Analysis of antiseizure drug‐related adverse reactions from the electronic health record using the common data model

Epilepsia ◽  
2020 ◽  
Vol 61 (4) ◽  
pp. 610-616 ◽  
Author(s):  
Sun Ah Choi ◽  
Hunmin Kim ◽  
Seok Kim ◽  
Sooyoung Yoo ◽  
Soyoung Yi ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Data Model ◽  
Adverse Reactions ◽  
Common Data Model ◽  
Health Record ◽  
The Common ◽  
Electronic Health

scholarly journals Transforming electronic health record polysomnographic data into the Observational Medical Outcome Partnership's Common Data Model: a pilot feasibility study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeong-Whun Kim ◽  
Seok Kim ◽  
Borim Ryu ◽  
Wongeun Song ◽  
Ho-Young Lee ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Data Model ◽  
University Hospital ◽  
Common Data Model ◽  
Health Record ◽  
Medical Outcome ◽  
Obstructive Sleep ◽  
Electronic Health ◽  
Sleep Laboratories ◽  
Pilot Feasibility Study

AbstractWell-defined large-volume polysomnographic (PSG) data can identify subgroups and predict outcomes of obstructive sleep apnea (OSA). However, current PSG data are scattered across numerous sleep laboratories and have different formats in the electronic health record (EHR). Hence, this study aimed to convert EHR PSG into a standardized data format—the Observational Medical Outcome Partnership (OMOP) common data model (CDM). We extracted the PSG data of a university hospital for the period from 2004 to 2019. We designed and implemented an extract–transform–load (ETL) process to transform PSG data into the OMOP CDM format and verified the data quality through expert evaluation. We converted the data of 11,797 sleep studies into CDM and added 632,841 measurements and 9,535 observations to the existing CDM database. Among 86 PSG parameters, 20 were mapped to CDM standard vocabulary and 66 could not be mapped; thus, new custom standard concepts were created. We validated the conversion and usefulness of PSG data through patient-level prediction analyses for the CDM data. We believe that this study represents the first CDM conversion of PSG. In the future, CDM transformation will enable network research in sleep medicine and will contribute to presenting more relevant clinical evidence.

scholarly journals ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data

JAMIA Open ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 10-14 ◽  
Author(s):  
Benjamin S Glicksberg ◽  
Boris Oskotsky ◽  
Nicholas Giangreco ◽  
Phyllis M Thangaraj ◽  
Vivek Rudrapatna ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Data Science ◽  
Demographic Data ◽  
R Package ◽  
Common Data Model ◽  
Health Record ◽  
Massachusetts Institute Of Technology ◽  
Data Types ◽  
Electronic Health Record Data ◽  
Electronic Health

Abstract Objectives Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement. Materials and methods We have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format. Results ROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept. Conclusion ROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu).

scholarly journals Characterizing Post-Acute Sequelae of SARS-CoV-2 Infection across Claims and Electronic Health Record Databases

2021 ◽  
Author(s):  
Matthew E Spotnitz ◽  
George Hripcsak ◽  
Patrick B Ryan ◽  
Karthik Natarajan
Keyword(s):  
Electronic Health Record ◽  
Large Scale ◽  
Medical Center ◽  
Respiratory Illness ◽  
Public Health Issue ◽  
Common Data Model ◽  
Health Record ◽  
Electronic Health

Structured Abstract Importance: Post-acute sequelae of SARS-CoV-2 infection (PASC) is emerging as a major public health issue. Objective: We characterized the incidence of PASC, or related symptoms and diagnoses, for COVID-19 and influenza patients. Design: Retrospective cohort study. Setting: Our data sources were the IBM MarketScan Commercial Claims and Encounters (CCAE), Optum Electronic Health Record (EHR) and Columbia University Irving Medical Center (CUIMC) databases that were transformed to the Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM) and were part of the Observational Health Sciences and Informatics (OHDSI) network. Participants: The COVID-19 cohort consisted of patients with a diagnosis of COVID-19 or positive lab test of SARS-CoV-2 after January 1st 2020 with a follow up period of at least 30 days. The influenza cohort consisted of patients with a diagnosis of influenza between October 1, 2018 and May 1, 2019 with a follow up period of at least 30 days. Intervention: Infection with COVID-19 or influenza. Main Outcomes and Measures: Post-acute sequelae of SARS-CoV-2 infection (PASC), or related diagnoses, for COVID-19 and influenza patients. Results: In aggregate, we characterized the post-acute experience for over 440,000 patients who were diagnosed with COVID-19 or tested positive for SARS-COV-2. The long term sequelae that had a higher incidence in the COVID-19 compared to Influenza cohorts were altered smell or taste, myocarditis, acute kidney injury, dyspnea and alopecia. Additionally, the long term incidences of respiratory illness, musculoskeletal disease, and psychiatric disorders for the COVID-19 population were higher than expected. Conclusions and Relevance: The long term sequelae of COVID-19 and influenza may be different. Further characterization of PASC on large scale observational healthcare databases is warranted.

scholarly journals Temporal Relationship of Computed and Structured Diagnoses in Electronic Health Record Data

2020 ◽  
Author(s):  
Wade L. Schulz ◽  
H. Patrick Young ◽  
Andreas Coppi ◽  
Bobak J. Mortazavi ◽  
Zhenqiu Lin ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Low Density Lipoprotein ◽  
Vital Signs ◽  
Density Lipoprotein ◽  
Clinical Information ◽  
Common Data Model ◽  
Administrative Claims ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Electronic Health

