.

BackgroundPersonal health record (PHR) technology can be used to support workplace health promotion, and prevent social and economic losses related to workers’ health management. PHR services can not only ensure interoperability, security, privacy, and data quality, but also consider the user’s perspective in their design.ObjectiveUsing Fast Healthcare Interoperability Resources (FHIR) and national health care data sets, this study aimed to design and develop an app for providing worker-centered, interconnected PHR services.MethodsThis study considered the user’s perspective, using the human-centered design (HCD) methodology, to develop a PHR app suitable for occupational health. We developed a prototype after analyzing quantitative and qualitative data collected from workers and a health care professional group, after which we performed a usability evaluation. We structured workers’ PHR items based on the analyzed data, and ensured structural and semantic interoperability using FHIR, Systematized Nomenclature of Medicine–Clinical Terms (SNOMED-CT), and Logical Observation Identifiers Names and Codes (LOINC). This study integrated workers’ health information scattered across different Korean institutions through an interface method, and workers’ PHRs were managed through a cloud server, using Azure API for FHIR.ResultsIn total, 562 workers from industrial parks participated in the quantitative study. The preferred data items for PHR were medication, number of steps walked, diet, blood pressure, weight, and blood glucose. The preferred features were ability to access medical checkup results, health information content provision, consultation record inquiry, and teleconsultation. The worker-centered PHR app collected data on, among others, life logs, vital signs, and medical checkup results; offered health care services such as reservation and teleconsultation; and provided occupational safety and health information through material safety data sheet search and health questionnaires. The app reflected improvements in user convenience and app usability proposed by 19 participants (7 health care professionals and 12 end users) in the usability evaluation. The After-Scenario Questionnaire (ASQ) was evaluated with a mean score of 5.90 (SD 0.34) out of 7, and the System Usability Scale (SUS) was evaluated a mean score of 88.7 (SD 4.83) out of 100.ConclusionsThe worker-centered PHR app integrates workers’ health information from different institutions and provides a variety of health care services from linked institutions through workers’ shared PHR. This app is expected to increase workers’ autonomy over their health information and support medical personnel’s decision making regarding workers’ health in the workplace. Particularly, the app will provide solutions for current major PHR challenges, and its design, which considers the user’s perspective, satisfies the prerequisites for its utilization in occupational health.

Examining the potential benefits of the influenza vaccine against SARS-CoV-2: A retrospective cohort analysis of 74,754 patients.

IntroductionRecently, several single center studies have suggested a protective effect of the influenza vaccine against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This study utilizes a continuously updated Electronic Medical Record (EMR) network to assess the possible benefits of influenza vaccination mitigating critical adverse outcomes in SARS-CoV-2 positive patients from 56 healthcare organizations (HCOs).MethodsThe de-identified records of 73,346,583 patients were retrospectively screened. Two cohorts of 37,377 patients, having either received or not received influenza vaccination six months–two weeks prior to SARS-CoV-2 positive diagnosis, were created using Common Procedural Terminology (CPT) and logical observation identifiers names and codes (LOINC) codes. Adverse outcomes within 30, 60, 90, and 120 days of positive SARS-CoV-2 diagnosis were compared between cohorts. Outcomes were assessed with stringent propensity score matching including age, race, ethnicity, gender, hypertension, diabetes, hyperlipidemia, chronic obstructive pulmonary disease (COPD), obesity, heart disease, and lifestyle habits such as smoking.ResultsSARS-CoV-2-positive patients who received the influenza vaccine experienced decreased sepsis (p<0.01, Risk Ratio: 1.361–1.450, 95% CI:1.123–1.699, NNT:286) and stroke (p<0.02, RR: 1.451–1.580, 95% CI:1.075–2.034, NNT:625) across all time points. ICU admissions were lower in SARS-CoV-2-positive patients receiving the influenza vaccine at 30, 90, and 120 days (p<0.03, RR: 1.174–1.200, 95% CI:1.003–1.385, NNT:435), while approaching significance at 60 days (p = 0.0509, RR: 1.156, 95% CI:0.999–1.338). Patients who received the influenza vaccine experienced fewer DVTs 60–120 days after positive SARS-CoV-2 diagnosis (p<0.02, RR:1.41–1.530, 95% CI:1.082–2.076, NNT:1000) and experienced fewer emergency department (ED) visits 90–120 days post SARS-CoV-2-positive diagnosis (p<0.01, RR:1.204–1.580, 95% CI: 1.050–1.476, NNT:176).ConclusionOur analysis outlines the potential protective effect of influenza vaccination in SARS-CoV-2-positive patients against adverse outcomes within 30, 60, 90, and 120 days of a positive diagnosis. Significant findings favoring influenza vaccination mitigating the risks of sepsis, stroke, deep vein thrombosis (DVT), emergency department (ED) & Intensive Care Unit (ICU) admissions suggest a potential protective effect that could benefit populations without readily available access to SARS-CoV-2 vaccination. Thus further investigation with future prospective studies is warranted.

