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19th World Congress on Medical and Health Informatics (MEDINFO)

作者： Wang, Yu Ma, Shuang Ru, Hua Ni, Hongyi Li, Jingsong Zhejiang Lab Res Ctr Healthcare Data Sci Hangzhou Peoples R China Zhejiang Univ Coll Biomed Engn & Instrument Sci Engn Res Ctr EMR & Intelligent Expert Syst Key Lab Biomed EngnMinist Educ Hangzhou Peoples R China

ISBN: (纸本)9781643684574;9781643684567

Real-world data (RWD) could be a new way to evaluate the safety and efficacy of post-marketing drugs, while there is no common method for how to use RWD for drug evaluation. In this paper, we present a framework for real-world drug evaluation based on electronic medical record (EHR) data. We designed a data model customized for post-marketing drug evaluation and a unified post-marketing drug evaluation pipeline. The proposed framework can be applied to drug evaluations with different study paradigms for different purposes by flexible use of the proposed data model and pipeline. A prototype system has been developed according to the framework. Real-world EHRs in a tertiary hospital in China between 2010 and 2020 were converted to the proposed data model, and as a test case, we conducted a research on the risk of allergic reactions to cefodizime and ceftriaxone using the prototype system.

关键词： Post-marketing drug evaluation real-world data electronic health records common data model

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Kamino: A Scalable Architecture to Support Medical AI Research Using Large Real World data 12

Kamino: A Scalable Architecture to Support Medical AI Resear...

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12th IEEE International Conference on Healthcare Informatics (IEEE-ICHI)

作者： Lin, Fongci Young, Patrick He, Huan Huang, Jimin Gagne, Roger Rice, Daniel Price, Nathan Byron, Will Hu, Yan Felker, Donn Button, Will Meeker, Deniella Hsiao, Allen Xu, Hua Tone, Charles, Jr. Schulz, Wade Yale Univ Sch Med Sect Biomed Informat & Data Sci New Haven CT 06520 USA Yale Univ Sch Med Dept Lab Med New Haven CT USA Yale New Haven Hosp Digital & Technol Solut New Haven CT USA Univ Texas Hlth Sci Houston Mcwilliam Sch Biomed Informat Houston TX USA Yale Univ Yale Sch Med Pediat New Haven CT USA Yale Univ Yale Sch Med Emergency Med New Haven CT USA

ISBN: (纸本)9798350383744;9798350383737

Electronic Health Records (EHRs) represent a crucial data source for real-world evidence generation. To facilitate biomedical studies using EHRs, standard data models like the OMOP CDM have been developed. Nevertheless, recent advancements in biomedical AI research that leverage EHRs have introduced new challenges, encompassing security considerations, large-scale data retrieval, and computational resource management, including GPUs. This paper introduces Kamino, an innovative architectural solution tailored to support biomedical AI research using EHR data. Kamino offers a user-friendly interface with features designed for efficient team access management in accordance with regulatory requirements. It facilitates direct data retrieval from an OMOP CDM instance and includes a resource allocation system based on Kubernetes orchestration. Here, we demonstrate the practical application and utility of Kamino through a clinical natural language processing task. We firmly believe that such a tool will significantly expedite AI research conducted with EHR data within academic institutions.

关键词： Health Informatics Observational Medical Outcomes Partnership common data model data Governance System Design

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RHEA: Real-World Observational Health data Exploration Application

RHEA: Real-World Observational Health Data Exploration Appli...

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19th World Congress on Medical and Health Informatics (MEDINFO)

作者： Seol, Soobeen Park, Jimyung Kim, Chungsoo Lee, Dong Yun Park, Rae Woong Ajou Univ Dept Biomed Sci Grad Sch Med Suwon South Korea Ajou Univ Dept Biomed Informat Sch Med Suwon South Korea

ISBN: (纸本)9781643684574;9781643684567

We developed a standardized framework named RHEA to represent longitudinal status of patient with cancer. RHEA generates a dashboard to visualize patients' data in the Observational Medical Outcomes Partnership-common data model format. The generated dashboard consists of three main parts for providing the macroscopic characteristics of the patient: 1) cohort-level visualization, 2) individual-level visualization and 3) cohort generation.

