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远程医疗监测能否识别慢性阻塞性肺疾病的急性加重并减少住院次数?系统数据分析

Does Telehealth Monitoring Identify Exacerbations of Chronic Obstructive Pulmonary Disease and Reduce Hospitalisations? An Analysis of System Data.

作者信息

Kargiannakis Melissa, Fitzsimmons Deborah A, Bentley Claire L, Mountain Gail A

机构信息

Faculty of Health Sciences, Western University, London, ON, Canada.

School of Nursing and Allied Health, Liverpool John Moores University, Liverpool, United Kingdom.

出版信息

JMIR Med Inform. 2017 Mar 22;5(1):e8. doi: 10.2196/medinform.6359.

DOI:10.2196/medinform.6359
PMID:28330829
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5382257/
Abstract

BACKGROUND

The increasing prevalence and associated cost of treating chronic obstructive pulmonary disease (COPD) is unsustainable. Health care organizations are focusing on ways to support self-management and prevent hospital admissions, including telehealth-monitoring services capturing physiological and health status data. This paper reports on data captured during a pilot randomized controlled trial of telehealth-supported care within a community-based service for patients discharged from hospital following an exacerbation of their COPD.

OBJECTIVE

The aim was to undertake the first analysis of system data to determine whether telehealth monitoring can identify an exacerbation of COPD, providing clinicians with an opportunity to intervene with timely treatment and prevent hospital readmission.

METHODS

A total of 23 participants received a telehealth-supported intervention. This paper reports on the analysis of data from a telehealth monitoring system that captured data from two sources: (1) data uploaded both manually and using Bluetooth peripheral devices by the 23 participants and (2) clinical records entered as nursing notes by the clinicians. Rules embedded in the telehealth monitoring system triggered system alerts to be reviewed by remote clinicians who determined whether clinical intervention was required. We also analyzed data on the frequency and length (bed days) of hospital admissions, frequency of hospital Accident and Emergency visits that did not lead to hospital admission, and frequency and type of community health care service contacts-other than the COPD discharge service-for all participants for the duration of the intervention and 6 months postintervention.

RESULTS

Patients generated 512 alerts, 451 of which occurred during the first 42 days that all participants used the equipment. Patients generated fewer alerts over time with typically seven alerts per day within the first 10 days and four alerts per day thereafter. They also had three times more days without alerts than with alerts. Alerts were most commonly triggered by reports of being more tired, having difficulty with self-care, and blood pressure being out of range. During the 8-week intervention, and for 6-month follow-up, eight of the 23 patients were hospitalized. Hospital readmission rates (2/23, 9%) in the first 28 days of service were lower than the 20% UK norm.

CONCLUSIONS

It seems that the clinical team can identify exacerbations based on both an increase in alerts and the types of system-generated alerts as evidenced by their efforts to provided treatment interventions. There was some indication that telehealth monitoring potentially delayed hospitalizations until after patients had been discharged from the service. We suggest that telehealth-supported care can fulfill an important role in enabling patients with COPD to better manage their condition and remain out of hospital, but adequate resourcing and timely response to alerts is a critical factor in supporting patients to remain at home.

TRIAL REGISTRATION

International Standard Randomized Controlled Trial Number (ISRCTN): 68856013; http://www.isrctn.com/ISRCTN68856013 (Archived by WebCite at http://www.webcitation.org/6ofApNB2e).

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d824/5382257/d5e7ec3e5cbc/medinform_v5i1e8_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d824/5382257/cc83f9cd1ced/medinform_v5i1e8_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d824/5382257/ddfc7fecbf29/medinform_v5i1e8_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d824/5382257/d5e7ec3e5cbc/medinform_v5i1e8_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d824/5382257/cc83f9cd1ced/medinform_v5i1e8_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d824/5382257/ddfc7fecbf29/medinform_v5i1e8_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d824/5382257/d5e7ec3e5cbc/medinform_v5i1e8_fig3.jpg
摘要

背景

慢性阻塞性肺疾病(COPD)的患病率不断上升,治疗相关费用难以为继。医疗保健机构正致力于支持自我管理并预防住院,包括通过远程健康监测服务获取生理和健康状况数据。本文报告了在一项试点随机对照试验中收集的数据,该试验针对社区服务中因COPD急性加重住院后出院的患者,提供远程健康支持护理。

目的

旨在对系统数据进行首次分析,以确定远程健康监测能否识别COPD急性加重,为临床医生提供及时干预治疗并预防再次住院的机会。

方法

共有23名参与者接受了远程健康支持干预。本文报告了对远程健康监测系统数据的分析,该系统从两个来源收集数据:(1)23名参与者手动上传以及使用蓝牙外围设备上传的数据;(2)临床医生作为护理记录录入的临床记录。远程健康监测系统中嵌入的规则触发系统警报,由远程临床医生进行审查,以确定是否需要临床干预。我们还分析了所有参与者在干预期间及干预后6个月内的住院频率和时长(住院天数)、未导致住院的医院急诊就诊频率,以及除COPD出院服务外的社区医疗服务接触频率和类型。

结果

患者共产生了512次警报,其中451次发生在所有参与者使用该设备的前42天内。随着时间推移,患者产生的警报减少,最初10天内通常每天有7次警报,此后每天有4次警报。他们无警报的天数是有警报天数的三倍。警报最常由感到更疲倦、自我护理困难以及血压超出范围的报告触发。在为期8周的干预期间及6个月的随访中,23名患者中有8人住院。服务开始后的前28天内再次住院率(2/23,9%)低于英国20%的标准水平。

结论

临床团队似乎能够根据警报增加以及系统生成的警报类型来识别急性加重,这从他们提供治疗干预的努力中得到了证明。有迹象表明,远程健康监测可能会将住院延迟到患者从该服务出院之后。我们认为,远程健康支持护理在使COPD患者更好地管理病情并避免住院方面可以发挥重要作用,但充足的资源配备和对警报的及时响应是支持患者居家的关键因素。

试验注册

国际标准随机对照试验编号(ISRCTN):68856013;http://www.isrctn.com/ISRCTN68856013(由WebCite存档于http://www.webcitation.org/6ofApNB2e)

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