药剂师专业知识对大型学术医疗系统综合电子咨询项目接受率的影响。

Innovations in pharmacy Pub Date : 2024-08-21 eCollection Date: 2024-01-01 DOI:10.24926/iip.v15i3.6278
Carrie Freed, Cynthia King, Brandon Soltesz, M David Gothard, Bushra Altabbaa, Aleksandra Majstorovic
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引用次数: 0

摘要

背景:尽管医疗保健系统中的医疗专业人员都在使用电子会诊(e-consults),但在特殊疾病状态之外,药剂师使用电子会诊仍是新鲜事物。我们需要开展更多的研究来填补文献空白,从而帮助药剂师在电子会诊项目中发挥最佳作用。目标:本研究旨在评估药剂师的专业知识对电子会诊结果的影响。研究方法本研究对一家大型学术医疗系统在 2020 年 3 月 1 日至 2022 年 8 月 31 日期间由药剂师完成的所有药学电子会诊进行了回顾性审查。这项研究被视为质量改进,无需获得机构审查委员会的批准。电子会诊使用报告进行识别。关键数据收集点包括电子会诊疾病状态、下达订单的医疗服务提供者、药剂师的专业以及建议结果。主要结果是专家与非专家药剂师建议的接受率差异。次要结果包括总体实施率、随时间推移的实施率、医疗机构类型之间的接受率、实施时间和药剂师响应时间。专家/非专家二分法的接受率通过皮尔逊卡方检验进行比较。结果共有 375 份电子会诊符合纳入标准,涉及 19 种疾病。最常见的三种疾病包括糖尿病(27.0%)、疼痛治疗(13.1%)和心理健康(11.0%)。近 60% 的电子会诊是与专家进行的疾病会诊。由专家完成的电子会诊与非专家完成的电子会诊相比,医疗服务提供者的接受率更高(分别为 62.6% 对 39.6%,p = 0.002)。总体实施率为 51.8%。医生(MD/DOs)接受药剂师建议的比例为 55.6%,高级注册护士(APRNs)为 64.7%,医生助理(PAs)为 100.0%,其他专业人员为 25.0%(p = 0.033)。建议实施的平均时间为 16.5 天(SD = 29.4 天)。药剂师做出回应的平均时间为 1.1 天(标准差 = 1.4 天)。结论综合电子会诊计划在纳入专家药剂师后会更加成功。
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Impact of Pharmacist Expertise on Acceptance Rates in a Comprehensive E-Consult Program within a Large Academic Health System.

Background: Although electronic consults (e-consults) are utilized in healthcare systems by medical professionals, use of e-consults by pharmacy remains novel outside of niche disease states. Additional research is required to fill literature gaps to assist in optimizing the pharmacist's role in e-consult programs. Objective: This study aimed to assess the impact of pharmacist expertise on e-consult outcomes. Methods: This study was a retrospective review of all pharmacy e-consults completed by pharmacists at a large academic health system between March 1st, 2020, and August 31st, 2022. This was deemed quality improvement and did not require Institutional Review Board approval. E-consults were identified using a report. Key data collection points included e-consult disease state, ordering provider, pharmacists' specialty, and recommendation result. The primary outcome was the difference in acceptance rates of expert versus non-expert pharmacist recommendations. Secondary outcomes included the overall implementation rate, implementation rate over time, acceptance rate between provider types, time to implementation, and pharmacist response time. Acceptance rates were compared between expert/non-expert dichotomy via Pearson chi-square test. Results: A total of 375 e-consults met inclusion criteria and spanned 19 unique disease states. The three most common included diabetes mellitus (27.0%), pain management (13.1%), and mental health (11.0%). Nearly 60% of e-consults were in a disease with an expert. The provider acceptance rate was higher when e-consults were completed by an expert versus non-expert (62.6% versus 39.6% respectively, p = 0.002). The overall implementation rate was 51.8%. Physicians (MD/DOs) accepted the pharmacist's recommendations 55.6% of the time, advanced practice registered nurses (APRNs) 64.7%, physician assistants (PAs) 100.0%, and other professionals 25.0% (p = 0.033). Mean time to recommendation implementation was 16.5 days (SD = 29.4 days). Mean time to pharmacist response was 1.1 days (SD = 1.4 days). Conclusions: Comprehensive e-consult programs are more successful when integrating expert pharmacists.

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