Investigation of Data Representation Issues in Computerizing Clinical Practice Guidelines in China
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Objectives: From the point of view of clinical data representation, this study attempted to identify obstacles in translating clinical narrative guidelines into computer interpretable format and integrating the guidelines with data in Electronic Health Records in China. Methods: Based on SAGE and K4CARE formulism, a Chinese clinical practice guideline for hypertension was modeled in Protégé by building an ontology that had three components: flowchart, node, and vMR. Meanwhile, data items imperative in Electronic Health Records for patients with hypertension were reviewed and compared with those from the ontology so as to identify conflicts and gaps between. Results: A set of flowcharts was built. A flowchart comprises three kinds of node: State, Decision, and Act, each has a set of attributes, including data input/output that exports data items, which then were specified following ClinicalStatement of HL7 vMR. A total of 140 data items were extracted from the ontology. In modeling the guideline, some narratives were found too inexplicit to formulate, and encoding data was quite difficult. Additionally, it was found in the healthcare records that there were 8 data items left out, and 10 data items defined differently compared to the extracted data items. Conclusions: The obstacles in modeling a clinical guideline and integrating with data in Electronic Health Records include narrative ambiguity of the guideline, gaps and inconsistencies in representing some data items between the guideline and the patient’ records, and unavailability of a unified medical coding system. Therefore, collaborations among various participants in developing guidelines and Electronic Health Record specifications is needed in China.
Abstract
Objectives: From the point of view of clinical data representation, this study attempted to identify obstacles in translating clinical narrative guidelines into computer interpretable format and integrating the guidelines with data in Electronic Health Records in China. Methods: Based on SAGE and K4CARE formulism, a Chinese clinical practice guideline for hypertension was modeled in Protégé by building an ontology that had three components: flowchart, node, and vMR. Meanwhile, data items imperative in Electronic Health Records for patients with hypertension were reviewed and compared with those from the ontology so as to identify conflicts and gaps between. Results: A set of flowcharts was built. A flowchart comprises three kinds of node: State, Decision, and Act, each has a set of attributes, including data input/output that exports data items, which then were specified following ClinicalStatement of HL7 vMR. A total of 140 data items were extracted from the ontology. In modeling the guideline, some narratives were found too inexplicit to formulate, and encoding data was quite difficult. Additionally, it was found in the healthcare records that there were 8 data items left out, and 10 data items defined differently compared to the extracted data items. Conclusions: The obstacles in modeling a clinical guideline and integrating with data in Electronic Health Records include narrative ambiguity of the guideline, gaps and inconsistencies in representing some data items between the guideline and the patient’ records, and unavailability of a unified medical coding system. Therefore, collaborations among various participants in developing guidelines and Electronic Health Record specifications is needed in China.
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