Automated identification of extreme-risk events in clinical incident reports

Journal of the American Medical Informatics Association

Volumn 19, Issue E1, 2012, Pages

  Ong, Mei Sing ^a   Magrabi, Farah ^a   Coiera, Enrico ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; AUTOMATION; BAYESIAN LEARNING; DATA BASE; INCIDENT REPORT; INFORMATION RETRIEVAL; MEDICAL INFORMATICS; MEDICAL INFORMATION SYSTEM; PATIENT SAFETY; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE;

ARTIFICIAL INTELLIGENCE; BAYES THEOREM; HUMANS; MEDICAL ERRORS; RISK MANAGEMENT; SUPPORT VECTOR MACHINES;

EID: 84863550886     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2011-000562     Document Type: Article

References (33)

1. Kohn L, Corrigan J, Donaldson M. TO err is Human: Building a safer Health System. Committee on Quality of Health Care in America. Washing DC: US Institute of Medicine, National Academy Press, 1999:86e101.
2. Beckmann U, Bohringer C, Carless R, et al. Evaluation of two methods for quality improvement in intensive care: facilitated incident monitoring and retrospective medical chart review. Crit Care Med 2003;31:1006e11.
3. O'Neil AC, Petersen LA, Cook EF, et al. Physician reporting compared with medicalrecord review to identify adverse medical events. Ann Intern Med 1993;119:370e6.
4. Bagian JP, Lee C, Gosbee J, et al. Developing and deploying a patient safety program in a large health care delivery system: you can't fix what you don't know about. Jt Comm J Qual Improv 2001;27:522e32.
5. Vincent C, Stanhope N, Crowley-Murphy M. Reasons for not reporting adverse incidents: an empirical study. J Eval Clin Pract 1999;5:13e21.
6. Williams SD, Ashcroft DM. Medication errors: how reliable are the severity ratings reported to the national reporting and learning system? Int J Qual Health Care 2009;21:316e20.
7. University Centre for Clinical Governance Research in Health. Evaluation of the safety improvement program in New South Wales: Study no 6 report on program outcomes. Kensington: Centre for Clinical Governance Research, University of New South Wales. http://www.med.unsw.edu.au/medweb.nsf/resources/SIP2/$file/Final +SIP+Study+6.pdf (accessed 6 Apr 2011).
8. Hor SY, Iedema R, Williams K, et al. Multiple accountabilities in incident reporting and management. Qual Health Res 2010;20:1091e100.
9. NSW Health. Clinical Incident Management in the NSW public health system, JanuaryeJune 2009. http://www.health.nsw.gov.au/resources/quality/incidentmgt/ pdf/incident_management_2009_01to06.pdf (accessed 6 Apr 2011).
10. Megaputer Intelligence Application of Polyanalyst to Flight Safety Data at Southwest AirlinesdProof-of-concept Demonstration of Data and Text Mining. http://flightsafety. org/files/flight_safety_data_sw.pdf (accessed 6 Jun 2011).
11. Srivastava AN, Akella R, Cruz S. Enabling Discovery of Recurring Anomalies in Aerospace Problem Reports Using High-Dimensional Clustering Techniques. http://ti. arc.nasa.gov/m/pub-archive/archive/IEEE_TextMiningPaper_vrs4.pdf (accessed 6 Jun 2011).
12. Castle JP, Stutz JC, McIntosh DM. Automatic discovery of anomalies reported in aerospace systems health and safety documents. American Institute of Aeronautics and Astronautics 2007 Conference and Exhibit. Rohnert Park, California: American Institute of Aeronautics and Astronautics, 2007. http://ti.arc.nasa.gov/m/pub-archive/ 1322h/1322%20(Castle).pdf (accessed 5 Jan 2012).
13. Govindan M, Van Citters AD, Nelson EC, et al. Automated detection of harm in healthcare with information technology: a systematic review. Qual Saf Health Care 2010;19:e11.
14. D'Avolio LW, Litwin MS, Rogers SO Jr, et al. Automatic identification and classification of surgical margin status from pathology reports following prostate cancer surgery. AMIA Annu Symp Proc 2007;11:160e4.
15. Webber WC, Cooper GF, Hanbury P, et al. Creating a text classifier to detect radiology reports describing mediastinal findings associated with inhalation anthrax and other disorders. J Am Med Inform Assoc 2003;10: 494e503.
16. Hripcsak G, Austin JH, Alderson PO, et al. Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports. Radiology 2002;224:157e63.
17. Visweswaran S, Hanbury P, Saul M, et al. Detecting adverse events in discharge summaries using variations on the simple bayes model. AMIA Annu Symp Proc 2003:689e93.
18. Melton GB, Hripcsak G. Automated detection of adverse events using natural language processing of discharge summaries. J Am Med Inform Assoc 2005;12:448e57.
19. Botsis T, Nguyen MD, Woo EJ, et al. Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection. J Am Med Inform Assoc 2011;18:631e8.
20. Ong MS, Magrabi F, Coiera E. Automated categorisation of clinical incident reports using statistical text classification. Qual Saf Health Care 2010;19:e55.
21. Runciman WB. The Australian patient safety Foundation. Anaesth Intensive Care 1988;16:114e16.
22. Runciman WB. Lessons from the Australian Patient Safety Foundation: setting up a national patient safety surveillance systemeis this the right model? Qual Saf Health Care 2002;11:246e51.
23. Runciman W, Hibbert P, Thomson R, et al. Towards an international classification for patient safety: key concepts and terms. Int J Qual Health Care 2009;21:18e26.
24. Sebastiani F. Machine learning in automated text categorization. ACM Computing Surveys 2002;34:1e47.
25. Joachims T. Text categorization with support vector machines: learning with many relevant features. Machine Learning: ECML-98. Lect Notes Comput Sci 1998;1398:137e42.
26. NSW Health. Severity Assessment Code (SAC) November 2005. http://www.health. nsw.gov.au/pubs/2005/pdf/sac_matrix.pdf (accessed 6 Apr 2011).
27. Akbani R, Kwek S, Japkowicz N. Applying support vector machines to imbalanced datasets. Lect Notes Comput Sci 2004;3201:39e50.
28. Weiss SM, Indurkhya N, Zhang T, et al. Text mining: Predictive Methods for Analyzing Unstructured Information. Spinger, 2005:30.
29. Fürnkranz J. A study using n-gram features for text categorization. Technical Report OEFAI-TR-98-30. Wien, Austria: Austrian Research Institute for Artificial Intelligence, 1998. http://en.scientificcommons.org/43006206 (accessed 5 Jan 2012).
30. Scholkopf B, Smola AJ. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2001:17.
31. Weka 3: Data Mining Software. http://www.cs.waikato.ac.nz/ml/weka (accessed 6 Apr 2011).
32. Kish L. Statistical Design for Research. John Wiley and Sons, 2004:113e14.
33. Billings CE. The NASA Aviation Safety Reporting System: lessons learned from voluntary incident reporting. In: Proceedings of Enhancing Patient Safety and Reducing Errors in Health Care. Chicago: National Patient Safety Foundation, 1999:97e100.