Preliminary feasibility assessment of CDM-based active surveillance using current status of medical device data in medical records and OMOP-CDM

Choi, Soo Jeong; Kim, Jin Kuk; Nam, Ki Chang; Lee, Suehyun; Kim, Ju Han; Lee, You Kyoung

doi:10.1038/s41598-021-03332-6

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…NLRRBC transfusion in patients subjected to SLP can be considered for reporting to the hemovigilance system as a prescription error of inappropriate blood components. However, passive surveillance is limited by underreporting [ 6 , 12 ], and NLRRBC transfusion has never been reported as a prescription error in the three institutions involved this study. We adopted CDM analysis to evaluate the success rate of SLP using real-world data.…”

Section: Discussionmentioning

confidence: 99%

“…However, there was no report of how many patients were overlooked among those who met the criteria for using LR blood components, because it is difficult to review large numbers of medical records for various patient groups. Recently, the common data model (CDM) has been used to review large datasets from multiple institutions [ 6 ]. A CDM is an informational model that allows the transformation of data contained in different databases to a common format, in which all coding and vocabulary are prespecified and standardized [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Common Data Model-based Analysis of Selective Leukoreduction Protocol Compliance at Three Hospitals

2022

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Background: The selective leukoreduction protocol (SLP) is limited in that patients who require it can be overlooked. We estimated SLP compliance (SLPC) using the Observational Medical Outcomes Partnership common data model (CDM).Methods: Patients were classified into eight groups: pre-and post-hematology disease (A and B), pre-and post-solid organ transplantation (C and D), solid cancer (E), immunodeficiency (F), anticancer therapy (G), and cardiovascular surgery (H). We examined the red blood cell (RBC) transfusion history from three hospital datasets comprising approximately three million patients over 20 years using CDM-based analysis. SLPC was calculated as the percentage of patients who received only leukoreduced RBCs in total patients transfused RBCs.Results: In total, 166,641 patients from three hospitals were enrolled in this study. From 2001 to 2021, SLPC in all groups, except H, tended to increase, although there were differences among the hospitals. Based on the most recent values (2017-2021), the SLPC in groups A, B, D, and G was maintained at ≥ 75% until 1,095 days before or after diagnosis or treatment. Groups E, F, and H had < 50% SLPC one day after diagnosis and treatment.Conclusions: CDM analysis supports the review of large datasets for SLPC evaluation. Although SLPC tended to improve in most patient groups, additional education and monitoring are needed for groups that continue to show low SLPC.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Common Data Model-based Analysis of Selective Leukoreduction Protocol Compliance at Three Hospitals

2022

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“…7–11 The assumption is that CDM-based solutions can facilitate meaningful collaborations by standardizing data collection and analysis processes across institutions, including distributed analytics. However, CDM-based solutions have not been widely used for medical device evaluation studies, 12 , 13 and the applicability of CDMs to medical device evaluation studies, 14 particularly whether they capture sufficient granularity of device identifiers and aggregate codes for procedures, remains an unanswered question.…”

Section: Introductionmentioning

confidence: 99%

Integrating real-world data to assess cardiac ablation device outcomes in a multicenter study using the OMOP common data model for regulatory decisions: implementation and evaluation

Jiang

Brandt

et al. 2023

JAMIA Open

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The objective of this study is to describe application of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to support medical device real-world evaluation in a National Evaluation System for health Technology Coordinating Center (NESTcc) Test-Case involving 2 healthcare systems, Mercy Health and Mayo Clinic. CDM implementation was coordinated across 2 healthcare systems with multiple hospitals to aggregate both medical device data from supply chain databases and patient outcomes and covariates from electronic health record data. Several data quality assurance (QA) analyses were implemented on the OMOP CDM to validate the data extraction, transformation, and load (ETL) process. OMOP CDM-based data of relevant patient encounters were successfully established to support studies for FDA regulatory submissions. QA analyses verified that the data transformation was robust between data sources and OMOP CDM. Our efforts provided useful insights in real-world data integration using OMOP CDM for medical device evaluation coordinated across multiple healthcare systems.

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Using automated methods to detect safety problems with health information technology: a scoping review

Surian

Wang

Coiera

et al. 2022

Journal of the American Medical Informatics Association

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Objective To summarize the research literature evaluating automated methods for early detection of safety problems with health information technology (HIT). Materials and Methods We searched bibliographic databases including MEDLINE, ACM Digital, Embase, CINAHL Complete, PsycINFO, and Web of Science from January 2010 to June 2021 for studies evaluating the performance of automated methods to detect HIT problems. HIT problems were reviewed using an existing classification for safety concerns. Automated methods were categorized into rule-based, statistical, and machine learning methods, and their performance in detecting HIT problems was assessed. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses extension for Scoping Reviews statement. Results Of the 45 studies identified, the majority (n = 27, 60%) focused on detecting use errors involving electronic health records and order entry systems. Machine learning (n = 22) and statistical modeling (n = 17) were the most common methods. Unsupervised learning was used to detect use errors in laboratory test results, prescriptions, and patient records while supervised learning was used to detect technical errors arising from hardware or software issues. Statistical modeling was used to detect use errors, unauthorized access, and clinical decision support system malfunctions while rule-based methods primarily focused on use errors. Conclusions A wide variety of rule-based, statistical, and machine learning methods have been applied to automate the detection of safety problems with HIT. Many opportunities remain to systematically study their application and effectiveness in real-world settings.

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Preliminary feasibility assessment of CDM-based active surveillance using current status of medical device data in medical records and OMOP-CDM

Cited by 3 publications

References 39 publications

Common Data Model-based Analysis of Selective Leukoreduction Protocol Compliance at Three Hospitals

Common Data Model-based Analysis of Selective Leukoreduction Protocol Compliance at Three Hospitals

Integrating real-world data to assess cardiac ablation device outcomes in a multicenter study using the OMOP common data model for regulatory decisions: implementation and evaluation

Using automated methods to detect safety problems with health information technology: a scoping review

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