Clinical Research Informatics and Electronic Health Record Data

Richesson, Rachel L.; Horvath, Monica M.; Rusincovitch, Shelley A.

doi:10.15265/iy-2014-0009

Cited by 36 publications

(18 citation statements)

References 86 publications

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“…The heterogeneity of data models is a challenge in reusing data from multiple data custodians [39]. Therefore, the distributed data must be harmonized through standardization.…”

Section: Introductionmentioning

confidence: 99%

Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation

Yigzaw

Michalas

Bellika

2017

BMC Med Inform Decis Mak

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BackgroundTechniques have been developed to compute statistics on distributed datasets without revealing private information except the statistical results. However, duplicate records in a distributed dataset may lead to incorrect statistical results. Therefore, to increase the accuracy of the statistical analysis of a distributed dataset, secure deduplication is an important preprocessing step.MethodsWe designed a secure protocol for the deduplication of horizontally partitioned datasets with deterministic record linkage algorithms. We provided a formal security analysis of the protocol in the presence of semi-honest adversaries. The protocol was implemented and deployed across three microbiology laboratories located in Norway, and we ran experiments on the datasets in which the number of records for each laboratory varied. Experiments were also performed on simulated microbiology datasets and data custodians connected through a local area network.ResultsThe security analysis demonstrated that the protocol protects the privacy of individuals and data custodians under a semi-honest adversarial model. More precisely, the protocol remains secure with the collusion of up to N − 2 corrupt data custodians. The total runtime for the protocol scales linearly with the addition of data custodians and records. One million simulated records distributed across 20 data custodians were deduplicated within 45 s. The experimental results showed that the protocol is more efficient and scalable than previous protocols for the same problem.ConclusionsThe proposed deduplication protocol is efficient and scalable for practical uses while protecting the privacy of patients and data custodians.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0389-x) contains supplementary material, which is available to authorized users.

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“…The heterogeneity of data models is a challenge in reusing data from multiple data custodians [39]. Therefore, the distributed data must be harmonized through standardization.…”

Section: Introductionmentioning

confidence: 99%

Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation

Yigzaw

Michalas

Bellika

2017

BMC Med Inform Decis Mak

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“…Pragmatic clinical trials, registries, and other clinical/translational research employing EHR data for clinical phenotypes rather than manual abstraction rely on having as accurate and complete value sets to define primary and co-morbid conditions as feasible [19,20]. Concern typically arises about missing diagnosis data, not yet entered in the EHR by clinicians on the patient's Problem List or as Encounter Diagnoses.…”

Section: Clinical Phenotyping For Clinical-translational Studies Usinmentioning

confidence: 99%

SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets (Preprint)

Chu¹,

Kannan²,

Basit³

et al. 2018

Preprint

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Background: Defining clinical phenotypes from EHR-derived data proves crucial for clinical decision support, population health endeavors, and translational research. EHR diagnoses now commonly draw from a finely-grained clinical terminologyeither native SNOMED CT, or a vendor-supplied terminology mapped to SNOMED CT concepts as the standard for EHR interoperability. Accordingly, electronic clinical quality measures (eCQMs) increasingly define clinical phenotypes with SNOMED CT value sets. The work of creating and maintaining list-based value sets proves daunting, as does vetting that their contents accurately represent the clinicallyintended condition. Objective: To compare an intensional (concept hierarchy-based) vs. extensional (list-based) value set approach to defining clinical phenotypes using SNOMED CT encoded data from EHRs, by evaluating value set conciseness, time to create, and completeness. Methods: Starting from published CMS 2018 high-priority eCQMs, we selected ten clinical conditions referenced by those eCQMs. For each, the published SNOMED CT list-based (extensional) value set was downloaded from the Value Set Authority Center (VSAC). Ten corresponding SNOMED CT hierarchy-based intensional value sets for the same conditions were identified within our EHR. From each hierarchybased intensional value set, an exactly equivalent full extensional value set was derived enumerating all included descendant SNOMED CT concepts. Comparisons were then made between (a) (VSAC) downloaded list-based (extensional) value sets, (b) corresponding hierarchy-based intensional value sets for the same conditions, and (c) derived list-based (extensional) value sets exactly equivalent to the hierarchy-based intensional value sets. Value set conciseness was assessed by the number of SNOMED CT concepts needed for definition. Time to construct the value sets for local use was measured. Value set completeness was assessed by comparing contents of the downloaded extensional vs. intensional value sets. Two measures of content completeness were made: for individual SNOMED CT concepts, and for the mapped diagnosis clinical terms available for selection within the EHR by clinicians. Results: The 10 hierarchy-based intensional value sets proved far simpler and faster to construct than exactly equivalent derived extensional value set lists, requiring a median 3 vs. 78 concepts to define, and 5 vs. 37 min to build. The hierarchy-based intensional value sets also proved more complete: in comparison, the 10 downloaded 2018 extensional value sets contained a median of just 35% of the intensional value sets' SNOMED CT concepts and 65% of mapped EHR clinical terms. Conclusions: In the EHR era, defining conditions preferentially should employ SNOMED CT concept hierarchy-based (intensional) value sets, rather than 1 extensional lists. By doing so, clinical guideline and eCQM authors can more readily engage specialists in vetting condition subtypes to include and exclude, and streamline broad EHR implementation of condition-specific ...

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“…The approach continues the tradition of past reviews of the IMIA Yearbook by focusing on a relatively small number of publications that are representative of current work in clinical research informatics [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Clinical Research Informatics: Supporting the Research Study Lifecycle

Johnson¹

2017

Yearb Med Inform

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SummaryObjectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years [2015][2016]. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.

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Clinical Research Informatics and Electronic Health Record Data

Cited by 36 publications

References 86 publications

Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation

Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation

SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets (Preprint)

Clinical Research Informatics: Supporting the Research Study Lifecycle

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