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Medical specialists are primarily interested in researching health care as a potential replacement for conventional healthcare methods nowadays. COVID-19 creates chaos in society regardless of the modern technological evaluation involved in this sector. Due to inadequate medical care and timely, accurate prognoses, many unexpected fatalities occur. As medical applications have expanded in their reaches along with their technical revolution, therefore patient monitoring systems are getting more popular among the medical actors. The Internet of Things (IoT) has met the requirements for the solution to deliver such a vast service globally at any time and in any location. The suggested model shows a wearable sensor node that the patients will wear. Monitoring client metrics like blood pressure, heart rate, temperature, etc., is the responsibility of the sensor nodes, which send the data to the cloud via an intermediary node. The sensor-acquired data are stored in the cloud storage for detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Among the different cloud-based predictive models now being used, the model having the highest accuracy will be treated as the resultant model. This resultant model will be further used for the data dissemination mechanism by which the concerned medical actors will be provided an alert message for a proper medication in a desirable manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Background: We sought to use existing in-patient surveillance data to investigate the risk of hospital-acquired antimicrobial-resistant organisms (ARO) among patients with COVID-19 infection. Methods: Prospective case capture was done for patients admitted with COVID-19, as well as those admitted with ARO and Clostridioides difficile infections (CDI). Odds ratios (OR) were used to measure the strength of association between COVID-19 infection and the risk of acquiring hospital-acquired ARO and CDI. Results: The odds of acquiring ARO/CDI were statistically higher among patients with hospital-acquired and community-acquired COVID-19 infections (OR=2.68 and 1.79 respectively) compared to persons without COVID-19 (OR=0.53). Conclusions: Our results show an association between COVID-19 infection and the acquisition of ARO/CDI in the in-patient setting. This finding suggests that prolonged hospitalization may expose patients to hospital-acquired infections, and this may have relevance in the management of patients requiring hospitalization for extended periods of time.
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BACKGROUND: The increase in telehealth usage has sustained since the beginning of the COVID-19 pandemic. While Remote Patient Monitoring (RPM) programs are abundantly used in the management of adults, pediatric RPM programs remain rare. METHODS: An RPM department was developed to serve several, multi-specialty pediatric programs. This department uses a centralized nursing team that manages all patients enrolled in RPM programs. Each program is unique and created in partnership with the centralized nurses and the ambulatory care teams. The various programs allow for transmission of patient- and caregiver-generated health data and consistent communication between the patient or caregiver and the managing providers, allowing for real-time plan adaptation. FINDINGS: Over 1200 patients have been managed through the 18 various RPM programs. Approximately 300 patients are monitored each month by the centralized nursing team. Patient and caregiver experience has been high due to resources offered including on-demand video visits and text messaging with the nursing team. DISCUSSION: Multi-specialty RPM departments help to expand the reach of an institution and provide care to more patients. Quality improvement must be ongoing to ensure equity of participation and perceived benefit of the programs for both providers and patients and caregivers. APPLICATION TO PRACTICE: Pediatric RPM programs can improve patient care delivery by decreasing days away from home while improving access to care. Ensuring equitable opportunity for patient participation is imperative in achieving success for an RPM department.
