title: Clinical decision support with automated text processing for cervical cancer screening.
abstract: OBJECTIVE
  To develop a computerized clinical decision support system (CDSS) for cervical cancer screening that can interpret free-text Papanicolaou (Pap) reports.
  
  
  METHODS
  The CDSS was constituted by two rulebases: the free-text rulebase for interpreting Pap reports and a guideline rulebase. The free-text rulebase was developed by analyzing a corpus of 49 293 Pap reports. The guideline rulebase was constructed using national cervical cancer screening guidelines. The CDSS accesses the electronic medical record (EMR) system to generate patient-specific recommendations. For evaluation, the screening recommendations made by the CDSS for 74 patients were reviewed by a physician.
  
  
  CONCLUSIONS
  Evaluation revealed that the CDSS outputs the optimal screening recommendations for 73 out of 74 test patients and it identified two cases for gynecology referral that were missed by the physician. The CDSS aided the physician to amend recommendations in six cases. The failure case was because human papillomavirus (HPV) testing was sometimes performed separately from the Pap test and these results were reported by a laboratory system that was not queried by the CDSS. Subsequently, the CDSS was upgraded to look up the HPV results missed earlier and it generated the optimal recommendations for all 74 test cases.
  
  
  CONCLUSIONS
  Single institution and single expert study.
  
  
  CONCLUSIONS
  An accurate CDSS system could be constructed for cervical cancer screening given the standardized reporting of Pap tests and the availability of explicit guidelines. Overall, the study demonstrates that free text in the EMR can be effectively utilized through natural language processing to develop clinical decision support tools.
authors.affiliation: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota 55905, USA. waghsk@gmail.com
authors.forename: Kavishwar B
authors.initials: KB
authors.lastname: Wagholikar
authors.forename: Kathy L
authors.initials: KL
authors.lastname: MacLaughlin
authors.forename: Michael R
authors.initials: MR
authors.lastname: Henry
authors.forename: Robert A
authors.initials: RA
authors.lastname: Greenes
authors.forename: Ronald A
authors.initials: RA
authors.lastname: Hankey
authors.forename: Hongfang
authors.initials: H
authors.lastname: Liu
authors.forename: Rajeev
authors.initials: R
authors.lastname: Chaudhry
mesh_terms: D000368:Aged; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008403:Mass Screening; D009323:Natural Language Processing; D065006:Papanicolaou Test; D012680:Sensitivity and Specificity; D002583:Uterine Cervical Neoplasms / Q000517:prevention & control*; D014626:Vaginal Smears