Sarah P Slight1, Clare L Tolley2, David W Bates3, Rachel Fraser4, Theophile Bigirumurame5, Adetayo Kasim6, Konstantinos Balaskonis7, Steven Narrie8, Andrew Heed4, E John Orav9, Neil W Watson4. 1. School of Pharmacy, Newcastle University, Newcastle upon Tyne, UK; The Newcastle upon Tyne Hospitals National Health Service Foundation Trust, Newcastle upon Tyne, UK; The Centre for Patient Safety Research and Practice, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA; Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK. Electronic address: sarah.slight@newcastle.ac.uk. 2. The Newcastle upon Tyne Hospitals National Health Service Foundation Trust, Newcastle upon Tyne, UK; Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK. 3. The Centre for Patient Safety Research and Practice, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Department of Health and Health Policy and Management, Harvard TH Chan School of Public Health, Boston, MA, USA; Harvard TH Chan School of Public Health, Boston, MA, USA. 4. The Newcastle upon Tyne Hospitals National Health Service Foundation Trust, Newcastle upon Tyne, UK. 5. Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK. 6. Wolfson Research Institute for Health and Wellbeing, Stockton on Tees, UK. 7. Newcastle Gateshead Clinical Commissioning Group, Newcastle Upon Tyne, UK. 8. Northumbria Healthcare National Health Service Foundation Trust, Newcastle upon Tyne, UK. 9. The Centre for Patient Safety Research and Practice, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.
Abstract
BACKGROUND: WHO's Third Global Patient Safety Challenge, Medication Without Harm, focused on reducing the substantial burden of iatrogenic harm associated with medications by 50% in the next 5 years. We aimed to assess whether the number and type of medication errors changed as an electronic prescribing system was optimised over time in a UK hospital. METHODS: We did a prospective observational study at a tertiary-care teaching hospital. Eight senior clinical pharmacists reviewed patients' records and collected data across four adult wards (renal, cardiology, general medical, and orthopaedic surgical) over a 2-year period (from Sept 29, 2014, to June 9, 2016). All medication errors and potential and actual adverse drug events were documented and the number of medication errors measured over the course of four time periods 7-10 weeks long. Pharmacists also recorded instances where the electronic prescribing system contributed to an error (system-related errors). A negative-binomial model and a Poisson model were used to identify factors related to medication error rates. FINDINGS: 5796 primary errors were recorded over the four time periods (period 1, 47 days [Sep 29-Dec 2, 2014]; period 2, 38 days [April 20-June 12, 2015, for the renal, medical, and surgical wards and April 20-June 15, 2015, for the cardiology ward]; period 3, 35 days [Sep 28-Nov 27, 2015] for the renal ward, 37 days [Sep 28-Nov 23, 2015] for the medical ward, and 40 days [Sep 28-Nov 20, 2015] for the cardiology and surgical wards; and period 4, 37 days [Feb 22-April 15, 2015] for the renal and medical wards and 39 days for the cardiology [April 13-June 7, 2015] and surgery [April 18-June 9, 2015] wards; unanticipated organisational factors prevented data collection on some days during each time period). There was no change in the rate of primary medication errors per admission over the observation periods: 1·53 medication errors in period 1, 1·44 medication errors in period 2, 1·70 medication errors in period 3, and 1·43 medication errors in period 4, per admission. By contrast, the overall rate of different types of medication errors decreased over the four periods. The most common types of error were medicine-reconciliation, dose, and avoidable delay-of-treatment errors. Some types of errors appeared to reduce over time (eg, dose errors [from 52 errors in period 1 to 19 errors in period 4, per 100 admissions]), whereas others increased (eg, inadequate follow-up of therapy [from 12 errors in period 1 to 24 errors in period 4, per 100 admissions]). We also found a reduction in the rates of potential adverse drug events between the first three periods and period 4. 436 system-related errors were recorded over the study period. INTERPRETATION: Although the overall rates of primary medication errors per admission did not change, we found a reduction in some error types and a significant decrease in the rates of potential adverse drug events over a 2-year period, during which system optimisation occurred. Targeting some error types could have the added benefit of reducing others, which suggests that system optimisation could ultimately help improve patient safety and outcomes. FUNDING: No funding.
BACKGROUND: WHO's Third Global Patient Safety Challenge, Medication Without Harm, focused on reducing the substantial burden of iatrogenic harm associated with medications by 50% in the next 5 years. We aimed to assess whether the number and type of medication errors changed as an electronic prescribing system was optimised over time in a UK hospital. METHODS: We did a prospective observational study at a tertiary-care teaching hospital. Eight senior clinical pharmacists reviewed patients' records and collected data across four adult wards (renal, cardiology, general medical, and orthopaedic surgical) over a 2-year period (from Sept 29, 2014, to June 9, 2016). All medication errors and potential and actual adverse drug events were documented and the number of medication errors measured over the course of four time periods 7-10 weeks long. Pharmacists also recorded instances where the electronic prescribing system contributed to an error (system-related errors). A negative-binomial model and a Poisson model were used to identify factors related to medication error rates. FINDINGS: 5796 primary errors were recorded over the four time periods (period 1, 47 days [Sep 29-Dec 2, 2014]; period 2, 38 days [April 20-June 12, 2015, for the renal, medical, and surgical wards and April 20-June 15, 2015, for the cardiology ward]; period 3, 35 days [Sep 28-Nov 27, 2015] for the renal ward, 37 days [Sep 28-Nov 23, 2015] for the medical ward, and 40 days [Sep 28-Nov 20, 2015] for the cardiology and surgical wards; and period 4, 37 days [Feb 22-April 15, 2015] for the renal and medical wards and 39 days for the cardiology [April 13-June 7, 2015] and surgery [April 18-June 9, 2015] wards; unanticipated organisational factors prevented data collection on some days during each time period). There was no change in the rate of primary medication errors per admission over the observation periods: 1·53 medication errors in period 1, 1·44 medication errors in period 2, 1·70 medication errors in period 3, and 1·43 medication errors in period 4, per admission. By contrast, the overall rate of different types of medication errors decreased over the four periods. The most common types of error were medicine-reconciliation, dose, and avoidable delay-of-treatment errors. Some types of errors appeared to reduce over time (eg, dose errors [from 52 errors in period 1 to 19 errors in period 4, per 100 admissions]), whereas others increased (eg, inadequate follow-up of therapy [from 12 errors in period 1 to 24 errors in period 4, per 100 admissions]). We also found a reduction in the rates of potential adverse drug events between the first three periods and period 4. 436 system-related errors were recorded over the study period. INTERPRETATION: Although the overall rates of primary medication errors per admission did not change, we found a reduction in some error types and a significant decrease in the rates of potential adverse drug events over a 2-year period, during which system optimisation occurred. Targeting some error types could have the added benefit of reducing others, which suggests that system optimisation could ultimately help improve patient safety and outcomes. FUNDING: No funding.
Authors: S Gallier; A Topham; P Nightingale; M Garrick; I Woolhouse; M A Berry; T Pankhurst; E Sapey; S Ball Journal: BMC Med Inform Decis Mak Date: 2022-05-03 Impact factor: 3.298
Authors: Michael Patrick O'Shea; Cormac Kennedy; Eileen Relihan; Kieran Harkin; Martina Hennessy; Michael Barry Journal: BMC Med Inform Decis Mak Date: 2021-06-21 Impact factor: 2.796