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Article

New Web-Based Ventilator Monitoring System Consisting of Central and Remote Mobile Applications in Intensive Care Units

by
Kyuseok Kim
1,2,†,
Yeonkyeong Kim
2,3,†,
Young Sam Kim
3,
Kyu Bom Kim
2,4,*,‡ and
Su Hwan Lee
3,*,‡
1
Department of Biomedical Engineering, Eulji University, 533, Sanseong-daero, Sujung-gu, Seongnam-si 13135, Republic of Korea
2
2TS Corporation, 16, Digital-ro, 32ga-gil, Guro-gu, Seoul 08393, Republic of Korea
3
Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, Yonsei University College of Medicine, 50-1, Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
4
Department of Radiation Convergence Engineering, Yonsei University, 1, Yeonsedae-gil, Heungeopmyeon, Wonju-si 26493, Republic of Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
‡
These authors also contributed equally to this work.
Appl. Sci. 2024, 14(15), 6842; https://doi.org/10.3390/app14156842
Submission received: 17 July 2024 / Revised: 31 July 2024 / Accepted: 4 August 2024 / Published: 5 August 2024
(This article belongs to the Special Issue Monitoring of Human Physiological Signals)
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Abstract

:
A ventilator central monitoring system (VCMS) that can efficiently respond to and treat patients’ respiratory issues in intensive care units (ICUs) is critical. Using Internet of Things (IoT) technology without loss or delay in patient monitoring data, clinical staff can overcome spatial constraints in patient respiratory management by integrated monitoring of multiple ventilators and providing real-time information through remote mobile applications. This study aimed to establish a VCMS and assess its effectiveness in an ICU setting. A VCMS comprises central monitoring and mobile applications, with significant real-time information from multiple patient monitors and ventilator devices stored and managed through the VCMS server, establishing an integrated monitoring environment on a web-based platform. The developed VCMS was analyzed in terms of real-time display and data transmission. Twenty-one respiratory physicians and staff members participated in usability and satisfaction surveys on the developed VCMS. The data transfer capacity derived an error of approximately 10 − 7 , and the difference in data transmission capacity was approximately 1.99 × 10 − 7 ± 9.97 × 10 − 6 with a 95% confidence interval of − 1.16 × 10 − 7 to 5.13 × 10 − 7 among 18 ventilators and patient monitors. The proposed VCMS could transmit data from various devices without loss of information within the ICU. The medical software validation, consisting of 37 tasks and 9 scenarios, showed a task completion rate of approximately 92%, with a 95% confidence interval of 88.81–90.43. The satisfaction survey consisted of 23 items and showed results of approximately 4.66 points out of 5. These results demonstrated that the VCMS can be readily used by clinical ICU staff, confirming its clinical utility and applicability. The proposed VCMS can help clinical staff quickly respond to the alarm of abnormal events and diagnose and treat based on longitudinal patient data. The mobile applications overcame space constraints, such as isolation to prevent respiratory infection transmission of clinical staff for continuous monitoring of respiratory patients and enabled rapid consultation, ensuring consistent care.

