ABSTRACT
The aim of this study was to determine the proportion of near-miss dispensing errors in hospital pharmacies in Japan. A prospective multi-center observational study was conducted between December 2018 and March 2019. The primary objective was to determine the proportion of near-miss dispensing errors in hospital pharmacy departments. The secondary objective was to determine the predictive factors for near-miss dispensing errors using multiple logistic regression analysis. The study was approved by the ethical committee at The Institute of Medical Sciences, University of Tokyo, Japan. A multi-center prospective observational study was conducted in 20 hospitals comprising 8862 beds. Across the 20 hospitals, we assessed data from 553 pharmacists and 53039 prescriptions. A near-miss dispensing error proportion of 0.87% (n = 461) was observed in the study. We found predictive factors for dispensing errors in day-time shifts: a higher number of drugs in a prescription, higher number of quantified drugs, such as liquid or powder formula, in a prescription, and higher number of topical agents in a prescription; but we did not observe for career experience level for clinical pharmacists. For night-time and weekend shifts, we observed a negative correlation of near-miss dispensing errors with clinical pharmacist experience level. We found an overall incidence of near-miss dispensing errors of 0.87%. Predictive factors for errors in night-time and weekend shifts was inexperienced pharmacists. We recommended that pharmacy managers should consider education or improved work flow to avoid near-miss dispensing errors by younger pharmacists, especially those working night or weekend shifts.
Subject(s)
Near Miss, Healthcare , Pharmacies , Hospitals , Humans , Japan , Medication Errors/prevention & control , Pharmacists , Powders , Prospective StudiesABSTRACT
We reported previously that spline interpolation is effective as a pretreatment before analyzing clinical data by time series. However, further improvement is required to understand the detailed tendency of clinical data. In this study, the tendency of interpolated hematological data was investigated in the period between the most tolerated dose (MTD) and low-dose chemotherapy (LDC) for colorectal cancer. All patients were received both MTD and LDC. Hematological data, white blood cell count (WBC), red blood cell count (RBC) and mean corpuscular volume (MCV), were interpolated. The accuracy of interpolation was verified using leave-one-out cross-validation. The difference, Δ(i), was calculated from interpolated data and exhibited as a function of time. The predictions of RBC and MCV were accurate with high correlation coefficients, although the interpolation of WBC data was inaccurate. A marked difference was observed in the trend of Δ(i) between LDC and MTD periods. SD-RBC showed significant differences between LDC and MTD periods. The SD-MCV average in the LDC period was larger than in the MTD period. SD-MCV showed no significant difference. An attractor plot of Δ(i) in RBC clarified the tendency of the interpolated RBC data. There is a possibility that Δ(i) of RBC and/or SD-RBC may contribute to monitoring adverse reactions and decision of medication. Moreover, it is also useful to check on attractor plot of Δ(i) in RBC together with SD-RBC in order to find out untoward reactions and decision of medication.
Subject(s)
Antineoplastic Agents/administration & dosage , Colorectal Neoplasms/drug therapy , Data Interpretation, Statistical , Drug Monitoring/methods , Hematologic Tests , Adult , Aged , Female , Humans , Male , Middle Aged , Pharmacovigilance , Time FactorsABSTRACT
PURPOSE: Time series analysis may be helpful to estimate hematological data on gastric cancer patients who receive S 1, but untreated raw clinical data are not suitable for this approach. Hematological monitoring data interpolated by spline were analyzed by an attractor plot, which is a form of time series analysis. METHODS: Hematological data of three gastric cancer patients were interpolated by cubic spline. The leave-one-out cross validation method was carried out and an attractor plot was adopted to evaluate red blood count (RBC) data. RESULTS: Well-predicted data, such as RBC, changed slightly; however, data with great deviation, such as the white blood count (WBC), were poorly predicted. The reaction of marrow function to chemotherapy was observable by spline interpolation of RBC data. Furthermore, an attractor plot clarified the tendency of the interpolated hematological monitoring data. CONCLUSIONS: It is suggested that spline interpolation is effective as a pretreatment to analyze clinical data from a time series.
