Novel Methodology for Triage and Prioritizing Using “Big Data” Patients with Chronic Heart Diseases Through Telemedicine Environmental

Abstract

Problem Statement: Improper triage and prioritization of big-data patients may result in erroneous strategic decisions. An example of such wrong decision making includes the triage of patients with chronic heart disease to low-priority groups. Incorrect decisions may jeopardize the patients’ health. Objective: This study aims to evaluate and score the big data of patients with chronic heart disease and of those who require urgent attention. The assessment is based on multicriteria decision making in a telemedical environment to improve the triage and prioritization processes. Methods: A hands-on study was performed. A total of 500 patients with chronic heart disease manifested in different symptoms and under various emergency levels were evaluated on the basis of the following four main measures. An electrocardiogram sensor was used to measure the electrical signals of the contractile activity of the heart over time. A SpO2 sensor was employed to determine the blood oxygen saturation levels of the patients. A blood pressure sensor was used to obtain the physiological data of the systolic and diastolic blood pressures of the patients. Finally, a non-sensory measurement (text frame) was conducted to assess chest pain and breathing. The patients were prioritized on the basis of a set of measurements by utilizing integrated back-forward adjustment for weight computation and technique for order performance by similarity to ideal solution. Discussion Results: Patients with the most urgent cases were given the highest priority level, whereas those with the least urgent cases were assigned with the lowest priority level among all patients’ scores. The first three patients assigned to the medical committee of doctors were proven to be the most critical emergency cases with the highest priority level on the basis of their clinical symptoms. By contrast, the last three patients were proven to be the least critical emergency cases and given the lowest priority levels relative to other patients. The throughput measurement in terms of scalability based on our proposed algorithm was more efficient than that of the benchmark algorithm. Finally, the new method for determining the “big data” patients characteristics based on “4Vs” was suggested.

Healthcare Driven by Big Data Analytics

“…In the emergency department, patient information, triage and prioritization become a complex decision-making process during peak times or when many patients need to be assessed at once. For example, patients with chronic heart disease are harder to prioritize and establishing a triage priority is challenging because it is necessary to asses each patient with chronic heart disease according to multiple symptoms such as vital signs, oxygen saturation, activity level, edema, etc (Salman et al, 2017).…”

Multi-Biological Laboratory Examination Framework for the Prioritization of Patients with COVID-19 Based on Integrated AHP and Group VIKOR Methods

“…A prioritization process is often conducted to ensure that care is given in an appropriate and timely manner. [13][14][15][16] Therefore, patient prioritization primarily aims to identify patients who can safely wait and those who cannot. [17][18][19][20][21][22] Patient condition should be the primary tool in assessing the priority in accordance with medical guidelines.…”

A Uniform Intelligent Prioritisation for Solving Diverse and Big Data Generated From Multiple Chronic Diseases Patients Based on Hybrid Decision-Making and Voting Method

“…Each patient must have a multigroup triage of the mentioned diseases to assess their emergency conditions. The rapid progression of information and communication technology plays an increasingly important role in telemedicine, given that it improves patients' quality of life and generates massive data, specifically in Tier 3 [14]. According to previous studies, patients with heart diseases who died from arrhythmia are considered 'big data'.…”

“…Big data refer to three main aspects, i.e. high-volume, high-velocity and high-variety form of data [19], which are explained as follows [14]- [16]. High volume refers to the quantity of data generated by wearable sensors and devices for each chronic disease (e.g.…”