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Objective: Structural reimbursement can be an important factor for large-scale implementing and upscaling of remote patient monitoring (RPM). During the COVID-19 pandemic, the Dutch Healthcare Authority expanded regulations, creating novel opportunities to reimburse RPM. Despite these regulations, barriers to the reim-bursement of RPM remain. This study aimed to identify the barriers and facilitators of structural reimbursement of RPM in hospital care in the Netherlands and to propose actionable recommendations.Methods: This is an exploratory qualitative study with relevant stakeholders in the Dutch purchasing market: the Dutch Healthcare Authority, health insurers, and healthcare providers. Semi-structured interviews were held between October and December of 2020. All interviews were conducted using a digital medium, transcribed verbatim, and thematically analyzed.Results: Multiple perceived barriers were mentioned: wrong pocket problems (i.e. the entity that bears the costs of implementation does not receive the benefits), no uniform quality and outcome indicators, lack of willingness to redesign care pathways by providers, and difficulties implementing cross-sector models. Perceived facilitators included interdisciplinary cooperation and transparency, the use of alternative payment models, increase in the total number of patients per RPM project, and the optional reimbursement scheme. Conclusion: Our interviews found barriers and facilitators concerning structural reimbursement of RPM in hos-pital settings in the Netherlands. Our results emphasize that the successful integration of structural reimburse-ment requires: 1) understanding the improvement potential of RPM by creating business cases, 2) co-creation (redesigning care paths) from the outset of an RPM project, 3) and allocating financial risk by providers and insurers.Public Interest Summary: The COVID-19 pandemic has demonstrated the strong potential of consultation and monitoring patients at a distance. Remote patient monitoring -the use of information technologies for moni-toring patients at a distance -is seen as a potential solution to urgent challenges in the healthcare system. Nevertheless, embedding remote patient monitoring innovations into routine healthcare is often challenging, partly due to difficulties in reimbursing these initiatives. Barriers to reimbursing remote patient monitoring included organizational factors, no uniform quality and outcome indicators, and difficulties using different payment models. Perceived facilitators included an increase in the total number of patients per project, better interdisciplinary cooperation and transparency, and help from the Dutch Healthcare Authority. Introducing these insights into healthcare policy dialogues could support reimbursement of remote patient monitoring and stim-ulate the collaboration of healthcare stakeholders responsible for implementing and scaling up remote patient monitoring projects.
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Recently, smart medical devices have become preva-lent in remote monitoring of patients and the delivery of medication. The ongoing Covid-19 pandemic situation has boosted the upward trend of the popularity of smart medical devices in the healthcare system. Simultaneously, different device manufacturers and technologies compete for a share in a smart medical device's market, which forces the integration of diverse smart medical de-vices into a common healthcare ecosystem. Hence, modern unified healthcare communication systems (UHCSs) combine ISO/IEEE 11073 and Health Level Seven (HL7) communication standards to support smart medical devices' interoperability and their communication with healthcare providers. Despite their advantages in supporting various smart medical devices and communication technologies, these standards do not provide any security and suffer from vulnerabilities. Existing studies provide stand-alone security solutions to components of UHCSs and do not cover UHCSs holistically. In this paper, we perform a systematic threat analysis of UHCSs that relies on attack-defense tree (ADTree) formalisms. Considering the attack landscape and defense ecosys-tem, we build an ADTree for UHCSs and convert the ADTree to stochastic timed automata (STA) to perform quantitative analysis. Our analysis using UPPAAL SMC shows that the Man-in-the-Middle and unauthorized remote access attacks are the most probable attacks that a malicious entity could pursue, causing mistreatment to patients. We also extract valuable information about the top threats, the likelihood of performing different individual and simultaneous attacks, and the expected cost for attackers. © 2022 IEEE.
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A multichannel interaction service is a practice whereby organizations communicate and interact with their existing customers and potential new customers through different channels. This article presents a brief case study of multichannel interaction in healthcare services, which studies the viability of continuous multichannel interaction for personalized healthcare services to enable health professionals to follow up and monitor patients in home-based care. Furthermore, this study aims to explore the possibility of the continuity and complementarity of the interactions across different communication channels with the patients. The data used for this study was gathered during the first wave of the COVID-19 pandemic. This study showed that despite this type of interaction being relatively new in healthcare services, it has considerable potential for improving the relationship between patients, health professionals, and care providers. Upon completion of the data analysis, several conclusions were drawn. One such conclusion was the ability to maintain continuity of interaction across multiple channels, as well as the synergy between the different channels of interaction available to patients and the impact this has on the way patients and health professionals interact. Additionally, it was determined that the complementarity of different interaction channels is crucial when implementing multichannel interaction services. Furthermore, the implementation of this solution resulted in improved communication between patients and health professionals. Also, it has decreased health professional's workload and reduced care providers costs regarding remote patient follow-up. © 2023 by the authors.
