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Automated recommendation for cervical cancer screening and surveillance.

Wagholikar KB, MacLaughlin KL, Casey PM, Kastner TM, Henry MR, Hankey RA, Peters SG, Greenes RA, Chute CG, Liu H, Chaudhry R - Cancer Inform (2014)

Bottom Line: The CDS system extracted relevant patient information from the electronic health record and applied the guideline model with an overall accuracy of 87%.Providers without CDS assistance needed an average of 1 minute 39 seconds to decide on recommendations for management of abnormal findings.Overall, our work demonstrates the feasibility and potential utility of automated recommendation system for cervical cancer screening and surveillance.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Statistics and Informatics, Mayo Clinic Rochester, MN, USA.

ABSTRACT
Because of the complexity of cervical cancer prevention guidelines, clinicians often fail to follow best-practice recommendations. Moreover, existing clinical decision support (CDS) systems generally recommend a cervical cytology every three years for all female patients, which is inappropriate for patients with abnormal findings that require surveillance at shorter intervals. To address this problem, we developed a decision tree-based CDS system that integrates national guidelines to provide comprehensive guidance to clinicians. Validation was performed in several iterations by comparing recommendations generated by the system with those of clinicians for 333 patients. The CDS system extracted relevant patient information from the electronic health record and applied the guideline model with an overall accuracy of 87%. Providers without CDS assistance needed an average of 1 minute 39 seconds to decide on recommendations for management of abnormal findings. Overall, our work demonstrates the feasibility and potential utility of automated recommendation system for cervical cancer screening and surveillance.

No MeSH data available.


A portion of the unified guideline flowchart (attached as Supplementary File).
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Related In: Results  -  Collection


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f1-cin-suppl.3-2014-001: A portion of the unified guideline flowchart (attached as Supplementary File).

Mentions: We reviewed guidelines from the American Cancer Society (ACS), US Preventive Services Task Force (USPSTF), American College of Obstetricians and Gynecologists (ACOG), and the ASCCP.16–20 While the ACS, USPSTF, and ACOG guidelines cover screening, ASCCP guidelines provide the recommendations for surveillance. We constructed a “unified guideline model” as a flow diagram (Fig. 1), which conglomerates the logic embedded in the relevant guidelines. The flow diagram consists of nodes and edges – the nodes represent concepts in patient information (eg, age) and the edges correspond to the possible values of the concepts (eg, age > 30 years). The flow diagram has an inverted tree structure, starting at a single node and ending in multiple leaf nodes. Multiple paths can be traced from the starting node to terminate in the leaf nodes, and each path corresponds to a unique patient scenario encountered in clinical practice. We modified the unified model during our study to incorporate updates to the national guidelines. The latest version of the model is attached in Supplementary File 1.


Automated recommendation for cervical cancer screening and surveillance.

Wagholikar KB, MacLaughlin KL, Casey PM, Kastner TM, Henry MR, Hankey RA, Peters SG, Greenes RA, Chute CG, Liu H, Chaudhry R - Cancer Inform (2014)

A portion of the unified guideline flowchart (attached as Supplementary File).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4214690&req=5

f1-cin-suppl.3-2014-001: A portion of the unified guideline flowchart (attached as Supplementary File).
Mentions: We reviewed guidelines from the American Cancer Society (ACS), US Preventive Services Task Force (USPSTF), American College of Obstetricians and Gynecologists (ACOG), and the ASCCP.16–20 While the ACS, USPSTF, and ACOG guidelines cover screening, ASCCP guidelines provide the recommendations for surveillance. We constructed a “unified guideline model” as a flow diagram (Fig. 1), which conglomerates the logic embedded in the relevant guidelines. The flow diagram consists of nodes and edges – the nodes represent concepts in patient information (eg, age) and the edges correspond to the possible values of the concepts (eg, age > 30 years). The flow diagram has an inverted tree structure, starting at a single node and ending in multiple leaf nodes. Multiple paths can be traced from the starting node to terminate in the leaf nodes, and each path corresponds to a unique patient scenario encountered in clinical practice. We modified the unified model during our study to incorporate updates to the national guidelines. The latest version of the model is attached in Supplementary File 1.

Bottom Line: The CDS system extracted relevant patient information from the electronic health record and applied the guideline model with an overall accuracy of 87%.Providers without CDS assistance needed an average of 1 minute 39 seconds to decide on recommendations for management of abnormal findings.Overall, our work demonstrates the feasibility and potential utility of automated recommendation system for cervical cancer screening and surveillance.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Statistics and Informatics, Mayo Clinic Rochester, MN, USA.

ABSTRACT
Because of the complexity of cervical cancer prevention guidelines, clinicians often fail to follow best-practice recommendations. Moreover, existing clinical decision support (CDS) systems generally recommend a cervical cytology every three years for all female patients, which is inappropriate for patients with abnormal findings that require surveillance at shorter intervals. To address this problem, we developed a decision tree-based CDS system that integrates national guidelines to provide comprehensive guidance to clinicians. Validation was performed in several iterations by comparing recommendations generated by the system with those of clinicians for 333 patients. The CDS system extracted relevant patient information from the electronic health record and applied the guideline model with an overall accuracy of 87%. Providers without CDS assistance needed an average of 1 minute 39 seconds to decide on recommendations for management of abnormal findings. Overall, our work demonstrates the feasibility and potential utility of automated recommendation system for cervical cancer screening and surveillance.

No MeSH data available.