背景:保持药物依从性对于患有精神疾病的人来说可能是具有挑战性的。基于对电子健康记录(EHR)中的问题模式进行自动检测的临床决策支持系统(CDSS)有可能通过建议基于证据的行动方案来实现对非依从性事件(“标志”)的早期干预。然而,现有文献显示,在跟踪低风险病例时,多重障碍-感知到的缺乏益处,数据的真实性,以人为中心的设计问题,等。-在现实世界的环境中对临床医生进行随访。这项研究调查了社区心理健康诊所中与随访不遵守提示相关的临床医生决策行为模式。
方法:后续提示,记录临床医生的反应,由CDSS软件(AI2)启用。使用主题综合方法分析回顾提示后记录的去识别的临床医生笔记-从描述临床医生评论开始,然后整理与设计相关的分析主题,并行,描述后续行为的先验类别。测试了从文献中得出的有关随访类别与客户和药物亚型特征的关系的假设。
结果:大多数客户未被随访(n=260;78%;随访:n=71;22%)。决策说明中出现的分析主题建议了环境因素-客户环境,他们的临床关系,以及医疗需求介导的临床医生如何与CDSS标志互动。发现药物亚型和随访之间存在显着差异,与抗精神病药和抗焦虑药相比,抗抑郁药的随访可能性较小(χ2=35.196,44.825;p<0.001;v=0.389,0.499);在采取行动的时间之间,遵循0和未遵循1个标志(M0=31.78;M1=45.55;U=12,119;p<0.001;2=.05)。
结论:这些分析鼓励积极纳入消费者和护理人员的意见,非EHR数据流,并更好地将来自并行卫生系统和其他临床医生的数据纳入CDSS设计,以鼓励后续行动。
Maintaining medication adherence can be challenging for people living with mental ill-health. Clinical decision support systems (CDSS) based on automated detection of problematic patterns in Electronic Health Records (EHRs) have the potential to enable early intervention into non-adherence events (\"flags\") through suggesting evidence-based courses of action. However, extant literature shows multiple barriers-perceived lack of benefit in following up low-risk cases, veracity of data, human-centric design concerns, etc.-to clinician follow-up in real-world settings. This study examined patterns in clinician decision making behaviour related to follow-up of non-adherence prompts within a community mental health clinic.
The prompts for follow-up, and the recording of clinician responses, were enabled by CDSS software (AI2). De-identified clinician notes recorded after reviewing a prompt were analysed using a thematic synthesis approach-starting with descriptions of clinician comments, then sorting into analytical themes related to design and, in parallel, a priori categories describing follow-up behaviours. Hypotheses derived from the literature about the follow-up categories\' relationships with client and medication-subtype characteristics were tested.
The majority of clients were Not Followed-up (n = 260; 78%; Followed-up: n = 71; 22%). The analytical themes emerging from the decision notes suggested contextual factors-the clients\' environment, their clinical relationships, and medical needs-mediated how clinicians interacted with the CDSS flags. Significant differences were found between medication subtypes and follow-up, with Anti-depressants less likely to be followed up than Anti-Psychotics and Anxiolytics (χ2 = 35.196, 44.825; p < 0.001; v = 0.389, 0.499); and between the time taken to action Followed-up0 and Not-followed up1 flags (M0 = 31.78; M1 = 45.55; U = 12,119; p < 0.001; η2 = .05).
These analyses encourage actively incorporating the input of consumers and carers, non-EHR data streams, and better incorporation of data from parallel health systems and other clinicians into CDSS designs to encourage follow-up.