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- All objects are now connected thanks to the technological advancements in the medical industry using IoT. IoT has been applied in a wide range of fields, including daily life. But the primary impact of IoT in healthcare is simply amazing. The project's
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Current remote monitoring of COVID-19 patients relies on manual symptom reporting, which is highly dependent on patient compliance. In this research, we present a machine learning (ML)-based remote monitoring method to estimate patient recovery from COVID-19 symptoms using automatically collected wearable device data, instead of relying on manually collected symptom data. We deploy our remote monitoring system, namely eCOVID, in two COVID-19 telemedicine clinics. Our system utilizes a Garmin wearable and symptom tracker mobile app for data collection. The data consists of vitals, lifestyle, and symptom information which is fused into an online report for clinicians to review. Symptom data collected via our mobile app is used to label the recovery status of each patient daily. We propose a ML-based binary patient recovery classifier which uses wearable data to estimate whether a patient has recovered from COVID-19 symptoms. We evaluate our method using leave-one-subject-out (LOSO) cross-validation, and find that Random Forest (RF) is the top performing model. Our method achieves an F1-score of 0.88 when applying our RF-based model personalization technique using weighted bootstrap aggregation. Our results demonstrate that ML-assisted remote monitoring using automatically collected wearable data can supplement or be used in place of manual daily symptom tracking which relies on patient compliance. IEEE
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Covid-19, which has spread throughout the world, has reportedly caused millions of deaths. Among the causes of the patient's death is the phase after the patient is declared negative for COVID, but there is a cytokine storm. In this study, an IoT-based technology was proposed to be able to detect abnormalities in COVID-19 patients, even though they already had a negative Covid status based on the PCR test. The implementation of this technology allows former Covid patients to be monitored from anywhere as long as they are connected to the internet, using designed wearable devices and dedicated mobile apps for them. Based on experiment result, all the sensors have the ability to work and sense patient body indicators with error below 5%. This study demonstrated the flawless use of a mobile app dedicated to monitor patients' health during the pandemic. When patient health condition indicating exposed to cytokine storm, a warning notification is appear at the mobile app. © 2022 IEEE.
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Mucormycosis once considered a rare disease with an incidence of 0.005 to 1.7 per million, has become one of the greatest menaces during the coronavirus disease (COVID-19) pandemic. India alone has contributed to nearly 70% of the global caseload of COVID-associated mucormycosis (CAM) and it had even been declared as a notifiable disease. Second wave of COVID-19 pandemic saw a steep rise in the incidence of mucormycosis and these patients have been presenting to anesthesiologists for various surgical procedures due to its primary or secondary sequelae. Rhino-orbito-cerebral mucormycosis (ROCM) is the commonest manifestation and is caused by Rhizopus arrhizus. Injudicious use of corticosteroids in vulnerable patients could have been a major contributing factor to the sudden rise in ROCM during the pandemic. Concerns related to anesthetic management include COVID-19 infection and post COVID sequalae, common presence of uncontrolled diabetes mellitus, possibility of difficult mask-ventilation and/or intubation, various drug therapy-associated adverse effects, and interaction of these drugs with anesthetic agents. Thorough preoperative optimization, multidisciplinary involvement, perioperative care, and vigilance go a long way in improving overall outcomes in these patients. Copyright © 2022 Saudi Journal of Anesthesia Published by Wolters Kluwer - Medknow.
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Introduction: Data are limited on the effectiveness of remote patient monitoring (RPM) for acute illnesses, including COVID-19. We conducted a study to determine if enrollment in a COVID-19 RPM program was associated with better outcomes. Methods: From March through September 2020, patients with respiratory symptoms and presumptive COVID-19 were referred to the health system's COVID-19 RPM program. We conducted a retrospective cohort study comparing outcomes for patients enrolled in the RPM (n = 4,435) with those who declined enrollment (n = 2,742). Primary outcomes were emergency room, hospital, and intensive care unit admissions, and death. We used logistic regression to adjust for demographic differences and known risk factors for severe COVID-19. Results: Patients enrolled in the RPM were less likely to have risk factors for severe COVID-19. There was a significant decrease in the odds of death for the group enrolled in the RPM (adjusted odds ratio [OR] = 0.50; 95% confidence interval [CI], 0.30-0.83) and a nonsignificant decrease in the odds of the other primary outcomes. Increased number of interactions with the RPM significantly decreased the odds of hospital admission (OR = 0.92; 95% CI, 0.88-0.95). Conclusions: COVID-19 RPM enrollment was associated with decreased odds of death, and the more patients interacted with the RPM, the less likely they were to require hospital admission. RPM is a promising tool that has the potential to improve patient outcomes for acute illness, but controlled trials are necessary to confirm these findings.
