Recognizing and preventing unacknowledged prescribing errors associated with polypharmacy.

IF 3.2 3区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Archives of Public Health Pub Date : 2024-09-04 DOI:10.1186/s13690-024-01381-7
Giovanna Gentile, Antonio Del Casale, Ottavia De Luca, Gerardo Salerno, Sara Spirito, Martina Regiani, Matteo Regiani, Saskia Preissner, Monica Rocco, Robert Preissner, Maurizio Simmaco, Marina Borro
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Abstract

Background: Prescribing errors put an enormous burden on health and the economy, claiming implementation of effective methods to prevent/reduce them. Polypharmacy regimens (five or more drugs) are highly prone to unacknowledged prescribing errors, since the complex network of drug-drug interactions, guidelines and contraindications is challenging to be adequately evaluated in the prescription phase, especially if different doctors are involved. Clinical decision support systems aimed at polypharmacy evaluation may be crucial to recognize and correct prescribing errors.

Methods: A commercial clinical decision support system (Drug-PIN®) was applied to estimate the frequency of unrecognized prescribing errors in a group of 307 consecutive patients accessing the hospital pre-admission service of the Sant'Andrea Hospital of Rome, Italy, in the period April-June 2023. Drug-PIN® is a two-step system, first scoring the risk (low, moderate or high) associated with a certain therapy-patient pair, then allowing therapy optimization by medications exchanges. We defined prescribing errors as cases where therapy optimization could achieve consistent reduction of the Drug-PIN® calculated risk.

Results: Polypharmacy was present in 205 patients, and moderate to high risk for medication harm was predicted by Drug-PIN® in 91 patients (29.6%). In 58 of them (63.7%), Drug-PIN® guided optimization of the therapy could be achieved, with a statistically significant reduction of the calculated therapy-associated risk score. Patients whose therapy cannot be improved have a statistically significant higher number of used drugs. Considering the overall study population, the rate of avoidable prescribing errors was 18.89%.

Conclusions: Results suggest that computer-aided evaluation of medication-associated harm could be a valuable and actionable tool to identify and prevent prescribing errors in polypharmacy. We conducted the study in a Hospital pre-admission setting, which is not representative of the general population but represents a hotspot to intercept fragile population, where a consistent fraction of potentially harmful polypharmacy regimens could be promptly identified and corrected by systematic use of adequate clinical decision support tools.

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认识和预防与多种药物治疗相关的未被承认的处方错误。
背景:处方错误给健康和经济造成了巨大负担,因此需要实施有效的方法来预防/减少处方错误。多种药物治疗方案(五种或更多种药物)极易出现未被发现的处方错误,因为在处方阶段,尤其是在涉及不同医生的情况下,很难对药物之间的相互作用、指南和禁忌等复杂网络进行充分评估。以多药评估为目标的临床决策支持系统可能是识别和纠正错误处方的关键:方法:应用商业临床决策支持系统(Drug-PIN®)对 2023 年 4 月至 6 月期间在意大利罗马圣安德烈医院入院前服务处就诊的 307 名连续患者中未识别处方错误的频率进行估计。Drug-PIN® 是一种分两步进行的系统,首先对与某种疗法-患者配对相关的风险(低、中或高)进行评分,然后通过换药优化疗法。我们将处方错误定义为通过优化治疗可持续降低 Drug-PIN® 计算风险的情况:结果:205 名患者存在多重用药,Drug-PIN® 预测 91 名患者(29.6%)存在中度至高度用药风险。其中 58 名患者(占 63.7%)在 Drug-PIN® 的指导下优化了治疗方案,计算出的治疗相关风险评分在统计学上显著降低。据统计,无法改善治疗的患者使用的药物数量明显增加。从整个研究人群来看,可避免的处方错误率为 18.89%:研究结果表明,计算机辅助的药物相关危害评估可以作为一种有价值的、可操作的工具,用于识别和预防多种药物治疗中的处方错误。我们在医院的入院前环境中进行了这项研究,该环境并不代表普通人群,但却代表了拦截脆弱人群的一个热点,在该环境中,通过系统地使用适当的临床决策支持工具,可以及时发现并纠正潜在有害的多种药物治疗方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Archives of Public Health
Archives of Public Health Medicine-Public Health, Environmental and Occupational Health
CiteScore
4.80
自引率
3.00%
发文量
244
审稿时长
16 weeks
期刊介绍: rchives of Public Health is a broad scope public health journal, dedicated to publishing all sound science in the field of public health. The journal aims to better the understanding of the health of populations. The journal contributes to public health knowledge, enhances the interaction between research, policy and practice and stimulates public health monitoring and indicator development. The journal considers submissions on health outcomes and their determinants, with clear statements about the public health and policy implications. Archives of Public Health welcomes methodological papers (e.g., on study design and bias), papers on health services research, health economics, community interventions, and epidemiological studies dealing with international comparisons, the determinants of inequality in health, and the environmental, behavioural, social, demographic and occupational correlates of health and diseases.
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