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Transforming French Electronic health Records into the observational Medical Outcome Partnership's Common data Model: A Feasibility Study

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APPLIED CLINICAL informatics 2020年第1期11卷 13-22页

作者： Lamer, Antoine Depas, Nicolas Doutreligne, Matthieu Parrot, Adrien Verloop, David Defebvre, Marguerite-Marie Ficheur, Gregoire Chazard, Emmanuel Beuscart, Jean-Baptiste Univ Lille Ctr Etud & Rech Informat Med CHU Lille EA 2694 Lille France Minist Affaires Sociales & Sante Bur Etat Sante Populat Direct Rech Etud & Stat Observat Sante & Assurance Malad Paris France Univ Paris 05 Paris France AP HP Web INnovat Donnees Direct Syst Informat Paris France ARS Hauts de France Serv Etud & Stat Lille France

Background Common data models (CDMs) enable data to be standardized, and facilitate data exchange, sharing, and storage, particularly when the data have been collected via distinct, heterogeneous systems. Moreover, CDMs provide tools for data quality assessment, integration into models, visualization, and analysis. The observational medical outcome partnership (OMOP) provides a CDM for organizing and standardizing databases. Common data models not only facilitate data integration but also (and especially for the OMOP model) extends the range of available statistical analyses. Objective This study aimed to evaluate the feasibility of implementing French national electronic health records in the OMOP CDM. Methods The OMOP's specifications were used to audit the source data, specify the transformation into the OMOP CDM, implement an extract-transform-load process to feed data from the French health care system into the OMOP CDM, and evaluate the final database. Results Seventeen vocabularies corresponding to the French context were added to the OMOP CDM's concepts. Three French terminologies were automatically mapped to standardized vocabularies. We loaded nine tables from the OMOP CDM's "standardized clinical data" section, and three tables from the "standardized health system data" section. Outpatient and inpatient data from 38,730 individuals were integrated. The median (interquartile range) number of outpatient and inpatient stays per patient was 160 (19-364). Conclusion Our results demonstrated that data from the French national health care system can be integrated into the OMOP CDM. One of the main challenges was the use of international OMOP concepts to annotate data recorded in a French context. The use of local terminologies was an obstacle to conceptual mapping;with the exception of an adaptation of the International Classification of Diseases 10th Revision, the French health care system does not use international terminologies. It would be interesting to ext

关键词： data integration secondary use observational medical outcome partnership observational health data sciences and informatics

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Leaf: an open-source, model-agnostic, data-driven web application for cohort discovery and translational biomedical research

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JOURNAL OF THE AMERICAN MEDICAL informatics ASSOCIATION 2020年第1期27卷 109-118页

作者： Dobbins, Nicholas J. Spital, Clifford H. Black, Robert A. Morrison, Jason M. de Veer, Bas Zampino, Elizabeth Harrington, Robert D. Britt, Bethene D. Stephens, Kari A. Wilcox, Adam B. Tarczy-Hornoch, Peter Mooney, Sean D. Univ Washington Dept Biomed Informat & Med Educ UW Med Res IT Box 358047 Seattle WA 98195 USA Univ Washington UW Med Res IT Seattle WA 98195 USA Univ Washington Populat Hlth Analyt Seattle WA 98195 USA St Jude Childrens Res Hosp Clin Res Informat Memphis TN USA Univ Washington Dept Med Div Infect Dis Seattle WA 98195 USA Univ Washington UW Med Analyt Seattle WA 98195 USA Univ Washington Dept Psychiat & Behav Sci Seattle WA 98195 USA Univ Washington Dept Biomed Informat & Med Educ Box 358047 Seattle WA 98195 USA

