scholarly journals Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership

2015 ◽  
Vol 06 (03) ◽  
pp. 536-547 ◽  
Author(s):  
F.S. Resnic ◽  
S.L. Robbins ◽  
J. Denton ◽  
L. Nookala ◽  
D. Meeker ◽  
...  
Keyword(s):  
Health Systems ◽  
Data Model ◽  
Comparative Effectiveness ◽  
Large Scale ◽  
Medication Administration ◽  
Use Cases ◽  
Common Data Model ◽  
Medical Outcomes ◽  
Data Set ◽  
Case Examples

SummaryBackground: Adoption of a common data model across health systems is a key infrastructure requirement to allow large scale distributed comparative effectiveness analyses. There are a growing number of common data models (CDM), such as Mini-Sentinel, and the Observational Medical Outcomes Partnership (OMOP) CDMs.Objective: In this case study, we describe the challenges and opportunities of a study specific use of the OMOP CDM by two health systems and describe three comparative effectiveness use cases developed from the CDM.Methods: The project transformed two health system databases (using crosswalks provided) into the OMOP CDM. Cohorts were developed from the transformed CDMs for three comparative effectiveness use case examples. Administrative/billing, demographic, order history, medication, and laboratory were included in the CDM transformation and cohort development rules.Results: Record counts per person month are presented for the eligible cohorts, highlighting differences between the civilian and federal datasets, e.g. the federal data set had more outpatient visits per person month (6.44 vs. 2.05 per person month). The count of medications per person month reflected the fact that one system‘s medications were extracted from orders while the other system had pharmacy fills and medication administration records. The federal system also had a higher prevalence of the conditions in all three use cases. Both systems required manual coding of some types of data to convert to the CDM.Conclusion: The data transformation to the CDM was time consuming and resources required were substantial, beyond requirements for collecting native source data. The need to manually code subsets of data limited the conversion. However, once the native data was converted to the CDM, both systems were then able to use the same queries to identify cohorts. Thus, the CDM minimized the effort to develop cohorts and analyze the results across the sites.FitzHenry F, Resnic FS, Robbins SL, Denton J, Nookala L, Meeker D, Ohno-Machado L, Matheny ME. A Case Report on Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership. Appl Clin Inform 2015; 6: 536–547http://dx.doi.org/10.4338/ACI-2014-12-CR-0121

scholarly journals Transforming Anesthesia Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study (Preprint)

2021 ◽  
Author(s):  
Antoine Lamer ◽  
Osama Abou-Arab ◽  
Alexandre Bourgeois ◽  
Adrien Parrot ◽  
Benjamin Popoff ◽  
...  
Keyword(s):  
Data Warehouse ◽  
Operating Room ◽  
Local Anesthesia ◽  
Data Model ◽  
Large Scale ◽  
Longitudinal Research ◽  
Data Reuse ◽  
Common Data Model ◽  
Anesthesia Care ◽  
Medical Outcomes

BACKGROUND Electronic health records (EHRs, such as those created by an anesthesia management system) generate a large amount of data that can notably be reused for clinical audits and scientific research. The sharing of these data and tools is generally affected by the lack of system interoperability. To overcome these issues, Observational Health Data Sciences and Informatics (OHDSI) developed the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to standardize EHR data and promote large-scale observational and longitudinal research. Anesthesia data have not previously been mapped into the OMOP CDM. OBJECTIVE The primary objective was to transform anesthesia data into the OMOP CDM. The secondary objective was to provide vocabularies, queries, and dashboards that might promote the exploitation and sharing of anesthesia data through the CDM. METHODS Using our local anesthesia data warehouse, a group of 5 experts from 5 different medical centers identified local concepts related to anesthesia. The concepts were then matched with standard concepts in the OHDSI vocabularies. We performed structural mapping between the design of our local anesthesia data warehouse and the OMOP CDM tables and fields. To validate the implementation of anesthesia data into the OMOP CDM, we developed a set of queries and dashboards. RESULTS We identified 522 concepts related to anesthesia care. They were classified as demographics, units, measurements, operating room steps, drugs, periods of interest, and features. After semantic mapping, 353 (67.7%) of these anesthesia concepts were mapped to OHDSI concepts. Further, 169 (32.3%) concepts related to periods and features were added to the OHDSI vocabularies. Then, 8 OMOP CDM tables were implemented with anesthesia data and 2 new tables (EPISODE and FEATURE) were added to store secondarily computed data. We integrated data from 5,72,609 operations and provided the code for a set of 8 queries and 4 dashboards related to anesthesia care. CONCLUSIONS Generic data concerning demographics, drugs, units, measurements, and operating room steps were already available in OHDSI vocabularies. However, most of the intraoperative concepts (the duration of specific steps, an episode of hypotension, etc) were not present in OHDSI vocabularies. The OMOP mapping provided here enables anesthesia data reuse.