关键词： common data model data visualization electronic health records

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A national unmet needs assessment for CTSA-affiliated electronic health record data networks: A customer discovery approach

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JOURNAL OF CLINICAL AND TRANSLATIONAL SCIENCE 2024年第1期8卷 e137-e137页

作者： Mora, Nallely Mehall, Madeline Lennox, Lindsay A. Pincus, Harold A. Charron, David Morrato, Elaine H. Loyola Univ Parkinson Sch Hlth Sci & Publ Hlth Chicago IL 60660 USA Loyola Univ Inst Translat Med Chicago IL 60660 USA Colorado Clin & Translat Sci Inst Aurora CO USA Columbia Univ Irving Inst Clin & Translat Res New York NY USA New York State Psychiat Inst & Hosp New York NY USA Univ Calif Berkeley Haas Sch Business NSF I Corps Program Berkeley CA USA Univ Calif Berkeley Haas Sch Business NIH I Corps Program Berkeley CA USA

Introduction: The expansion of electronic health record (EHR) data networks over the last two decades has significantly improved the accessibility and processes around data sharing. However, there lies a gap in meeting the needs of Clinical and Translational Science Award (CTSA) hubs, particularly related to real-world data (RWD) and real-world evidence (RWE).Methods: We adopted a mixed-methods approach to construct a comprehensive needs assessment that included: (1) A Landscape Context analysis to understand the competitive environment;and (2) Customer Discovery to identify stakeholders and the value proposition related to EHR data networks. Methods included surveys, interviews, and a focus ***: Thirty-two CTSA institutions contributed data for analysis. Fifty-four interviews and one focus group were conducted. The synthesis of our findings pivots around five emergent themes: (1) CTSA segmentation needs vary according to resources;(2) Team science is key for success;(3) Quality of data generates trust in the network;(4) Capacity building is defined differently by researcher career stage and CTSA existing resources;and (5) Researchers' unmet ***: Based on the results, EHR data networks like ENACT that would like to meet the expectations of academic research centers within the CTSA consortium need to consider filling the gaps identified by our study: foster team science, improve workforce capacity, achieve data governance trust and efficiency of operation, and aid Learning Health Systems with validating, applying, and scaling the evidence to support quality improvement and high-value care. These findings align with the NIH NCATS Strategic Plan for data Science.

关键词： Clinical and translational science award clinical informatics common data model data network real-world data electronic health records implementation science real-world evidence

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Performance of a semi-automated approach for risk estimation using a common data model for longitudinal healthcare databases

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STATISTICAL METHODS IN MEDICAL RESEARCH 2013年第1期22卷 97-112页

作者： Hoa Van Le Beach, Kathleen J. Powell, Gregory Pattishall, Ed Ryan, Patrick Mera, Robertino M. Univ N Carolina Dept Epidemiol Gillings Sch Global Publ Hlth Chapel Hill NC 27599 USA GlaxoSmithKline Res Triangle Pk NC USA

Different structures and coding schemes may limit rapid evaluation of a large pool of potential drug safety signals using multiple longitudinal healthcare databases. To overcome this restriction, a semi-automated approach utilising common data model (CDM) and robust pharmacoepidemiologic methods was developed;however, its performance needed to be evaluated. Twenty-three established drug-safety associations from publications were reproduced in a healthcare claims database and four of these were also repeated in electronic health records. Concordance and discrepancy of pairwise estimates were assessed between the results derived from the publication and results from this approach. For all 27 pairs, an observed agreement between the published results and the results from the semi-automated approach was greater than 85% and Kappa coefficient was 0.61, 95% CI: 0.19-1.00. Ln(IRR) differed by less than 50% for 13/27 pairs, and the IRR varied less than 2-fold for 19/27 pairs. Reproducibility based on the intra-class correlation coefficient was 0.54. Most covariates (>90%) in the publications were available for inclusion in the models. Once the study populations and inclusion/exclusion criteria were obtained from the literature, the analysis was able to be completed in 2-8 h. The semi-automated methodology using a CDM produced consistent risk estimates compared to the published findings for most selected drug-outcome associations, regardless of original study designs, databases, medications and outcomes. Further assessment of this approach is useful to understand its roles, strengths and limitations in rapidly evaluating safety signals.