1. Introduction

Patient monitoring in real time to detect life-threatening situations and make an accurate diagnosis plays an important role in the intensive care unit (ICU) [1]. When monitoring and managing high-risk patients, the course of treatment evaluation is critical [2,3,4]. Physicians integrate multiple time-sensitive and complex physiological data acquired through various monitoring devices (e.g., electroencephalography and photoelectric devices for the saturation of partial pressure oxygen) to make diagnostic and treatment decisions [5,6].
Ventilator monitoring, the hallmark of the ICU, keeps patients with respiratory failure alive by delivering oxygen to the alveoli during artificially controlled breathing. Mechanical ventilation is used to rest respiratory muscles and manipulate breathing patterns to enhance ventilation and oxidation when normal gas exchange cannot be expected from spontaneous breathing alone. Unlike spontaneous breathing, which starts through negative pressure due to the contraction of the diaphragm, most ventilators induce inspiration through positive pressure [7]. These ventilators can help clinical staff make a comprehensive judgment by providing various characteristic waveforms, such as the volume–pressure loop, flow–pressure loop, auto-positive end-expiratory pressure, and air leak [8,9]. For instance, the monitor for mechanical ventilation displays the patient’s response after adjusting the ventilation system and can select the optimal control parameters for patients with respiratory failure [10]. These parameters are monitored in real time by clinical staff and require immediate attention, as they play a critical role in acute hypoxic and hypercapnic respiratory failure, as well as severe metabolic acidosis or alkalosis [11,12].
Although it is very important to monitor the ventilator in real time, there are several limitations in terms of monitoring patients in the ICU. First, the ability to comprehensively diagnose and treat incidents caused by ventilator-induced lung injuries and customize responses for each patient is limited [7,13]. Appropriate parameter control is required based on continuous observation and trend review by the clinical staff to provide gas exchange with a ventilator that can minimize lung damage. In this case, it is necessary to comprehensively store and manage the patient’s whole-cycle information and provide it to the clinical staff. This can play a crucial role in preventing lung damage caused by ventilators through breathing management tailored to precision medicine [13]. Second, the noise level in an ICU with various monitoring devices and many clinical staff members is very high, which can distract the clinical staff from focusing on individual patient ventilator monitoring. Excessive sound pressure levels in ICUs are often reported to be in the 50–70 dB(A) range, with levels above 40 dB(A) known to cause difficulty in concentration [14,15,16,17]. The World Health Organization (WHO) guidelines recommend that levels should not exceed 35 dB(A) in most spaces where patients are treated or observed, as patients are less able to cope with stress [18]. In addition, when false and nonactionable alarms that do not require specific intervention are excluded from the total number of alarms in the ICU, the number of actionable alarms ranges from <1% to 26% [19]. Therefore, excessive alarms can cause clinical staff to lose focus on patients, which means that they may disable or silence alarms [17,19]. Ultimately, there is a need to reduce alarm fatigue among the clinical staff through central monitoring. Finally, respiratory diseases, such as the coronavirus disease 2019 (COVID-19), require new treatment protocols for respiratory monitoring in the ICU. Respiratory infectious diseases readily transmit to others, increasing the infection risk for clinical staff and demand for additional isolation beds [20]. Monitoring patients with respiratory failure who are on mechanical ventilator or extracorporeal membrane oxygenation (ECMO) therapy in an isolated room is difficult [21,22,23]. Contactless real-time integration of ventilator monitoring is required to minimize infections in clinical staff and reduce the nursing workload in ICUs.
Tele-ICUs can be an alternative to overcome these problems. Since the tele-ICU system was first implemented in 2000 by the Sentara Hospital System in the United States of America, many hospitals have been using similar systems, such as electronic ICUs, ICU telemedicine, remote ICUs, and virtual ICUs [24]. ICUs need higher medical resources than general wards in terms of cost and clinical staff. Tele-ICUs not only improve them but also confirm the possibility of improving the recovery and long-term outcomes of critically ill patients, and can potentially improve process quality [25,26]. Using tele-ICUs, clinicians can quickly monitor and treat patients, even if they are physically distant from clinicians and nurses in the ICU. Off-site clinicians can also remotely access and participate in real-time patient treatment and efficiently use distributed clinical resources by overcoming spatial constraints [27,28]. Furthermore, tele-ICUs can further improve centralized systems, expanding critical care capacity in community settings [29]. Comprehensive programs are growing in tele-ICUs. Diagnosing and treating a single critical patient requires various measured biosignal information and an evidence-based treatment plan based on long-term follow-up records [30]. Thus, the integration and acceptance of monitoring programs in tele-ICUs is an inevitable trend that provides efficient communication even with collaboration among the clinical staff, not with the monitored department [31]. The proposed web-based ventilator central monitoring system (VCMS) accurately meets these requirements.
Therefore, this study aimed to develop a VCMS utilizing information and communication technology (ICT) in tele-ICUs to overcome the limitations of conventional ventilator monitoring and investigate its usefulness. We believe that a VCMS can efficiently respond to and treat patients’ respiratory issues in the ICU. Key contributions of the study include (1) integration of individualized ventilator monitoring into one monitoring platform with Internet of Things (IoT) technology to enable comprehensive patient management, (2) establishment of a dedicated server for the VCMS for longitudinal analysis of each patient to assist pulmonology physicians and nurse practitioners in making patient care decisions, and (3) effective monitoring of patients by medical staff using the VCMS, even in a noisy ICU environment. Figure 1 shows the proposed VCMS framework, and this can overcome the space constraints of clinical staff regarding patient respiratory management by integrating and monitoring multiple ventilation systems using IoT technology without losing or delaying patient monitoring data and providing real-time information through remote mobile applications. We developed a VCMS and performed a clinical experiment to demonstrate its viability in the ICU.

2. Materials and Methods

2.1. Development of the Proposed VCMS

Figure 2 shows a simplified framework for the proposed VCMS in the ICU. In short, it receives many real-time respiratory measurement signals from multiple ventilators and patient monitoring systems. Currently, respiratory signals are delivered to the server through the internet environment in the ICU. The responding server may become overloaded and crash if much information is received in real time. This poses a serious risk, especially for ventilators and patient monitors, as they require continuous real-time monitoring. Thus, the load-balancing web application is supported by reducing the traffic load to improve request efficiency, and HAProxy and Nginx can be applied to perform the round-robin algorithm [32].
An application was developed to collect data and deliver control commands through TCP/IP socket connections for patient waves and numerical data from the ventilators and patient monitors through a load balancer. It was developed in Java (using the Apache MINA framework as a data collection server) to support operating system (OS) independence [33]. The collected data were saved in Kafka (or Apache Kafka) to make them available to consumers. It is a distributed data-streaming platform that provides unification, high throughput, and low latency for managing real-time data feeds. It exchanges the ventilator monitoring data (waveforms and numerical parameters) through a high-capacity and high-speed message queue [34]. ELK (ElasticSearch, Logstash, and Kibana) stacks were used to quickly analyze, store, and retrieve large amounts of data stored in Kafka. ELK provides meaningful insights into processing and analyzing large amounts of data in real time in the IoT field [35]. Logstash implements the collection, aggregation, parsing, and delivery of log or transaction data from various sources (e.g., DB and CSV files) to ElasticSearch. ElasticSearch retrieves and aggregates data from LogStash to obtain interesting information, and Kibana supports the visualization and monitoring of ElasticSearch using a quick search. However, ElasticSearch is limited to storing real-time patient respiratory data. To overcome this problem, MariaDB, a relational database for standardized data management, was used in the proposed VCMS [36]. The VCMS has the advantage of storing, quickly searching, and analyzing patient respiratory data, and it can monitor multiple patients in real time. As this is limited to processing through ELK and MariaDB, a remote dictionary server (Redis), an in-memory DB that processes data in memory using a key-value structure, was applied [37]. Data acquired in real-time were transmitted to a spring open-source application based on the Java enterprise from Redis [38], and real-time respiratory data were delivered to the central monitor and mobile applications through a load balancer in the web socket format in the ICU. Data encryption standard and advanced encryption standard (AES) encryption and transmission methods were applied to implement cybersecurity for the transmitted data [39]. As needed, clinical staff can check longitudinal information (e.g., trend review, event review, and patient longitudinal information) by requesting the repository for the comprehensive diagnosis of individual patients in central monitoring and mobile applications.