Logical Observation Identifiers, Names, and Codes Nursing Subcommittee Update.

Matney, Susan A. PhD, RNC-OB, FAAN, FACMI, FHIMSS, FAMIA, FHL7; Anderson, Lisa MSN, RN-BCEditor(s): Carter-Templeton, Heather D. PhD, RN-BC Author Information

Medical big data for smart healthcare

As most people expect, the COVID-19 pandemic will change our life radically. One of the directions is expected to minimize off-line contacts in daily lives and healthcare is surely not the exception. Big hospitals, which are centralized systems evolved to deliver healthcare most effectively, can become the media of the spread of diseases in some cases and the adoption of a decentralized healthcare system will be a helpful strategy to prevent it. This strategy can be implemented through a smart healthcare system, and it will be one more necessity of a smart healthcare system in our society. Smart healthcare, which will be delivered mainly through online contact, is a very comprehensive concept enabling otherwise not possible healthcare such as health monitoring using life-log data from the wearable device and far beyond the online consultation with a doctor. Although there will be many social, ethical, and technical challenges for smart healthcare, the discussion today will be limited to two challenges, interoperability and protecting the data subjects' rights. Smart healthcare needs to collect and process comprehensive health information from a vast number of data subjects during its development and delivery and the data subjects' rights are highly vulnerable during these processes. Collecting and exchanging health information from various sources such as hospitals, clinics, smartwatches, and so on, inevitably raises the issue of interoperability of the data from different sources. The implementation of smart healthcare requires technical solutions to both challenges. General Data Protection Regulation, recently issued by the EU, deals the data subjects' rights very seriously, such as the right to be informed, the right of access, the right to rectification, the right to erasure, the right to restrict processing, the right to data portability, the right to object, and the rights in relation to automated decision making and profiling. Technical innovations required to protect these data subjects' rights will be discussed here. Compared to traditional healthcare which mostly dealt with medical data generated within the healthcare facility itself, smart healthcare requires health data beyond the healthcare facilities. Not to mention the life-log data from different wearable devices, even the medical data from different hospitals are not interoperable. Although many health information standards have been proposed, such as Systematized Nomenclature of Medicine Clinical Terms (SMOMED CT), Logical Observation Identifier Name Code (LOINC), Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR), and so on, the adoption of these standards in hospital information system in Korea has been very slow. This low adoption of health information standards in our hospital information system obstructs the so-called, meaningful use of electronic medical records. The necessity of adopting health information standards will be refreshed today. Smart healthcare requires the active participation of health consumers and active participation of health consumers requires their health literacy. Health literacy of health consumers will be fostered by innovative technical aids such as data visualization, the decision support system for consumers, and more. Health literacy will the most powerful driving force of smart healthcare implementation.

Association of Tyrosine Kinase Inhibitors with Vascular Adverse Events: A Cohort Study Using Pooled Electronic Health Record