Objective: Academic medical centers and health systems are increasingly challenged with supporting appropriate secondary use of clinical data. Enterprise data warehouses have emerged as central resources for these data, but often require an informatician to extract meaningful information, limiting direct access by end users. To overcome this challenge, we have developed Leaf, a lightweight self-service web application for querying clinical data from heterogeneous data models and sources. Materials and Methods: Leaf utilizes a flexible biomedical concept system to define hierarchical concepts and ontologies. Each Leaf concept contains both textual representations and SQL query building blocks, exposed by a simple drag-and-drop user interface. Leaf generates abstract syntax trees which are compiled into dynamic SQL queries. Results: Leaf is a successful production-supported tool at the University of Washington, which hosts a central Leaf instance querying an enterprise data warehouse with over 300 active users. Through the support of UW Medicine (https://***), the Institute of Translational health sciences (https://***), and the National Center for data to health (https://***/cd2h/), Leaf source code has been released into the public domain at https://***/uwrit/leaf. Discussion: Leaf allows the querying of single or multiple clinical databases simultaneously, even those of different data models. This enables fast installation without costly extraction or duplication. Conclusions: Leaf differs from existing cohort discovery tools because it does not specify a required data model and is designed to seamlessly leverage existing user authentication systems and clinical databases in situ. We believe Leaf to be useful for health system analytics, clinical research data warehouses, precision medicine bio-banks, and clinical studies involving large patient cohorts.

关键词： biomedical informatics leaf cohort discovery observational health data sciences and informatics data integration cloud computing

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Web services for data warehouses: OMOP and PCORnet on i2b2

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JOURNAL OF THE AMERICAN MEDICAL informatics ASSOCIATION 2018年第10期25卷 1331-1338页

作者： Klann, Jeffrey G. Phillips, Lori C. Herrick, Christopher Joss, Matthew A. H. Wagholikar, Kavishwar B. Murphy, Shawn N. Partners Healthcare Res Informat Sci & Comp Boston MA USA Harvard Med Sch Boston MA USA Massachusetts Gen Hosp Dept Med Boston MA 02114 USA Massachusetts Gen Hosp Dept Neurol Boston MA 02114 USA

Objective: healthcare organizations use research data models supported by projects and tools that interest them, which often means organizations must support the same data in multiple models. The healthcare research ecosystem would benefit if tools and projects could be adopted independently from the underlying data model. Here, we introduce the concept of a reusable application programming interface (API) for healthcare and show that the i2b2 API can be adapted to support diverse patient-centric data models. Materials and Methods: We develop methodology for extending i2b2's pre-existing API to query additional data models, using i2b2's recent "multi-fact-table querying" feature. Our method involves developing data-model-specific i2b2 ontologies and mapping these to query non-standard table structure. Results: We implement this methodology to query OMOP and PCORnet models, which we validate with the i2b2 query tool. We implement the entire PCORnet data model and a five-domain subset of the OMOP model. We also demonstrate that additional, ancillary data model columns can be modeled and queried as i2b2 "modifiers." Discussion: i2b2' s REST API can be used to query multiple healthcare data models, enabling shared tooling to have a choice of backend data stores. This enables separation between data model and software tooling for some of the more popular open analytic data models in healthcare. Conclusion: This methodology immediately allows querying OMOP and PCORnet using the i2b2 API. It is released as an open-source set of Docker images, and also on the i2b2 community wiki.

关键词： medical informatics data integration data models ontology-driven data representation Patient Centered Outcomes Research Institute observational health data sciences and informatics informatics for Integrating Biology and the Bedside

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Assessing the Use of German Claims data Vocabularies for Research in the observational Medical Outcomes Partnership Common data Model: Development and Evaluation Study

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JMIR MEDICAL informatics 2023年 11卷 e47959页

作者： Henke, Elisa Zoch, Michele Kallfelz, Michael Ruhnke, Thomas Leutner, Liz Annika Spoden, Melissa Guenster, Christian Math, Dipl Sedlmayr, Martin Bathelt, Franziska Tech Univ Dresden Inst Med Informat & Biometry Carl Gustav Carus Fac Med Dresden Germany Odysseus Data Serv GmbH Berlin Germany AOK Res Inst Wissensch Inst AOK Berlin Germany Thiem Res GmbH Cottbus Germany Tech Univ Dresden Carl Gustav Carus Fac Med Inst Med Informat & Biometry Fetscherstr 74 D-01307 Dresden Germany

