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Int J Risk Saf Med ; 27(4): 169-75, 2015.
Article in English | MEDLINE | ID: mdl-26756890

ABSTRACT

BACKGROUND: Despite efforts to improve the patients' safety, medical errors especially prescription errors can lead to morbidity and mortality in patients. The present study was conducted to assess the prescription errors in the intensive care units (ICU) in Shiraz, Southwest of Iran. METHODS: We reviewed the all recorded orders in the two ICU wards of the Shiraz largest hospital in the south of Iran. Data were collected from the two wards and hospital archive using a structured checklist. Descriptive statistics, Chi-square and logistic regression tests were used to analyze the data. RESULTS: Among the 2230 recorded prescriptions for 40 hospitalized patients, 387 prescribed orders (251 in the General ICU and 136 in the Central ICU) had at least one error which occurred in the three months of the study. The study revealed that illegible orders have the highest error frequency in the two wards. The mean prescription error in the two ICU wards was 17.3 (0.19 errors in the General and 0.14 errors in The Central ICU, respectively). Lack of drug dosage was more than that in the larger wards (P = 0.037); moreover, illegible order and mistaken dosage were more in smaller wards (OR 1.84, CI = 1.18-2.86 and OR 2.55, CI = 1.08-6.00, P = 0.007 and P = 0.031, respectively). CONCLUSION: The rate of prescription errors in ICU wards was high and it was higher in crowded wards. Illegible orders were the majority of important errors in prescriptions. In the majority of orders, physicians did not write the drug form and drug dose which could be potentially harmful to patients. It is recommended that a computerized physician order should be used because it can decrease prescription errors.


Subject(s)
Drug Prescriptions/statistics & numerical data , Intensive Care Units/statistics & numerical data , Medication Errors/statistics & numerical data , Chi-Square Distribution , Drug Dosage Calculations , Drug Prescriptions/standards , Handwriting , Hospital Records/statistics & numerical data , Hospitals, Teaching , Humans , Iran , Logistic Models , Medical Records/statistics & numerical data , Medication Errors/classification , Retrospective Studies
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