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Population of the information model is also helped by defining value sets of permitted concepts. For example,
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| CapRefId | lung-site-value-set |
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| CapRefType | Figure |
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below shows a list of concepts which are considered legitimate finding sites for lung nodules.
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| CapId | lung-site-value-set |
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| CapType | Figure |
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Lung site value set |
In cases where the lexical forms are required by NLU and not represented in SNOMED CT, a synonym may be added. Postcoordination may also be used to define new clinical ideas. Value sets based on SNOMED CT content are used to gather evidence of clinical confidence for a specific condition. For example, "Signs and Symptoms of Exacerbation of Congestive Heart Failure (CHF)" is a value set which leverages the SNOMED CT hierarchy. 49 SNOMED CT concepts and their descendants are used to define this value set, a fragment of which is shown in
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| CapRefId | signs-and-symptoms-of-exacerbation-of-chf-value-set |
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| CapRefType | Figure |
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below.
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| CapId | signs-and-symptoms-of-exacerbation-of-chf-value-set |
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| CapType | Figure |
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Signs and symptoms of exacerbation of CHF value set |
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CDS interventions are presented to clinicians via web-based applications, such as Fluency for ImagingTM (FFI), M*Modal's speech reporting and workflow management solution. Computer-Assisted Physician Documentation (CAPD) technology presents radiologists with real-time, automated quality-improvement feedback. In
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| CapRefId | m-modal-fluency-for-imaging-application-with-cds-feedback-through-capd-technology |
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| CapRefType | Figure |
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below, a clinical decision support notification is presented to a radiologist while dictating a CT lung screening study which had the same finding as described in
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| CapRefId | finding-information-model-using-snomed-ct-concepts |
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| CapRefType | Figure |
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above. The clinical reasoning reaches a conclusion that the patient has “
Unchanged solid nodule < 6mm” which is classified as “LungRads Category 2” based on
American College of Radiologists (ACR) recommendations and follow-up of “
Repeat (low dose CT scan) LDCT in 6 months”.
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| CapId | m-modal-fluency-for-imaging-application-with-cds-feedback-through-capd-technology |
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| CapType | Figure |
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M*Modal Fluency for Imaging application with CDS feedback through CAPD technology |
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| CapRefId | critical-finding-dashboard-showing-compliant-and-non-compliant-reporting-of-critical-findings-found |
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| CapRefType | Figure |
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below is a screen capture of a dashboard which indicates how often a particular critical finding was found, and how often the radiologist fulfilled the follow up requirements. All critical findings in this application are identified using SNOMED CT codes defined in a value set.
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| CapId | critical-finding-dashboard-showing-compliant-and-non-compliant-reporting-of-critical-findings-found |
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| CapType | Figure |
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Critical finding dashboard showing compliant and non-compliant reporting of critical findings found |
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The CDI use case leverages M*Modal's CDS reasoning layer to identify encounters that do not capture clinical diagnosis at the appropriate level of specificity in concepts such as laterality, acuity, severity, episode of care, risk of mortality, etc., to support ICD-10 and quality requirements. The M*Modal system delivers these insights to CDI Specialists in the back end to automate chart review while also generating summarized supporting evidence of clinical indicators that include information on vital signs, labs, symptoms/diseases, medications, social history, etc. as shown in
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| CapRefId | m-modal-evidence-sheet-of-clinical-indicators-to-support-the-back-end-cdi-specialist-workflow |
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below.
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| CapId | m-modal-evidence-sheet-of-clinical-indicators-to-support-the-back-end-cdi-specialist-workflow |
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| CapType | Figure |
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M*Modal evidence sheet of clinical indicators to support the back-end CDI Specialist workflow |
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