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Telehealth programs and wearable sensors that enable patients to monitor their vital signs have expanded due to the COVID-19 pandemic. The electronic National Early Warning Score (e-NEWS) system helps identify and respond to acute illness.
This study aimed to implement and evaluate a comprehensive telehealth system to monitor vital signs using e-NEWS for patients receiving integrated home-based medical care (iHBMC). The goal was to improve the early detection of patient deterioration and enhance care delivery in home settings. The system was deployed to optimize remote monitoring in iHBMC and reduce emergency visits and hospitalizations.
The study was conducted at a medical center and its affiliated home health agency in central Taiwan from November 1, 2022, to October 31, 2023. Patients eligible for iHBMC were enrolled, and sensor data from devices such as blood pressure monitors, thermometers, and pulse oximeters were transmitted to a cloud-based server for e-NEWS calculations at least twice per day over a 2-week period. Patients with e-NEWSs up to 4 received nursing or physician recommendations and interventions based on abnormal physiological data, with reassessment occurring after 2 hours.
A total of 28 participants were enrolled, with a median age of 84.5 (IQR 79.3‐90.8) years, and 32% (n=9) were male. All participants had caregivers, with only 5 out of 28 (18%) able to make decisions independently. The system was implemented across one medical center and its affiliated home health agency. Of the 28 participants, 27 completed the study, while 1 exited early due to low blood pressure and shortness of breath. The median e-NEWS value was 4 (IQR 3‐6), with 397 abnormal readings recorded. Of the remaining 27 participants, 8 participants had earlier home visits due to abnormal readings, 6 required hypertension medication adjustments, and 9 received advice on oxygen supplementation. Overall, 24 out of 28 (86%) participants reported being satisfied with the system.
This study demonstrated the feasibility of implementing a telehealth system integrated with e-NEWS in iHBMC settings, potentially aiding in the early detection of clinical deterioration. Although caregivers receive training and resources for their tasks, the system may increase their workload, which could lead to higher stress levels. The small sample size, short monitoring duration, and regional focus in central Taiwan may further limit the applicability of the findings to areas with differing countries, regions, and health care infrastructures. Further research is required to confirm its impact.
Home hospitalization, a patient-centered approach, offers benefits to individuals with chronic diseases by both reducing the frequency of their hospital visits and providing care within the comfort of their homes [
In health care, connecting real-world objects with electronic devices such as wearable sensors for critical data collection has become essential. This integration is supported by cloud computing, which processes health data to better enhance human well-being. For instance, telemonitoring, widely adopted during the COVID-19 pandemic, uses pulse oximetry and symptom recording to detect any deterioration in one’s health [
The early warning score is a comprehensive physiological scoring system that evaluates parameters such as respiratory rate, oxygen supplementation and saturation, body temperature, blood pressure, pulse rate, and level of consciousness. This scoring system has been formally recognized by the National Health Service in the United Kingdom and is used as an alert system to identify emergency patients in inpatient settings [
The primary aim of this study was to establish and evaluate the feasibility of a comprehensive telemonitoring system integrated with the electronic NEWS (e-NEWS) system for monitoring vital signs in patients receiving integrated home-based medical care (iHBMC). Specifically, the study sought to assess the system’s ability to detect early signs of clinical deterioration and trigger timely interventions by health care professionals. Key objectives included evaluating the system’s completion rates for vital sign monitoring, identifying abnormal e-NEWS values and their associated interventions, measuring patient and caregiver satisfaction, and exploring operational challenges such as network connectivity and caregiver burden. This implementation report focused exclusively on the feasibility pilot study, aiming to highlight the lessons learned. The report was organized in accordance with the iCHECK-DH (Guidelines and Checklist for Reporting on Digital Health Implementations) [
A multidisciplinary team comprising medical, technical, and support personnel was essential. The medical team included a physician responsible for overseeing patient care and clinical decision-making, a pharmacist tasked with conducting medication reviews and offering therapeutic recommendations, and a nurse case manager who monitored patients, responded to alerts, and coordinated care delivery. Furthermore, the team included a therapist and a nutritionist to address rehabilitative and nutritional needs, respectively. On the technical side, an information technology support engineer was required to maintain the telemonitoring systems, while a health informatics specialist managed the integration of telemonitoring data into the electronic health record (EHR). Additionally, the support staff included an administrative professional responsible for documentation management, billing, and maintenance of patient records, as detailed in
Development and implementation process of the e-NEWS–based mobile health care monitoring system. e-NEWS: electronic National Early Warning Score.