关键词： common data model drug safety observational database pharmacovigilance propensity score safety signal risk estimation

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Second primary malignancy risk in thyroid cancer and matched patients with and without radioiodine therapy analysis from the observational health data sciences and informatics

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EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 2022年第10期49卷 3547-3556页

作者： Kim, Seok Bang, Ji-In Boo, Dachung Kim, Borham Choi, In Young Ko, SooJeong Yoo, Ie Ryung Kim, Kwangsoo Kim, Junmo Joo, YoungHwan Ryoo, Hyun Gee Paeng, Jin Chul Park, Jung Mi Jang, Woncheol Kim, Byungwon Chung, Yangha Yang, Dongyoon Yoo, Sooyoung Lee, Ho-Young Seoul Natl Univ Bundang Hosp Off eHlth Res & Business 173rd St Seongnam Si 436707 South Korea CHA Univ CHA Bundang Med Ctr Dept Nucl Med Seongnam South Korea Catholic Univ Korea Coll Med Dept Med Informat Seoul South Korea Catholic Univ Korea Dept Biomed & Hlth Sci Seoul South Korea Catholic Univ Korea Seoul St Marys Hosp Coll Med Dept RadiolDiv Nucl Med Seoul South Korea Seoul Natl Univ Hosp Transdisciplinary Dept Med & Adv Technol Seoul South Korea Seoul Natl Univ Med Res Ctr Med Big Data Res Ctr Seoul South Korea Seoul Natl Univ Interdisciplinary Program Bioengn Seoul South Korea Seoul Natl Univ Hosp Biomed Res Inst Seoul South Korea Seoul Natl Univ Coll Med Dept Nucl Med Seoul South Korea Seoul Natl Univ Hosp Dept Nucl Med Seoul South Korea Soonchunhyang Univ Bucheon Hosp Dept Nucl Med Gyeonggido South Korea Seoul Natl Univ Dept Stat Seoul South Korea Kyungpook Natl Univ Dept Stat Daegu South Korea Seoul Natl Univ Bundang Hosp Dept Nucl Med Off eHlth Res & Business 166 Gumi Ro Seongnam Si 436707 South Korea

Purpose Risk of second primary malignancy (SPM) after radioiodine (RAI) therapy has been continuously debated. The aim of this study is to identify the risk of SPM in thyroid cancer (TC) patients with RAI compared with TC patients without RAI from matched cohort. Methods Retrospective propensity-matched cohorts were constructed across 4 hospitals in South Korea via the Observational Health data Science and Informatics (OHDSI), and electrical health records were converted to data of common data model. TC patients who received RAI therapy constituted the target group, whereas TC patients without RAI therapy constituted the comparative group with 1:1 propensity score matching. Hazard ratio (HR) by Cox proportional hazard model was used to estimate the risk of SPM, and meta-analysis was performed to pool the HRs. Results Among a total of 24,318 patients, 5,374 patients from each group were analyzed (mean age 48.9 and 49.2, women 79.4% and 79.5% for target and comparative group, respectively). All hazard ratios of SPM in TC patients with RAI therapy were <= 1 based on 95% confidence interval(CI) from full or subgroup analyses according to thyroid cancer stage, time-at-risk period, SPM subtype (hematologic or non-hematologic), and initial age (< 30 years or >= 30 years). The HR within the target group was not significantly higher (< 1) in patients who received over 3.7 GBq of I-131 compared with patients who received less than 3.7 GBq of I-131 based on 95% CI. Conclusion There was no significant difference of the SPM risk between TC patients treated with I-131 and propensity-matched TC patients without I-131 therapy.

