Clinical decision support with automated text processing for cervical cancer screening

Objective To develop a computerized clinical decision support system (CDSS) for cervical cancer screening that can interpret free-text Papanicolaou (Pap) reports. Materials and 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. Results and Discussion 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. Limitations Single institution and single expert study. Conclusion 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.

[1]  Alan R. Aronson,et al.  Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program , 2001, AMIA.

[2]  Steven H. Brown,et al.  Automated identification of postoperative complications within an electronic medical record using natural language processing. , 2011, JAMA.

[3]  Jonathan M. Teich,et al.  Grand challenges in clinical decision support , 2008, J. Biomed. Informatics.

[4]  Diane Solomon,et al.  American Cancer Society Guideline for the Early Detection of Cervical Neoplasia and Cancer , 2003, Journal of lower genital tract disease.

[5]  Screening for cervical cancer: recommendations and rationale. , 2003, The American journal of nursing.

[6]  S Kamen,et al.  The task force. , 1976, Journal of hospital dental practice.

[7]  M. Gokhale,et al.  Tracking Abnormal Cervical Cancer Screening: Evaluation of an EMR-Based Intervention , 2010, Journal of General Internal Medicine.

[8]  Richard P. Moser,et al.  Adherence to cervical cancer screening guidelines for U.S. women aged 25-64: data from the 2005 Health Information National Trends Survey (HINTS). , 2009, Journal of women's health.

[9]  George Hripcsak,et al.  Review Paper: Detecting Adverse Events Using Information Technology , 2003, J. Am. Medical Informatics Assoc..

[10]  Li Zhi-gang,et al.  2006 Consensus Guidelines for the Management of Women with Abnormal Cervical Cancer Screening Tests , 2008 .

[11]  John F. Hurdle,et al.  Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research , 2008, Yearbook of Medical Informatics.

[12]  Robert A. Greenes,et al.  Clinical Decision Support: The Road Ahead , 2006 .

[13]  Peter J. Haug,et al.  Combining decision support methodologies to diagnose pneumonia , 2001, AMIA.

[14]  Craig E. Kuziemsky,et al.  A review on systematic reviews of health information system studies , 2010, J. Am. Medical Informatics Assoc..

[15]  D T Janerich,et al.  The screening histories of women with invasive cervical cancer, Connecticut. , 1995, American journal of public health.

[16]  Sunghwan Sohn,et al.  Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications , 2010, J. Am. Medical Informatics Assoc..

[17]  Ninad K. Mishra,et al.  Research Paper: A Rule-based Approach for Identifying Obesity and Its Comorbidities in Medical Discharge Summaries , 2009, J. Am. Medical Informatics Assoc..

[18]  P. Effler,et al.  Statewide system of electronic notifiable disease reporting from clinical laboratories: comparing automated reporting with conventional methods. , 1999, JAMA.

[19]  Richard N. Shiffman,et al.  Accuracy of a computerized clinical decision-support system for asthma assessment and management , 2011, J. Am. Medical Informatics Assoc..

[20]  Mark Sherman,et al.  The 2001 Bethesda System: terminology for reporting results of cervical cytology. , 2002, JAMA.

[21]  Diane Solomon,et al.  2006 consensus guidelines for the management of women with abnormal cervical cancer screening tests. , 2007, American journal of obstetrics and gynecology.

[22]  A. Jha,et al.  The promise of electronic records: around the corner or down the road? , 2011, JAMA.

[23]  Rajeev Chaudhry,et al.  Use of a Web-based clinical decision support system to improve abdominal aortic aneurysm screening in a primary care practice , 2012, Journal of evaluation in clinical practice.

[24]  Amy M Karch,et al.  Acetaminophen's hidden dangers. , 2003, The American journal of nursing.

[25]  A. Berg,et al.  U.S. Preventive services task force. Behavioral counseling in primary care to promote physical activity: recommendation and rationale. , 2003, The American journal of nursing.

[26]  Scott T. Weiss,et al.  Characterization of Patients who Suffer Asthma Exacerbations using Data Extracted from Electronic Medical Records , 2008, AMIA.

[27]  Carol Friedman,et al.  A broad-coverage natural language processing system , 2000, AMIA.

