Abstract
BackgroundThe use of big data and machine learning within clinical decision support systems (CDSSs) has the potential to transform medicine through better prognosis, diagnosis and automation of tasks. Real-time application of machine learning algorithms, however, is dependent on data being present and entered prior to, or at the point of, CDSS deployment. Our aim was to determine the feasibility of automating CDSSs within electronic health records (EHRs) by investigating the timing, data categorization, and completeness of documentation of their individual components of two common Clinical Decision Rules (CDRs) in the Emergency Department.MethodsThe CURB-65 severity score and HEART score were randomly selected from a list of the top emergency medicine CDRs. Emergency department (ED) visits with ICD-9 codes applicable to our CDRs were eligible. The charts were reviewed to determine the categorization components of the CDRs as structured and/or unstructured, median times of documentation, portion of charts with all data components documented as structured data, portion of charts with all structured CDR components documented before ED departure. A kappa score was calculated for interrater reliability.ResultsThe components of the CDRs were mainly documented as structured data for the CURB-65 severity score and HEART score. In the CURB-65 group, 26.8% of charts had all components documented as structured data, and 67.8% in the HEART score. Documentation of some CDR components often occurred late for both CDRs. Only 21 and 11% of patients had all CDR components documented as structured data prior to ED departure for the CURB-65 and HEART score groups, respectively. The interrater reliability for the CURB-65 score review was 0.75 and 0.65 for the HEART score.ConclusionOur study found that EHRs may be unable to automatically calculate popular CDRs—such as the CURB-65 severity score and HEART score—due to missing components and late data entry.
Highlights
The use of big data and machine learning within clinical decision support systems (CDSSs) has the potential to transform medicine through better prognosis, diagnosis and automation of tasks
One hundred and forty-five charts were reviewed for the CURB-65 rule and HEART score, each
The HEART score was able to be calculated 67.8% of the time and the CURB-65 rule 26.8% of the time from the provided structured data
Summary
The use of big data and machine learning within clinical decision support systems (CDSSs) has the potential to transform medicine through better prognosis, diagnosis and automation of tasks. Our aim was to determine the feasibility of automating CDSSs within electronic health records (EHRs) by investigating the timing, data categorization, and completeness of documentation of their individual components of two common Clinical Decision Rules (CDRs) in the Emergency Department. Real-time application of machine learning algorithms, is dependent on data being present and entered prior to, or at the point of, CDSS deployment [3]. While simple CDSSs may request the user to enter in data necessary to run the algorithm, this process becomes infeasible when tens or even hundreds of data elements are needed, requiring some form of automated capture and real-time integration of data from the electronic health record (EHR) [4]. There is limited knowledge on when components necessary for calculation of CDSS in the ED are available in the EHR, what data type (structured vs unstructured) they exist in, and data completeness for specific algorithms [7]
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