2.2. Experiment Conditions

To verify the proposed VCMS, an MV2000 EVO5 ventilator (MEK-ICS, Paju-si, Gyeonggi-do, Republic of Korea) and an MP1300 patient monitor (MEK-ICS, Paju-si, Gyeonggi-do, Republic of Korea) were used. The MV2000 EVO5 supports 16 ventilation modes, including volume-assisted controlled ventilation (V-ACV), pressure-based assisted controlled ventilation (P-ACV), high-flow nasal therapy (O2 stream), bi-level positive airway pressure (bi-level), and spontaneous ventilation (SPONT).
The corresponding modes can be adjusted using 41 parameters. The adjustment range and alarm values for the 12 representative ventilator parameters are summarized in Table 1. It offers a 15″ color TFT (resolution: 1024 × 768 pixels), touchscreen, and knob HDMI for individual displays. The MP1300 also provides a total of 75 parameters for 12 items, such as wave, electrocardiogram, SpO2, non-invasive blood pressure (NBP), and invasive blood pressure (IBP); the upper/lower limits and alarm settings of 10 representative parameters are summarized in Table 2. The MP1300 has a 12.1″ color TFT touch screen, an encoder, and a +12 V mobile DC input.
The hardware interface of the VCMS server operates under the following specifications: OS: Ubuntu 20.04 64 bit, CPU: Intel® Core™ i7-9700 3.60 GHz, 8 Cores, GPU: NVDIA GeForce RTX 2080, RAM:32 GB, HDD:1 TB, SSD:512 GB, and RJ-48 network supported. In addition, the hardware interface of the client in the VCMS is as follows: OS: Windows 7 32/64 bit, CPU: Intel® Core™ i3-4150 3.50 GHz, RAM: 4 GB, and RJ-48 network and wireless support. The display monitor for the VCMS has a resolution of 1920 × 1080 pixels and a size of 23″. The software versions used for the VCMS are Visual Studio 2019 and dotnet-sdk-5.0, JDK 8, Node.Js 14.15.0, Python 3.7, MariaDB 10.3.21, ElasticSearch 7.12, and Kafka 2.5.0. TCP/IP communication with a data agent was performed by a local area network (LAN) based on the RS-232 and Ethernet protocols to deliver respiratory information from ventilators and patient monitors. Mobile applications communicate wirelessly fidelity (WiFi) through a cloud network in a hospital. Here, the hub and LAN cables are supported by the maximum packet rates of 10, 100, and 1 Gb/s full-duplex Ethernet in the IEEE 802.3 specification [40].
This prospective study was approved by the Institutional Review Board (1-2022-0036 and 25 July 2022) of Severance Hospital in South Korea. This study used a qualitative, multi-case design. Three patients participated in the study, and a demo mode of 15 patient monitors and ventilators was used in the ICU. The patients were aged 60–81 years, with an average of 37.6 days of stay in the ICU. For the demo mode, a case study was conducted using the mode implemented in the MCK-ICS device of the patient monitor and ventilator, which implements and transmits virtual information to the VCMS in the same manner as measured in real patients. The clinical staff in the ICU had 18 patient monitors and ventilators with 24 h/7 days of continuous monitoring.

2.3. Data Analysis

The technical goal of the VCMS is to enable data transmitted from the ventilator and the patient monitors to be observed simultaneously in synchronization with the central monitor and mobile applications and to enable storage, search, and analysis in the database without loss of information. To evaluate the accuracy of data communication, the ideal data transfer capacity (DTC) per second of the ventilator and patient monitor was calculated, which can be defined by Equation (1):
D T C : V T   o r   D T C : P M = 14 × N w a v e × M w a v e + 32 × N P a r a × N B e d   ( b p s )
where DTC:VT and DTC:PM are the data transfer capacities of the ventilator and patient monitor, respectively. The wave and numerical data comprised 14-bit and 32-bit signed integrator arrays, respectively. Therefore, the capacity of the wave data per second can be defined by multiplying the number of wave data per second (Nwave), the number of matrix elements of each wave data point (Mwave), and 14 bits. In addition, the capacity of numerical data per second can be calculated by multiplying the number of parameters (NPara) by 16 bits. Finally, DTC:VT and DTC:PM can be calculated by summing the DTC of the wave data and numerical data and then multiplying by the number of beds (NBed). Quantitative verification was performed by comparing the realistic DTC of the ventilator and patient monitor. Another assessment is data transmission rapidity (DTR). Depending on the size of each DTC, it is necessary to verify whether data are transmitted at the correct time during serial communication from the device to the server. The error compared to the outlier can be expressed as Equation (2):
T R   d i f f e r e n c e = T M , D T C − T I , D T C T I , D T C × 100   ( % )
where T I , D T C and T M , D T C are ideal and measured transfer times, respectively. The T I , D T C values were close to one second for each DTC.