关键词： Thyroid cancer Iodine-131 Neoplasm Second primary Risk common data model Observational study

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Haematology Outcomes Network in Europe (HONEUR)-A collaborative, interdisciplinary platform to harness the potential of real-world data in hematology

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EUROPEAN JOURNAL OF HAEMATOLOGY 2022年第2期109卷 138-145页

作者： Bardenheuer, Kristina Van Speybroeck, Michel Hague, Clare Nikai, Enkeleida Price, Martin Janssen Pharmaceut NV Beerse Belgium

Introduction There remains a need to optimize treatments and improve outcomes among patients with hematologic malignancies. The timely synthesis and analysis of real-world data could play a key role. Objectives The Haematology Outcomes Network in Europe (HONEUR) is a federated data network (FDN) that aims to overcome the challenges of heterogenous data collected from different registries, hospitals, and other databases in different countries. It has the functionality required to analyze data from various sources in a time efficient manner, while preserving local data security and governance. With this, research studies can be performed that can increase knowledge and understanding of the management of patients with hematologic malignancies. Methods HONEUR uses the Observational Medical Outcomes Partnership (OMOP) common data model, which allows analysis scripts to be run by multiple sites using their own data, ultimately generating aggregated results. Furthermore, distributed analytics can be used to run statistical analyses across multiple sites, as if data were pooled. The external governance model ensures high-quality standards, while data ownership is retained locally. Twenty partners from nine countries are now participating, with data from more than 26 000 patients available for analysis. Research questions that can be addressed through HONEUR include assessments of natural disease history, treatment patterns, and clinical effectiveness. Conclusions The HONEUR FDN marks an important step forward in increasing the value of information routinely captured by individual hospitals, registries and other database holders, thus enabling larger-scale studies to be undertaken rapidly and efficiently.

关键词： common data model data analysis evidence generation federated data network Haematology Outcomes Network in Europe Observational Medical Outcomes Partnership real-world evidence

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Invited Commentary: Standards, Inputs, and Outputs-Strategies for Improving data-Sharing and Consortia-Based Epidemiologic Research

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AMERICAN JOURNAL OF EPIDEMIOLOGY 2022年第1期191卷 159-162页

作者： Lacey, James V., Jr. Benbow, Jennifer L. City Hope Natl Med Ctr Dept Computat & Quantitat Med Beckman Res Inst Div Hlth Analyt Duarte CA 91010 USA

data-sharing improves epidemiologic research, but the sharing of data frustrates epidemiologic researchers. The inefficiencies of current methods and options for data-sharing are increasingly documented and easily understood by any study group that has shared its data and any researcher who has received shared data. In this issue of the Journal, Temprosa et al. (Am J Epidemiol. 2022;191(1):147-158) describe how the Consortium of Metabolomics Studies (COMETS) developed and deployed a flexible analytical platform to eliminate key pain points in large-scale metabolomics research. COMETS Analytics includes an online tool, but its cloud computing and technology are the supporting rather than the leading actors in this script. The COMETS team identified the need to standardize diverse and inconsistent metabolomics and covariate data and models across its many participating cohort studies, and then developed a flexible tool that gave its member studies choices about how they wanted to meet the consortium's analytical requirements. Different specialties will have different specific research needs and will probably continue to use and develop an array of diverse analytical and technical solutions for their projects. COMETS Analytics shows how important-and enabling-the upstream attention to data standards and data consistency is to producing high-quality metabolomics, consortia-based, and large-scale epidemiology research.