scholarly journals The Risk of Osteoporosis and Osteoporotic Fracture Following the Use of Irritable Bowel Syndrome Medical Treatment: An Analysis Using the OMOP CDM Database

2021 ◽  
Vol 10 (9) ◽  
pp. 2044
Author(s):  
Gyu Lee Kim ◽  
Yu Hyeon Yi ◽  
Hye Rim Hwang ◽  
Jinmi Kim ◽  
Youngmin Park ◽  
...  
Keyword(s):  
Irritable Bowel Syndrome ◽  
Osteoporotic Fracture ◽  
Data Model ◽  
Large Scale ◽  
Osteoporotic Fractures ◽  
Common Data Model ◽  
Medical Outcomes ◽  
Treatment Of Osteoporosis ◽  
Increased Risk ◽  
Irritable Bowel

Patients with irritable bowel syndrome (IBS) are at increased risk of osteoporosis and osteoporotic fracture. This study investigated whether IBS medication attenuated the rate of osteoporosis and osteoporotic fracture risk. We conducted a retrospective large-scale multicenter study across eight hospital databases encoded in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The primary outcome was the incidence of osteoporosis, whereas secondary outcomes were osteoporotic fractures. After 1:4 matching, 24,723 IBS patients, 78,318 non-IBS patients, 427,640 non-IBS patients with IBS medication, and 827,954 non-IBS patients without IBS medication were selected. The risk of osteoporosis was significantly increased in the IBS group compared to the non-IBS group (hazard ratio (HR) 1.33; confidence interval (CI) 1.17~1.51). Even in patients who were not diagnosed with IBS, the risk of osteoporosis was significantly increased in those with IBS medication compared to those without (HR 1.77, CI 1.62~1.93). The risk of osteoporotic fracture was significantly increased in the IBS medication group (HR 1.69, CI 1.55~1.84). Patients exposed to IBS treatment even without IBS diagnosis were at increased risk of osteoporosis and osteoporotic fracture. Early diagnosis and treatment of osteoporosis should be considered in patients who have received medication for IBS symptoms.

scholarly journals Transforming Anesthesia Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study

10.2196/29259 ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. e29259
Author(s):  
Antoine Lamer ◽  
Osama Abou-Arab ◽  
Alexandre Bourgeois ◽  
Adrien Parrot ◽  
Benjamin Popoff ◽  
...  
Keyword(s):  
Data Warehouse ◽  
Operating Room ◽  
Local Anesthesia ◽  
Data Model ◽  
Large Scale ◽  
Longitudinal Research ◽  
Data Reuse ◽  
Common Data Model ◽  
Anesthesia Care ◽  
Medical Outcomes

