Reducing waiting time for remote patients in telemedicine with considering treated patients in emergency department based on body sensors technologies and hybrid computational algorithms: Toward scalable and efficient real time healthcare monitoring system

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Highlights

  • Considering remote patients with EDs patients in one healthcare system is a demand.

  • TPM model considers the increasing in the number of remote and EDs patients.

  • TPM reduces the waiting time for remote and EDs patients to get medical services.

  • TPM considers the variation in triage levels for patients based on many features.

  • TPM solves complex decision problems in prioritizing patients with heart diseases.

Abstract

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.

Keywords

Healthcare services
Patient monitoring
Sensor
Emergency Department ED
Chronic disease
Disaster

Abbreviations

BFAWC
Back Forward Adjustments for Weight Computing
BP
Blood Pressure
DS
Dempster-Sheffer
ED
Emergency Departments
FCM
Fuzzy Clustering Mean
MCI
Mass Casualty Incident
MLAHP
Multi-Layer for Analytic Hierarchy Process
MSHA
Multi source healthcare architecture
TPM
Triaging and Prioritizing Model
PC
Priority code
TOPSIS
Technique for Order of Preference by Similarity to Ideal Solution

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