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.


Workflow and data relationships. Patient interactions with the care providers are depicted on the right side, leading to creation of data, which need to be collectively analyzed for decision making.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4214690&req=5

f2-cin-suppl.3-2014-001: Workflow and data relationships. Patient interactions with the care providers are depicted on the right side, leading to creation of data, which need to be collectively analyzed for decision making.

Mentions: To ensure accuracy of data extraction, reconciliation is performed across multiple sources.21Figure 2 summarizes the information sources. For example, the history of hysterectomy that determines if a patient should have cervical, vaginal, or no cytology testing is determined by searching for hysterectomy in surgical reports, problem list, clinical notes, and annual survey responses. Similarly, history of cervical cancer is inferred from problem list, searching cervical cytology reports, and the patient survey information.


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)

Workflow and data relationships. Patient interactions with the care providers are depicted on the right side, leading to creation of data, which need to be collectively analyzed for decision making.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2-cin-suppl.3-2014-001: Workflow and data relationships. Patient interactions with the care providers are depicted on the right side, leading to creation of data, which need to be collectively analyzed for decision making.
Mentions: To ensure accuracy of data extraction, reconciliation is performed across multiple sources.21Figure 2 summarizes the information sources. For example, the history of hysterectomy that determines if a patient should have cervical, vaginal, or no cytology testing is determined by searching for hysterectomy in surgical reports, problem list, clinical notes, and annual survey responses. Similarly, history of cervical cancer is inferred from problem list, searching cervical cytology reports, and the patient survey information.

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.