The QOCA (Quanta Omni Cloud Care; Quanta Computer Inc) APC (artificial intelligence patient care) system was selected due to its compatibility with Taiwan’s national health infrastructure, its integration capabilities with existing EHR, and its ease of use for patients and caregivers. The system supported various sensor devices, allowing for interoperability with different telemonitoring tools. The platform used secure data encryption to protect patient privacy, and the cloud-based nature of the system ensured scalability for potential nationwide implementation. The decision to use this system was also influenced by its adaptability for future upgrades, such as the integration of artificial intelligence to predict health deterioration.
Patients eligible for the iHBMC program, launched by the National Health Insurance (NHI) plan of Taiwan, resided in their own homes (excluding care facilities) and had a confirmed medical need as assessed by the health care team [
The research received approval from the institutional review board of the medical center (CE22459B), ensuring all procedures adhered to the established study protocol, standard regulations, and ethical principles outlined in the Declaration of Helsinki. Informed consent was obtained from all participants, who were informed of the study’s purpose, procedures, and their right to withdraw at any time. While the data were not anonymized, they were safeguarded through encryption, restricted access, and secure handling, with any issues managed by the medical center. In the event of adverse reactions or harm resulting from this study, compensation was provided by the medical center.
Patient demographics were investigated, including age, gender, disease diagnosis, medications, disease duration, comorbidities, and tube dependency, encompassing various types of medical tubes, such as the nasogastric tube, urinary catheter, and tracheostomy tube. Furthermore, the NEWSs comprised 7 vital signs: respiration rate, oxygen saturation, systolic blood pressure, heart rate, temperature, level of consciousness, and oxygen supplementation. Different scores were assigned based on varying degrees of abnormalities [
The telemonitoring system used standardized communication protocols, such as Fast Health Interoperability Resources, to ensure seamless data integration with existing hospital EHR systems. The use of these protocols allowed for real-time synchronization of patient data across different health care platforms, facilitating continuity of care between home-based care providers and hospital systems.
The telemonitoring system was implemented with collaboration from several key entities. The study was conducted at a medical center and its affiliated home health care agency in central Taiwan, where health care professionals including physicians, nurses, and pharmacists managed patient care and telemonitoring operations from November 1, 2022, to October 31, 2023. Technical support for the system was provided by Quanta Computer Inc, which developed the QOCA APC system, a cloud-based infrastructure for real-time monitoring of patient vital signs. The platform ensured smooth data transmission and timely alerts. Although the Taiwanese government did not directly fund the system, the iHBMC model supported by the NHI program aligned with national health care goals.
The sustainability of the telemonitoring system depended on several factors, including the availability of funding, integration with existing health care infrastructure, and the long-term engagement of caregivers and patients. This study demonstrated the feasibility of the system within a short time frame; however, for widespread adoption, a sustainable business model was necessary. This included securing government support or public-private partnerships to offset the costs of system maintenance, training, and ongoing support for users. Future research also considered the environmental sustainability of telemonitoring systems, particularly regarding the use of electronic devices and data storage solutions.
The telemonitoring system was implemented in central Taiwan, covering patients enrolled in iHBMC programs affiliated with a medical center. Although the study had a regional focus, the system had the potential for scalability to other regions across Taiwan. In this pilot phase, the study covered 28 patients, representing a small but significant portion of the eligible population within the hospital’s home care program. In
Architecture of the QOCA APC system and conversion of physiological data to e-NEWS format. APC: artificial intelligence patient care; e-NEWS: electronic National Early Warning Score; QOCA: Quanta Omni Cloud Care.
The baseline characteristics of the patients were depicted in
Baseline characteristics of the participants (N=28).
