Department of Pharmacy Practice and Science, The University of Arizona College of Pharmacy, Tucson, 85721, USA.
Res Social Adm Pharm. 2012 Nov-Dec;8(6):523-32. doi: 10.1016/j.sapharm.2011.12.005. Epub 2012 Jan 20.
Relatively little is known about how e-prescribing impacts outpatient prescribing errors. Comparing these data with problems identified with other prescription conveyance methods will help researchers identify system problems and offer solutions.
The objectives of this study were to (1) measure the incidence of prescription problems that required pharmacist intervention, (2) determine the types and relative frequencies of prescription conveyance that contain problems that require pharmacist intervention, and (3) estimate the pharmacy personnel time and related practice expenses for prescriptions requiring intervention.
This study used an observational prospective design examining data from 2 community chain grocery store pharmacies. The primary outcome was number of interventions for each prescription conveyance type. Variables of interest included (1) the type of medication(s) involved in the intervention, (2) how the pharmacist was alerted to the potential problem, (3) reason for the intervention, (4) pharmacists' actions based on the intervention, (5) time spent during the resolution of the intervention, and (6) costs based on pharmacy personnel time. Chi-square analysis with a Bonferroni correction was used to compare percentage intervention rates between prescription conveyances. E-prescribing was used as the reference group to compare across interventions. A Kruskal-Wallis rank test was used to compare the time on task values for the interventions.
Pharmacists reviewed 1678 new prescriptions and intervened on 153 (9.1%) during 13 days of data collection. A total of 11 hours and 58 minutes were required to perform all interventions for an overall average of 4.9 (standard deviation=0.34) minutes per intervention. The most common reasons for pharmacists' intervention on e-prescriptions were excessive quantity/duration (18.2%) and violating legal requirements (18.2%). The percentages of interventions were significantly different between e-prescribing (11.7%) and both faxed (3.9%) and verbal (5.1%) orders (P<.0001 and P<.01, respectively), with faxed and verbal interventions occurring less frequently. The difference in the intervention rates between e-prescribing (11.7%) and handwritten (15.4%) prescription conveyances were not statistically significant.
When comparing e-prescribing with handwritten prescriptions requiring interventions, no significant differences existed. Results suggest that pharmacists must intervene on e-prescriptions as at the same rate as handwritten prescriptions.
关于电子处方对门诊处方错误的影响，我们知之甚少。将这些数据与其他处方传递方式中发现的问题进行比较，将有助于研究人员识别系统问题并提供解决方案。
本研究的目的是：(1) 测量需要药剂师干预的处方问题的发生率；(2) 确定需要药剂师干预的处方传递类型和相对频率；(3) 估算需要干预的处方的药剂师时间和相关实践费用。
本研究采用观察性前瞻性设计，检查了 2 家社区连锁杂货店药店的数据。主要结果是每种处方传递类型的干预次数。感兴趣的变量包括：(1) 干预所涉及的药物种类；(2) 药剂师如何发现潜在问题；(3) 干预的原因；(4) 药剂师根据干预采取的行动；(5) 解决干预所需的时间；(6) 基于药剂师时间的成本。采用卡方检验(Chi-square analysis)，并使用 Bonferroni 校正比较不同处方传递方式的干预率。将电子处方作为参考组，进行干预比较。采用 Kruskal-Wallis 秩检验比较干预任务的时间值。
在 13 天的数据收集期间，药剂师审查了 1678 份新处方，共干预了 153 份处方(9.1%)。完成所有干预措施共花费 11 小时 58 分钟，平均每次干预时间为 4.9(标准差=0.34)分钟。电子处方干预最常见的原因是剂量/持续时间过大(18.2%)和违反法律要求(18.2%)。电子处方与传真(3.9%)和口头(5.1%)医嘱的干预率有显著差异(P<.0001 和 P<.01)，传真和口头医嘱的发生率较低。电子处方(11.7%)与手写处方(15.4%)干预率之间的差异无统计学意义。
将电子处方与需要干预的手写处方进行比较时，两者之间没有显著差异。结果表明，药剂师必须以与手写处方相同的速度干预电子处方。