2.4. Medical Software Validation

The proposed VCMS is a device that consolidates the display, storage, and analysis of signals measured from existing ventilators and patient monitors into one integrated platform to maintain the measurement performance of each ventilator and patient monitor while enabling medical staff to use the VCMS platform efficiently. Regarding the comprehensive evaluation of the developed VCMS, a medical software validation was performed to evaluate the clinical usefulness of the clinical staff and the quantitative performance of the software interface in terms of hospitalization, monitoring display, zoom-in, alarms, waveforms and parameters, trend review, event review, and discharge from the hospital. A total of 21 volunteers participated in the medical software validation. All volunteers were clinical staff with expertise in respiratory internal medicine. Summary information on the participants is shown in Table 3.
The monitor and tablet PC had 24″ 1920 × 1080 and 10.5″ 1920 × 1200 resolutions, respectively. Temperature, humidity, and illumination at the evaluation site were measured at 21–24 °C, 30–60%, and 31–1200 lx, respectively. The medical software validation established an evaluation protocol based on the International Electrotechnical Commission (IEC) 62366-1:2015 [41] and the IEC technical report (TR) 62366-2:2016 [42]. In addition, relevant IEC and International Organization for Standardization (ISO) technical documents were referred to [43,44,45,46,47,48]. The items used to evaluate the user task of the VCMS are listed in Table 4. Moreover, the viability of the proposed VCMS system was reviewed by performing a satisfaction evaluation of the use of the VCMS by clinical staff who participated in the actual test. It was organized to respond on a 5-point Likert scale [49] in relation to task performance. Participants were given a maximum of 5 points for satisfaction with the question and a minimum of 1 point for dissatisfaction. After participating in the evaluation, they were evaluated in an independent environment. Participants’ opinions on the satisfaction evaluation items were used to examine the connection with the task success rate. A list of the satisfaction evaluations is presented in Table 5.

3. Results

3.1. Real-Time Display in VCMS

Figure 3 shows an example of a web-based central patient monitoring display in the developed VCMS. Figure 3a shows the full screen of the VCMS, which displays real-time information from patient monitors and ventilators. The wave and numeric data can be aligned and monitored horizontally for each patient, and the desired wave parameter and numeric data can be changed and displayed. Hospitalization was placed in drag-and-drop mode on the desired thumbnail in conjunction with the hospital information system and electronic medical record to increase the convenience of clinical staff management. Each patient display parameter can be set independently, and the monitoring efficiency and expertise of the clinical staff can be maximized by storing the usage log records of each administrator. Figure 3b shows an enlarged thumbnail image of box A among the patient-monitored thumbnails. Wave data were configured to be displayed in real-time by designating up to three parameters, allowing time-series waveform data to be drawn continuously. This is expressed as a new waveform by erasing the existing waveform data from left to right. This is the same as the waveform drawing on the patient monitor, and the left to right. This is the same as the waveform drawing on the patient monitor, and the time for clinical staff to adapt and master the VCMS devices can be minimized. The numerical parameters were also configured to display values transmitted in real time from the patient monitor and to display up to nine values in one thumbnail. Each parameter can also be set independently of the patient’s monitor. Figure 3c shows an enlarged thumbnail image of box B among the ventilator thumbnails. The display method was the same as that for the abovementioned patient monitor. Figure 3c shows the screen of the VCMS when an alarm occurs in the V-ACV mode. The parameter thumbnail that triggers the alarm and the cause of the occurrence on the upper-right side are displayed together with the red area. At that time, a high-decibel voice is generated by a speaker connected to the VCMS so that the clinical staff can immediately determine the patient and the cause of the alarm. The clinical staff can adjust the alarm level, and the default value is set below the dB(A) recommended by the WHO [18]. When an alarm occurs in various parameters, the red area is displayed on the parameter thumbnails simultaneously, and the alarm produces an indication in the upper-right and is displayed every 1 s. All alarms are maintained until the data are transmitted within the threshold value for each parameter, which can be set differently from the parameter thresholds of the patient monitor and ventilator.
Figure 4a shows a display window for event reviews, with detailed alarm information. A tab displays alarm events that occurred for each patient and shows the alarm occurrence, event type, cause of occurrence, and patient ID. An additional tab displays more detailed information when the alarm is clicked. Figure 4b shows the display window of the trend review, which can be used to check the information for various waves and numerical parameters in the time series. It is possible to select the desired parameters, and up to five parameters can be simultaneously checked. Various time zones can be set and displayed in the time-series table, and the numerical information can be checked in the table below using the drag-and-drop or direction keys on the blue line. In addition, the time-series data of the desired parameters can be exported through anonymization within the scope of the authority of the clinical staff. Event and trend reviews provide longitudinal respiratory and biometric information for individual patients, enabling continuous monitoring even if the clinical staff shifts and supporting comprehensive diagnosis because long-term patient monitoring can be seen at a glance.