Characteristics | Values | |
Age (years), median (IQR) | 85 (79‐91) | |
Female | 19 (68) | |
Male | 9 (32) | |
BMI (kg/m2), median (IQR) | 22 (20‐26) | |
Illiterate | 7 (25) | |
Primary school | 13 (46) | |
Junior high school | 6 (21) | |
Senior high school | 1 (4) | |
University | 1 (4) | |
Single, widowed, or divorced | 18 (64) | |
Married | 10 (36) | |
Oneself | 0 (0) | |
Others | 28 (100) | |
Oneself | 5 (18) | |
Others | 23 (82) | |
Dementia | 11 (39) | |
Diabetes mellitus | 10 (36) | |
Congestive heart failure | 8 (29) | |
Cerebrovascular accident | 8 (29) | |
Solid tumor | 7 (25) | |
Myocardial infarction | 5 (18) | |
Peptic ulcer disease | 4 (14) | |
Chronic kidney disease | 4 (14) | |
Peripheral vascular disease | 1 (4) | |
Chronic obstructive pulmonary disease | 1 (4) | |
Connective tissue disease | 1 (4) | |
Nasogastric tubes, n (%) | 15 (54) | |
Urinary catheters, n (%) | 14 (50) | |
Tracheostomy, n (%) | 1 (4) | |
Age-adjusted Charlson comorbidity index, median (IQR) | 7 (5‐9) | |
Polypharmacy, n (%) | 16 (57) | |
Barthel index of activities of daily living, median (IQR) | 0 (0‐19) | |
Glasgow coma scale, median (IQR) | 12 (10-15) |
The percentage of vital signs above the medium level (n=27).
Respiration rate, n (%) | Oxygen saturations, n (%) | Any supplemental oxygen, n (%) | Temperature, n (%) | Systolic blood pressure, n (%) | Heart rate, n (%) | e-NEWS | |
Day 1, morning | 1 (4) | 1 (4) | 8 (30) | 2 (7) | 1 (4) | 0 (0) | 16 (59) |
Day 1, night | 1 (4) | 2 (7) | 7 (26) | 2 (7) | 0 (0) | 0 (0) | 12 (44) |
Day 2, morning | 1 (4) | 3 (11) | 7 (26) | 2 (7) | 3 (11) | 0 (0) | 12 (44) |
Day 2, night | 1 (4) | 2 (7) | 7 (26) | 1 (4) | 1 (4) | 0 (0) | 11 (41) |
Day 3, morning | 1 (4) | 4 (15) | 7 (26) | 1 (4) | 3 (11) | 0 (0) | 16 (59) |
Day 3, night | 1 (4) | 1 (4) | 7 (26) | 0 (0) | 4 (15) | 0 (0) | 12 (44) |
Day 4, morning | 1 (4) | 2 (7) | 7 (26) | 1 (4) | 5 (19) | 0 (0) | 15 (56) |
Day 4, night | 1 (4) | 3 (11) | 7 (26) | 0 (0) | 3 (11) | 0 (0) | 13 (48) |
Day 5, morning | 1 (4) | 5 (19) | 7 (26) | 1 (4) | 4 (15) | 0 (0) | 16 (59) |
Day 5, night | 1 (4) | 3 (11) | 7 (26) | 1 (4) | 3 (11) | 0 (0) | 14 (52) |
Day 6, morning | 1 (4) | 6 (22) | 7 (26) | 1 (4) | 4 (15) | 0 (0) | 16 (59) |
Day 6, night | 0 (0) | 4 (15) | 7 (26) | 0 (0) | 2 (7) | 0 (0) | 16 (59) |
Day 7, morning | 0 (0) | 4 (15) | 7 (26) | 0 (0) | 4 (15) | 1 (4) | 14 (52) |
Day 7, night | 1 (4) | 2 (7) | 7 (26) | 0 (0) | 2 (7) | 0 (0) | 13 (48) |
Day 8, morning | 1 (4) | 3 (11) | 7 (26) | 0 (0) | 3 (11) | 0 (0) | 15 (56) |
Day 8, night | 0 (0) | 4 (15) | 7 (26) | 0 (0) | 3 (11) | 0 (0) | 14 (52) |
Day 9, morning | 1 (4) | 4 (15) | 7 (26) | 0 (0) | 4 (15) | 1 (4) | 13 (48) |
Day 9, night | 1 (4) | 2 (7) | 7 (26) | 0 (0) | 3 (11) | 0 (0) | 13 (48) |
Day 10, morning | 2 (7) | 4 (15) | 7 (26) | 0 (0) | 4 (15) | 0 (0) | 14 (52) |
Day 10, night | 2 (7) | 2 (7) | 7 (26) | 0 (0) | 3 (11) | 1 (4) | 14 (52) |
Day 11, morning | 2 (7) | 3 (11) | 7 (26) | 0 (0) | 4 (15) | 1 (4) | 15 (56) |
Day 11, night | 2 (7) | 2 (7) | 7 (26) | 0 (0) | 3 (11) | 0 (0) | 12 (44) |
Day 12, morning | 2 (7) | 4 (15) | 7 (26) | 0 (0) | 4 (15) | 0 (0) | 14 (52) |
Day 12, night | 1 (4) | 2 (7) | 7 (26) | 0 (0) | 3 (11) | 0 (0) | 15 (56) |
Day 13, morning | 2 (7) | 7 (26) | 7 (26) | 0 (0) | 4 (15) | 1 (4) | 17 (63) |
Day 13, night | 1 (4) | 1 (4) | 7 (26) | 0 (0) | 4 (15) | 0 (0) | 15 (56) |
Day 14, morning | 2 (7) | 5 (19) | 7 (26) | 0 (0) | 7 (26) | 0 (0) | 16 (59) |
Day 14, night | 2 (7) | 5 (19) | 7 (26) | 0 (0) | 3 (11) | 0 (0) | 14 (52) |
ae-NEWS: electronic National Early Warning Score.