Background Electronic health records (EHRs, such as those created by an anesthesia management system) generate a large amount of data that can notably be reused for clinical audits and scientific research. The sharing of these data and tools is generally affected by the lack of system interoperability. To overcome these issues, Observational Health Data Sciences and Informatics (OHDSI) developed the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to standardize EHR data and promote large-scale observational and longitudinal research. Anesthesia data have not previously been mapped into the OMOP CDM. Objective The primary objective was to transform anesthesia data into the OMOP CDM. The secondary objective was to provide vocabularies, queries, and dashboards that might promote the exploitation and sharing of anesthesia data through the CDM. Methods Using our local anesthesia data warehouse, a group of 5 experts from 5 different medical centers identified local concepts related to anesthesia. The concepts were then matched with standard concepts in the OHDSI vocabularies. We performed structural mapping between the design of our local anesthesia data warehouse and the OMOP CDM tables and fields. To validate the implementation of anesthesia data into the OMOP CDM, we developed a set of queries and dashboards. Results We identified 522 concepts related to anesthesia care. They were classified as demographics, units, measurements, operating room steps, drugs, periods of interest, and features. After semantic mapping, 353 (67.7%) of these anesthesia concepts were mapped to OHDSI concepts. Further, 169 (32.3%) concepts related to periods and features were added to the OHDSI vocabularies. Then, 8 OMOP CDM tables were implemented with anesthesia data and 2 new tables (EPISODE and FEATURE) were added to store secondarily computed data. We integrated data from 5,72,609 operations and provided the code for a set of 8 queries and 4 dashboards related to anesthesia care. Conclusions Generic data concerning demographics, drugs, units, measurements, and operating room steps were already available in OHDSI vocabularies. However, most of the intraoperative concepts (the duration of specific steps, an episode of hypotension, etc) were not present in OHDSI vocabularies. The OMOP mapping provided here enables anesthesia data reuse.

scholarly journals From OpenEHR to FHIR and OMOP Data Model for Microbiology Findings

2021 ◽  
Author(s):  
Eugenia Rinaldi ◽  
Sylvia Thun
Keyword(s):  
Infection Control ◽  
Data Model ◽  
Data Exchange ◽  
Control Group ◽  
Common Data Model ◽  
University Hospitals ◽  
Data Set ◽  
Related Data ◽  
National Initiative ◽  
Data Elements

HiGHmed is a German Consortium where eight University Hospitals have agreed to the cross-institutional data exchange through novel medical informatics solutions. The HiGHmed Use Case Infection Control group has modelled a set of infection-related data in the openEHR format. In order to establish interoperability with the other German Consortia belonging to the same national initiative, we mapped the openEHR information to the Fast Healthcare Interoperability Resources (FHIR) format recommended within the initiative. FHIR enables fast exchange of data thanks to the discrete and independent data elements into which information is organized. Furthermore, to explore the possibility of maximizing analysis capabilities for our data set, we subsequently mapped the FHIR elements to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). The OMOP data model is designed to support the conduct of research to identify and evaluate associations between interventions and outcomes caused by these interventions. Mapping across standard allows to exploit their peculiarities while establishing and/or maintaining interoperability. This article provides an overview of our experience in mapping infection control related data across three different standards openEHR, FHIR and OMOP CDM.

scholarly journals Pharmacovigilance and Clinical Environment: Utilizing OMOP-CDM and OHDSI Software Stack to Integrate EHR Data

2021 ◽  
Author(s):  
Vlasios K. Dimitriadis ◽  
George I. Gavriilidis ◽  
Pantelis Natsiavas
Keyword(s):  
Information Technology ◽  
Electronic Health Records ◽  
Drug Safety ◽  
Data Model ◽  
Health Data ◽  
Common Data Model ◽  
Clinical Environment ◽  
Drug Reactions ◽  
Medical Outcomes ◽  
Integrate Data

