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

ABSTRACT: Well-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.

SUBMITTER: Kim JW

PROVIDER: S-EPMC8007756 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCommon data models (CDMs) enable data to be standardized, and facilitate data exchange, sharing, and storage, particularly when the data have been collected via distinct, heterogeneous systems. Moreover, CDMs provide tools for data quality assessment, integration into models, visualization, and analysis. The observational medical outcome partnership (OMOP) provides a CDM for organizing and standardizing databases. Common data models not only facilitate data integration but also (and especially for the OMOP model) extends the range of available statistical analyses.ObjectiveThis study aimed to evaluate the feasibility of implementing French national electronic health records in the OMOP CDM.MethodsThe OMOP's specifications were used to audit the source data, specify the transformation into the OMOP CDM, implement an extract-transform-load process to feed data from the French health care system into the OMOP CDM, and evaluate the final database.ResultsSeventeen vocabularies corresponding to the French context were added to the OMOP CDM's concepts. Three French terminologies were automatically mapped to standardized vocabularies. We loaded nine tables from the OMOP CDM's "standardized clinical data" section, and three tables from the "standardized health system data" section. Outpatient and inpatient data from 38,730 individuals were integrated. The median (interquartile range) number of outpatient and inpatient stays per patient was 160 (19-364).ConclusionOur results demonstrated that data from the French national health care system can be integrated into the OMOP CDM. One of the main challenges was the use of international OMOP concepts to annotate data recorded in a French context. The use of local terminologies was an obstacle to conceptual mapping; with the exception of an adaptation of the International Classification of Diseases 10th Revision, the French health care system does not use international terminologies. It would be interesting to extend our present findings to the 65 million people registered in the French health care system.

Project description:BackgroundFalls in acute care settings threaten patients' safety. Researchers have been developing fall risk prediction models and exploring risk factors to provide evidence-based fall prevention practices; however, such efforts are hindered by insufficient samples, limited covariates, and a lack of standardized methodologies that aid study replication.ObjectiveThe objectives of this study were to (1) convert fall-related electronic health record data into the standardized Observational Medical Outcome Partnership's (OMOP) common data model format and (2) develop models that predict fall risk during 2 time periods.MethodsAs a pilot feasibility test, we converted fall-related electronic health record data (nursing notes, fall risk assessment sheet, patient acuity assessment sheet, and clinical observation sheet) into standardized OMOP common data model format using an extraction, transformation, and load process. We developed fall risk prediction models for 2 time periods (within 7 days of admission and during the entire hospital stay) using 2 algorithms (least absolute shrinkage and selection operator logistic regression and random forest).ResultsIn total, 6277 nursing statements, 747,049,486 clinical observation sheet records, 1,554,775 fall risk scores, and 5,685,011 patient acuity scores were converted into OMOP common data model format. All our models (area under the receiver operating characteristic curve 0.692-0.726) performed better than the Hendrich II Fall Risk Model. Patient acuity score, fall history, age ≥60 years, movement disorder, and central nervous system agents were the most important predictors in the logistic regression models.ConclusionsTo enhance model performance further, we are currently converting all nursing records into the OMOP common data model data format, which will then be included in the models. Thus, in the near future, the performance of fall risk prediction models could be improved through the application of abundant nursing records and external validation.

Project description:BackgroundElectronic 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.ObjectiveThe 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.MethodsUsing 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.ResultsWe 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.ConclusionsGeneric 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.

