Show Your Work: Do Prescripition Annotations Impact Near Miss Medication Errors?

描述（由申请人提供）：药房是处方药错误检测系统的组成部分。然而，由于许多处方本身缺乏明确性，再加上缺乏关于处方的患者特定数据，经常会要求开处方者澄清或提供更多细节。在可以电子地生成处方并且这些处方可以与电子健康记录接口的时代，可以自动地在处方上包括更多信息。在某些情况下，数据表明，尽管了解包括这些信息的价值，但临床医生很少将其包括在处方中，从而放弃了通过包括这些数据获得的潜在安全性收益。其目标是评估将患者药物、过敏、体重、剂量指南和其他处方策略数据纳入电子处方的价值。为了实现这一目标，研究人员将：（1）评估药剂师致电医生的基线率和原因;（2）对电子处方作者进行随机试验，包括和不包括打印的患者特定处方策略数据，以确定这些数据对药剂师回电率和类型的影响。对于本项目的两个目标，他们将从研究期间处理的召回数据表和药房日志表中收集信息，以计算每1000张处方的召回率，并确定当处方包括处方策略数据时，处方者召回的原因是否发生变化。这项研究将提供有价值的数据，一个技术上容易，但不常见的实现过程中的电子处方书写工具。因为这些数据很容易提供给与电子健康记录集成的处方作者，如果这项研究发现了好处，这些结果可能很容易转化为实践。这项研究还将提供有价值的基线数据，以确定对电子处方进行额外修改的影响。

DESCRIPTION (PROVIDED BY APPLICANT): Pharmacists are an integral part of the error detection system for prescription medications. However, the lack of clarity inherent in many prescriptions, coupled with the lack of patient-specific data on prescriptions, often generates calls back to prescribers for clarification or for additional detail. In an era where prescriptions may be generated electronically, and where these prescriptions may be interfaced with electronic health records, it is possible to include more information on the prescription automatically. In some cases, data have shown that, despite understanding the value of including such information, clinicians rarely include it on prescriptions, thereby foregoing the potential safety gains to be had by including these data. The goal is to assess the value of incorporating patient medications, allergies, weights, dosing guidelines and other prescribing-strategy data onto an electronic prescription. To accomplish this goal, the investigators will: (1) assess the baseline rate and reasons for calls from pharmacists to physicians; and (2) conduct a randomized trial of an electronic prescription writer with and without printed patient-specific prescribing strategy data included to determine the impact of these data on the rate and type of pharmacist callbacks. For both aims of this project, they will collect information from call back data sheets and pharmacy log sheets processed during study periods to calculate a rate of callbacks per 1000 prescriptions, as well as to determine whether the reasons for callbacks to prescribers change when prescriptions include prescribing strategy data. This study will provide valuable data about a technically easy but uncommonly implemented process among electronic prescription-writing tools. Because these data are readily available to prescription writers that are integrated with electronic health records, if this study uncovers benefit, these results may be easily translated into practice. This study also will provide valuable baseline data to determine the impact of additional modifications to the electronic prescription.