Information Technology (IT) and specialized systems could have a prominent role towards the support of drug safety processes, both in the clinical context but also beyond that. PVClinical project aims to build an IT platform, enabling the investigation of potential Adverse Drug Reactions (ADRs). In this paper, we outline the utilization of Observational Medical Outcomes Partnership – Common Data Model (OMOP-CDM) and the openly available Observational Health Data Sciences and Informatics (OHDSI) software stack as part of PVClinical platform. OMOP-CDM offers the capacity to integrate data from Electronic Health Records (EHRs) (e.g., encounters, patients, providers, diagnoses, drugs, measurements and procedures) via an accepted data model. Furthermore, the OHDSI software stack provides valuable analytics tools which could be used to address important questions regarding drug safety quickly and efficiently, enabling the investigation of potential ADRs in the clinical environment.

scholarly journals LOD Development System for Medical Information Standard

2018 ◽  
Vol 7 (3.33) ◽  
pp. 225
Author(s):  
Hee-kyung Moon ◽  
Sung-kook Han ◽  
Chang-ho An
Keyword(s):  
Data Model ◽  
Medical Information ◽  
Open Data ◽  
Linked Open Data ◽  
Common Data Model ◽  
Medical Outcomes ◽  
Systematic Analysis ◽  
Development System ◽  
Observational Database

This paper describes Linked Open Data(LOD) development system and its application of medical information standard as Observational Medical Outcomes Partnership(OMOP) Common Data Model(CDM). The OMOP CDM allows for the systematic analysis of disparate observational database in each hospital. This paper describes a LOD instance development system based on SII. It can generate the application-specified instance development system automatically. Therefore, we applied by medical information standard as OMOP CDM to LOD development system. As a result, it was confirmed that there is no problem in applying to the standardization of medical information using the LOD development system.  

scholarly journals 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 (Preprint)

2020 ◽  
Author(s):  
Seungho Jeon ◽  
Jeongeun Seo ◽  
Sukyoung Kim ◽  
Jeongmoon Lee ◽  
Jong-Ho Kim ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Model ◽  
Personal Information ◽  
Health Data ◽  
Common Data Model ◽  
Patient Privacy ◽  
Medical Outcomes ◽  
Cloud Computing System ◽  
Privacy Models ◽  
Identification Strategy

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, l-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, l-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, l-diversity, and t-closeness privacy models but also the overall possibility of re-identification. CONCLUSIONS Our proposed de-identification strategy effectively enhanced the privacy of the CDM database, thereby encouraging clinical research involving multiple centers.

scholarly journals Characteristics and outcomes of patients with COVID-19 with and without prevalent hypertension: a multinational cohort study

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e057632
Author(s):  
Carlen Reyes ◽  
Andrea Pistillo ◽  
Sergio Fernández-Bertolín ◽  
Martina Recalde ◽  
Elena Roel ◽  
...  
Keyword(s):  
Cohort Study ◽  
Data Model ◽  
Distress Syndrome ◽  
Adverse Outcomes ◽  
Common Data Model ◽  
Medical Outcomes ◽  
Hypertension Status ◽  
Hospitalised Patients ◽  
The Usa

