Developing a natural language processing application for measuring the quality of colonoscopy procedures

Journal of the American Medical Informatics Association

Volumn 18, Issue SUPPL. 1, 2011, Pages 150-156

  Harkema, Henk ^a   Chapman, Wendy W ^b   Saul, Melissa ^a   Dellon, Evan S ^c   Schoen, Robert E ^a   Mehrotra, Ateev ^a  

^a UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; COLONOSCOPY; HUMAN; MEASUREMENT; NATURAL LANGUAGE PROCESSING; QUALITY CONTROL; RELIABILITY; COMPUTER SYSTEM; DATA MINING; HEALTH CARE QUALITY; STANDARD;

COLONOSCOPY; COMPUTER SYSTEMS; DATA MINING; HUMANS; NATURAL LANGUAGE PROCESSING; QUALITY OF HEALTH CARE;

EID: 84861530893     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2011-000431     Document Type: Article

References (73)

1. McGlynn EA, Asch SM, Adams J, et al. The quality of health care delivered to adults in the United States. N Engl J Med 2003;348:2635-45.
2. Chassin MR, Galvin RW. The urgent need to improve health care quality. Institute of Medicine National Roundtable on Health Care Quality. JAMA 1998;280:1000-5.
3. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: Institute of Medicine of The National Academies, 2001.
4. Performance Measurement: Accelerating Improvement. Washington, DC: Institute of Medicine of The National Academies, 2005.
5. Diamond CC, Rask KJ, Kohler SA. Use of paper medical records versus administrative data for measuring and improving health care quality: are we still searching for a gold standard? Dis Manag 2001;4:121-30.
6. Iezzoni LI. Assessing quality using administrative data. Ann Intern Med 1997;127:666-47.
7. Zhan C, Miller MR. Administrative data based patient safety research: a critical review. Qual Saf Health Care 2003;12(Suppl 2):ii58-63.
8. Pawlson LG, Scholle SH, Powers A. Comparison of administrative-only versus administrative plus chart review data for reporting HEDIS Hybrid Measures. Am J Manag Care 2007;13:553-8.
9. Lieberman DA. Pitfalls of using administrative data for research. Dig Dis Sci 2010;55:1721-5.
10. Tierney WM, Overhage JM, McDonald CJ. Toward electronic medical records that improve care. Ann Intern Med 1995;122:725-6.
11. Eddy DM. Performance measurement: problems and solutions. Health Aff (Millwood) 1998;17:7-25.
12. Bates DW, Gawande AA. Improving Safety with Information Technology. N Engl J Med 2003;348:2526-34.
13. Patient Safety: Achieving a New Standard for Care. Washington, DC: Institute of Medicine of The National Academies, 2004.
14. Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006;144:742-52.
15. Vogt TM, Ickin M, Ahmed F, et al. The prevention index: using technology to improve quality assessment. Health Serv Res 2004;39:511-30.
16. Persell SD, Kho AN, Thompson JA, et al. Improving hypertension quality measurement using electronic health records. Med Care 2009;47:388-94.
17. Tang PC, Ralston M, Arrigotti MF, et al. Comparison of methodologies for calculating quality measures based on administrative data versus clinical data from an electronic health record system: implications for performance measures. J Am Med Inform Assoc 2007;14:10-15.
18. O'Toole MF, Kmetik KS, Bossley H, et al. Electronic health record systems: the vehicle for implementing performance measures. Am Heart Hosp J 2005;3:88-93.
19. Baron JR. Quality improvement with an electronic health record: achievable, but not automatic. Ann Intern Med 2007;147:549-662.
20. Linder JA, Kaleba EO, Kmetik KS. Using electronic health records to measure physician performance for acute conditions in primary care: empirical evaluation of the community-acquired pneumonia clinical quality measure set. Med Care 2009;47:208-16.
21. Maddocks H, Marshall JN, Stewart M, et al. Quality of congestive heart failure care: assessing measurement of care using electronic medical records. Can Fam Physician 2010;56:e432-7.
22. Baker DW, Persell SD, Thompson JA, et al. Automated review of electronic health records to assess quality of care for outpatients with heart failure. Ann Intern Med 2007;146:270-7.
23. Waye JD, Rex DK, Williams CB. Colonoscopy: Principles and Practice. Malden, MA: John Wiley and Sons, 2009.
