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 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 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

10.2196/19597 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e19597
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.

Assessment of inter-institutional post-operative hypoparathyroidism status using a common data model (Preprint)

2021 ◽  
Author(s):  
Joon-Hyop Lee ◽  
Suhyun Kim ◽  
Kwangsoo Kim ◽  
Young Jun Chai ◽  
Hyeong Won Yu ◽  
...  
Keyword(s):  
Data Model ◽  
Tumor Resection ◽  
Health Data ◽  
Common Data Model ◽  
Tertiary Institutions ◽  
Logistic Analysis ◽  
Large Databases ◽  
Tertiary Hospitals ◽  
Tingling Sensation ◽  
Transient Hypoparathyroidism

BACKGROUND Post-thyroidectomy hypoparathyroidism may result in various transient or permanent symptoms, ranging from tingling sensation to severe breathing difficulties. Its incidence varies among surgeons and institutions, making it difficult to determine its actual incidence and associated factors. OBJECTIVE This study attempted to estimate the incidence of post-operative hypoparathyroidism in patients at two tertiary institutions that share a common data model, the Observational Health Data Sciences and Informatics. METHODS This study used the Common Data Model to extract explicitly specified encoding and relationships among concepts using standardized vocabularies. The EDI-codes of various thyroid disorders and thyroid operations were extracted from two separate tertiary hospitals between January 2013 and December 2018. Patients were grouped into no evidence of/transient/permanent hypoparathyroidism groups to analyze the likelihood of hypoparathyroidism occurrence related to operation types and diagnosis RESULTS Of the 4848 eligible patients at the two institutions who underwent thyroidectomy, 1370 (28.26%) experienced transient hypoparathyroidism and 251 (5.18%) experienced persistent hypoparathyroidism. Univariate logistic regression analysis predicted that, relative to total bilateral thyroidectomy, radical tumor resection was associated with a 48% greater likelihood of transient hypoparathyroidism and a 102% greater likelihood of persistent hypoparathyroidism. Moreover, multivariate logistic analysis found that radical tumor resection was associated with a 50% greater likelihood of transient hypoparathyroidism and a 97% greater likelihood of persistent hypoparathyroidism than total bilateral thyroidectomy. CONCLUSIONS These findings, by integrating and analyzing two databases, suggest that this analysis could be expanded to include other large databases that share the same Observational Health Data Sciences and Informatics protocol.

scholarly journals Identification of patients with hemoglobin SS/Sβ0 thalassemia disease and pain crises within electronic health records

2018 ◽  
Vol 2 (11) ◽  
pp. 1172-1179 ◽  
Author(s):  
Ashima Singh ◽  
Javier Mora ◽  
Julie A. Panepinto
Keyword(s):  
Electronic Health Records ◽  
Acute Care ◽  
Sensitivity And Specificity ◽  
Data Model ◽  
High Sensitivity ◽  
Common Data Model ◽  
Health Records ◽  
Key Points ◽  
Electronic Health

Key Points The algorithms have high sensitivity and specificity to identify patients with hemoglobin SS/Sβ0 thalassemia and acute care pain encounters. Codes conforming to common data model are provided to facilitate adoption of algorithms and standardize definitions for EHR-based research.

scholarly journals Common Causes and Characteristics of Adverse Drug Reactions in Older Adults: A Retrospective Study

2020 ◽  
Author(s):  
Seong-Dae Woo ◽  
Jiwon Yoon ◽  
Go-Eun Doo ◽  
Youjin Park ◽  
Youngsoo Lee ◽  
...  
Keyword(s):  
Older Adults ◽  
Adverse Drug Reactions ◽  
Common Data Model ◽  
Drug Reactions ◽  
Medical Outcomes ◽  
Younger Patients ◽  
Iodinated Contrast ◽  
Common Causes ◽  
Aging Populations ◽  
Pharmacovigilance Center

Abstract Background Aging populations are often accompanied by comorbidity and polypharmacy, leading to increases in adverse drug reactions (ADRs). We sought to evaluate the causes and characteristics of ADRs in older Korean adults (≥ 65 years) in comparison to younger individuals (< 65 years).Methods Of 37,523 cases reported at a Korean pharmacovigilance center from 2011 to 2018, we reviewed 18,842 ADRs of certain or probable causality on the basis of WHO-UMC criteria. Subjects exposed to major culprits were extracted from cohorts transformed to the Observational Medical Outcomes Partnership Common Data Model during the study period.Results In total, 4,152 (22.0%) ADRs were reported for 3,437 older adults (mean age, 74.6 years and 57.3% female). Tramadol (rate ratio, 1.32; 95% confidence interval [CI], 1.21–1.44; P < 0.001) and fentanyl (1.49, 1.16–1.92, P = 0.002) posed higher risks of ADRs in the older adults, whereas nonsteroidal anti-inflammatory drugs (NSAIDs) (0.35, 0.30–0.40, P < 0.001) and iodinated contrast media (ICM) (0.82, 0.76–0.89, P < 0.001) posed lower risks. Ratios of serious ADRs to NSAIDs (odds ratio, 2.16; 95% CI, 1.48–3.15; P < 0.001) and ICM (2.09, 1.36–3.21, P = 0.001) were higher in the older adults than in the younger patients. Analgesics primarily elicited cutaneous ADRs in the younger patients and gastrointestinal reactions in the older adults. ICM more commonly led to anaphylaxis in the older adults than the younger patients (3.0% vs. 1.6%, P = 0.019).Conclusion For early detection of ADRs in older adults, better understanding of differences in the causes and characteristics thereof in comparison to the general population is needed.

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 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 Potential and Challenges of Patient-Generated Health Data for High-Quality Cancer Care

2015 ◽  
Vol 11 (3) ◽  
pp. 195-197 ◽  
Author(s):  
Arlene E. Chung ◽  
Ethan M. Basch
Keyword(s):  
Information Technology ◽  
Electronic Health Records ◽  
Clinical Data ◽  
Cancer Care ◽  
Health Data ◽  
Multiple Streams ◽  
High Quality ◽  
Health Records ◽  
Quality Cancer Care ◽  
Intelligent Filtering

Electronic health records and information technology that allow for customizable alerts, intelligent filtering of data, and meaningful aggregation of multiple streams of patient-generated health data with clinical data will be integral to the successful integration of patient-generated health data into routine cancer care.

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