Background

Scalability challenge in real time healthcare monitoring system relates to several issues. One of the insistent issues is the increasing in the number of patients. Increasing in the patients’ number causes long queue and increase the waiting time for the patients in their seeking for healthcare services. Thus, an ethical issue raises as the healthcare providers should provide fast services for all patients. Recent studies have proposed scalable models that are limited to (1) triaging remote patients for the optimal emergency level and (2) prioritizing remote patients with the highest triage level to receive immediate healthcare services. However, these studies have shown limitations, that is, (1) they have not addressed the waiting time for all patients with different triage levels in the same waiting queue; and (2) they have not considered Emergency Department EDs patients. Therefore, considering the remote patients with the treated patients in EDs in one healthcare system is a demand, to efficiently handle all the patients' requests and productively manage the medical resources.

Objective

This study aims to reduce the waiting time for the remote patients in telemedicine with considering treated patients in EDs. The study presents a scalable telemedicine model to improve the ability of real time healthcare monitoring system in accommodating the increasing number of patients with chronic heart disease by reducing their waiting time for healthcare services, prioritizing the patients who have the most emergency cases and provide all the patients by fast healthcare services. The proposed model called Triaging and Prioritizing Model “TPM”.

Method

The proposed model “TPM” considers triaging and prioritizing all patients (remote and EDs patients) as two sequential processes. The TPM was formulated to triage the patients based on hybrid algorithms which combine Evidence-Theory with Fuzzy Cluster Means (FCM) and then prioritize the patients based on dedicated computational algorithm. A simulation, on 580 chronic heart diseases patients, was implemented. The patients considered as they have different emergency levels based on four vital data acquisition tools: electrocardiogram sensor, blood pressure sensor, oxygen saturation sensor and a text input as non-sensory based acquisition tool.

Results

Computational results show the superiority of the proposed model (TPM) in accommodating large numbers of patients and reducing their waiting time for services compared with relevant benchmark studies. In 1,185 min, TPM managed the (580) patients’ requests. By contrast, the benchmark managed only 256 patients at the same amount of time. In addition to that, TPM shows improvements in terms of waiting time and services provisioning rates compared with benchmark methods.

Conclusion

All patients with the different emergency levels receive services with less waiting time compared with the relevant studies. The proposed model (TPM) model considers both of remote patients and treated patients in EDs efficiently. TPM improves response time for the medical services, reduces waiting time for all patients and consequently, saves more lives.

中文翻译：

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基于身体传感器技术和混合计算算法的急诊科，考虑到急诊科就诊患者，从而减少了远程医疗中的轮候时间：迈向可扩展且高效的实时医疗监控系统

背景

小号实时医疗监控系统中的可量测性挑战涉及几个问题。坚持的问题之一是患者数量的增加。患者人数的增加导致排队的时间较长，并增加了患者寻求医疗服务的等待时间。因此，由于医疗保健提供者应为所有患者提供快速服务，因此出现了道德问题。最近的研究提出了可扩展的模型，该模型仅限于（1）对远程患者进行分类以达到最佳紧急情况，以及（2）对具有最高分类水平的远程患者进行优先排序以立即获得医疗服务。但是，这些研究显示出局限性，即：（1）他们未解决所有在同一等待队列中具有不同分类级别的患者的等待时间；（2）他们尚未考虑急诊科急诊科的急诊科病人。因此，需要在一个医疗保健系统中考虑偏远患者和急诊急救患者，以有效地处理所有患者的要求并有效地管理医疗资源。

目的

这项研究旨在通过考虑急诊科治疗的患者，减少远程医疗中的远程患者等待时间。该研究提出了一种可扩展的远程医疗模型，以通过减少等待时间的医疗保健服务，优先考虑最紧急情况的患者并提供所有这些服务来提高实时医疗保健监视系统的能力，以适应越来越多的慢性心脏病患者。快速的医疗保健服务为患者提供帮助。提议的模型称为“分类和优先级模型”“ TPM”。

方法

提出的模型“ TPM”考虑对所有患者（远程和EDs患者）进行分类和优先级划分为两个连续过程。根据混合算法，将证据理论与模糊聚类均值（FCM）相结合，制定TPM对患者进行分类，然后根据专用计算算法对患者进行优先排序。对580名慢性心脏病患者进行了模拟。基于四种重要数据采集工具，患者被认为具有不同的紧急程度：心电图传感器，血压传感器，血氧饱和度传感器和作为基于非感官的采集工具的文本输入。

结果

计算结果表明，与相关基准研究相比，该模型（TPM）在容纳大量患者并减少服务等待时间方面具有优势。在1,185分钟内，TPM处理了（580）位患者的请求。相比之下，该基准在相同时间段仅处理了256名患者。除此之外，与基准方法相比，TPM显示出在等待时间和服务供应率方面的改进。

结论

与相关研究相比，所有处于不同紧急程度的患者都获得了更少的等待时间的服务。提议的模型（TPM）模型有效地考虑了急诊科的远程患者和接受治疗的患者。TPM缩短了医疗服务的响应时间，减少了所有患者的等待时间，从而挽救了更多生命。