24. Jemal A, Siegel R, Xu J, et al. Cancer statistics, 2010. CA Cancer J Clin 2010;60:277-300.
25. Winawer SJ, Zauber AG, Ho MN, et al. Prevention of colorectal cancer by colonoscopic polypectomy. The National Polyp Study Workgroup. N Engl J Med 1993;329:1977-81.
26. Levin B, Lieberman DA, McFarland B, et al. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. Gastroenterology 2008;134:1570-95.
27. Phillips KA, Liang SY, Ladabaum U, et al. Trends in colonoscopy for colorectal cancer screening. Med Care 2007;45:160-7.
28. Kaminski MF, Regula J. Colorectal cancer screening by colonoscopydcurrent issues. Digestion 2007;76:20-5.
29. Chen S, Rex DK. Endoscopist is comparable to age and gender as predictor of adenomas at colonoscopy. Am J Gastroenterol 2005;100:S393.
30. Lieberman DA, Faigel DO, Logan JR, et al. Assessment of the quality of colonoscopy reports: results from a multicenter consortium. Gastrointest Endosc 2009;69:645-53.
31. Rex DK, Bond JH, Winawer S, et al. Quality in the technical performance of colonoscopy and the continuous quality improvement process for colonoscopy: recommendations of the U.S. Multi-Society Task Force on Colorectal Cancer. Am J Gastroenterol 2002;97:1296-308.
32. Kaminski MF, Regula J, Kraszewska E, et al. Quality indicators for colonoscopy and the risk of interval cancer. N Engl J Med 2010;362:1795-803.
33. Barclay RL, Vicari JJ, Doughty AS, et al. Colonoscopic withdrawal times and adenoma detection during screening colonoscopy. N Engl J Med 2006;355:2533-41.
34. Jurafsky D, Martin JH. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. 2nd edn. Upper Saddle River, NJ: Pearson-Prentice Hall, 2009.
35. Indurkhya N, Damerau FJ. Handbook of Natural Language Processing. 2nd edn. Boca Raton, FL: CRC Press, 2010.
36. Allen J. Natural Language Understanding. 2nd edn. Redwood City, CA: Benjamin/ Cummings, 1995.
37. Friedman C. A Broad-Coverage Natural Language Processing System. Proc AMIA Symp 2000:270-4.
38. Hazlehurst B, Frost HR, Sittig DF, et al. MediClass: A system for detecting and classifying encounter-based clinical events in any electronic medical record. J Am Med Inform Assoc 2005;12:517-29.
39. Christensen LM, Harkema H, Irwin J, et al. ONYX: a system for the semantic analysis of clinical text. Proc BioNLP 2009. Stroudsburg, PA: Association for Computational Linguistics, 2009:19-27.
40. Savova GK, Masanz JJ, Ogren PV, et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 2010;17:507-13.
41. Crowley RS, Castine M, Mitchell K, et al. caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research. J Am Med Inform Assoc 2010;17:253-64.
42. Zou Q, Chu WW, Morioka C, et al. IndexFinder: a method of extracting key concepts from clinical texts for indexing. AMIA Annu Symp Proc 2003:763-7.
43. Li D, Savova GK, Schuler KK. Conditional Random Fields and Support Vector Machines for Disorder Named Entity Recognition in Clinical Texts. Proc BioNLP 2008. Stroudsburg, PA: Association for Computational Linguistics, 2008:94-5.
44. Denny JC, Spickard A, Johnson KB, et al. Evaluation of a method to identify and categorize section headers in clinical documents. J Am Med Inform Assoc 2009;16:806-15.
45. Roberts A, Gaizauskas R, Hepple M. Extracting clinical relationships from patient narratives. Proc BioNLP 2008. Stroudsburg, PA: Association for Computational Linguistics, 2008:10-18.
46. Zhou L, Melton GB, Parsons S, et al. A temporal constraint structure for extracting temporal information from clinical narrative. J Biomed Inform 2006;39:424-39.
47. Harkema H, Dowling JW, Thornblade T, et al. ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports. J Biomed Inform 2009;42:839-51.
48. Wang X, Hripcsak G, Markatou M, et al. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc 2009;16:328-37.
49. Aramaki E, Miura Y, Tonoike M, et al. Extraction of adverse drug effects from clinical records. Stud Health Technol Inform 2010;160:739-43.
50. Pakhomov S, Weston SA, Jacobsen SJ, et al. electronic medical records for clinical research: application to the identification of heart failure. Am J Manag Care 2007;13 (6 Pt 1):281-8.
