26th October 2018, Volume 131 Number 1484

Jerome Ng, Penny Andrew, Paul Muir, Monique Greene, Sabitha Mohan, Jacqui Knight, Phil Hider, Peter Davis, Mary Seddon, Shane Scahill, Jeff Harrison, Lifeng Zhou, Vanessa Selak, Carlene Lawes, Geetha Galgali, Joanna Broad, Marilyn Crawley, Wynn Pevreal, Neil Houston, Tamzin Brott, David Ryan, Jocelyn Peach, Andrew Brant, Dale Bramley

Adverse drug events (ADEs) account for a significant proportion of all iatrogenic harm (up to 38%)1 and improvement initiatives have been introduced into New Zealand hospitals to prevent them.2 The…

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Summary

The routine measurement of adverse drug events (ADE) is important for monitoring and informing improvement, but current detection tools are manual and too resource intensive. Our research, for the first time, shows ADEs can be reliably and sustainably measured using clinical coding surveillance (CCS). Using CCS, almost 12,000 ADEs over two years were detected in hospitalised patients. Most ADEs originated from the community setting.

Abstract

Aim

To explore the feasibility and reliability of Clinical Coding Surveillance (CCS) for the routine monitoring of Adverse Drug Events (ADE) and describe the characteristics of harm identified through this approach in a large district health board (DHB).

Method

All hospital admissions at Waitemata DHB from 2015 to 2016 with an ADE-related ICD10-AM code of Y40-Y59, X40-X49 or T36-T50 were extracted from clinical coded data. The data was analysed using descriptive statistics, statistical process control and Pareto charts. Two clinicians assessed a random sample of 140 ADEs for their accuracy against what was clinically documented in medical records.

Results

A total of 11,999 ADEs were identified in 244,992 admissions (4.9 ADEs per 100 admissions). ADEs were more prevalent in older adults and associated with longer average length of stays and medicines such as analgesics, antibiotics, anticoagulants and diuretics. Only 2,164 (18%) of ADEs were classified as originating within hospital. Of ADEs originating outside of the hospital, the main causes were poisoning by psychotropics, anti-epileptics and anti-parkinsonism agents and non-opioid analgesics. Clinicians agreed that 91% of ADE positive admissions were accurately classified as per clinical documentation.

Conclusion

CCS is a feasible and reliable approach for the routine monitoring of ADEs in hospitals.

Author Information

Jerome Ng, Lead Advisor, Improvement, Research & Informatics, Institute for Innovation and Improvement (i3), Waitemata DHB, Auckland; Penny Andrew, Director, Institute for Innovation and Improvement (i3), Waitemata DHB, Auckland; Paul Muir, Medical Fellow, Planning, Funding and Outcomes, Waitemata DHB, Auckland; Monique Greene, Information Analyst, Institute for Innovation and Improvement (i3), Waitemata DHB, Auckland; Sabitha Mohan, Clinical Coding Auditor, Health Information Group, Waitemata DHB, Auckland; Jacqui Knight, Clinical Coding Team Leader, Health Information Group, Waitemata DHB, Auckland; Phil Hider, Senior Lecturer, Department of Population Health, University of Otago, Christchurch; Peter Davis, Professor, Centre of Methods and Policy Application in the Social Sciences (COMPASS), University of Auckland, Auckland;
Mary Seddon, Independent Consultant, Seddon Healthcare Quality, Auckland;
Shane Scahill, Senior Lecturer, School of Management, Massey University, Auckland;
Jeff Harrison, Associate Professor, School of Pharmacy, University of Auckland, Auckland;
Lifeng Zhou, Chief Advisor for Asian International Collaboration, Waitemata District Health Board, Auckland; Vanessa Selak, Senior Lecturer, School of Population Health, University of Auckland, Auckland; Carlene Lawes, Public Health Physician (Surgical), Institute for Innovation and Improvement (i3), Waitemata DHB, Auckland; Geetha Galgali, Public Health Physician (Maternity), Child, Women and Family, Waitemata DHB, Auckland; Joanna Broad, Senior Research Fellow, Department of Geriatric Medicine, University of Auckland, Auckland; Marilyn Crawley, Chief Pharmacist, Pharmacy Department, Waitemata DHB, Auckland; Wynn Pevreal, Medication Safety Pharmacist, Pharmacy Department, Waitemata DHB, Auckland (Died 24 April 2018); Neil Houston, Clinical Director for Safety and Quality in Primary Care, Waitemata DHB, Auckland; Tamzin Brott, Executive Director—Allied Health, Scientific & Technical Professions, Waitemata DHB, Auckland;
David Ryan, Information Systems Change Manager, Health Information Group, Waitemata DHB, Auckland; Jocelyn Peach, Director of Nursing and Midwifery, Waitemata DHB, Auckland;
Andrew Brant, Chief Medical Officer, Waitemata DHB, Auckland; Dale Bramley, Chief Executive Officer, Waitemata DHB, Auckland.

