scholarly journals Assessment of Inter-Institutional Post-Operative Hypoparathyroidism Status Using a Common Data Model

2021 ◽  
Vol 10 (19) ◽  
pp. 4454
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

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. 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. 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. 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. 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.

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 Assessment of Inter-Institutional Post-Operative Hypoparathyroidism Status Using A Common Data Model

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 ◽  
Transient Hypothyroidism ◽  
Large Databases ◽  
Tingling Sensation ◽  
Multivariate Logistic Analysis

Abstract 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. 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. 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 hypothyroidism and a 102% greater likelihood of persistent hypothyroidism. Moreover, multivariate logistic analysis found that radical tumor resection was associated with a 50% greater likelihood of transient hypothyroidism and a 97% greater likelihood of persistent hypothyroidism than total bilateral thyroidectomy. In addition to estimating the incidence of and risk factors for post-operative hypoparathyroidism at two institutions, 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 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.

Blockchain-authenticated sharing of cancer patient genomic and clinical outcomes data.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e19358-e19358
Author(s):  
Shohei Burns ◽  
Eric Andrew Collisson
Keyword(s):  
Cancer Patient ◽  
Cancer Patients ◽  
Real World ◽  
Data Model ◽  
Data Extraction ◽  
Data Representation ◽  
Health Data ◽  
Patient Data ◽  
Common Data Model ◽  
Patient Privacy

e19358 Background: Efficiently sharing health data produced during standard care could dramatically accelerate progress in cancer treatments but various barriers make this difficult. Not sharing these data to ensure patient privacy is at the cost of little to no learning from real-world data produced during cancer care. Furthermore, recent research has demonstrated a willingness of cancer patients to share their treatment experiences to fuel research, despite potential risks to privacy. The objective of this study was to design, pilot, and release a decentralized, scalable, efficient, economical, and secure strategy for dissemination of de-identified clinical and genomic data with a focus on late stage cancer. Methods: We created and piloted a blockchain-authenticated system to enable securely sharing de-identified patient data derived from standard of care imaging, genomic testing, and electronic health records (EHR), called the Cancer Gene Trust (CGT). We prospectively consented and collected data for a pilot cohort (n = 18), which we uploaded to CGT. EHR data were extracted from both a hospital cancer registry and a common data model format to identify optimal data extraction and dissemination practices. Specifically, we scored and compared the level of completeness between two EHR data extraction formats against the gold-standard source documentation for patients with available data (n = 17). Results: While the total completeness scores were greater for the registry reports than the common data model, this difference was not statistically significant. We did find that some specific data fields, such as histology site, were better captured using the registry reports, which can be used to improve the continually adapting common data model. In terms of the overall pilot study, we found that CGT enables rapid integration of real-world cancer patient data in a more clinically useful timeframe. We also developed an open-source web application to allow users to seamlessly search, browse, explore, and download CGT data. Conclusions: Our pilot demonstrates the willingness of cancer patients to participate in data sharing and how blockchain-enabled structures can maintain relationships between individual data elements while preserving patient privacy, empowering findings by third party researchers and clinicians. We demonstrate the feasibility of CGT as a framework to share health data trapped in silos to further cancer research. Further studies to optimize data representation, stream, and integrity are required.

scholarly journals Using UMLS for electronic health data standardization and database design

2020 ◽  
Vol 27 (10) ◽  
pp. 1520-1528 ◽  
Author(s):  
Andrew P Reimer ◽  
Alex Milinovich
Keyword(s):  
Data Model ◽  
Health Data ◽  
Third Party ◽  
Primary Data ◽  
Data Repository ◽  
Common Data Model ◽  
Secondary Use ◽  
Mapping Process ◽  
Electronic Health ◽  
Multiple Domains

Abstract Objective Patients that undergo medical transfer represent 1 patient population that remains infrequently studied due to challenges in aggregating data across multiple domains and sources that are necessary to capture the entire episode of patient care. To facilitate access to and secondary use of transport patient data, we developed the Transport Data Repository that combines data from 3 separate domains and many sources within our health system. Methods The repository is a relational database anchored by the Unified Medical Language System unique concept identifiers to integrate, map, and standardize the data into a common data model. Primary data domains included sending and receiving hospital encounters, medical transport record, and custom hospital transport log data. A 4-step mapping process was developed: 1) automatic source code match, 2) exact text match, 3) fuzzy matching, and 4) manual matching. Results 431 090 total mappings were generated in the Transport Data Repository, consisting of 69 010 unique concepts with 77% of the data being mapped automatically. Transport Source Data yielded significantly lower mapping results with only 8% of data entities automatically mapped and a significant amount (43%) remaining unmapped. Discussion The multistep mapping process resulted in a majority of data been automatically mapped. Poor matching of transport medical record data is due to the third-party vendor data being generated and stored in a nonstandardized format. Conclusion The multistep mapping process developed and implemented is necessary to normalize electronic health data from multiple domains and sources into a common data model to support secondary use of data.

scholarly journals Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study (Preprint)

2019 ◽  
Author(s):  
Yue Yu ◽  
Kathryn Ruddy ◽  
Aaron Mansfield ◽  
Nansu Zong ◽  
Andrew Wen ◽  
...  
Keyword(s):  
Text Mining ◽  
Adverse Events ◽  
Signal Detection ◽  
Immune Checkpoint ◽  
Data Model ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Health Data ◽  
Common Data Model ◽  
Electronic Health Record Data

BACKGROUND Immune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles. OBJECTIVE The aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration–approved immune checkpoint inhibitors. METHODS In our framework, we first used the Food and Drug Administration’s Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categories to discover potentially new irAE signals. RESULTS By our text-mining pipeline, 490 irAE terms were identified from drug labels, and 918 terms were identified from the literature. In addition, of the 94 positive signals detected using CDM-based FAERS, 53 signals (56%) were labeled signals, 10 (11%) were unlabeled published signals, and 31 (33%) were potentially new signals. CONCLUSIONS We demonstrated that our approach is effective for irAE signal detection and filtration. Moreover, our CDM-based framework could facilitate adverse drug events detection and filtration toward the goal of next-generation pharmacovigilance that seamlessly integrates electronic health record data for improved signal detection.

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.

scholarly journals Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study

10.2196/17353 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e17353
Author(s):  
Yue Yu ◽  
Kathryn Ruddy ◽  
Aaron Mansfield ◽  
Nansu Zong ◽  
Andrew Wen ◽  
...  
Keyword(s):  
Text Mining ◽  
Adverse Events ◽  
Signal Detection ◽  
Immune Checkpoint ◽  
Data Model ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Health Data ◽  
Common Data Model ◽  
Electronic Health Record Data

Background Immune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles. Objective The aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration–approved immune checkpoint inhibitors. Methods In our framework, we first used the Food and Drug Administration’s Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categories to discover potentially new irAE signals. Results By our text-mining pipeline, 490 irAE terms were identified from drug labels, and 918 terms were identified from the literature. In addition, of the 94 positive signals detected using CDM-based FAERS, 53 signals (56%) were labeled signals, 10 (11%) were unlabeled published signals, and 31 (33%) were potentially new signals. Conclusions We demonstrated that our approach is effective for irAE signal detection and filtration. Moreover, our CDM-based framework could facilitate adverse drug events detection and filtration toward the goal of next-generation pharmacovigilance that seamlessly integrates electronic health record data for improved signal detection.

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