ObjectiveTo characterise patients with and without prevalent hypertension and COVID-19 and to assess adverse outcomes in both inpatients and outpatients.Design and settingThis is a retrospective cohort study using 15 healthcare databases (primary and secondary electronic healthcare records, insurance and national claims data) from the USA, Europe and South Korea, standardised to the Observational Medical Outcomes Partnership common data model. Data were gathered from 1 March to 31 October 2020.ParticipantsTwo non-mutually exclusive cohorts were defined: (1) individuals diagnosed with COVID-19 (diagnosed cohort) and (2) individuals hospitalised with COVID-19 (hospitalised cohort), and stratified by hypertension status. Follow-up was from COVID-19 diagnosis/hospitalisation to death, end of the study period or 30 days.OutcomesDemographics, comorbidities and 30-day outcomes (hospitalisation and death for the ‘diagnosed’ cohort and adverse events and death for the ‘hospitalised’ cohort) were reported.ResultsWe identified 2 851 035 diagnosed and 563 708 hospitalised patients with COVID-19. Hypertension was more prevalent in the latter (ranging across databases from 17.4% (95% CI 17.2 to 17.6) to 61.4% (95% CI 61.0 to 61.8) and from 25.6% (95% CI 24.6 to 26.6) to 85.9% (95% CI 85.2 to 86.6)). Patients in both cohorts with hypertension were predominantly >50 years old and female. Patients with hypertension were frequently diagnosed with obesity, heart disease, dyslipidaemia and diabetes. Compared with patients without hypertension, patients with hypertension in the COVID-19 diagnosed cohort had more hospitalisations (ranging from 1.3% (95% CI 0.4 to 2.2) to 41.1% (95% CI 39.5 to 42.7) vs from 1.4% (95% CI 0.9 to 1.9) to 15.9% (95% CI 14.9 to 16.9)) and increased mortality (ranging from 0.3% (95% CI 0.1 to 0.5) to 18.5% (95% CI 15.7 to 21.3) vs from 0.2% (95% CI 0.2 to 0.2) to 11.8% (95% CI 10.8 to 12.8)). Patients in the COVID-19 hospitalised cohort with hypertension were more likely to have acute respiratory distress syndrome (ranging from 0.1% (95% CI 0.0 to 0.2) to 65.6% (95% CI 62.5 to 68.7) vs from 0.1% (95% CI 0.0 to 0.2) to 54.7% (95% CI 50.5 to 58.9)), arrhythmia (ranging from 0.5% (95% CI 0.3 to 0.7) to 45.8% (95% CI 42.6 to 49.0) vs from 0.4% (95% CI 0.3 to 0.5) to 36.8% (95% CI 32.7 to 40.9)) and increased mortality (ranging from 1.8% (95% CI 0.4 to 3.2) to 25.1% (95% CI 23.0 to 27.2) vs from 0.7% (95% CI 0.5 to 0.9) to 10.9% (95% CI 10.4 to 11.4)) than patients without hypertension.ConclusionsCOVID-19 patients with hypertension were more likely to suffer severe outcomes, hospitalisations and deaths compared with those without hypertension.

scholarly journals Extract, Transform, Load Framework for the Conversion of Health Databases to OMOP

2021 ◽  
Author(s):  
Juan C. Quiroz ◽  
Tim Chard ◽  
Zhisheng Sa ◽  
Angus Ritchie ◽  
Louisa Jorm ◽  
...  
Keyword(s):  
Data Model ◽  
Web Application ◽  
Research Community ◽  
Common Data Model ◽  
Medical Outcomes ◽  
Data Manipulation ◽  
Community Needs ◽  
Mapping Process ◽  
The Cost ◽  
Manipulation Language

ABSTRACTObjectiveDevelop an extract, transform, load (ETL) framework for the conversion of health databases to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) that supports transparency of the mapping process, readability, refactoring, and maintainability.Materials and MethodsWe propose an ETL framework that is metadata-driven and generic across source datasets. The ETL framework reads mapping logic for OMOP tables from YAML files, which organize SQL snippets in key-value pairs that define the extract and transform logic to populate OMOP columns.ResultsWe developed a data manipulation language (DML) for writing the mapping logic from health datasets to OMOP, which defines mapping operations on a column-by-column basis. A core ETL pipeline converts the DML in YAML files and generates an ETL script. We provide access to our ETL framework via a web application, allowing users to upload and edit YAML files and obtain an ETL SQL script that can be used in development environments.DiscussionThe structure of the DML and the mapping operations defined in column-by-column operations maximizes readability, refactoring, and maintainability, while minimizing technical debt, and standardizes the writing of ETL operations for mapping to OMOP. Our web application allows institutions and teams to reuse the ETL pipeline by writing their own rules using our DML.ConclusionThe research community needs tools that reduce the cost and time effort needed to map datasets to OMOP. These tools must support transparency of the mapping process for mapping efforts to be reused by different institutions.

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