Acknowledgements

We gratefully acknowledge the following people for their support and advice: Ross S, Wallace I, Gilmour K, Farmer E, Wood P, Doogue M, Wilkinson J, Young M, Li Y, Hamblin R and Armstrong D.

Correspondence

Dr Jerome Ng, Institute for Innovation and Improvement, Waitemata DHB West Wing, Taharoto Building (Building 1), North Shore Hospital Site, Takapuna, Private Bag 93-503, Auckland 0740.

Correspondence Email

jerome.ng@waitematadhb.govt.nz

Competing Interests

Nil.

References

  1. Council of Europe Expert Group on Safe Medication Practices. Creation of a better medication safety culture in Europe: building up safe medication practices 2006 [cited 2010 17/03/2010]. Available from: http://www.gs1health.net/downloads/medication.safety.report.2007.pdf
  2. SQM. Safe and Quality Use of Medicines 2005-2007 Report: Ministry of Health; 2008 [cited 2010 19/05/2010]. Available from: http://www.safeuseofmedicines.co.nz/Portals/0/About/S&QuseofMeds05to07.pdf
  3. Ng J, Scahill S, Harrison J. Getting the foundations right for the measurement of medication safety: the need for a meaningful conceptual frame N Z Med J. 2017; 130(1452):54–62.
  4. Robb G, Loe E, Maharaj A, Hamblin R, Seddon M. Medication-related patient harm in New Zealand hospitals. N Z Med J. 2017; 130(1460):21–32.
  5. Sapere Research Group. Report A: A framework for the measurement of medication-related harm. 2013.
  6. Briant R, Ali W, Lay-Yee R, Davis P. Representative case series from public hospital admissions 1998 I: drug and related therapeutic adverse events. N Z Med J. 2004; 117(1188).
  7. Davis P, Lay-Yee R, Briant R. Adverse events in New Zealand Public Hospitals: Principal Findings from a National Survey Wellington: Ministry of Health; 2001 [cited 2010 10/02/2010]. Available from: http://www.moh.govt.nz/publications/adverseevents
  8. Kunac D, Reith D. Preventable medication-related events in hospitalised children in New Zealand. N Z Med J. 2008; 121(1272):17–32.
  9. Seddon M, Jackson A, Cameron C, Young M, Escott L, Maharaj A, et al. The Adverse Drug Event Collaborative: a joint venture to measure medication-related patient harm. N Z Med J. 2013; 126(1368):9–20.
  10. Hohl C, Karpov A, Reddekopp L, Stausberg J. ICD-10 codes used to identify adverse drug events in administrative data: a systematic review. J Am Med Inform Assoc. 2014; 21:547–57.
  11. Parikh S, Christensen D, Stuchbery P, Peterson J, Hutchinson A, Jackson T. Exploring in-hospital adverse drug events using ICD-10 codes. Australian Health Review. 2014; 38(4):454–60.
  12. Hodgkinson M, Dirnbauer N, Larmour I. Identification of Adverse Drug Reactions Using the ICD-10 Australian Modification Clinical Coding Surveillance. Journal of Pharmacy Practice and Research Volume. 2009; 39(1):19–23.
  13. Ministry of Health. National Minimum Dataset (hospital events): Ministry of Health; 2017 [updated 17 August 2015; cited 2017 06/11/2017]. Available from: http://www.health.govt.nz/nz-health-statistics/national-collections-and-surveys/collections/national-minimum-dataset-hospital-events
  14. National Health Board. National Minimum Dataset (Hospital Events) Data Dictionary. Wellington: Ministry of Health, 2014.
  15. National Centre for Classification in Health. Volume. International statistical classification of diseases and related health problems 10th revision Australian modification (ICD-10-AM). 3rd ed. Sydney: National Centre for Classification in Health; 2002.
  16. Australian Commission on Safety and Quality in Health Care. Classification of Hospital Acquired Diagnoses (CHADx) Australian Commission on Safety and Quality in Health Care; 2014 [cited 2014 15/12/2014]. Available from: http://www.safetyandquality.gov.au/our-work/information-strategy/health-information-standards/classification-of-hospital-acquired-diagnoses-chadx/