Project description:BackgroundPatient-monitoring software generates a large amount of data that can be reused for clinical audits and scientific research. The Observational Health Data Sciences and Informatics (OHDSI) consortium developed the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to standardize electronic health record data and promote large-scale observational and longitudinal research.ObjectiveThis study aimed to transform primary care data into the OMOP CDM format.MethodsWe extracted primary care data from electronic health records at a multidisciplinary health center in Wattrelos, France. We performed structural mapping between the design of our local primary care database and the OMOP CDM tables and fields. Local French vocabularies concepts were mapped to OHDSI standard vocabularies. To validate the implementation of primary care data into the OMOP CDM format, we applied a set of queries. A practical application was achieved through the development of a dashboard.ResultsData from 18,395 patients were implemented into the OMOP CDM, corresponding to 592,226 consultations over a period of 20 years. A total of 18 OMOP CDM tables were implemented. A total of 17 local vocabularies were identified as being related to primary care and corresponded to patient characteristics (sex, location, year of birth, and race), units of measurement, biometric measures, laboratory test results, medical histories, and drug prescriptions. During semantic mapping, 10,221 primary care concepts were mapped to standard OHDSI concepts. Five queries were used to validate the OMOP CDM by comparing the results obtained after the completion of the transformations with the results obtained in the source software. Lastly, a prototype dashboard was developed to visualize the activity of the health center, the laboratory test results, and the drug prescription data.ConclusionsPrimary care data from a French health care facility have been implemented into the OMOP CDM format. Data concerning demographics, units, measurements, and primary care consultation steps were already available in OHDSI vocabularies. Laboratory test results and drug prescription data were mapped to available vocabularies and structured in the final model. A dashboard application provided health care professionals with feedback on their practice.

Project description:BackgroundThe Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) has been implemented on various claims and electronic health record (EHR) databases, but has not been applied to a hospital transactional database. This study addresses the implementation of the OMOP CDM on the U.S. Premier Hospital database.MethodsWe designed and implemented an extract, transform, load (ETL) process to convert the Premier hospital database into the OMOP CDM. Standard charge codes in Premier were mapped between the OMOP version 4.0 Vocabulary and standard charge descriptions. Visit logic was added to impute the visit dates. We tested the conversion by replicating a published study using the raw and transformed databases. The Premier hospital database was compared to a claims database, in regard to prevalence of disease.FindingsThe data transformed into the CDM resulted in 1% of the data being discarded due to data errors in the raw data. A total of 91.4% of Premier standard charge codes were mapped successfully to a standard vocabulary. The results of the replication study resulted in a similar distribution of patient characteristics. The comparison to the claims data yields notable similarities and differences amongst conditions represented in both databases.DiscussionThe transformation of the Premier database into the OMOP CDM version 4.0 adds value in conducting analyses due to successful mapping of the drugs and procedures. The addition of visit logic gives ordinality to drugs and procedures that wasn't present prior to the transformation. Comparing conditions in Premier against a claims database can provide an understanding about Premier's potential use in pharmacoepidemiology studies that are traditionally conducted via claims databases.Conclusion and next stepsThe conversion of the Premier database into the OMOP CDM 4.0 was completed successfully. The next steps include refinement of vocabularies and mappings and continual maintenance of the transformed CDM.

Project description:BACKGROUND:The secondary use of electronic health records (EHRs) promises to facilitate medical research. We reviewed general data requirements in observational studies and analyzed the feasibility of conducting observational studies with structured EHR data, in particular diagnosis and procedure codes. METHODS:After reviewing published observational studies from the University Hospital of Erlangen for general data requirements, we identified three different study populations for the feasibility analysis with eligibility criteria from three exemplary observational studies. For each study population, we evaluated the availability of relevant patient characteristics in our EHR, including outcome and exposure variables. To assess data quality, we computed distributions of relevant patient characteristics from the available structured EHR data and compared them to those of the original studies. We implemented computed phenotypes for patient characteristics where necessary. In random samples, we evaluated how well structured patient characteristics agreed with a gold standard from manually interpreted free texts. We categorized our findings using the four data quality dimensions "completeness", "correctness", "currency" and "granularity". RESULTS:Reviewing general data requirements, we found that some investigators supplement routine data with questionnaires, interviews and follow-up examinations. We included 847 subjects in the feasibility analysis (Study 1 n?=?411, Study 2 n?=?423, Study 3 n?=?13). All eligibility criteria from two studies were available in structured data, while one study required computed phenotypes in eligibility criteria. In one study, we found that all necessary patient characteristics were documented at least once in either structured or unstructured data. In another study, all exposure and outcome variables were available in structured data, while in the other one unstructured data had to be consulted. The comparison of patient characteristics distributions, as computed from structured data, with those from the original study yielded similar distributions as well as indications of underreporting. We observed violations in all four data quality dimensions. CONCLUSIONS:While we found relevant patient characteristics available in structured EHR data, data quality problems may entail that it remains a case-by-case decision whether diagnosis and procedure codes are sufficient to underpin observational studies. Free-text data or subsequently supplementary study data may be important to complement a comprehensive patient history.

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

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