关键词： common data model data analysis data harmonization data pooling epidemiologic methods metabolomics prospective studies

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The Value of Federated data Networks in Oncology: What Research Questions Do They Answer? Outcomes From a Systematic Literature Review

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VALUE IN HEALTH 2022年第5期25卷 855-868页

作者： Hunger, Matthias Bardenheuer, Kristina Passey, Alun Schade, Rene Sharma, Ruchika Hague, Clare ICON plc Global Hlth Econ Outcomes Res & Epidemiol Dublin Ireland Janssen Pharmaceut NV Beerse Belgium Epi Hlth Econ Market Access & Reimbursement Janssen EMEA Johnson & Johnson Pl 1 D-41470 Neuss Germany

Objectives: Real-world evidence (RWE) plays an important role in addressing key research questions of interest to healthcare decision makers. Federated data networks (FDNs) apply novel technology to enable the conduct of RWE studies with multiple partners, without the need to share the individual partner's data set. A systematic review of the published literature was performed to determine which types of research questions can best be addressed through FDNs, specifically in the field of oncology. Methods: Systematic searches of MEDLINE and Embase were undertaken to identify the types of research questions that had been addressed in studies using FDNs. Additional information was retrieved about study characteristics, statistical methods, Results: In total, 40 publications were included where research questions on the following had been addressed (multiple categories possible): disease natural history (58%), safety surveillance (18%), treatment pathways (15%), comparative effectiveness (10%), and cost/resource use studies (3%)-13% of studies had to be left uncategorized. A total of 50% of the studies were run with data partners in networks of #5. The size of the networks ranged from 227 patients to .5 million patients. Statistical methods used included distributed learning and distributed regression methods. Conclusions: Further work is needed to raise awareness of the important role that FDNs can play in leveraging readily available RWE to address key research questions of interest in cancer and the benefits to the research community in engaging in federated data initiatives with a long-term perspective.

关键词： common data model data analysis evidence generation federated data network Observational Medical Outcomes Partnership oncology real-world evidence systematic literature review

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Fitness of real-world data for clinical trial data collection: Results and lessons from a HARMONY Outcomes ancillary study

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CLINICAL TRIALS 2022年第6期19卷 655-664页

作者： Hammill, Bradley G. Leimberger, Jeffrey D. Lampron, Zachary Raman, Sudha R. O'Brien, Emily C. Wurst, Keele E. Mountcastle, Sally Cunnington, Marianne Janmohamed, Salim Curtis, Lesley H. Duke Clin Res Inst 215 Morris St Durham NC 27701 USA Duke Univ Sch Med Durham NC USA GlaxoSmithKline Stevenage Herts England GlaxoSmithKline Res Triangle Pk NC USA GlaxoSmithKline Brentford England

Background Despite the extensive use of real-world data for retrospective, observational clinical research, our understanding of how real-world data might increase the efficiency of data collection in patient-level randomized clinical trials is largely unknown. The structure of real-world data is inherently heterogeneous, with each source electronic health record and claims database different from the next. Their fitness-for-use as data sources for multisite trials in the United States has not been established. Methods For a subset of participants in the HARMONY Outcomes Trial, we obtained electronic health record data from recruiting sites or Medicare claims data from the Centers for Medicare & Medicaid Services. For baseline characteristics and follow-up events, we assessed the level of agreement between these real-world data and data documented in the trial database. Results Real-world data-derived demographic information tended to agree with trial-reported demographic information, although real-world data were less accurate in identifying medical history. The ability of real-world data to identify baseline medication usage differed by real-world data source, with claims data demonstrating substantially better performance than electronic health record data. The limited number of lab results in the collected electronic health record data matched closely with values in the trial database. There were not enough follow-up events in the ancillary study population to draw meaningful conclusions about the performance of real-world data for identification of events. Based on the conduct of this ancillary study, the challenges and opportunities of using real-world data within clinical trials are discussed. Conclusion Based on a subset of participants from the HARMONY Outcomes Trial, our results suggest that electronic health record or claims data, as currently available, are unlikely to be a complete substitute for trial data collection of medical history or baseline lab

关键词： Electronic health record claims data common data model

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