Leong Elizabeth, Chang Stephane, Yearwood Kristi, Eeles Eamonn, Yerkovich Stephanie, Ling Carolina, Teodorczuk Andrew, Dissanayaka Nadeeka
Internal Medicine Services, The Prince Charles Hospital, Brisbane, Queensland, Australia.
School of Medicine, Northside Clinical School, The Prince Charles Hospital, Brisbane, Queensland, Australia.
Australas J Ageing. 2025 Mar;44(1):e70000. doi: 10.1111/ajag.70000.
OBJECTIVE(S): The identification of cause(s) of delirium remains a clinical challenge within medicine. Our group have previously successfully developed and tested the Aetiology in Delirium-Decision Support Tool (AiD-DST). The AiD-DST is designed to help medical professionals close the gap on the detection of cause(s) of delirium. Here, we report on use of AiD-DST in the real-world setting.
A real-world implementation study of the AiD-DST within a general medical ward of a metropolitan hospital was conducted over a 10-week period. A mixed method evaluation was performed based upon the RE-AIM Framework that incorporates reach, effectiveness, adoption, implementation and maintenance of an intervention.
Reach: fifty-three out of 87 (61%) eligible doctors consented to participation in the study.
A mean of 4.3 diagnoses were generated per patient with no difference in frequency when compared with historical control (z = 1.36; p = .17). Average usability score was 5.86 (SD = 1.15) on a 7-point scale, with 93% of respondents being satisfied with the AiD-DST. Free text feedback comprised themes of accessibility, ergonomics, diagnostic accuracy and applicability of AiD-DST to related conditions.
Instrument completion rate was 98% (n = 49/50), with a median completion time of 90 s. Maintenance: Sixty-seven % of uses of AiD-DST occurred in the second half of the study (p = .3). Following the initiation period there was an increase in use (r = .79; p = 02).
Proof of principle was demonstrated for local implementation of a diagnostic support tool (AiD-DST).
谵妄病因的识别仍然是医学领域的一项临床挑战。我们团队之前成功开发并测试了谵妄病因决策支持工具（AiD-DST）。AiD-DST旨在帮助医疗专业人员缩小在谵妄病因检测方面的差距。在此，我们报告AiD-DST在实际环境中的使用情况。
在一家大都市医院的普通内科病房对AiD-DST进行了为期10周的实际应用研究。基于RE-AIM框架进行了混合方法评估，该框架纳入了干预措施的覆盖范围、有效性、采用情况、实施情况和维持情况。
覆盖范围：87名符合条件的医生中有53名（61%）同意参与研究。
每位患者平均产生4.3个诊断结果，与历史对照相比频率无差异（z = 1.36；p = 0.17）。在7分制量表上，平均可用性评分为5.86（标准差 = 1.15），93%的受访者对AiD-DST满意。自由文本反馈包括AiD-DST的可及性、人体工程学、诊断准确性以及对相关病症的适用性等主题。
工具完成率为98%（n = 49/50），中位完成时间为90秒。维持情况：67%的AiD-DST使用发生在研究的后半段（p = 0.3）。在启动期之后使用量有所增加（r = 0.79；p = 0.02）。
证明了诊断支持工具（AiD-DST）在本地实施的原理。