The percentage of vital signs above the high level (n=27).
Respiration rate, n (%) | Oxygen saturations, n (%) | Any supplemental oxygen, n (%) | Temperature, n (%) | Systolic blood pressure, n (%) | Heart rate, n (%) | e-NEWS | |
Day 1, morning | 1 (4) | 1 (4) | 0 (0) | 2 (7) | 0 (0) | 0 (0) | 1 (4) |
Day 1, night | 1 (4) | 1 (4) | 0 (0) | 2 (7) | 0 (0) | 0 (0) | 3 (11) |
Day 2, morning | 1 (4) | 1 (4) | 0 (0) | 2 (7) | 0 (0) | 0 (0) | 5 (19) |
Day 2, night | 1 (4) | 1 (4) | 0 (0) | 1 (4) | 0 (0) | 0 (0) | 1 (4) |
Day 3, morning | 1 (4) | 1 (4) | 0 (0) | 1 (4) | 0 (0) | 0 (0) | 3 (11) |
Day 3, night | 1 (4) | 1 (4) | 0 (0) | 0 (0) | 1 (4) | 0 (0) | 2 (7) |
Day 4, morning | 1 (4) | 2 (7) | 0 (0) | 1 (4) | 1 (4) | 0 (0) | 4 (15) |
Day 4, night | 1 (4) | 2 (7) | 0 (0) | 0 (0) | 1 (4) | 0 (0) | 3 (11) |
Day 5, morning | 1 (4) | 2 (7) | 0 (0) | 1 (4) | 1 (4) | 0 (0) | 5 (19) |
Day 5, night | 1 (4) | 1 (4) | 0 (0) | 1 (4) | 2 (7) | 0 (0) | 2 (7) |
Day 6, morning | 1 (4) | 1 (4) | 0 (0) | 1 (4) | 1 (4) | 0 (0) | 5 (19) |
Day 6, night | 0 (0) | 1 (4) | 0 (0) | 0 (0) | 2 (7) | 0 (0) | 2 (7) |
Day 7, morning | 0 (0) | 4 (15) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 5 (19) |
Day 7, night | 1 (4) | 1 (4) | 0 (0) | 0 (0) | 1 (4) | 0 (0) | 2 (7) |
Day 8, morning | 1 (4) | 2 (7) | 0 (0) | 0 (0) | 1 (4) | 0 (0) | 4 (15) |
Day 8, night | 0 (0) | 2 (7) | 0 (0) | 0 (0) | 2 (7) | 0 (0) | 2 (7) |
Day 9, morning | 1 (4) | 2 (7) | 0 (0) | 0 (0) | 1 (4) | 0 (0) | 5 (19) |
Day 9, night | 1 (4) | 1 (4) | 0 (0) | 0 (0) | 2 (7) | 0 (0) | 4 (15) |
Day 10, morning | 2 (7) | 2 (7) | 0 (0) | 0 (0) | 1 (4) | 0 (0) | 6 (22) |
Day 10, night | 2 (7) | 1 (4) | 0 (0) | 0 (0) | 3 (11) | 0 (0) | 4 (15) |
Day 11, morning | 2 (7) | 2 (7) | 0 (0) | 0 (0) | 2 (7) | 0 (0) | 8 (30) |
Day 11, night | 2 (7) | 1 (4) | 0 (0) | 0 (0) | 3 (11) | 0 (0) | 4 (15) |
Day 12, morning | 2 (7) | 2 (7) | 0 (0) | 0 (0) | 3 (11) | 0 (0) | 8 (30) |
Day 12, night | 1 (4) | 1 (4) | 0 (0) | 0 (0) | 1 (4) | 0 (0) | 3 (11) |
Day 13, morning | 2 (7) | 1 (4) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 5 (19) |
Day 13, night | 1 (4) | 1 (4) | 0 (0) | 0 (0) | 1 (4) | 0 (0) | 4 (15) |
Day 14, morning | 2 (7) | 2 (7) | 0 (0) | 0 (0) | 1 (4) | 0 (0) | 7 (26) |
Day 14, night | 2 (7) | 1 (4) | 0 (0) | 0 (0) | 1 (4) | 0 (0) | 6 (22) |
ae-NEWS: electronic National Early Warning Score.