3.2. Quantitative Evaluation of Real-Time Data Transmission

Verifying whether the real-time data transmitted from multiple patient monitors and ventilators is displayed on a central monitor and remote mobile applications without loss of information is one of the most important evaluations in the proposed VCMS.
Figure 5 shows the comparison results of the error between the ideal and measured DTC according to the number of patient monitors and ventilators. The wave parameters sent from the patient monitor and ventilator to the VCMS server were 14 and 5, respectively, and the numerical parameters were 57 and 41, respectively. The wave parameters of the patient monitor included seven electrocardiogram channels: respiratory rate, oxygen saturation (SpO2), four IBP channels, and carbon dioxide. The ventilator measured pressure, flow, volume, end-tidal carbon dioxide, and SpO2. The ideal DTC for the number of patient monitors and ventilators was calculated using Equation (1). The ideal DTC, when each patient monitor and ventilator are one, is as follows: the DTC of a patient monitor = 21,424 bit/s and that of a ventilator = 5512 bit/s. For clinical evaluation, up to 18 units of maximum patient monitoring and ventilator use were measured in the ICU over a measurement period of 3 months. The measured DTC error was less than 10−7, ranging from 1/1 to 18/18 (number of patient monitors/number of ventilators).
Figure 6 shows the results of the DTR difference according to the DTC in a scatter plot. A total of 36 patient monitors and ventilators were measured up to 484,848 bit/s; the mean of the measured DTR difference was 1.99 × 10−7, and the standard deviation was 9.97 × 10−6. The 95% confidence interval (CI) was measured from −1.16 × 10−7 to 5.13 × 10−7. This result indicates that the measurement data were delivered in real time without information loss, except for essential losses that can occur through errors in delivering the bits received during communication [50].

3.3. Results of the Medical Software Validation

Investigating the success rate and satisfaction of the analytical and empirical approaches with the developed VCMS is valuable for reviewing its clinical usefulness and practical availability.
Figure 7a shows the accumulated bar graph of each task performed according to Table 4. The participants’ task performance can be classified into three categories. Completed (C) is successful completion of the task without usage error and a close call; completed with issues (CI) is the final completed task, although it could have been used incorrectly; incomplete (NC) involves usage error or a task being completed that is different from the manufacturer’s intention or expectation, and when the user finds it difficult to perform the task and asks for help to complete the task. Success in the task includes only C and CI. Figure 7a shows the result of calculating the task completion rate for each scenario based on Equation (2). Nineteen items obtained class C, and there were two items with a 100% success rate, including C and CI classes, showing a 100% task success rate from a total of 21. There were 32 items with a task success rate > 90%, accounting for 86.4% of the total items. The largest number of CI classes was counted as three users each in tasks 23 and 29, and the largest number of NC classes was in tasks 5 and 17, with six participants each. In particular, the CI class also included two users in the case of task 17, which had the lowest success rate of 71% among all tasks. This error was caused by the less intuitive nature of the alarm setting menu, because the volume icons in the OS are more familiar. Except for task 17, 36 items had 80% or higher task success rates. Figure 7b shows the task completion rates for each scenario. The average task success rate per scenario was 92%, and the 95% CI was 88.81 ± 90.43. The scenario with the highest task completion rate was 100% with the waveform setting (Task 5), and the lowest task completion rate was 86% with hospitalization (Task 1). Due to the difficulty of the menu approach in task 1, many participants performed their duties with confusion and with the help of the host. This problem occurs when participants first encounter a program and can be overcome through mastery. In the case of hospital discharge, a button was produced under the name “remove” during the medical software validation, and this confused the participants. Therefore, we fixed the problem by renaming it “discharge”, which is more commonly used in clinical practice.
Figure 8 shows a bar graph of the satisfaction survey results by scenario according to Table 5. The satisfaction survey was conducted for 15 min for participants who participated in the task after the summative test was completed and was conducted without host or external influences. The average satisfaction with the 23 items was 4.66 out of 5. The “alarm setting” of scenario 6 showed the highest satisfaction, with 4.82 points, and the “discharge” of scenario 9 showed the lowest satisfaction, with 4.52 points. “Overall” scenario 10 proved the usefulness of the VCMS with 4.67 points, reflecting the display trend and operation method of the existing patient monitor and ventilator as closely as possible. Thus, this high level of clinical staff satisfaction indicates the need for the developed VCMS compared to various existing monitoring devices and suggests that it may have real-world utility in the ICU.