  17. Kumpula E-K, Nada-Raja S, Norris P, Quigley P. A descriptive study of intentional self-poisoning from New Zealand national registry data. Australian and New Zealand Journal of Public Health. 2017; 41(5):535–40.
  18. Nishtala P, Ndukwe H, Chyou T, Salahudeen M, Narayan S. An overview of pharmacoepidemiology in New Zealand: medical databases, registries and research achievements. N Z Med J. 2017; 130(1449):52–8.
  19. Parkin L, Paul C, Herbison GP. Simvastatin dose and risk of rhabdomyolysis: Nested case–control study based on national health and drug dispensing data. International Journal of Cardiology. 2014; 174(1):83–9.
  20. Hider P, Parker K, von Randow M, Milne B, Lay-Yee R, Davis P. Can patient safety indicators monitor medical and surgical care at New Zealand public hospitals? N Z Med J. 2014; 127(1405):32–44.
  21. World Health Organization. Technical Report No 498: International Drug Monitoring, The Role of National Centres. Geneva: The Institute, 1972.
  22. Rains M, Thompson T. The New Zealand Casemix System: An Overview. Wellington, NZ: Casemix Working Group, 2015.
  23. HealthRoundTable. 3.1: Complications Rate & Analysis (CHADx): HealthRoundTable; 2016 [cited 2016 21.03.2016]. Available from: http://www.hed.nhs.uk/portalaustralia/Module.aspx?reportID=10
  24. Langley GJ, Moen RD, Nolan KM, Nolan TW, Norman CL, Provost LP. The improvement guide: a practical approach to enhancing organizational performance: John Wiley & Sons; 2009.
  25. Benneyan JC, Lloyd RC, Plsek PE. Statistical process control as a tool for research and healthcare improvement. Quality and Safety in Health Care. 2003; 12(6):458–64.
  26. Perla R, Provost L, Murray SK. The run chart: a simple analytical tool for learning from variation in healthcare processes. BMJ Quality & Safety. 2011; 20:46–51.
  27. Stausberg J. International prevalence of adverse drug events in hospitals: an analysis of routine data from England, Germany, and the USA. BMC Health Services Research. 2014; 14(1):125.
  28. Bates D, Cullen D, Laird N, et al. Incidence of adverse drug events and potential adverse drug events: implications for prevention. JAMA. 1995; 274:29–34.
  29. Abernethy AP, Herndon JE, Wheeler JL, Rowe K, Marcello J, Patwardhan M. Poor Documentation Prevents Adequate Assessment of Quality Metrics in Colorectal Cancer. Journal of Oncology Practice. 2009; 5(4):167–74.
  30. Graber ML, Wachter RM, Cassel CK. Bringing diagnosis into the quality and safety equations. JAMA. 2012; 308(12):1211–2.
  31. Cao F, Sun X, Wang X, Li B, Li J, Pan Y. Ontology-based knowledge management for personalized adverse drug events detection. Studies in Health Technology & Informatics. 2011; 169:699–703.
  32. Ng J, Scahill S, Harrison J. Stakeholder views do matter: a conceptual framework for medication safety measurement. Journal of Pharmaceutical Health Services Research. 2018; 9(1):21–31.
  33. Ng J, Andrew P, Crawley M, Pevreal W, Peach J. Assessing a hospital medication system for patient safety: findings and lessons learnt from trialling an Australian modified tool at Waitemata District Health Board. N Z Med J. 2016; 129(1430).
  34. Jha A, Kuperman G, Teich J, et al. Identifying adverse drug events: development of a computer-based monitor and comparison with chart review and stimulated voluntary report. J Am Med Inform Assoc. 1998; 5:305–14.
  35. Sandars J, Esmail A. The frequency and nature of medical error in primary care: understanding the diversity across studies. Family Practice. 2003;20(3):231-6.

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