IntroductionTyrosine Kinase Inhibitor (TKI)-associated vascular adverse events (VAE) have emerged as one of the challenges in the management of chronic myeloid leukemia (CML). Available knowledge on association of TKIs with VAE mostly comes from randomized clinical trials but there are concerns about comprehensiveness of the reported data. This is because different trials have relied on differing definitions to capture VAE. It is generally considered that TKI-associated VAE reported in clinical trials are underestimates. In this retrospective cohort study, we used a clinical informatics approach to collect data on CML pts from multiple, distinct electronic health records across several different healthcare systems and report on the prevalence of VAE in CML cohorts treated on different TKIs.MethodsExplorys Enterprise Performance Management (EPM) is a HIPAA-compliant database containing de-identified clinical data on 50 million pts from 26 healthcare networks comprising 360 hospitals since 1999. As this database is HIPAA-compliant, IRB approval was waived by Cleveland Clinic IRB. In this study, we used the EPM database's “power search” tool to create CML cohort and then identified VAE events occurring after TKI initiation using specific temporal relationships. Power searches for data extraction relies on associating clinical informatics standard ontologies that includes Current Procedural Terminology (CPT) (for procedures), International Classification of Diseases 9th edition (ICD-9) and 10th edition (for diagnoses), logical observation identifiers names and codes (LOINC) (for laboratory information), Medical Subject Headings (MeSH) (for medical topics), RxNorm (for pharmacy information), and Systematized Nomenclature for Medicine - Clinical Terms (SNOMED-CT) (for medical terms). We created temporal attributes linking the CML cohort to outcome of interest (VAE) using SNOMED-CT diagnosis for VAE (included coronary, cerebrovascular and peripheral arterial disease, venous thrombosis among others) and subsequently mapping it with RxNorm (all anticoagulants or antiplatelet agents in clinical use for the study time period) and CPT codes (use of inferior vena cava filter or thrombectomy). All generated quantitative data using EPM database are rounded to the nearest 10 for the purposes of de-identification.ResultsUsing Explorys, we identified a total of 2750 CML pts who were treated with imatinib, 680 pts with dasatinib, 600 pts with nilotinib and 870 pts with 2 or more TKI's. Of the five TKIs, we excluded bosutinib and ponatinib from analysis because of low pt numbers (<100). Baseline demographic characteristics showing overall distribution of atherosclerotic disease in CML pts treated with these three TKIs are shown in Table 1. Of note, there was a high prevalence of coronary arteriosclerosis in these TKI-treated cohorts - 29% in imatinib cohort, 28% in nilotinib group and 22% in pts who received dasatinib. A total of 780 VAE (28%) were identified after initiation of imatinib - 19% (520/2750) of them were arterial events and 9.5% (260/2750) were venous. Following initiation of nilotinib, 160 VAE occurred with a prevalence rate of 26.6% - 18.3% (110/600) of them were arterial and 8.3% (50/600) were venous. After starting dasatinib, 150 VAE was identified with a prevalence rate of 22% - 14.7% (100/680) of them were arterial events and 7.4% (50/680) were venous. In CML pts treated with 2 or more TKIs, the prevalence of VAE was highest at 36.8% (320/870) - 24.1% (210/870) were arterial and 12.6% were venous events.Conclusions:In this population based study of CML pts using clinical informatics approach, we report a much higher rate of VAE, both arterial and venous in TKI cohorts compared to that reported in clinical trials. Ontology based linking of CML with outcomes of interest and cross confirmation with subsequent anticoagulation treatment received ensures accuracy of the reported events using this approach. Arterial vascular events ranged from 15-20% whereas venous events were reported in the range of 8-9% for the three TKIs examined. Surprisingly high rates of VAE noted in the imatinib cohort could be the result of lead time bias because of earlier introduction of imatinib. Additional analysis of this cohort is underway to characterize this association. [Display omitted] DisclosuresGerds:Incyte: Consultancy; CTI BioPharma: Consultancy. Advani:Takeda/ Millenium: Research Funding; Pfizer: Consultancy. Majhail:Sanofi: Honoraria; Anthem, Inc.: Consultancy. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees.

A National, Semantic-Driven, Three-Pillar Strategy to Enable Health Data Secondary Usage Interoperability for Research Within the Swiss Personalized Health Network: Methodological Study.

