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Standardizing Clinical Diagnoses: Evaluating Alternate Terminology Selection.

ABSTRACT: In most electronic health record (EHR) systems, clinicians record diagnoses using interface terminologies, such as Intelligent Medical Objects (IMO). When extracting data from EHRs for collaborative research, local codes are often transformed to standard terminologies for consistent analyses despite the potential for loss of fidelity. EHR diagnosis codes may be standardized directly during the Extract-Transform-Load (ETL) process to the "Meaningful Use" clinical data exchange standard, SNOMED-CT, or to the International Classification of Diseases (ICD) terminologies commonly used for billing. We examined the performance of ETL standardization via the direct IMO mapping to SNOMED-CT, and via IMO mapping to ICD-9-CM or ICD-10-CM followed by UMLS mapping to SNOMED-CT. We found that for both ICD-9-CM and ICD-10-CM, only 24-27% of diagnosis codes map to the same SNOMED-CT code selected by the direct IMO-SNOMED crosswalk. We identified that differences in mapping lead to loss in the granularity and laterality of the initial diagnosis.

SUBMITTER: Burrows EK

PROVIDER: S-EPMC7233070 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Standardizing Clinical Diagnoses: Evaluating Alternate Terminology Selection.

Burrows Evanette K EK Razzaghi Hanieh H Utidjian Levon L Bailey L Charles LC

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science 20200530

In most electronic health record (EHR) systems, clinicians record diagnoses using interface terminologies, such as Intelligent Medical Objects (IMO). When extracting data from EHRs for collaborative research, local codes are often transformed to standard terminologies for consistent analyses despite the potential for loss of fidelity. EHR diagnosis codes may be standardized directly during the Extract-Transform-Load (ETL) process to the "Meaningful Use" clinical data exchange standard, SNOMED-CT ...[more]

PMID: 32477625

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Project description:Context.—Lower Anogenital Squamous Terminology (LAST) standardization recommended p16INK4a immunohistochemistry (p16 IHC) for biopsies diagnosed morphologically as cervical intraepithelial neoplasia (CIN) grade 2 (CIN2) to classify them as low-grade or high-grade squamous intraepithelial lesions (HSILs).Objective.—To describe the relationships of p16 IHC and other biomarkers associated with cervical cancer risk with biopsy diagnoses.Design.—A statewide, stratified sample of cervical biopsies diagnosed by community pathologists (CPs), including 1512 CIN2, underwent a consensus, expert pathologist panel (EP) review (without p16 IHC results), p16 IHC interpretation by a third pathology group, and human papillomavirus (HPV) genotyping, results of which were grouped hierarchically according to cancer risk. Antecedent cytologic interpretations were also available.Results.—Biopsies were more likely to test p16 IHC positive with increasing severity of CP diagnoses, overall (Ptrend ≤ .001) and within each HPV risk group (Ptrend ≤ .001 except for low-risk HPV [Ptrend < .010]). All abnormal grades of CP-diagnosed biopsies were more likely to test p16 IHC positive with a higher HPV risk group (Ptrend < .001), and testing p16 IHC positive was associated with higher HPV risk group than testing p16 IHC negative for each grade of CP-diagnosed biopsies (P < .001). p16 IHC-positive, CP-diagnosed CIN2 biopsies were less likely than CP-diagnosed CIN3 biopsies to test HPV16 positive, have an antecedent HSIL+ cytology, or to be diagnosed as CIN3+ by the EP (P < .001 for all). p16 IHC-positive, CP-diagnosed CIN1 biopsies had lower HPV risk groups than p16 IHC-negative, CP-diagnosed CIN2 biopsies (P < .001).Conclusions.—p16 IHC-positive, CP-diagnosed CIN2 appears to be lower cancer risk than CP-diagnosed CIN3. LAST classification of "HSIL" diagnosis, which includes p16 IHC-positive CIN2, should annotate the morphologic diagnosis (CIN2 or CIN3) to inform all management decisions, which is especially important for young (<30 years) women diagnosed with CIN2 for whom surveillance rather than treatment is recommended.

Project description:BackgroundWith the globalization of clinical trials, a growing emphasis has been placed on the standardization of the workflow in order to ensure the reproducibility and reliability of the overall trial. Despite the importance of workflow evaluation, to our knowledge no previous studies have attempted to adapt existing modeling languages to standardize the representation of clinical trials. Unified Modeling Language (UML) is a computational language that can be used to model operational workflow, and a UML profile can be developed to standardize UML models within a given domain. This paper's objective is to develop a UML profile to extend the UML Activity Diagram schema into the clinical trials domain, defining a standard representation for clinical trial workflow diagrams in UML.MethodsTwo Brazilian clinical trial sites in rheumatology and oncology were examined to model their workflow and collect time-motion data. UML modeling was conducted in Eclipse, and a UML profile was developed to incorporate information used in discrete event simulation software.ResultsEthnographic observation revealed bottlenecks in workflow: these included tasks requiring full commitment of CRCs, transferring notes from paper to computers, deviations from standard operating procedures, and conflicts between different IT systems. Time-motion analysis revealed that nurses' activities took up the most time in the workflow and contained a high frequency of shorter duration activities. Administrative assistants performed more activities near the beginning and end of the workflow. Overall, clinical trial tasks had a greater frequency than clinic routines or other general activities.ConclusionsThis paper describes a method for modeling clinical trial workflow in UML and standardizing these workflow diagrams through a UML profile. In the increasingly global environment of clinical trials, the standardization of workflow modeling is a necessary precursor to conducting a comparative analysis of international clinical trials workflows.

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Dataset Information

Standardizing Clinical Diagnoses: Evaluating Alternate Terminology Selection.

Publications

Standardizing Clinical Diagnoses: Evaluating Alternate Terminology Selection.

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