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实施抗精神病药副作用的数字化临床决策支持工具:一项焦点小组研究。

Implementing a digital clinical decision support tool for side effects of antipsychotics: a focus group study.

机构信息

Faculty of Health and Life Sciences, Oxford Brookes University, Oxford, UK.

Department of Psychiatry, University of Oxford, Oxford, UK.

出版信息

Evid Based Ment Health. 2019 May;22(2):56-60. doi: 10.1136/ebmental-2019-300086. Epub 2019 Apr 15.

Abstract

BACKGROUND

In medicine, algorithms can inform treatment decisions by combining the most up-to-date evidence about side effect profiles of medications, which are comparable in efficacy. Their use provides opportunities for improved shared clinician-patient decision-making when initiating therapy. We designed a decision support tool (DST) that incorporated the latest evidence regarding antipsychotic side effects. The tool allowed patients to select one side effect commonly associated with antipsychotics that they wished to avoid; the tool then provided a list of suggested medications and ones to avoid.

OBJECTIVE

To explore qualitatively the acceptability and usefulness of the DST from the perspectives of patients and psychiatrists.

METHODS

This qualitative study took place at a mental health and community hospital in Oxford, UK, in 2018. Four patients/carers and four psychiatrists were recruited to two focus groups to explore their perceptions of the tool. Data were thematically analysed.

FINDINGS

Findings demonstrated a high degree of acceptability and potential usability of the DST for patients and psychiatrists. The main themes to emerge relating to the DST were 'prescribing preferences and practices', 'consideration and awareness of side effects', 'app content, layout and accessibility', 'influence on clinical practice' and 'role in decision-making'.

CONCLUSIONS

A proof-of-concept clinical study will incorporate the recommendations produced from the findings into the tool's design.

CLINICAL IMPLICATIONS

Digital DSTs provide opportunities for the most up-to-date information on medication side effects to be used as the basis for shared clinician-patient decision-making. This tool has the potential to improve adherence to psychiatric medication, with benefits to clinical outcomes and healthcare resourcing.

摘要

背景

在医学领域,算法可以通过结合关于药物副作用的最新证据来为治疗决策提供信息,这些证据在疗效上是可比的。当开始治疗时,它们的使用为改善临床医生和患者共同决策提供了机会。我们设计了一个决策支持工具(DST),其中包含了关于抗精神病药副作用的最新证据。该工具允许患者选择他们希望避免的一种常见的抗精神病药副作用;然后,该工具提供了一份建议药物和避免药物的清单。

目的

从患者和精神科医生的角度定性探索 DST 的可接受性和有用性。

方法

这项定性研究于 2018 年在英国牛津的一家心理健康和社区医院进行。招募了 4 名患者/照顾者和 4 名精神科医生参加两个焦点小组,以探讨他们对该工具的看法。数据进行了主题分析。

结果

研究结果表明,患者和精神科医生对 DST 的接受程度和潜在可用性都很高。与 DST 相关的主要主题包括“处方偏好和实践”、“考虑和意识到副作用”、“应用程序的内容、布局和可访问性”、“对临床实践的影响”和“在决策中的作用”。

结论

一项概念验证临床研究将把从研究结果中提出的建议纳入到工具的设计中。

临床意义

数字 DST 为使用药物副作用的最新信息作为临床医生和患者共同决策的基础提供了机会。该工具有可能提高对精神科药物的依从性,从而改善临床结果和医疗资源利用。

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