4. Discussion

4.1. Novelty of VCMS

This study introduced our newly developed web-based ventilator monitoring system and demonstrated the quality of real-time data transmission and usability in the VCMS, which consists of central monitoring and remote mobile applications. The novelty of the proposed VCMS is that it can monitor multiple ventilators and monitoring devices of patients based on the web browser. Existing IoT-based ventilator monitoring research has focused on single ventilators, with approaches for contactless monitoring to prevent infection by medical staff and lower prices [51,52]. The VCMS makes it possible to respond to ICU patients quickly, and the ventilator information can be accessed and closely monitored remotely, making it beneficial for patient management and reducing medical staff fatigue when monitoring multiple ventilators and ICU patient monitoring devices. In particular, the web-based integrated monitoring platform allows medical staff to manage ICU patients without being distracted by location. Figure 9 shows the real-time web-based display in the VCMS between the ventilator device and mobile application. The proposed VCMS provides a comprehensive diagnosis by recording and displaying different biometric signals and vital signs. Providing event and trend reviews overcomes the limitations of judgments based on temporary phenomena, thereby enabling evidence-based care.
VCMS development increases viability in terms of task completion rate and satisfaction considering the convenience and familiarity of the clinical staff. Lack of experience can be an important issue for ICU nurses. When these issues are combined with problems such as a lack of clinical staff and poor supervision, the likelihood of errors increases [53]. Thus, repeated training and hands-on experience should be performed. However, training clinical staff on various equipment and devices requires considerable time. With the VCMS, the adaptation period for using new equipment related to the quality of care was minimized. The same operation was performed with the patient monitor and ventilator equipment for the VCMS. Based on the medical software validation survey results, feedback was used to shorten the training period for additional VCMS tasks (e.g., hospitalization, events, and trend review operations). In the medical software validation, a task completion rate of >80% and high satisfaction of 4.67 points with only 20 min of task introduction and use training indicated that participants could easily use the VCMS without much difficulty.
The developed VCMS might play an important role in implementing tele-ICUs successfully. Central monitoring, remote mobile application monitoring, and managing each ventilator might improve the concentration of the clinical staff on the patient. These advantages may contribute to lower ICU maintenance costs and staff fatigue.

4.2. Limitations and Future Research Directions

The proposed VCMS has two primary potential applications for performance improvement. First is machine learning-based respiratory disease prediction. Based on accumulated patient information, patient prognosis and prediction might be performed based on clinical experience. Recently, computer-aided diagnosis based on machine learning has been introduced, and features highly correlated with diseases have been identified. Giang et al. investigated the prediction of ventilator-associated pneumonia using five machine learning models: logistic regression, multilayer perceptron, random forest, support vector machines, and gradient-boosted trees [54]. These were learned 48 h after mechanical ventilation. The model performance was evaluated based on the area under the receiver operating characteristic curve (AUROC) in a 20% hold-out test set, and the highest value was 0.854. Shashikumar et al. developed and validated a deep learning model for mechanical ventilation [55]. Secured COVID-19 patient data from the University of California San Diego Health and Massachusetts General Hospital and an open-source software-based deep learning model for optimizing tracheal intubation timing and improving patient treatment were proposed and verified using AUROC. This showed a higher AUROC than the baseline model and ROX score. We plan to conduct precision medicine research using big respiratory and biosignal data based on data accumulated with the VCMS. Second, because the VCMS is web-based, it allows medical institutions to monitor home ventilators. It integrates home ventilation based on external cloud services. Many studies have shown that home ventilation using non-invasive ventilation can improve sleep and quality of life, reduce exacerbations and hospital admissions, and improve survival rates [56]. It can significantly change the management of patients with chronic respiratory failure, improve access to treatment, and reduce emergency room visits and hospitalizations, which can positively affect treatment continuity and reduce healthcare costs [57]. A patient’s full-cycle observations are expected to be made by communicating the real-time information of the home ventilator with the VCMS using an external cloud service, which can suggest a new paradigm for pulmonary treatment and rehabilitation. However, additional problems, such as collecting patient personal information and strengthening the cybersecurity of cloud services, must be overcome by combining home ventilators and the VCMS [58].

5. Conclusions

This work focuses on developing and evaluating the proposed VCMS to store and manage comprehensive information from patient monitors and ventilators and provide integrated monitoring in real time. This study indicates that providing real-time data from multiple patient monitors and ventilators helps clinical staff enhance their quality of care and early-response capabilities for alarms in central monitoring, aids in remote mobile applications, and stores without information loss. The medical software validation was also conducted with 21 pulmonology physicians and staff members with ICU experience. Clinical staff could easily operate with little introduction and training in the VCMS; the average task completion rate was over 80%, and improvement work was carried out through feedback. Furthermore, the satisfaction survey proved the usefulness and applicability of the developed VCMS, with a score of 4.67 out of 5. Nevertheless, challenges remain in improving the proposed VCMS, such as machine learning-based respiratory abnormality prediction using big data and cybersecurity for integrating home ventilators. We expect that the developed VCMS will become a core technology for tele-ICUs, which will help efficiently utilize scarce medical resources during national crises, such as infectious disease outbreaks.

Author Contributions

Conceptualization, K.K., Y.K., Y.S.K., K.B.K. and S.H.L.; formal analysis, K.K., Y.K. and K.B.K.; investigation, K.K., Y.S.K. and S.H.L.; methodology, K.K., Y.K., K.B.K. and S.H.L.; software, K.K.; validation, K.B.K. and S.H.L.; writing—original draft, K.K. and Y.K.; writing—review and editing, Y.S.K., K.B.K. and S.H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Korea Medical Device Development Fund, awarded by the Korean government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health and Welfare, the Ministry of Food and Drug Safety) (project number: RS-2020-KD000032); the National Research Foundation of Korea (NRF) grant, funded by the Korea government (MSIT) (RS-2023-00252863); and the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (RS-2023-00239193, RS-2023-00243656).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Boards of Severance Hospital (1-2022-0036 and 25 July 2022).

