School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester, UK.
Drug Saf. 2010 Nov 1;33(11):1027-44. doi: 10.2165/11538310-000000000-00000.
Pharmacists have an essential role in improving drug usage and preventing prescribing errors (PEs). PEs at the interface of care are common, sometimes leading to adverse drug events (ADEs). This was the first study to investigate, using a computerized search method, the number, types, severity, pharmacists' impact on PEs and predictors of PEs in the context of electronic prescribing (e-prescribing) at hospital discharge.
This was a retrospective, observational, 4-week study, carried out in 2008 in the Medical and Elderly Care wards of a 904-bed teaching hospital in the northwest of England, operating an e-prescribing system at discharge. Details were obtained, using a systematic computerized search of the system, of medication orders either entered by doctors and discontinued by pharmacists or entered by pharmacists. Meetings were conducted within 5 days of data extraction with pharmacists doing their routine clinical work, who categorized the occurrence, type and severity of their interventions using a scale. An independent senior pharmacist retrospectively rated the severity and potential impact, and subjectively judged, based on experience, whether any error was a computer-related error (CRE). Discrepancies were resolved by multidisciplinary discussion. The Statistical Package for Social Sciences was used for descriptive data analysis. For the PE predictors, a multivariate logistic regression was performed using STATA 7. Nine predictors were selected a priori from available prescribers', patients' and drug data.
There were 7920 medication orders entered for 1038 patients (doctors entered 7712 orders; pharmacists entered 208 omitted orders). There were 675 (8.5% of 7920) interventions by pharmacists; 11 were not associated with PEs. Incidences of erroneous orders and patients with error were 8.0% (95% CI 7.4, 8.5 [n = 630/7920]) and 20.4% (95% CI 18.1, 22.9 [n = 212/1038]), respectively. The PE incidence was 8.4% (95% CI 7.8, 9.0 [n = 664/7920]). The top three medications associated with PEs were paracetamol (acetaminophen; 30 [4.8%]), salbutamol (albuterol; 28 [4.4%]) and omeprazole (25 [4.0%]). Pharmacists intercepted 524 (83.2%) erroneous orders without referring to doctors, and 70% of erroneous orders within 24 hours. Omission (31.0%), drug selection (29.4%) and dosage regimen (18.1%) error types accounted for >75% of PEs. There were 18 (2.9%) serious, 481 (76.3%) significant and 131 (20.8%) minor erroneous orders. Most erroneous orders (469 [74.4%]) were rated as of significant severity and significant impact of pharmacists on PEs. CREs (n = 279) accounted for 44.3% of erroneous orders. There was a significant difference in severity between CREs and non-CREs (χ2 = 38.88; df = 4; p < 0.001), with CREs being less severe than non-CREs. Drugs with multiple oral formulations (odds ratio [OR] 2.1; 95% CI 1.25, 3.37; p = 0.004) and prescribing by junior doctors (OR 2.54; 95% CI 1.08, 5.99; p = 0.03) were significant predictors of PEs.
PEs commonly occur at hospital discharge, even with the use of an e-prescribing system. User and computer factors both appeared to contribute to the high error rate. The e-prescribing system facilitated the systematic extraction of data to investigate PEs in hospital practice. Pharmacists play an important role in rapidly documenting and preventing PEs before they reach and possibly harm patients. Pharmacists should understand CREs, so they complement, rather than duplicate, the e-prescribing system's strengths.
药剂师在改善药物使用和预防处方错误(PEs)方面发挥着重要作用。在护理界面发生的 PEs 很常见,有时会导致药物不良事件(ADEs)。这是第一项使用计算机搜索方法研究电子处方(e-prescribing)出院时 PEs 的数量、类型、严重程度、药剂师的影响以及 PEs 预测因素的研究。
这是一项回顾性、观察性的四周研究,于 2008 年在英格兰西北部一家 904 张病床的教学医院的医疗和老年护理病房进行,在出院时使用电子处方系统。通过系统地计算机搜索系统,获得了医生输入并由药剂师停止或由药剂师输入的药物医嘱的详细信息。在数据提取后的 5 天内与正在进行常规临床工作的药剂师进行了会议,他们使用量表对其干预的发生、类型和严重程度进行了分类。一位独立的高级药剂师回顾性地评估了严重程度和潜在影响,并根据经验主观判断是否存在任何错误是计算机相关错误(CRE)。通过多学科讨论解决差异。使用社会科学统计软件包进行描述性数据分析。对于 PE 预测因素,使用 STATA 7 进行了多变量逻辑回归。从可用的处方者、患者和药物数据中预先选择了九个预测因素。
为 1038 名患者输入了 7920 种药物医嘱(医生输入了 7712 种医嘱;药剂师输入了 208 种遗漏医嘱)。药剂师进行了 675 次(7920 次中的 8.5%)干预;其中 11 次与 PEs 无关。错误医嘱和出现错误的患者的发生率分别为 8.0%(95%CI7.4,8.5[n=630/7920])和 20.4%(95%CI18.1,22.9[n=212/1038])。PE 的发生率为 8.4%(95%CI7.8,9.0[n=664/7920])。与 PEs 相关的前三种药物是对乙酰氨基酚(acetaminophen;30[4.8%])、沙丁胺醇(albuterol;28[4.4%])和奥美拉唑(omeprazole;25[4.0%])。药剂师在未咨询医生的情况下拦截了 524 次(83.2%)错误医嘱,并且在 24 小时内处理了 70%的错误医嘱。遗漏(31.0%)、药物选择(29.4%)和剂量方案(18.1%)错误类型占 PEs 的>75%。有 18 个(2.9%)严重、481 个(76.3%)显著和 131 个(20.8%)轻微错误医嘱。大多数错误医嘱(469[74.4%])被评为严重程度显著,药剂师对 PEs 的影响显著。CREs(n=279)占错误医嘱的 44.3%。CREs 和非 CREs 的严重程度存在显著差异(χ2=38.88;df=4;p<0.001),CREs 的严重程度低于非 CREs。具有多种口服剂型的药物(比值比[OR]2.1;95%CI1.25,3.37;p=0.004)和初级医生处方(OR2.54;95%CI1.08,5.99;p=0.03)是 PEs 的显著预测因素。
即使使用电子处方系统,出院时也经常发生 PEs。用户和计算机因素似乎都促成了高错误率。电子处方系统有助于系统地提取数据以研究医院实践中的 PEs。药剂师在记录和预防 PEs 方面发挥着重要作用,这些 PEs 在到达患者并可能伤害患者之前就得到了预防。药剂师应该了解 CREs,以便他们补充而不是重复电子处方系统的优势。