Improving the Accuracy of a Clinical Decision Support System for Cervical Cancer Screening and Surveillance

Abstract

Background: Clinical decision support systems (CDSS) for cervical cancer prevention are generally limited to identifying patients who are overdue for their next routine/next screening, and they do not provide recommendations for follow-up of abnormal results. We previously developed a CDSS to automatically provide follow-up recommendations based on the American Society of Colposcopy and Cervical Pathology (ASCCP) guidelines for women with both previously normal and abnormal test results leveraging information available in the electronic medical record (EMR).

Objective: Enhance the CDSS by improving its accuracy and incorporating changes to reflect the latest revision of the guidelines.

Methods: After making enhancements to the CDSS, we evaluated the performance of the clinical recommendations on 393 patients selected through stratified sampling from a set of 3,704 patients in a nonclinical setting. We performed chart review of individual patient's record to evaluate the performance of the system. An expert clinician assisted by a resident manually reviewed the recommendation made by the system and verified whether the recommendations were as per the ASCCP guidelines.

Results: The recommendation accuracy of the enhanced CDSS improved to 93%, which is a substantial improvement over the 84% reported previously. A detailed analysis of errors is presented in this article. We fixed the errors identified in this evaluation that were amenable to correction to further improve the accuracy of the system. The source code of the updated CDSS is available at https://github.com/ohnlp/MayoNlpPapCdss.

Conclusion: We made substantial enhancements to our earlier prototype CDSS with the updated ASCCP guidelines and performed a thorough evaluation in a nonclinical setting to improve the accuracy of the CDSS. The CDSS will be further refined as it is utilized in the practice.

Fig. 1

Cervical cancer screening and surveillance CDSS workflow. The workflow starts with a patient's clinic visit, which results in generation of different data sources. The system automatically reads from multiple sources of information and extracts primary data elements from individual documents. The data elements/variables are then reassembled across different time points. The decision logic rules apply on the temporal data elements to compute the care recommendation. The care recommendations are delivered to the point of care.

Fig. 2

Temporal assembly of primary and secondary data elements related to cervical cancer screening for a given patient.

Fig. 3

Example of a decision logic rule that computes care recommendation based on data elements and their respective values over a time period by CDSS. CDSS, clinical decision support systems.