Informed Consent Statement

Written informed consent was obtained from all subjects involved in this study.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

Authors Kyuseok Kim, Yeonkyeong Kim and Kyu Bom Kim were employed by the company 2TS Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Schematic illustration of proposed ventilator central monitoring system (VCMS) in the intensive care unit (ICU).
Figure 1. Schematic illustration of proposed ventilator central monitoring system (VCMS) in the intensive care unit (ICU).
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Figure 2. A simplified framework of the proposed VCMS in the ICU.
Figure 2. A simplified framework of the proposed VCMS in the ICU.
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Figure 3. Example of a patient monitoring display in a developed VCMS: (a) full screen of the VCMS that displays real-time information from up to 32 patient monitors and ventilators; (b) enlarged thumbnail image in box A among patient monitor thumbnails; (c) enlarged thumbnail image in box B among the ventilator thumbnails.
Figure 3. Example of a patient monitoring display in a developed VCMS: (a) full screen of the VCMS that displays real-time information from up to 32 patient monitors and ventilators; (b) enlarged thumbnail image in box A among patient monitor thumbnails; (c) enlarged thumbnail image in box B among the ventilator thumbnails.
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Figure 4. (a) Display window for event review with detailed alarm information and (b) the trend review display window, in which is possible to check time-series information for various waves and numerical parameters.
Figure 4. (a) Display window for event review with detailed alarm information and (b) the trend review display window, in which is possible to check time-series information for various waves and numerical parameters.
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Figure 5. Bar graph showing the error rate results of the ideal data transfer capacity (DTC) and measured DTC according to the number of patient monitors and ventilators.
Figure 5. Bar graph showing the error rate results of the ideal data transfer capacity (DTC) and measured DTC according to the number of patient monitors and ventilators.
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Figure 6. Scatter plot of data transmission rapidity (DTR) difference according to the DTC.
Figure 6. Scatter plot of data transmission rapidity (DTR) difference according to the DTC.
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Figure 7. (a) Accumulated bar graph for score for 37 tasks, and (b) task completion rate for each scenario. Here, the medical software validation for the VCMS was conducted with the items in Table 4.
Figure 7. (a) Accumulated bar graph for score for 37 tasks, and (b) task completion rate for each scenario. Here, the medical software validation for the VCMS was conducted with the items in Table 4.
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Figure 8. Bar graph of the satisfaction survey results by scenario according to Table 5 of participants who performed medical software validations on the developed VCMS.
Figure 8. Bar graph of the satisfaction survey results by scenario according to Table 5 of participants who performed medical software validations on the developed VCMS.
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Figure 9. Scene of real-time web-based display in the ventilator central monitoring system (VCMS) between the ventilator device and mobile application.
Figure 9. Scene of real-time web-based display in the ventilator central monitoring system (VCMS) between the ventilator device and mobile application.
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Table 1. Representative ventilator parameters and dimensions.
Table 1. Representative ventilator parameters and dimensions.
ParametersLow DimensionHigh Dimension
Range (Alarm)Range (Alarm)
VE Tidal0–2500 (200) mL51–2500 (600) mL
VE min0–49.9 (2.0) lpm0.1–50.0 (10.0) lpm
RESP.R2–179 (5) bpm3–180 (30) bpm
Ppeak0–119 (10) cmH2O1–120 (60) cmH2O
O218–100 (18)%19–100 (80)%
Air leak-50–500 (500) mL
APNEA-2–60 (20) s
SpO251–99 (90)%52–100 (100)%
PR25–245 (50) bpm30–250 (120) bpm
EtCO20–14.9 (4.0)%0–15.0 (10.0)%
FiCO20–14.9 (0.1)%0–15.0 (5.0)%
RESP1–179 (5) bpm2–180 (30) bpm
Abbreviations: VE, exhaled minute volume; RESP.R, respiratory resistance; Ppeak, peak pressure; PR, pulse rate; EtCO2, end tidal carbon dioxide; FiCO2, fractional concentration of carbon dioxide in inspired gas; RESP, respiration.
Table 2. Representative patient monitor parameters and dimensions.
Table 2. Representative patient monitor parameters and dimensions.
ParametersLow DimensionHigh Dimension
Range (Alarm)Range (Alarm)
Heart rate20–290 (50) bpm30–300 (120) bpm
ST segment−0.99 to −0.01 (−0.50) mm0.01–0.99 (0.50) mm
SpO251–99 (90)%52–100 (100)%
RESP2–145 (5) bpm10–150 (30) bpm
Temperature10–34 (32) °C20–38 (37) °C
APNEA-10–120 (60) s
NBP5–300 (80/70/60) * mmHg5–300 (150/130/120) * mmHg
IBP5–300 (80/70/60) * mmHg5–300 (150/130/120) * mmHg
EtCO20–14.9 (4.0)%0–15.0 (10.0)%
FiCO20–14.9 (0.1)%0–15.0 (5.0)%
FiCO20–14.9 (0.1)%0–15.0 (5.0)%
Abbreviations: SpO2, oxygen saturation; NBP, non-invasive blood pressure; IBP, invasive blood pressure; EtCO2, end tidal carbon dioxide; FiCO2, fractional concentration of carbon dioxide in inspired gas; * hypertension/normal blood pressure/hypotension.