Background: National classifications and terminologies already routinely used for documentation within patient care settings enable the unambiguous representation of clinical information. However, the diversity of different vocabularies across health care institutions and countries is a barrier to achieving semantic interoperability and exchanging data across sites. The observational Medical Outcomes Partnership (OMOP) Common data Model (CDM) enables the standardization of structure and medical terminology. It allows the mapping of national vocabularies into so-called standard concepts, representing normative expressions for international analyses and research. Within our project "Hybrid Quality Indicators Using Machine Learning Methods" (Hybrid-QI), we aim to harmonize source codes used in German claims data vocabularies that are currently unavailable in the OMOP ***: This study aims to increase the coverage of German vocabularies in the OMOP CDM. We aim to completely transform the source codes used in German claims data into the OMOP CDM without data loss and make German claims data usable for OMOP CDM-based ***: To prepare the missing German vocabularies for the OMOP CDM, we defined a vocabulary preparation approach consisting of the identification of all codes of the corresponding vocabularies, their assembly into machine-readable tables, and the translation of German designations into English. Furthermore, we used 2 proposed approaches for OMOP-compliant vocabulary preparation: the mapping to standard concepts using the observational health data sciences and informatics (OHDSI) tool Usagi and the preparation of new 2-billion concepts (ie, concept_id >2 billion). Finally, we evaluated the prepared vocabularies regarding completeness and correctness using synthetic German claims data and calculated the coverage of German claims data vocabularies in the OMOP ***: Our vocabulary preparation approach was able to map 3 missing German vo

关键词： OMOP CDM interoperability vocabularies claims data OHDSI observational Medical Outcomes Partnership common data model observational health data sciences and informatics

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Development and Validation of a Prognostic Classification Model Predicting Postoperative Adverse Outcomes in Older Surgical Patients Using a Machine Learning Algorithm: Retrospective observational Network Study

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JOURNAL OF MEDICAL INTERNET RESEARCH 2023年第1期25卷 e42259页

作者： Choi, Jung-Yeon Yoo, Sooyoung Song, Wongeun Kim, Seok Baek, Hyunyoung Lee, Jun Suh Yoon, Yoo-Seok Yoon, Seonghae Lee, Hae-Young Kim, Kwang-il Seoul Natl Univ Dept Internal Med Bundang Hosp Seongnam Si South Korea Seoul Natl Univ Off Ehlth Res & Business Bundang Hosp Seongnam Si South Korea Seoul Natl Univ Grad Sch Convergence Sci & Technol Dept Hlth Sci & Technol Seongnam Si South Korea G Sam Hosp Dept Surg Gunpo South Korea Seoul Natl Univ Dept Surg Bundang Hosp Seongnam Si South Korea Seoul Natl Univ Dept Surg Coll Med Seoul South Korea Seoul Natl Univ Dept Clin Pharmacol & Therapeut Bundang Hosp Seongnam Si South Korea Seoul Natl Univ Hosp Dept Internal Med Seoul South Korea Seoul Natl Univ Dept Internal Med Coll Med Seoul South Korea Seoul Natl Univ Bundang Hosp Dept Internal Med 82 Gumi Ro173 Beon Gil Seongnam Si 13620 South Korea

Background: Older adults are at an increased risk of postoperative morbidity. Numerous risk stratification tools exist, but effort and manpower are required. Objective: This study aimed to develop a predictive model of postoperative adverse outcomes in older patients following general surgery with an open-source, patient-level prediction from the observational health data sciences and informatics for internal and external validation. Methods: We used the observational Medical Outcomes Partnership common data model and machine learning algorithms. The primary outcome was a composite of 90-day postoperative all-cause mortality and emergency department visits. Secondary outcomes were postoperative delirium, prolonged postoperative stay (>= 75th percentile), and prolonged hospital stay (>= 21 days). An 80% versus 20% split of the data from the Seoul National University Bundang Hospital (SNUBH) and Seoul National University Hospital (SNUH) common data model was used for model training and testing versus external validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) with a 95% CI. Results: data from 27,197 (SNUBH) and 32,857 (SNUH) patients were analyzed. Compared to the random forest, Adaboost, and decision tree models, the least absolute shrinkage and selection operator logistic regression model showed good internal discriminative accuracy (internal AUC 0.723, 95% CI 0.701-0.744) and transportability (external AUC 0.703, 95% CI 0.692-0.714) for the primary outcome. The model also possessed good internal and external AUCs for postoperative delirium (internal AUC 0.754, 95% CI 0.713-0.794;external AUC 0.750, 95% CI 0.727-0.772), prolonged postoperative stay (internal AUC 0.813, 95% CI 0.800-0.825;external AUC 0.747, 95% CI 0.741-0.753), and prolonged hospital stay (internal AUC 0.770, 95% CI 0.749-0.792;external AUC 0.707, 95% CI 0.696-0.718). Compared with age or the Charlson comorbidity index, the model showed

