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

Abstract

Keywords► clinical decision support system ► ambulatory care information systems ► testing and evaluation of health information technology ► electronic health records ► knowledge delivery ► knowledge management AbstractBackground 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.

Artificial intelligence in clinical decision support systems for oncology

“…And there is another angle that is Collaborative intelligence, human and AI working synergistically. With the rapid development of CDSS, the CDSS for cervical cancer prevention automatically provides recommendations, accuracy of which is improved to 93% vs. the expert clinician as per the American Society of Colposcopy and Cervical Pathology (ASCCP) guidelines 76 . some doctors blindly follow the suggestions of the CDSS and think it will replace them in the future.…”

A Clinical Decision Support System for Assessing the Risk of Cervical Cancer: Development and Evaluation Study

“…The first phase of the study included determining the essential parameters for assessing the risk of cervical cancer. Initially, a list of these parameters was provided based on literature reviews [4,15,[20][21][22][23][24]. Subsequently, 20 gynecologists completed a 5-point Likert scale questionnaire (very important=5, important=4, moderately important=3, slightly important=2, and unimportant=1) to determine the most important parameters included on the list.…”

“…In the field of oncology, CDSSs can help assess the risk of cancer development by using clinical data and quantifying the impact of cancer risk factors [ 14 ]. These systems can also support early disease detection and allow GPs to provide a care plan when specialists are not available [ 15 , 16 ]. Although some similar systems have been previously developed for cervical cancer risk assessment, the number and types of input and output variables and the types of rules and algorithms used are different.…”

Addressing the Crisis of Diagnosis and Management of Migraine in Primary Care: A Summary of the American Headache Society FrontLine Primary Care Advisory Board

“…Tools will likely need to be multimodal to accommodate different ways of learning. Examples might include podcasts, online programs, point of care applications (apps), pathways for non‐headache specialists similar to the American Society for Colposcopy and Cervical Pathology 8, articles for the society magazines with group specific resources, for example, AFP, AAN Neurobytes of hot topics (~5 minutes), and an ongoing e‐newsletter about headache updates. There are existing guidelines and tools that can be used to decrease the need to build the program without a foundation of existing materials…”