Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership

FitzHenry, Fern; Resnic, Frederic S.; Robbins, Shira L.; Denton, Jason; Nookala, Lavanya; Meeker, Daniella; Ohno-Machado, Lucila; Matheny, Michael E.

doi:10.4338/aci-2014-12-cr-0121

Cited by 83 publications

(53 citation statements)

References 37 publications

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“…Thus, there is a need to understand whether the CDM is an organizational or institutional standard, which is why the CDM consortium promises to use only in this consortium. Another challenge of CDM is that data mapping must be done manually, step-by-step [34]. Data mapping is nearly impossible to automatize as each hospital has a unique data structure, and automatic data mapping without consideration of the discrepancies would result in definite biases.…”

Section: Hot Issues In Korea: the Common Data Modelmentioning

confidence: 99%

Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration

Kim

Yoon

2019

Endocrinol Metab

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Most people are now familiar with the concepts of big data, deep learning, machine learning, and artificial intelligence (AI) and have a vague expectation that AI using medical big data can be used to improve the quality of medical care. However, the expectation that big data could change the field of medicine is inconsistent with the current reality. The clinical meaningfulness of the results of research using medical big data needs to be examined. Medical staff needs to be clear about the purpose of AI that utilizes medical big data and to focus on the quality of this data, rather than the quantity. Further, medical professionals should understand the necessary precautions for using medical big data, as well as its advantages. No doubt that someday, medical big data will play an essential role in healthcare; however, at present, it seems too early to actively use it in clinical practice. The field continues to work toward developing medical big data and making it appropriate for healthcare. Researchers should continue to engage in empirical research to ensure that appropriate processes are in place to empirically evaluate the results of its use in healthcare.

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Section: Hot Issues In Korea: the Common Data Modelmentioning

confidence: 99%

Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration

Kim

Yoon

2019

Endocrinol Metab

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“…The ETL process of additional data items to OMOP can constitute a nontrivial task; however, there are multiple efforts to map EHR data to OMOP and institutions that already implement the CDM for other projects can start using the application without further work. 8 13 14 15 16 17 While other data models such as from i2b2 18 would have been conceivable, we decided for the use of the OMOP CDM for the integration of clinical and genomic data. We found this approach preferable because it naturally extended our previous local developments of established and well-maintained ETL processes to OMOP, as well as the standardized and yet easily extendable vocabularies and REST API.…”

Section: Discussionmentioning

confidence: 99%

EHR-Independent Predictive Decision Support Architecture Based on OMOP

et al. 2020

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Background The increasing availability of molecular and clinical data of cancer patients combined with novel machine learning techniques has the potential to enhance clinical decision support, example, for assessing a patient's relapse risk. While these prediction models often produce promising results, a deployment in clinical settings is rarely pursued. Objectives In this study, we demonstrate how prediction tools can be integrated generically into a clinical setting and provide an exemplary use case for predicting relapse risk in melanoma patients. Methods To make the decision support architecture independent of the electronic health record (EHR) and transferable to different hospital environments, it was based on the widely used Observational Medical Outcomes Partnership (OMOP) common data model (CDM) rather than on a proprietary EHR data structure. The usability of our exemplary implementation was evaluated by means of conducting user interviews including the thinking-aloud protocol and the system usability scale (SUS) questionnaire. Results An extract-transform-load process was developed to extract relevant clinical and molecular data from their original sources and map them to OMOP. Further, the OMOP WebAPI was adapted to retrieve all data for a single patient and transfer them into the decision support Web application for enabling physicians to easily consult the prediction service including monitoring of transferred data. The evaluation of the application resulted in a SUS score of 86.7. Conclusion This work proposes an EHR-independent means of integrating prediction models for deployment in clinical settings, utilizing the OMOP CDM. The usability evaluation revealed that the application is generally suitable for routine use while also illustrating small aspects for improvement.

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“…In addition to supporting research within a single organization, the information models derived in this study can enable the extension of common data models for comparison across settings such as those used by the Patient-Centered Outcomes Research Institute (PCORI) and National Center for Advancing Translational Science (NCATS). 8,9 …”

Section: Discussionmentioning

confidence: 99%

Modeling Flowsheet Data to Support Secondary Use

Westra

Christie

Johnson

et al. 2017

CIN: Computers, Informatics, Nursing

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The purpose of this study was to create information models from flowsheet data using a data-driven consensus based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same “thing,” but the names of these observations often differ, according to who performs documentation or the location of the service (e.g., pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring). Flowsheet data are challenging for secondary use due to the existence of multiple semantically equivalent measures representing the same concepts. Ten information models were created in this study: five related to quality measures (falls, pressure ulcers, venous thrombosis embolism, genitourinary system including catheter associated urinary tract infection, and pain management) and five high volume physiological systems: cardiac, gastrointestinal, musculoskeletal, respiratory, and expanded vital signs/anthropometrics. The value of the information models is that flowsheet data can be extracted and mapped for semantically comparable flowsheet measures from a clinical data repository regardless of the time frame, discipline, or setting in which documentation occurred. The 10 information models simplify the representation of the content in flowsheet data, reducing 1,552 source measures to 557 concepts. The amount of representational reduction ranges from 3% for Falls to 78% for the Respiratory System. The information models provide a foundation for including nursing and interprofessional assessments and interventions in common data models, to support research within and across health systems.

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Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership

Cited by 83 publications

References 37 publications

Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration

Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration

EHR-Independent Predictive Decision Support Architecture Based on OMOP

Modeling Flowsheet Data to Support Secondary Use

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