Browsing by Subject "informatics"

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    A Corpus-Driven Standardization Framework for Encoding Clinical Problems with SNOMED CT Expressions and HL7 FHIR
    (2020-12) Peterson, Kevin
    Free-text clinical problem descriptions are used throughout the medical record to communicate patients’ pertinent conditions. These summary-level representations of diagnoses and other clinical concerns underpin critical aspects of the modern patient record such as the problem list, and are key inputs to predictive models and clinical decision support applications. Given their importance to both clinical care and downstream analytics, representations of these clinical problems must be amenable to both human interpretation and machine processing. While free-text is expressive and provides the most transparent and unbiased view into the intent of the clinician, standardized and consistent representations of the semantics of these problem descriptions are necessary for contemporary data-driven healthcare systems. Free-text problem descriptions may be standardized and structured in a variety of ways. First, they may be encoded using a controlled terminology such as Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT). Even though a single code may inadequately capture the context, modifiers, and related information of a problem, codes may be combined, or “post-coordinated” into more complex structures called SNOMED CT Expressions. Next, alignment to standardized semantic and data models such as Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) allows for the most structured representation, but with higher implementation complexity. Competing usage priorities introduce a fundamental optimization problem in representing these entries – free-text is the most natural and useful form for clinicians, while structured and codified forms are computable and better suited for data analytics and interoperability. In this study, we introduce methods to minimize this conflict between structured and unstructured forms by proposing a framework for capturing the semantics of free-text clinical problems and transforming them into codified, structured formats using Natural Language Processing (NLP) techniques.
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    A Data Quality Framework for the Secondary Use of Electronic Health Information
    (2016-04) Johnson, Steven
    Electronic health record (EHR) systems are designed to replace paper charts and facilitate the delivery of care. Since EHR data is now readily available in electronic form, it is increasingly used for other purposes. This is expected to improve health outcomes for patients; however, the benefits will only be realized if the data that is captured in the EHR is of sufficient quality to support these secondary uses. This research demonstrated that a healthcare data quality framework can be developed that produces metrics that characterize underlying EHR data quality and it can be used to quantify the impact of data quality issues on the correctness of the intended use of the data. The framework described in this research defined a Data Quality (DQ) Ontology and implemented an assessment method. The DQ Ontology was developed by mining the healthcare data quality literature for important terms used to discuss data quality concepts and these terms were harmonized into an ontology. Four high-level data quality dimensions (CorrectnessMeasure, ConsistencyMeasure, CompletenessMeasure and CurrencyMeasure) categorized 19 lower level Measures. The ontology serves as an unambiguous vocabulary and allows more precision when discussing healthcare data quality. The DQ Ontology is expressed with sufficient rigor that it can be used for logical inference and computation. The data quality framework was used to characterize data quality of an EHR for 10 data quality Measures. The results demonstrate that data quality can be quantified and Metrics can track data quality trends over time and for specific domain concepts. The DQ framework produces scalar quantities which can be computed on individual domain concepts and can be meaningfully aggregated at different levels of an information model. The data quality assessment process was also used to quantify the impact of data quality issues on a task. The EHR data was systematically degraded and a measure of the impact on the correctness of CMS178 eMeasure (Urinary Catheter Removal after Surgery) was computed. This information can help healthcare organizations prioritize data quality improvement efforts to focus on the areas that are most important and determine if the data can support its intended use.