51. Al-Haddad MA, Friedlin J, Kesterson J, et al. Natural language processing for the development of a clinical registry: a validation study in intraductal papillary mucinous neoplasms. HPB (Oxford) 2010;12:688-95.
52. Li L, Chase HS, Patel CO, et al. Comparing ICD9-encoded diagnoses and NLPprocessed discharge summaries for clinical trials pre-screening: a case study. AMIA Anuu Symp Proc 2008:404-8.
53. Liu H, Friedman C. CliniViewer: a tool for viewing electronic medical records based on natural language processing and XML. Stud Health Technol Inform 2004;107:639-43.
54. Van Vleck TT, Elhadad N. Corpus-based problem selection for EHR note summarization. AMIA Annu Symp Proc 2010:817-21.
55. Chiang JH, Lin JW, Yang CW. Automated evaluation of electronic discharge notes to assess quality of care for cardiovascular diseases using Medical Language Extraction and Encoding System (MedLEE). J Am Med Inform Assoc 2010;17:245-52.
56. D'Avolio LW, Litwin MS, Rogers SO Jr, et al. Facilitating clinical outcomes assessment through the automated identification of quality measures for prostate cancer surgery. J Am Med Inform Assoc 2008;15:341-8.
57. Pakhomov S, Bjornsen S, Hanson P, et al. Quality performance measurement using the text of electronic medical records. Med Decis Making 2008;28:462-70.
58. D'Avolio LW, Nguyen TM, Farwell WR, et al. Evaluation of a generalizable approach to clinical information retrieval using the Automated Retrieval Console (ARC). J Am Med Inform Assoc 2010;17:375-83.
59. Denny JC, Choma NN, Peterson JF, et al. Natural Language Processing Improves Identification of Colorectal Cancer Testing in the Electronic Medical Record. Med Decis Making. Published Online First: 10 March 2011. doi:10.1177/ 0272989X11400418.
60. Denny JC, Peterson JF, Choma NN, et al. Extracting timing and status descriptors for colonoscopy testing from electronic medical records. J Am Med Inform Assoc 2010;17:383-8.
61. Rex DK, Petrini JL, Baron TH, et al. Quality indicators for colonoscopy. Gastrointest Endosc 2006;63(4 Suppl):S16-28.
62. Lieberman D, Nadel M, Smith RA, et al. Standardized colonoscopy reporting and data system: report of the quality assurance task group of the national colorectal cancer Roundtable. Gastrointest Endosc 2007;65:757-66.
63. Mehrotra A, Dellon ES, Schoen R, et al. Applying a natural language processing tool to electronic health records to measure the quality of colonoscopy procedures. (under review).
64. Yount RJ, Vries JK, Councill CD. The Medical Archival System: An Information Retrieval System Based on Distributed Parallel Processing. Inf Proc Manag 1991;27:379-89.
65. Dhir R, Patel AA, Winters S, et al. A Multidisciplinary Approach to Honest Broker Services for Tissue Banks and Clinical Data: A Pragmatic and Practical Model. Cancer. 2008;113(7):1705-15.
66. Cunningham H, Maynard D, Bontcheva K, et al. GATE: a framework and graphical development environment for robust NLP tools and applications. Proc 40th Anniversary Meeting of the Association for Computational Linguistics (ACL). Philadelphia, PA: 2002:168-75.
67. Chapman W, Conway M, Dowling J, et al. Challenges in adapting an NLP system for real-time surveillance. 9th Annual Conference of the International Society for Disease Surveillance. Park City, UT, 2010.
68. Chapman W, Harkema H. Identifying respiratory-related clinical conditions from ED reports with Topaz. Clin Med Res 2010;8:53.
69. Chu D. Clinical Feature Extraction from Emergency Department Reports for Biosurveillance [Master's Thesis]. Pittsburgh: University of Pittsburgh, 2007.
70. Lindberg DA, Humphreys BL, McCray AT. The unified medical language system. Methods Inf Med 1993;32:281-91.
71. Unified Medical Language System (UMLS) Documentation. Bethesda, MD: National Library of Medicine. http://www.nlm.nih.gov/research/umls/documentation.html.
72. Aronson AR. Effective mapping of biomedical text to the UMLS Metathesaurus: The MetaMap program. Proc AMIA Symp 2001:17-21.
73. MetaMap Transfer (MMTx). Bethesda, MD: National Library of Medicine. http:// mmtx.nlm.nih.gov/MMTx/.