[28]  E. Behan,et al.  Health Care Providers , 2012 .

[29]  V. Benard,et al.  Human papillomavirus and Papanicolaou tests screening interval recommendations in the United States. , 2011, American journal of obstetrics and gynecology.

[30]  Zahava Berkowitz,et al.  Cervical cancer screening with both human papillomavirus and Papanicolaou testing vs Papanicolaou testing alone: what screening intervals are physicians recommending? , 2010, Archives of internal medicine.

[31]  ACOG Practice Bulletin no. 109: Cervical cytology screening. , 2009, Obstetrics and gynecology.

[32]  Kavishwar B. Wagholikar,et al.  Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions , 2012, Journal of Medical Systems.

[33]  Zahava Berkowitz,et al.  Low-Risk Human Papillomavirus Testing and Other Nonrecommended Human Papillomavirus Testing Practices Among U.S. Health Care Providers , 2011, Obstetrics and gynecology.

[34]  David W. Bates,et al.  Guided medication dosing for elderly emergency patients using real-time, computerized decision support , 2012, J. Am. Medical Informatics Assoc..

[35]  Peter L. Elkin,et al.  The introduction of a diagnostic decision support system (DXplainTM) into the workflow of a teaching hospital service can decrease the cost of service for diagnostically challenging Diagnostic Related Groups (DRGs) , 2010, Int. J. Medical Informatics.

[36]  Galia Angelova,et al.  Contextualization in Automatic Extraction of Drugs from Hospital Patient Records , 2011, MIE.

[37]  I. Kohane,et al.  Electronic medical records for discovery research in rheumatoid arthritis , 2010, Arthritis care & research.

[38]  Stéfan Jacques Darmoni,et al.  Evaluation of natural language processing from emergency department computerized medical records for intra-hospital syndromic surveillance , 2011, BMC Medical Informatics Decis. Mak..

[39]  Ming Li,et al.  Natural Language Processing Improves Identification of Colorectal Cancer Testing in the Electronic Medical Record , 2012, Medical decision making : an international journal of the Society for Medical Decision Making.

[40]  M. Manos,et al.  Cervical cancer in women with comprehensive health care access: attributable factors in the screening process. , 2005, Journal of the National Cancer Institute.

[41]  George Hripcsak,et al.  Automated detection of adverse events using natural language processing of discharge summaries. , 2005, Journal of the American Medical Informatics Association : JAMIA.

[42]  Nancy M. Lorenzi,et al.  Social, organizational, and contextual characteristics of clinical decision support systems for intensive insulin therapy: A literature review and case study , 2010, Int. J. Medical Informatics.

[43]  Lucila Ohno-Machado,et al.  Natural language processing: an introduction , 2011, J. Am. Medical Informatics Assoc..

[44]  S. Taplin,et al.  Factors in quality care--the case of follow-up to abnormal cancer screening tests--problems in the steps and interfaces of care. , 2010, Journal of the National Cancer Institute. Monographs.

[45]  Hong Yu,et al.  Lancet: a high precision medication event extraction system for clinical text , 2010, J. Am. Medical Informatics Assoc..

[46]  U. P. S. T. Force,et al.  Screening for cervical cancer: recommendations and rationale. , 2003, American family physician.

[47]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[48]  Sarah P Zuber,et al.  Screening for Cervical Cancer , 2012 .

[49]  C. Wheeler,et al.  Natural history of human papillomavirus infections, cytologic and histologic abnormalities, and cancer. , 2008, Obstetrics and gynecology clinics of North America.

[50]  David A Haggstrom,et al.  Specialty Differences in Primary Care Physician Reports of Papanicolaou Test Screening Practices: A National Survey, 2006 to 2007 , 2009, Annals of Internal Medicine.

[51]  R. Lazarus,et al.  Viewpoint Paper: Electronic Support for Public Health: Validated Case Finding and Reporting for Notifiable Diseases Using Electronic Medical Data , 2009, J. Am. Medical Informatics Assoc..

[52]  Clement J. McDonald,et al.  What can natural language processing do for clinical decision support? , 2009, J. Biomed. Informatics.