1 Centre for e-Health, Department of Information and Communication Technology, Faculty of Engineering and Science, University of Agder, Kristiansand, Norway.
2 Sørlandet Hospital, Kristiansand, Norway.
J Telemed Telecare. 2019 Jan;25(1):46-53. doi: 10.1177/1357633X17735558. Epub 2017 Oct 10.
Patients with chronic obstructive pulmonary disease require help in daily life situations to increase their individual perception of security, especially under worsened medical conditions. Unnecessary hospital (re-)admissions and home visits by doctors or nurses shall be avoided. This study evaluates the results from a two-year telemedicine field trial for automatic health status assessment based on remote monitoring and analysis of a long time series of vital signs data from patients at home over periods of weeks or months.
After discharge from hospital treatment for acute exacerbations, 94 patients were recruited for follow-up by the trial system. The system supported daily measurements of pulse and transdermal peripheral capillary oxygen saturation at patients' homes, a symptom-specific questionnaire, and provided nurses trained to use telemedicine ("telenurses") with an automatically generated health status overview of all monitored patients. A colour code (green/yellow/red) indicated whether the patient was stable or had a notable deterioration, while red alerts highlighted those in most urgent need of follow-up. The telenurses could manually overwrite the status level based on the patients' conditions observed through video consultation.
Health status evaluation in 4970 telemonitor datasets were assessed retrospectively. The automatic health status determination (subgroup of 33 patients) showed green status at 46% of the days during a one-month monitoring period, 28% yellow status, and 19% red status (no data reported at 7% of the days). The telenurses manually downrated approximately 10% of the red or yellow alerts.
The evaluation of the defined real-time health status assessment algorithms, which involve static rules with personally adapted elements, shows limitations to adapt long-term home monitoring with adequate interpretation of day-to-day changes in the patient's condition. Thus, due to the given sensitivity and specificity of such algorithms, it seems challenging to avoid false high alerts.
慢性阻塞性肺疾病患者需要在日常生活中获得帮助，以提高其个体安全感，尤其是在病情恶化的情况下。应避免不必要的医院（再次）入院和医生或护士的家访。本研究评估了基于远程监测和分析患者家中数周或数月的长时间生命体征数据的自动健康状况评估的两年远程医疗现场试验的结果。
在因急性加重症出院后，有 94 名患者被纳入试验系统的随访。该系统支持患者在家中进行日常脉搏和经皮外周毛细血管血氧饱和度测量、特定症状问卷，并为接受远程医疗培训的护士（“远程护士”）提供所有监测患者的自动生成的健康状况概述。颜色代码（绿色/黄色/红色）表示患者是否稳定或有明显恶化，而红色警报则突出显示那些最需要随访的患者。远程护士可以根据视频咨询中观察到的患者情况手动覆盖状态级别。
回顾性评估了 4970 个远程监测数据集的健康状况评估。在一个月的监测期间，自动健康状况确定（33 名患者的子组）显示绿色状态占 46%的天数，黄色状态占 28%，红色状态占 19%（有 7%的天数没有数据报告）。远程护士手动下调了大约 10%的红色或黄色警报。
定义的实时健康状况评估算法的评估涉及具有个人适应元素的静态规则，显示出适应长期家庭监测的局限性，以充分解释患者病情的日常变化。因此，由于这些算法的给定灵敏度和特异性，似乎难以避免虚假高警报。
