演示自动化的不依从和服务脱离风险监测，并对严重精神疾病进行积极随访。

Demonstration of automated non-adherence and service disengagement risk monitoring with active follow-up for severe mental illness.

机构信息

Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.

Personal Health Informatics Lab, Flinders Digital Health Centre, Flinders University, Clovelly Park, SA, Australia.

出版信息

Aust N Z J Psychiatry. 2021 Oct;55(10):976-982. doi: 10.1177/0004867421998800. Epub 2021 Mar 21.

DOI:10.1177/0004867421998800

PMID:33745291

Abstract

AIMS

Medication cessation and service disengagement often precedes relapse in people with severe mental illnesses but currently specialist mental health services only become involved after a relapse. Early detection of non-adherence is needed to enable intervention to avert relapse. This paper aims to demonstrate how digitally automated non-adherence risk monitoring from Medicare data with active follow-up can work and perform in practice in a real-world mental health service setting.

METHODS

AI software is an automated risk monitoring tool to detect non-adherence using Medicare data. It was implemented prospectively in a cohort of 354 registered patients of a community mental health clinic between July 2019 and February 2020. Patients flagged as at risk by the software were reviewed by two clinicians. We describe the risks automatically flagged for non-adherence and the clinical responses. We examine differences in clinical and demographic factors in patients flagged at increased risk of non-adherence.

RESULTS

In total, 46.7% (142/304) were flagged by the software as at risk of non-adherence, and 22% (31/142) received an intervention following clinician review of their case notes. Patients flagged by the software were older in age and had more prior mental health treatment episodes. More alerts were associated with patients who had been transferred from the mental health service to the care of their general practitioners, and those with more alerts were more likely to receive a follow-up intervention.

CONCLUSION

Digitally automated monitoring for non-adherence risk is feasible and can be integrated into clinical workflows in community psychiatric and primary care settings. The technology may assist clinicians and services to detect non-adherence behaviour early, thereby triggering interventions that have the potential to reduce rates of mental health deterioration and acute illness relapse.

摘要

目的

在患有严重精神疾病的人群中，药物戒断和服务脱节通常先于复发，但目前专业精神卫生服务仅在复发后才介入。需要早期发现不依从，以便进行干预以避免复发。本文旨在展示如何使用医疗保险数据中的数字化自动不依从风险监测以及主动随访在现实精神卫生服务环境中实际运作和表现。

方法

人工智能软件是一种使用医疗保险数据检测不依从的自动风险监测工具。它在 2019 年 7 月至 2020 年 2 月期间在一个社区精神卫生诊所的 354 名登记患者队列中前瞻性实施。软件标记为有风险的患者由两名临床医生进行审查。我们描述了自动标记为不依从的风险以及临床反应。我们检查了在不依从风险增加的患者中临床和人口统计学因素的差异。