Journal of Preventive Medicine and Public Health

The Korean Society for Preventive Medicine (대한예방의학회)
권호 표지
DOI QR코드

DOI QR Code

Identifying Adverse Events Using International Classification of Diseases, Tenth Revision Y Codes in Korea: A Cross-sectional Study

  • Ock, Minsu (Department of Preventive Medicine, Ulsan University Hospital, University of Ulsan College of Medicine) ;
  • Kim, Hwa Jung (Department of Clinical Epidemiology and Biostatistics, Asan Medical Center) ;
  • Jeon, Bomin (Department of Preventive Medicine, University of Ulsan College of Medicine) ;
  • Kim, Ye-Jee (Department of Clinical Epidemiology and Biostatistics, Asan Medical Center) ;
  • Ryu, Hyun Mi (Department of Preventive Medicine, University of Ulsan College of Medicine) ;
  • Lee, Moo-Song (Department of Clinical Epidemiology and Biostatistics, Asan Medical Center)
  • Received : 2017.08.11
  • Accepted : 2017.12.05
  • Published : 2018.01.31
Download PDF
⟨ Previous Next ⟩

Abstract

Objectives: The use of administrative data is an affordable alternative to conducting a difficult large-scale medical-record review to estimate the scale of adverse events. We identified adverse events from 2002 to 2013 on the national level in Korea, using International Classification of Diseases, tenth revision (ICD-10) Y codes. Methods: We used data from the National Health Insurance Service-National Sample Cohort (NHIS-NSC). We relied on medical treatment databases to extract information on ICD-10 Y codes from each participant in the NHIS-NSC. We classified adverse events in the ICD-10 Y codes into 6 types: those related to drugs, transfusions, and fluids; those related to vaccines and immunoglobulin; those related to surgery and procedures; those related to infections; those related to devices; and others. Results: Over 12 years, a total of 20 817 adverse events were identified using ICD-10 Y codes, and the estimated total adverse event rate was 0.20%. Between 2002 and 2013, the total number of such events increased by 131.3%, from 1366 in 2002 to 3159 in 2013. The total rate increased by 103.9%, from 0.17% in 2002 to 0.35% in 2013. Events related to drugs, transfusions, and fluids were the most common (19 446, 93.4%), followed by those related to surgery and procedures (1209, 5.8%) and those related to vaccines and immunoglobulin (72, 0.3%). Conclusions: Based on a comparison with the results of other studies, the total adverse event rate in this study was significantly underestimated. Improving coding practices for ICD-10 Y codes is necessary to precisely monitor the scale of adverse events in Korea.

