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.

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

420 Evaluating the Rate of Referral for Obstructive Sleep Apnea in a Pre-Doctoral Dental Clinic Using the STOP-Bang Questionnaire

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A166-A166
Author(s):  
Nathan Guess ◽  
Henry Fischbach ◽  
Andy Ni ◽  
Allen Firestone
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
High Risk ◽  
Electronic Health Record ◽  
Health Record ◽  
Dental Clinic ◽  
Neck Size ◽  
Previous Diagnosis ◽  
Obstructive Sleep ◽  
Electronic Health

Abstract Introduction The STOP-Bang Questionnaire is a validated instrument to assess an individual’s risk for obstructive sleep apnea (OSA). The prevalence of OSA is estimated at 20% in the US with only 20% of those individuals properly diagnosed. Dentists are being asked to screen and refer patients at high risk for OSA for definitive diagnosis and treatment. The aim of this study was to determine whether patients in a dental school student clinic who were identified as high-risk for OSA, were referred for evaluation of OSA. Methods All new patients over the age of 18 admitted to The Ohio State University - College of Dentistry complete an “Adult Medical History Form”. Included in this study were 21,312 patients admitted between July 2017 and March 2020. Data were extracted from the history form to determine the STOP-Bang Score for all patients: age, sex, BMI, self-reported snoring-, stopped breathing/choking/gasping while sleeping-, high blood pressure-, neck size over 17” (males) or 16” (females)-, and tiredness. Each positive response is a point, for a maximum of 8 points possible. Additionally, any previous diagnosis of sleep apnea, and the patient’s history of referrals were extracted from the health record. According to clinic policy, if the patient did not have a previous diagnosis for OSA noted in the health history, and scored 5 or more on the STOP-Bang Questionnaire, they should receive a referral for an evaluation for OSA. Notes and referral forms were reviewed to determine if the appropriate referrals occurred for patients at high risk without a previous diagnosis. Results Of the 21,312 patients screened; 1098 (5.2%) screened high-risk for OSA, of which 398 had no previous diagnosis of OSA. Of these 398 patients, none (0%) had referrals for further evaluation for OSA. Conclusion The rate of appropriate referrals from a student dental clinic with an electronic health record was unacceptably low. Continued education and changes to the electronic health record are needed to ensure those at high-risk for OSA are appropriately referred and managed. Support (if any):

scholarly journals Prevalence and nature of statin drug-drug interactions in a university hospital by electronic health record mining

2017 ◽  
Vol 74 (4) ◽  
pp. 525-534 ◽  
Author(s):  
Camille Morival ◽  
Richard Westerlynck ◽  
Guillaume Bouzillé ◽  
Marc Cuggia ◽  
Pascal Le Corre
Keyword(s):  
Drug Interactions ◽  
Electronic Health Record ◽  
University Hospital ◽  
Health Record ◽  
Electronic Health ◽  
Statin Drug

scholarly journals Multisite validation of a simple electronic health record algorithm for identifying diagnosed obstructive sleep apnea

2020 ◽  
Vol 16 (2) ◽  
pp. 175-183 ◽  
Author(s):  
Brendan T. Keenan ◽  
H. Lester Kirchner ◽  
Olivia J. Veatch ◽  
Kenneth M. Borthwick ◽  
Vicki A. Davenport ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Electronic Health Record ◽  
Health Record ◽  
Obstructive Sleep ◽  
Electronic Health

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 Assessing the relation of the coded nursing care and nursing intensity data: Towards the exploitation of clinical data for administrative use and the design of nursing workload

2018 ◽  
Vol 26 (1) ◽  
pp. 114-128 ◽  
Author(s):  
Pia Liljamo ◽  
Ulla-Mari Kinnunen ◽  
Kaija Saranto
Keyword(s):  
Electronic Health Record ◽  
Linear Mixed Model ◽  
Resource Planning ◽  
University Hospital ◽  
Nursing Workload ◽  
Health Record ◽  
Intensity Data ◽  
Electronic Health Record Data ◽  
Generalised Linear Mixed Model ◽  
Electronic Health

Patient-care data from the electronic health record systems are increasingly in demand for re-use in administration and resource planning. Nursing documentation with coded concepts is expected to produce more reliable data, fulfilling better requirements for re-use. The aim was to ascertain what kind of relation exist between coded nursing diagnoses, nursing interventions, and nursing intensity and to discuss the possibilities for re-using nursing data for workload design. We analysed the retrospective nursing records of 794 patients documented by the Finnish Care Classification and nursing intensity data assessed by the Oulu Patient Classification over a 15-day period in nine inpatient units at a university hospital. Using the generalised linear mixed model, the clear relationship between the number of coded nursing notes and nursing intensity levels were ascertained. The number of coded nursing notes increases when the nursing intensity increases. The outcomes construct a good basis for continuing elaboration of electronic health record data re-use.

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.

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