Gentile Giovanna, Del Casale Antonio, De Luca Ottavia, Salerno Gerardo, Spirito Sara, Regiani Martina, Regiani Matteo, Preissner Saskia, Rocco Monica, Preissner Robert, Simmaco Maurizio, Borro Marina
, Via di Grottarossa 1035/1039, Rome, 00189, Italy.
Laboratory of Clinical Biochemistry, Advanced Molecular Diagnostic Unit, Sant'Andrea University Hospital, Via di Grottarossa 1035/1039, Rome, 00189, Italy.
Arch Public Health. 2024 Sep 4;82(1):146. doi: 10.1186/s13690-024-01381-7.
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.
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.
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%.
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.
处方错误给健康和经济带来了巨大负担，因此需要实施有效的方法来预防/减少这些错误。多重用药方案（五种或更多药物）极易出现未被识别的处方错误，因为药物相互作用、指南和禁忌的复杂网络在处方阶段很难得到充分评估，尤其是当涉及不同医生时。旨在评估多重用药的临床决策支持系统对于识别和纠正处方错误可能至关重要。
应用一种商业临床决策支持系统（Drug-PIN）来估计2023年4月至6月期间在意大利罗马圣安德烈亚医院接受入院前服务的307例连续患者中未被识别的处方错误频率。Drug-PIN是一个两步系统，首先对特定治疗-患者对相关的风险（低、中或高）进行评分，然后通过药物交换实现治疗优化。我们将处方错误定义为通过治疗优化可以持续降低Drug-PIN计算出的风险的情况。
205例患者存在多重用药情况，Drug-PIN预测91例患者（29.6%）存在中度至高度用药风险。其中58例患者（63.7%）通过Drug-PIN指导实现了治疗优化，计算出的治疗相关风险评分有统计学意义的降低。治疗无法改善的患者使用的药物数量在统计学上显著更多。考虑整个研究人群，可避免的处方错误率为18.89%。
结果表明，计算机辅助评估用药相关危害可能是识别和预防多重用药中处方错误的有价值且可操作的工具。我们在医院入院前环境中进行了这项研究，该环境不代表一般人群，但却是拦截脆弱人群的热点地区，通过系统使用适当的临床决策支持工具，可以迅速识别并纠正相当一部分潜在有害的多重用药方案。