BackgroundInteroperability is a well-known challenge in medical informatics. Current trends in interoperability have moved from a data model technocentric approach to sustainable semantics, formal descriptive languages, and processes. Despite many initiatives and investments for decades, the interoperability challenge remains crucial. The need for data sharing for most purposes ranging from patient care to secondary uses, such as public health, research, and quality assessment, faces unmet problems.ObjectiveThis work was performed in the context of a large Swiss Federal initiative aiming at building a national infrastructure for reusing consented data acquired in the health care and research system to enable research in the field of personalized medicine in Switzerland. The initiative is the Swiss Personalized Health Network (SPHN). This initiative is providing funding to foster use and exchange of health-related data for research. As part of the initiative, a national strategy to enable a semantically interoperable clinical data landscape was developed and implemented.MethodsA deep analysis of various approaches to address interoperability was performed at the start, including large frameworks in health care, such as Health Level Seven (HL7) and Integrating Healthcare Enterprise (IHE), and in several domains, such as regulatory agencies (eg, Clinical Data Interchange Standards Consortium [CDISC]) and research communities (eg, Observational Medical Outcome Partnership [OMOP]), to identify bottlenecks and assess sustainability. Based on this research, a strategy composed of three pillars was designed. It has strong multidimensional semantics, descriptive formal language for exchanges, and as many data models as needed to comply with the needs of various communities.ResultsThis strategy has been implemented stepwise in Switzerland since the middle of 2019 and has been adopted by all university hospitals and high research organizations. The initiative is coordinated by a central organization, the SPHN Data Coordination Center of the SIB Swiss Institute of Bioinformatics. The semantics is mapped by domain experts on various existing standards, such as Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), Logical Observation Identifiers Names and Codes (LOINC), and International Classification of Diseases (ICD). The resource description framework (RDF) is used for storing and transporting data, and to integrate information from different sources and standards. Data transformers based on SPARQL query language are implemented to convert RDF representations to the numerous data models required by the research community or bridge with other systems, such as electronic case report forms.ConclusionsThe SPHN strategy successfully implemented existing standards in a pragmatic and applicable way. It did not try to build any new standards but used existing ones in a nondogmatic way. It has now been funded for another 4 years, bringing the Swiss landscape into a new dimension to support research in the field of personalized medicine and large interoperable clinical data.

COVID-19 Vaccination Is Safe and Effective in Patients With Inflammatory Bowel Disease: Analysis of a Large Multi-institutional Research Network in the United States

COVID-19 Vaccination Is Safe and Effective in Patients With Inflammatory Bowel Disease: Analysis of a Large Multi-institutional Research Network in the United States

Postcoordination of LOINC Codes in SNOMED CT.

The objectives of this paper are to analyze the terminologies SNOMED CT and Logical Observation Identifiers Names and Codes (LOINC) and to provide a guideline for the translation of LOINC concepts to SNOMED CT. Verified research data sets were used for this study, so this experiment is replicable with other research data. 50 LOINC concepts of frequently performed laboratory services were translated to SNOMED CT. Information would be lost with pre-coordinated mapping but the compositional grammar of SNOMED CT allows for the linking of individual concepts into complicated postcoordinated expressions including all embedded information in LOINC concepts. All information can thus be transferred smoothly to SNOMED CT.

Aligning an interface terminology to the Logical Observation Identifiers Names and Codes (LOINC®).

ObjectiveOur study consists in aligning the interface terminology of the Bordeaux university hospital (TLAB) to the Logical Observation Identifiers Names and Codes (LOINC). The objective was to facilitate the shared and integrated use of biological results with other health information systems.Materials and MethodsWe used an innovative approach based on a decomposition and re-composition of LOINC concepts according to the transversal relations that may be described between LOINC concepts and their definitional attributes. TLAB entities were first anchored to LOINC attributes and then aligned to LOINC concepts through the appropriate combination of definitional attributes. Finally, using laboratory results of the Bordeaux data-warehouse, an instance-based filtering process has been applied.ResultsWe found a small overlap between the tokens constituting the labels of TLAB and LOINC. However, the TLAB entities have been easily aligned to LOINC attributes. Thus, 99.8% of TLAB entities have been related to a LOINC analyte and 61.0% to a LOINC system. A total of 55.4% of used TLAB entities in the hospital data-warehouse have been mapped to LOINC concepts. We performed a manual evaluation of all 1-1 mappings between TLAB entities and LOINC concepts and obtained a precision of 0.59.ConclusionWe aligned TLAB and LOINC with reasonable performances, given the poor quality of TLAB labels. In terms of interoperability, the alignment of interface terminologies with LOINC could be improved through a more formal LOINC structure. This would allow queries on LOINC attributes rather than on LOINC concepts only.