Patient vital signs and 14-day e-NEWS records. e-NEWS: electronic National Early Warning Score.
Feedback regarding the use of the remote care system was collected and included information on user satisfaction, perceived effectiveness of the system, ease of use, and any technical issues or challenges encountered during its implementation (
Using feedback from e-NEWS
Patients, n (%) | ||
Like | 24 (86) | |
Neutral | 3 (11) | |
Dislike | 1 (4) | |
Full 14 days | 27 (96) | |
Less than 14 days | 1 (4) |
ae-NEWS: electronic National Early Warning Score.
The system achieved a 100% completion rate for vital sign monitoring, with only 1 patient withdrawing from the study due to hospitalization. The real-time alert system ensured prompt responses to abnormal vital sign readings, minimizing the risk of adverse events.
Telemetric measurements using algorithms for risk assessment were still in the early stages [
Two key challenges in implementing an early warning score in primary care were the low probability of disease acuity and the weak correlation between NEWS and referral decisions. Understanding the full clinical impacts was crucial before the widespread adoption of the system in community settings [
An unintended consequence of the study was the increased workload for caregivers, who had to manage the telemonitoring devices and respond to alerts. While this responsibility did not result in negative health outcomes, it may represent an additional burden for some families, particularly those with limited technical expertise.
Telehealth is being increasingly used in patient home care and may help address challenges while also supporting home-based patients who are receiving medical care by improving their independence, self-management, and access to community care services, as well as reducing unnecessary hospital admissions [
The small sample size of 28 participants and the short duration of monitoring limit our study, restricting the generalizability of the findings to broader populations. While the results provide valuable insights into the feasibility of e-NEWS in iHBMC settings, they may not fully apply to groups with diverse demographics, health care needs, or environmental conditions. Additionally, our study’s regional focus in central Taiwan may limit the applicability of findings to regions with different health care infrastructures, caregiver resources, and technology access. Studies involving larger and more diverse patient populations across varied geographic settings would offer stronger evidence for broader application.
In this pilot study, e-NEWSs serve as a critical part of clinical decision-making, helping identify patients with abnormal physiological readings and prompting timely interventions. Patients with medium to high e-NEWSs trigger specific actions by the multidisciplinary team, such as medication adjustments, supplemental oxygen, and rescheduling of home visits for closer monitoring. This scoring system appears effective in preventing clinical deterioration, as suggested by a reduction in emergency room visits and a high completion rate of health assessments. However, further research with a broader cohort is essential to determine whether these trends consistently lead to positive outcomes and reduced hospital use across varied care environments.
The implementation also affects caregivers, who are responsible for monitoring devices, interpreting alerts, and supporting patient care based on e-NEWS indicators. Although caregivers receive training and resources to handle these responsibilities, the system adds to their daily workload, potentially increasing caregiver strain. To mitigate this, strategies such as ongoing technical support, streamlined device functionality, and educational workshops are used to ensure caregivers can manage these tasks without compromising their well-being. Future iterations of the e-NEWS system could further enhance caregiver support, incorporating features that reduce manual input and provide automated guidance, enabling more efficient caregiving while maintaining system effectiveness.
We would like to extend our gratitude to the Biostatistics Group, Department of Medical Research, Taichung Veterans General Hospital, for their valuable assistance with the statistical analysis performed in this study. We also thank Quanta Computer Inc for their help in constructing a platform, namely the QOCA (Quanta Omni Cloud Care) APC (artificial intelligence patient care) system. This project was made possible through research funding provided by the National Science and Technology Council, Taiwan (grant NSTC 111‐2622-B-075A-00).
None declared.
artificial intelligence patient care
electronic health record
electronic National Early Warning Score
Guidelines and Checklist for Reporting on Digital Health Implementations
integrated home-based medical care
National Early Warning Score
National Health Insurance
Quanta Omni Cloud Care
iCHECK-DH