AbstractThe electronic health record (EHR) holds the prospect of providing more complete and timely access to clinical information for studies, quality assessments, and quality improvement compared to other data sources, such as administrative claims. Our goal was to assess the completeness and timeliness of structured diagnoses in the EHR compared to computed diagnoses for hypertension (HTN), hyperlipidemia (HLD), and diabetes mellitus (DM). We determined the amount of time for a structured diagnosis to be recorded in the EHR from when an equivalent diagnosis could be computed from other structured data elements, such as vital signs and laboratory results. Using our local instance of EHR data in the PCORnet common data model (CDM) with encounters from January 1, 2012 through February 10, 2019, we identified patients with at least two observations above threshold separated by at least 30 days. The thresholds were outpatient blood pressure of ≥ 140/90 mmHg, any low-density lipoprotein ≥ 130 mg/dl, or any hemoglobin A1c ≥ 7%, respectively. The primary measure was the length of time between the computed diagnosis and the time at which a structured diagnosis could be identified within the EHR history or problem list. We found that 39.8% of those with HTN, 21.6% with HLD, and 1.0% with DM did not receive a corresponding structured diagnosis recorded in the EHR. For those who received a structured diagnosis, a mean of 389, 198, and 106 days elapsed before the patient had the corresponding diagnosis of HTN, HLD, or DM, respectively, recorded in the EHR. We identified a marked temporal delay between when a diagnosis can be computed or inferred and when an equivalent structured diagnosis is recorded within the EHR. These findings demonstrate the continued need for additional study of the EHR to avoid bias when using observational data and reinforce the need for computational approaches to identify clinical phenotypes.

Development of a Prediction Model for Incident Atrial Fibrillation using Machine Learning Applied to Harmonized Electronic Health Record Data

2019 ◽  
Author(s):  
Premanand Tiwari ◽  
Katie Colborn ◽  
Derek E. Smith ◽  
Fuyong Xing ◽  
Debashis Ghosh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Electronic Health Record ◽  
Data Reduction ◽  
Machine Learning Algorithms ◽  
Common Data Model ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Hyperparameter Selection ◽  
Electronic Health

AbstractAtrial fibrillation (AF) is the most common sustained cardiac arrhythmia, whose early detection could lead to significant improvements in outcomes through appropriate prescription of anticoagulation. Although a variety of methods exist for screening for AF, there is general agreement that a targeted approach would be preferred. Implicit within this approach is the need for an efficient method for identification of patients at risk. In this investigation, we examined the strengths and weaknesses of an approach based on application of machine-learning algorithms to electronic health record (EHR) data that has been harmonized to the Observational Medical Outcomes Partnership (OMOP) common data model. We examined data from a total of 2.3M individuals, of whom 1.16% developed incident AF over designated 6-month time intervals. We examined and compared several approaches for data reduction, sample balancing (re-sampling) and predictive modeling using cross-validation for hyperparameter selection, and out-of-sample testing for validation. Although no approach provided outstanding classification accuracy, we found that the optimal approach for prediction of 6-month incident AF used a random forest classifier, raw features (no data reduction), and synthetic minority oversampling technique (SMOTE) resampling (F1 statistic 0.12, AUC 0.65). This model performed better than a predictive model based only on known AF risk factors, and highlighted the importance of using resampling methods to optimize ML approaches to imbalanced data as exists in EHRs. Further studies using EHR data in other medical systems are needed to validate the clinical applicability of these findings.

An Empirical Study on Patients' Acceptance and Resistance Towards Electronic Health Record Sharing System

2018 ◽  
Vol 9 (2) ◽  
pp. 1-27 ◽  
Author(s):  
Kin Lok Keung ◽  
Carman Lee ◽  
K.K.H. Ng ◽  
Sing Sum Leung ◽  
K.L. Choy
Keyword(s):  
Electronic Health Record ◽  
Theoretical Models ◽  
Sunk Costs ◽  
Behavioural Intention ◽  
Health Record ◽  
Transition Costs ◽  
Effort Expectancy ◽  
The Common ◽  
Electronic Health ◽  
Major Data

This article aims at identifying significant factors influencing behavioural intention and resistance of patients toward electronic health record sharing systems by using PLS-SEM. A questionnaire was selected as the major data collection method and 243 responses were collected. Thus, this paper reviewed different theoretical models to illustrate the factors which influence the behavioural intention of patients towards the usage of the system and to identify the most important factors for acceptance and resistance of patients' respectively. The responses were then divided into two groups, specialist patients and normal patients, which had the common factors including performance expectancy and effort expectancy. For specialist patients, transition costs were identified as the only factor significantly affecting resistance to use. For normal patients, sunk costs and regret avoidance were found to be positively correlated with resistance to using of normal patients.

scholarly journals International Electronic Health Record-Derived COVID-19 Clinical Course Profiles: The 4CE Consortium

2020 ◽  
Author(s):  
Gabriel A Brat ◽  
Griffin M Weber ◽  
Nils Gehlenborg ◽  
Paul Avillach ◽  
Nathan P Palmer ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Laboratory Tests ◽  
Common Data Model ◽  
Health Record ◽  
Data Generation ◽  
Medical Outcomes ◽  
Electronic Health Record Data ◽  
Country Level ◽  
Electronic Health ◽  
Untapped Resource

ABSTRACTWe leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across 5 countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on comorbidities and temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

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