Harmonization of Detailed Clinical Models with Clinical Study Data Standards

Jiang, Guoqian; Evans, J.; Oniki, Thomas A.; Coyle, Joseph F.; Bain, Landen; Huff, Stanley M.; Kush, Rebecca; Chute, Christopher G.

doi:10.3414/me13-02-0019

Cited by 21 publications

(7 citation statements)

References 8 publications

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“…Huser and Cimino analyzed three public integrated data repositories (IDRs) and proposed desiderata for their common design patterns. 61 Potential candidates for CDM include Clinical Information Modeling Initiative, 62 Mini-Sentinel Common Data Model (recommended by US Food and Drug Administration, www.mini-sentinel.org ), i2b2 Star Schema, 63 and OMOP CDM. 33 , 35 , 36 , 64 Additionally, the Institute of Medicine has recently initiated an effort to standardize structured capture of social and behavioral domains in the EHR.…”

Section: Desideratamentioning

confidence: 99%

Desiderata for computable representations of electronic health records-driven phenotype algorithms

Thompson

Rasmussen

et al. 2015

Journal of the American Medical Informatics Association

106

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Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms.Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility.Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

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Section: Desideratamentioning

confidence: 99%

Desiderata for computable representations of electronic health records-driven phenotype algorithms

Thompson

Rasmussen

et al. 2015

Journal of the American Medical Informatics Association

106

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“…[23][24][25][26] In addition to the technical limitations of working with data at the scale of the EHR, there are also challenges in performing external validation across health care systems. [27][28][29] Nonetheless, with the increasing availability of cloud computing platforms 24 and data storage 30,31 as well as scalable computational models that can be developed and potentially shared across health care systems, opportunities to apply EHR data to clinical decision-making are emerging. For identifying patients at risk of AF, if an automated prediction algorithm could be applied to patient EHR data, then a clinical decision support system could be developed to guide clinicians toward aggressive screening approaches using standard wearable 32 or implantable telemetry 33 devices.…”

Section: Introductionmentioning

confidence: 99%

“…However, despite widespread adoption of EHRs as mandated under the Patient Protection and Affordable Care Act, there are limited examples of practical applications of EHR data to predict a meaningful clinical outcome . In addition to the technical limitations of working with data at the scale of the EHR, there are also challenges in performing external validation across health care systems . Nonetheless, with the increasing availability of cloud computing platforms and data storage as well as scalable computational models that can be developed and potentially shared across health care systems, opportunities to apply EHR data to clinical decision-making are emerging.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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IMPORTANCE Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, and its early detection could lead to significant improvements in outcomes through the appropriate prescription of anticoagulation medication. Although a variety of methods exist for screening for AF, a targeted approach, which requires an efficient method for identifying patients at risk, would be preferred. OBJECTIVE To examine machine learning approaches applied to electronic health record data that have been harmonized to the Observational Medical Outcomes Partnership Common Data Model for identifying risk of AF. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study used data from 2 252 219 individuals cared for in the UCHealth hospital system, which comprises 3 large hospitals in Colorado, from January 1, 2011, to October 1, 2018. Initial analysis was performed in December 2018; follow-up analysis was performed in July 2019. EXPOSURES All Observational Medical Outcomes Partnership Common Data Model-harmonized electronic health record features, including diagnoses, procedures, medications, age, and sex. MAIN OUTCOMES AND MEASURES Classification of incident AF in designated 6-month intervals, adjudicated retrospectively, based on area under the receiver operating characteristic curve and F1 statistic. RESULTS Of 2 252 219 individuals (1 225 533 [54.4%] women; mean [SD] age, 42.9 [22.3] years), 28 036 (1.2%) developed incident AF during a designated 6-month interval. The machine learning model that used the 200 most common electronic health record features, including age and sex, and random oversampling with a single-layer, fully connected neural network provided the optimal prediction of 6-month incident AF, with an area under the receiver operating characteristic curve of 0.800 and an F1 score of 0.110. This model performed only slightly better than a more basic logistic regression model composed of known clinical risk factors for AF, which had an area under the receiver operating characteristic curve of 0.794 and an F1 score of 0.079. CONCLUSIONS AND RELEVANCE Machine learning approaches to electronic health record data offer a promising method for improving risk prediction for incident AF, but more work is needed to show improvement beyond standard risk factors.

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“…According to the ISO/IEC 11179 standard one item can be represented by multiple codes and this set of UMLS codes is called the concept domain of the item. The value domain of the item is characterized by the data type and the optional code list [ 14 – 16 ].…”

Section: Methodsmentioning

confidence: 99%

ODMSummary: A Tool for Automatic Structured Comparison of Multiple Medical Forms Based on Semantic Annotation with the Unified Medical Language System

2016

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IntroductionMedical documentation is applied in various settings including patient care and clinical research. Since procedures of medical documentation are heterogeneous and developed further, secondary use of medical data is complicated. Development of medical forms, merging of data from different sources and meta-analyses of different data sets are currently a predominantly manual process and therefore difficult and cumbersome. Available applications to automate these processes are limited. In particular, tools to compare multiple documentation forms are missing. The objective of this work is to design, implement and evaluate the new system ODMSummary for comparison of multiple forms with a high number of semantically annotated data elements and a high level of usability.MethodsSystem requirements are the capability to summarize and compare a set of forms, enable to estimate the documentation effort, track changes in different versions of forms and find comparable items in different forms. Forms are provided in Operational Data Model format with semantic annotations from the Unified Medical Language System. 12 medical experts were invited to participate in a 3-phase evaluation of the tool regarding usability.ResultsODMSummary (available at https://odmtoolbox.uni-muenster.de/summary/summary.html) provides a structured overview of multiple forms and their documentation fields. This comparison enables medical experts to assess multiple forms or whole datasets for secondary use. System usability was optimized based on expert feedback.DiscussionThe evaluation demonstrates that feedback from domain experts is needed to identify usability issues. In conclusion, this work shows that automatic comparison of multiple forms is feasible and the results are usable for medical experts.

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Harmonization of Detailed Clinical Models with Clinical Study Data Standards

Cited by 21 publications

References 8 publications

Desiderata for computable representations of electronic health records-driven phenotype algorithms

Desiderata for computable representations of electronic health records-driven phenotype algorithms

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

ODMSummary: A Tool for Automatic Structured Comparison of Multiple Medical Forms Based on Semantic Annotation with the Unified Medical Language System

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