Table 3. Summary of evaluation participants.
Table 3. Summary of evaluation participants.
Medical Doctor (n = 6)Nurse (n = 15)
Age38 ± 833 ± 5
Period of career12.8 ± 9.311.1 ± 5.6
Experience using similar devicesGE Healthcare,
Philips Healthcare,
Nihon Kohden		GE Healthcare,
Philips Healthcare
Period of central monitoring system
<1 year10
1 to 3 years20
3 to 5 years04
5 to 10 years05
>10 years36
Table 4. List of medical software validation for the VCMS.
Table 4. List of medical software validation for the VCMS.
ScenarioNo.Task
1. Hospitalization1Search for registered devices.
2Add the patient monitoring display in the first column of the first row.
2. Screen mode3Change the screen to the maximum monitoring screen.
4Move the patient display you are monitoring one space to the right.
5The patient display automatically sorts the bed numbers in order.
3. Zoom in6Open the zoom-in screen.
7Check the ventilator mode is V-ACV.
8Stop the waveform.
9Check the graph data by moving the lines on the stopped waveform.
10Close the zoom-in screen.
4. Numeric parameter setting11Open the numeric parameter settings window.
12Change the PEEP tab to the VE MIN tab.
13Close the numeric parameter settings window.
5. Waveform setting14Open the waveform settings window.
15Change the volume waveform tab to the empty tab.
16Close the waveform settings window.
6. Alarm setting17Set the volume of the alarm to 3.
18Check for visual and auditory alarms that are occurring.
19Pause the audible alarm for two minutes.
20Open the parameter alarm setting window.
21Change the upper limit of the VE MIN tab to 15.
22Close the parameter alarm setting window.
23Disable the audible alarm.
7. Trend review24Open the trend review window.
25Change the PEEP tab to the VE MIN tab.
26Check out the data from the table for the previous hour.
27Check the table at the desired time by arbitrarily moving the vertical line.
28Check the table for the most recent data.
29Download data for parameters you want for the day to a chart file.
30Open the chart file and check the graph.
31Close the trend review window.
8. Event review32Check out recent events for the entire patient.
33Open the event review window.
34Check out the events that have occurred to date.
35Check the details of the most recent event in the patient’s event.
36Close the event review window.
9. Discharge37Please discharge the patient.
Table 5. List of satisfaction evaluation for the VCMS.
Table 5. List of satisfaction evaluation for the VCMS.
ScenarioNo.Category
1. Hospitalization1Are you satisfied with the way you search for registered devices?
2Do you think it is easy to add a patient monitoring window to admit?
2. Screen mode3Do you think changing the number of arrangements on the screen is easy?
4Do you think it is easy to monitor in the form of maximum arrangement?
5Do you think it is easy to move the patient display to the desired thumbnail?
6Is it useful to automatically align the patient display in bed number order?
3. Zoom in7Do you think it is easy to zoom in on the patient’s monitoring screen?
8Are you satisfied with the ability to stop the waveform?
9Is it convenient to check the graph by moving the lines on the stopped waveform?
4. Numeric parameter setting10Do you think it is easy to change the type of numeric parameters?
5. Waveform setting11Do you think it is easy to change the type of wave data?
6. Alarm setting12Are you satisfied with ability to set alarm limits for individual patients?
13Is it convenient to change the value by clicking the arrow button or typing directly?
14Is it easy to change the alarm volume?
7. Trend review15Do you think it is easy to see data trends over time for individual patients?
16Are you satisfied with the ability to drag and drop vertical lines to see data in the table for the desired time zone?
17Is it easy to check the data in that time zone by selecting a date and time?
18Do you think it is convenient to export data trends in a chart or Excel?
8. Event review19Do you think it is easy to check the events of the entire patient?
20Is it easy to check the events of individual patients that have occurred?
21Are you satisfied with the ability to see details such as the time, type, and content of the event?
9. Discharge22Is it easy to process the discharging a patient?
10. Overall23Please evaluate your overall satisfaction with the VCMS.
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Kim, K.; Kim, Y.; Kim, Y.S.; Kim, K.B.; Lee, S.H. New Web-Based Ventilator Monitoring System Consisting of Central and Remote Mobile Applications in Intensive Care Units. Appl. Sci. 2024, 14, 6842. https://doi.org/10.3390/app14156842

AMA Style

Kim K, Kim Y, Kim YS, Kim KB, Lee SH. New Web-Based Ventilator Monitoring System Consisting of Central and Remote Mobile Applications in Intensive Care Units. Applied Sciences. 2024; 14(15):6842. https://doi.org/10.3390/app14156842

Chicago/Turabian Style

Kim, Kyuseok, Yeonkyeong Kim, Young Sam Kim, Kyu Bom Kim, and Su Hwan Lee. 2024. "New Web-Based Ventilator Monitoring System Consisting of Central and Remote Mobile Applications in Intensive Care Units" Applied Sciences 14, no. 15: 6842. https://doi.org/10.3390/app14156842

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