Automated recommendation for cervical cancer screening and surveillance

Kavishwar B. Wagholikar, Kathy L. Aclaughlin, Petra Casey, Thomas Kastner, Michael Henry, Ronald Hankey, Steve G. Peters, Robert Greenes, Christopher G. Chute, Hongfang Liu, Rajeev Chaudhry

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
JournalCancer Informatics
StatePublished - Oct 15 2014


  • Cervical cancer
  • Clinical decision support
  • Colposcopy
  • Natural language processing
  • Papanicolaou test

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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