Keywords

References

  1. Institute of Medicine (US) Committee on Quality of Health Care in America. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academies Press; 2001, p.1-22.
  2. Kohn LT, Corrigan J, Donaldson MS; Institute of Medicine (US) Committee on Quality of Health Care in America. To err is human : building a safer health system. Washington, DC: National Academy Press; 2000, p.1-16.
  3. Vincent C. Patient safety; 2010 [cited 2018 Jan 12]. Available from: http://file.zums.ac.ir/ebook/011-Patient%20Safety-Charles%20Vincent-1405192216-BMJ%20Books-2010-432-$71.pdf.
  4. Murff HJ, Patel VL, Hripcsak G, Bates DW. Detecting adverse events for patient safety research: a review of current methodologies. J Biomed Inform 2003;36(1-2):131-143. https://doi.org/10.1016/j.jbi.2003.08.003
  5. Thomas EJ, Petersen LA. Measuring errors and adverse events in health care. J Gen Intern Med 2003;18(1):61-67. https://doi.org/10.1046/j.1525-1497.2003.20147.x
  6. de Vries EN, Ramrattan MA, Smorenburg SM, Gouma DJ, Boermeester MA. The incidence and nature of in-hospital adverse events: a systematic review. Qual Saf Health Care 2008;17(3): 216-223. https://doi.org/10.1136/qshc.2007.023622
  7. Vlayen A, Verelst S, Bekkering GE, Schrooten W, Hellings J, Claes N. Incidence and preventability of adverse events requiring intensive care admission: a systematic review. J Eval Clin Pract 2012;18(2):485-497. https://doi.org/10.1111/j.1365-2753.2010.01612.x
  8. Brennan TA, Leape LL, Laird NM, Hebert L, Localio AR, Lawthers AG, et al. Incidence of adverse events and negligence in hospitalized patients. Results of the Harvard Medical Practice Study I. N Engl J Med 1991;324(6):370-376. https://doi.org/10.1056/NEJM199102073240604
  9. Leape LL, Brennan TA, Laird N, Lawthers AG, Localio AR, Barnes BA, et al. The nature of adverse events in hospitalized patients. Results of the Harvard Medical Practice Study II. N Engl J Med 1991;324(6):377-384. https://doi.org/10.1056/NEJM199102073240605
  10. Thomas EJ, Studdert DM, Burstin HR, Orav EJ, Zeena T, Williams EJ, et al. Incidence and types of adverse events and negligent care in Utah and Colorado. Med Care 2000;38(3): 261-271. https://doi.org/10.1097/00005650-200003000-00003
  11. Baines R, Langelaan M, de Bruijne M, Spreeuwenberg P, Wagner C. How effective are patient safety initiatives? A retrospective patient record review study of changes to patient safety over time. BMJ Qual Saf 2015;24(9):561-571. https://doi.org/10.1136/bmjqs-2014-003702
  12. Luck J, Peabody JW, Dresselhaus TR, Lee M, Glassman P. How well does chart abstraction measure quality? A prospective comparison of standardized patients with the medical record. Am J Med 2000;108(8):642-649. https://doi.org/10.1016/S0002-9343(00)00363-6
  13. Forster AJ, Taljaard M, Bennett C, van Walraven C. Reliability of the peer-review process for adverse event rating. PLoS One 2012;7(7):e41239. https://doi.org/10.1371/journal.pone.0041239
  14. Ock M, Lee SI, Jo MW, Lee JY, Kim SH. Assessing reliability of medical record reviews for the detection of hospital adverse events. J Prev Med Public Health 2015;48(5):239-248. https://doi.org/10.3961/jpmph.14.049
  15. Zhan C, Miller MR. Administrative data based patient safety research: a critical review. Qual Saf Health Care 2003;12 Suppl 2:ii58-ii63. https://doi.org/10.1136/qhc.12.1.58
  16. Kaafarani HM, Rosen AK. Using administrative data to identify surgical adverse events: an introduction to the Patient Safety Indicators. Am J Surg 2009;198(5 Suppl):S63-S68. https://doi.org/10.1016/j.amjsurg.2009.08.008
  17. Hohl CM, Karpov A, Reddekopp L, Doyle-Waters M, Stausberg J. ICD-10 codes used to identify adverse drug events in administrative data: a systematic review. J Am Med Inform Assoc 2014;21(3):547-557. https://doi.org/10.1136/amiajnl-2013-002116
  18. van Mourik MS, van Duijn PJ, Moons KG, Bonten MJ, Lee GM. Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review. BMJ Open 2015; 5(8):e008424. https://doi.org/10.1136/bmjopen-2015-008424
  19. Ock M, Kim JH, Lee SI. A legal framework for improving patient safety in Korea. Health Policy Manag 2015;25(3):174-184 (Korean). https://doi.org/10.4332/KJHPA.2015.25.3.174
  20. Hwang JI, Chin HJ, Chang YS. Characteristics associated with the occurrence of adverse events: a retrospective medical record review using the Global Trigger Tool in a fully digitalized tertiary teaching hospital in Korea. J Eval Clin Pract 2014;20(1): 27-35. https://doi.org/10.1111/jep.12075
  21. Hwang JI, Kim J, Park JW. Adverse events in Korean Traditional Medicine Hospitals: a retrospective medical record review. J Patient Saf 2015. doi: https://doi.org/10.1097/PTS.000000000 0000190.
  22. Lee J, Lee JS, Park SH, Shin SA, Kim K. Cohort profile: the National Health Insurance Service-National Sample Cohort (NHIS-NSC), South Korea. Int J Epidemiol 2017;46(2):e15.
  23. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380 (9859):2095-2128. https://doi.org/10.1016/S0140-6736(12)61728-0
  24. Ock MS, Lee SI, Kim Y, Lee JH, Lee JY, Jo MW, et al. Development of case review form for detecting adverse events. Qual Improv Health Care 2015;21(1):66-76 (Korean). https://doi.org/10.14371/QIH.2015.21.1.66
  25. Verelst S, Jacques J, Van den Heede K, Gillet P, Kolh P, Vleugels A, et al. Validation of hospital administrative dataset for adverse event screening. Qual Saf Health Care 2010;19(5):e25.
  26. Wu TY, Jen MH, Bottle A, Molokhia M, Aylin P, Bell D, et al. Ten-year trends in hospital admissions for adverse drug reactions in England 1999-2009. J R Soc Med 2010;103(6):239-250. https://doi.org/10.1258/jrsm.2010.100113
  27. Jordan K, Porcheret M, Croft P. Quality of morbidity coding in general practice computerized medical records: a systematic review. Fam Pract 2004;21(4):396-412. https://doi.org/10.1093/fampra/cmh409
  28. Waller P, Shaw M, Ho D, Shakir S, Ebrahim S. Hospital admissions for 'drug-induced' disorders in England: a study using the Hospital Episodes Statistics (HES) database. Br J Clin Pharmacol 2005;59(2):213-219. https://doi.org/10.1111/j.1365-2125.2004.02236.x
  29. Patel H, Bell D, Molokhia M, Srishanmuganathan J, Patel M, Car J, et al. Trends in hospital admissions for adverse drug reactions in England: analysis of national hospital episode statistics 1998-2005. BMC Clin Pharmacol 2007;7:9.
  30. Ackroyd-Stolarz S, Bowles SK, Giffin L. Validating administrative data for the detection of adverse events in older hospitalized patients. Drug Healthc Patient Saf 2014;6:101-108.
  31. Graber ML. The incidence of diagnostic error in medicine. BMJ Qual Saf 2013;22 Suppl 2:ii21-ii27. https://doi.org/10.1136/bmjqs-2012-001615
  32. Ock M. Evaluating the feasibility of introducing open disclosure of patient safety incidents [dissertation]. Ulsan: University of Ulsan; 2016 (Korean).

Cited by

  1. 입원 시 상병의 수집 및 활용에 관한 보건의료정보관리사의 관점: 질적 연구 vol.26, pp.1, 2020, https://doi.org/10.14371/qih.2020.26.1.23
  2. Use of ICD‐10‐CM T codes in hospital claims data to identify adverse drug events in Taiwan vol.46, pp.2, 2021, https://doi.org/10.1111/jcpt.13308