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

描述（由申请人提供）：药剂师是处方药错误检测系统不可或缺的一部分。然而，许多处方本身缺乏清晰度，加上缺乏患者特定的处方数据，常常会要求处方者进行澄清或提供更多细节。在可以电子方式生成处方并且这些处方可以与电子健康记录交互的时代，可以自动包括关于处方的更多信息。在某些情况下，数据表明，尽管了解包含此类信息的价值，但临床医生很少将其包含在处方中，从而放弃了包含这些数据所带来的潜在安全收益。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) 对包含或不包含打印的患者特定处方策略数据的电子处方编写器进行随机试验，以确定这些数据对药剂师回电率和类型的影响。为了实现该项目的两个目标，他们将从研究期间处理的回调数据表和药房日志表中收集信息，以计算每 1000 个处方的回调率，并确定当处方包含处方策略数据时，回调处方者的原因是否会发生变化。这项研究将为电子处方书写工具中技术上简单但不常见的实施过程提供有价值的数据。由于与电子健康记录集成的处方作者可以轻松获得这些数据，因此如果这项研究发现了益处，这些结果可以很容易地转化为实践。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.