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. ? ? ?
| Johnson, Kevin B; Ho, Yun-Xian; Cala, Cather Marie et al. (2010) Showing Your Work: Impact of annotating electronic prescriptions with decision support results. J Biomed Inform 43:321-5 |