关键词： CDM common data model patient-level prediction OHDSI observational health data sciences and informatics postoperative outcome postoperative surgery elderly elder predict adverse event adverse outcome geriatric older adult ageing model algorithm

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Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the observational Medical Outcomes Partnership Common data Model (OMOP-CDM) in a Public Cloud-Computing Environment: Anonymization of Medical data Using Privacy Models

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JOURNAL OF MEDICAL INTERNET RESEARCH 2020年第11期22卷 e19597页

作者： Jeon, Seungho Seo, Jeongeun Kim, Sukyoung Lee, Jeongmoon Kim, Jong-Ho Sohn, Jang Wook Moon, Jongsub Joo, Hyung Joon Korea Univ Grad Sch Informat Secur Div Informat Secur Seoul South Korea Korea Univ Res Inst Med Bigdata Sci Seoul South Korea Korea Univ Cardiovasc Ctr Dept Cardiol Seoul South Korea Korea Univ Coll Med Dept Internal Med Div Infect Dis Seoul South Korea Korea Univ Coll Med Dept Internal Med 145 Anam Ro Seoul 02841 South Korea

Background: De-identifying personal information is critical when using personal health data for secondary research. The observational Medical Outcomes Partnership Common data Model (CDM), defined by the nonprofit organization observational health data sciences and informatics, has been gaining attention for its use in the analysis of patient-level clinical data obtained from various medical institutions. When analyzing such data in a public environment such as a cloud-computing system, an appropriate de-identification strategy is required to protect patient privacy. Objective: This study proposes and evaluates a de-identification strategy that is comprised of several rules along with privacy models such as k-anonymity, 1-diversity, and t-closeness. The proposed strategy was evaluated using the actual CDM database. Methods: The CDM database used in this study was constructed by the Anam Hospital of Korea University. Analysis and evaluation were performed using the ARX anonymizing framework in combination with the k-anonymity, 1-diversity, and t-closeness privacy models. Results: The CDM database, which was constructed according to the rules established by observational health data sciences and informatics, exhibited a low risk of re-identification: The highest re-identifiable record rate (11.3%) in the dataset was exhibited by the DRUG_EXPOSURE table, with a re-identification success rate of 0.03%. However, because all tables include at least one "highest risk" value of 100%, suitable anonymizing techniques are required;moreover, the CDM database preserves the "source values" (raw data), a combination of which could increase the risk of re-identification. Therefore, this study proposes an enhanced strategy to de-identify the source values to significantly reduce not only the highest risk in the k-anonymity, 1-diversity, and t-closeness privacy models but also the overall possibility of re-identification. Conclusions: Our proposed de-identification strategy effectivel

关键词： de-identification privacy anonymization common data model observational health data sciences and informatics

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Building the observational medical outcomes partnership's T-MSIS Analytic File common data model

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informatics in Medicine Unlocked 2023年 39卷 101259页

作者： Williams, Nick National Library of Medicine Lister Hill National Center for Biomedical Communications Bethesda MD United States

Objectives: This effort used databricks to create an observational Medical Outcomes Partnership (OMOP) Common data Model (CDM) for Transformed MSIS Analytic File (TAF) Medicaid records. Materials and methods: Our process included data volume and content assessment of TAF, translation mapping of TAF concepts to OMOP concepts and the creation of Extract Transform and Load (ETL) code. Results: The final CDM contained 119, 048, 562 individuals and 24, 806, 828,121 clinical observations from 2014 through 2018. Discussion: The transformation of TAF into OMOP can support the generation of evidence with special attention to low-income patients on public insurance. Such patients are perhaps underrepresented in academic medical center patient populations. Conclusion: Our effort successfully used databricks to transform TAF records into OMOP CDM. Our CDM can be used to generate evidence for OMOP network studies. © 2023

关键词： Centers for medicare and medicaid services Common data model Medicaid observational health data sciences and informatics observational medical outcomes partnership

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