Limits...
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.


Sample explanations generated by the decision support system.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4214690&req=5

f4-cin-suppl.3-2014-001: Sample explanations generated by the decision support system.

Mentions: The institutional EHR interface was enhanced to instantly display the recommendation generated by the reminder system. As shown in Figure 3, the recommendation is displayed when the provider navigates to the pathology reports in the EHR. The explanation for the recommendation is also displayed to inform the provider. Figure 4 summarizes a sample of possible explanations. An icon in the reminder tab links to an institutional guideline repository, which also lists local experts for consultation or clarification. The button in the lower right corner opens a pop-up for provider feedback about the CDS recommendation.


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)

Sample explanations generated by the decision support system.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4-cin-suppl.3-2014-001: Sample explanations generated by the decision support system.
Mentions: The institutional EHR interface was enhanced to instantly display the recommendation generated by the reminder system. As shown in Figure 3, the recommendation is displayed when the provider navigates to the pathology reports in the EHR. The explanation for the recommendation is also displayed to inform the provider. Figure 4 summarizes a sample of possible explanations. An icon in the reminder tab links to an institutional guideline repository, which also lists local experts for consultation or clarification. The button in the lower right corner opens a pop-up for provider feedback about the CDS recommendation.

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.