Screening electronic health record-related patient safety reports using machine learning

Journal of Patient Safety

Volumn 13, Issue 1, 2017, Pages 31-36

  Marella, William M ^a,b   Sparnon, Erin ^a,b   Finley, Edward ^a,b  

^a Pennsylvania Patient Safety Authority   (United States)

^b ECRI INSTITUTE   (United States)

Author keywords

Artificial intelligence; Electronic health records; Iatrogenic disease; Medical errors; Patient safety

Indexed keywords

ALGORITHM; AUTOMATION; BAYES THEOREM; DATA MINING; ELECTRONIC HEALTH RECORD; FACTUAL DATABASE; HUMAN; MACHINE LEARNING; MANDATORY REPORTING; MEDICAL INFORMATICS; PATIENT SAFETY; PENNSYLVANIA;

ALGORITHMS; AUTOMATION; BAYES THEOREM; DATA MINING; DATABASES, FACTUAL; ELECTRONIC HEALTH RECORDS; HUMANS; MACHINE LEARNING; MANDATORY REPORTING; MEDICAL INFORMATICS; PATIENT SAFETY; PENNSYLVANIA;

EID: 84897955248     PISSN: 15498417     EISSN: 15498425     Source Type: Journal    
DOI: 10.1097/PTS.0000000000000104     Document Type: Article

References (16)

1. Buntin MB, Burke MF, Hoaglin MC, et al. The benefits of health information technology: A review of the recent literature shows predominantly positive results. Health Aff (Millwood). 2011; 30: 464-471.
2. Committee on Patient Safety Health Information Technology, Institute of Medicine. Health IT and patient safety: Building safer systems for better care: The National Academies Press; 2012. Available at: Http://www.nap.edu/openbook.php?record-id=13269.
3. Reckmann MH, Westbrook JI, Koh Y, et al. Does computerized provider order entry reduce prescribing errors for hospital inpatients? A systematic review. J Am Med Inform Assoc. 2009; 16: 613-623.
4. Virapongse A, Bates DW, Shi P, et al. Electronic health records and malpractice claims in office practice. Arch Intern Med. 2008; 168: 2362-2367.
5. Quinn MA, Kats AM, Kleinman K, et al. The relationship between electronic health records and malpractice claims. Arch Intern Med. 2012; 172: 1187-1189.
6. Walker JM, Carayon P, Leveson N, et al. EHR safety: The way forward to safe and effective systems. J Am Med Inform Assoc. 2008; 15: 272-277.
7. Sittig DF, Classen DC. Safe electronic health record use requires a comprehensive monitoring and evaluation framework. JAMA. 2010; 303: 450-451.
8. Singh H, Classen DC, Sittig DF. Creating an oversight infrastructure for electronic health record-related patient safety hazards. J Patient Saf. 2011; 7: 169-174.
9. Ong MS, Magrabi F, Coiera E. Automated categorisation of clinical incident reports using statistical text classification. Qual Saf Health Care. 2010; 19: E55.
10. Ong MS, Magrabi F, Coiera E. Automated identification of extreme-risk events in clinical incident reports. J Am Med Inform Assoc. 2012; 19: E110-e118.
11. Chai KE, Anthony S, Coiera E, et al. Using statistical text classification to identify health information technology incidents. J Am Med Inform Assoc. 2013; 20: 980-985.
12. McKnight SD. Semi-supervised classification of patient safety event reports. J Patient Saf. 2012; 8: 60-64.
13. 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: 631-638.
14. Sparnon E, Marella WM. The role of the electronic health record in patient safety events. Pa Patient Saf Advis. 2012; 9: 113-121.
15. Magrabi F, Ong MS, Runciman W, et al. Using FDA reports to inform a classification for health information technology safety problems. J Am Med Inform Assoc. 2012; 19: 45-53.
16. Mierswa I, Wurst M, Klinkenberg R, et al. YALE: Rapid Prototyping for Complex Data Mining Tasks. KDD '06: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Philadelphia, PA: ACM; 2006: 935-940.