An Innovative Methodology for Big Data Visualization for Telemedicine

A Systematic Review of Knowledge Visualization Approaches Using Big Data Methodology for Clinical Decision Support

“…The PRISMA flow diagram (Figure 1) summarizes the screening process. The 17 studies remaining were included in the qualitative synthesis [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] that follows.…”

“…We identified four main groups of users of big data visualization applications for clinical decision support: (1) academicians (clinical researchers and clinical epidemiologists, nurse educators) [11,12,17,18,26], (2) administrators (hospital administrators and managers) [12,17,25], ancillary staff (caretakers, lab managers, pharmacists) [11,13,25]; (3) health care providers (clinicians, nurses, physicians) [11][12][13][14][15][16][18][19][20][21][22][23][24][25][26][27] and (4) patients [11,14,22,23]. More than 94% of the studies (n = 16) were developed to support clinical healthcare provider decision-making.…”

“…The main sources of the big data in public health and behavior include measures and records of electrical and electromagnetic signals from the body (n = 5, 29%) which comprise live data feeds from patient monitoring systems, electrocardiograph (ECG) and electroencephalography (EEG) signals, and sensor data from the Internet of Things (IOT). These approaches connect humans "to the Internet via sensors, and microprocessor chips that record and transmit data" such as brain activity, heart rate, sound waves, and body temperature" [22,23].…”

“…All 17 studies used categorical and numerical data information types, while more than 75% (n = 13) used text data types (clinician notes, laboratory results, notes, etc.). Applications in 13 studies used time series data, including real time signals in three studies (18%) [21,22,26]. Three studies [13,15,16] used maps in their applications to provide clinical decision support.…”

“…A variety of data collection methods were used, with more than half of the studies using data from EHR/EMR systems. The other most common method involved receiving data from continuous monitoring via EEG, ECG, bedside monitoring, or IOT [11,14,19,22,23,26]. Two studies used big databases [11,20], one study used data from a provincial pathology laboratory [13] and one study used user input data in combination with IOT data [14].…”