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INTRODUCTION: This study investigated how patients with COVID-19, telemonitoring (TM) teams, general practitioners (GPs) and primary care nurses in Belgium experienced remote patient monitoring (RPM) in 12 healthcare organizations, in relation to the patients' illness, health, and care needs, perceived quality of care, patient and health system outcomes, and implementation challenges. DESIGN: A qualitative research approach was adopted, including focus group discussions and semi-structured interviews. METHODS: Four different groups of participants were interviewed, that is, patients (n = 17), TM teams (n = 27), GPs (n = 16), and primary care nurses (n = 12). An interview guide was drafted based on a literature review. Interviews were transcribed verbatim, and NVivo was used for managing and analyzing the data. The QUAGOL method was used to guide the data analysis process and was adapted for the purpose of a thematic content analysis. RESULTS: All participants agreed that RPM-reassured patients. The overall perceived value of RPM for individual patients depended on how well the intervention matched with their needs. Patients who did not have the necessary language (Dutch/French speaking) and digital skills, who did not have the right equipment (smartphone or tablet), or who missed the necessary infrastructure (no internet coverage in their region) were often excluded. Remote patient monitoring also reassured healthcare professionals as it gave them information on a disease they had little knowledge about. Professionals involved in RPM experienced a high workload. All TM teams agreed that quality of data was a key factor to ensure an adequate follow-up, but they differed in what they found important. The logistic management of RPM was a challenge because of the contagious character of COVID-19, and the need for an effective information flow between the hospital team and primary care providers. Participants missed clarification about who was accountable for the care for patients in the projects. Primary care nurses and GPs missed access to RPM data. All agreed that the funding they received was not sufficient to cover all the costs associated with RPM. CONCLUSION: Healthcare professionals and patients perceive RPM as valuable and believe that the concept will have its place in the Belgium health system. However, current RPM practice is challenged by many barriers, and the sustainability of RPM implementation is low. CLINICAL RELEVANCE: Remote patient monitoring (RPM) was perceived as a valuable intervention for patients with COVID-19, but there were important concerns about unequal access to care. While the technology for RPM is available, the sustainability of implementation is low because of concerns with data quality, challenging logistics within projects, lack of data integration and communication, and a lack of an overarching guiding framework.
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Background: Remote patient monitoring (RPM) can be deployed as part of a tiered approach to open up hospital bed availability by allowing earlier discharge of patients with continued virtual monitoring. We describe the impact of RPM on length of stay (LOS) for patients with COVID-19. Methods: We deployed RPM during two COVID-19 surges at a tertiary academic hospital from March to June 2020 as a feasibility pilot to establish the infrastructure for RPM including electronic health record changes and virtual health center (VHC) protocols, and October 2020 to February 2021, during the second surge of COVID-19. Discharging patients received a wearable vital sign monitoring device, allowing real-time data transmission to the VHC using a smart phone application. The data, monitored 24 h a day for 8 days by a technician, had built-in escalation protocols to nurses and/or attending physicians. Results: We compared patients discharged with RPM with those discharged without RPM during both phases using a two-to-one-matched case-control design including age, sex, Charlson comorbidity index, and limited English proficiency. After including discharge with home oxygen therapy as an effect modifier, there was a significant association between shorter LOS and RPM for patients discharging without home oxygen (p = 0.0075) compared with patients not discharging on RPM. Discussion: Our study shows a strong association between a reduction in LOS for patients discharging with RPM but without home oxygen therapy, which can assist with hospital capacity. Conclusions: Home telemonitoring after discharge for patients with COVID-19 may reduce LOS.
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The COVID-19 pandemic pushed hospitals to deliver care outside of their four walls. To successfully scale virtual care delivery, it is important to understand how its implementation affects frontline workers, including their teamwork and patient-provider interactions. We conducted in-depth interviews of 17 clinicians and staff involved with the COVID-19 Virtual Observation Unit (CVOU) in the emergency department (ED) of an academic hospital. The program leveraged remote patient monitoring and mobile integrated health care. In the CVOU (vs. the ED), participants observed increases in interactions among clinicians and staff, patient participation in care delivery, attention to nonmedical factors, and involvement of coordinators and paramedics in patient care. These changes were associated with unintended, positive consequences for staff, namely, feeling heard, experience of meaningfulness, and positive attitudes toward virtual care. This study advances research on reconfiguration of roles following implementation of new practices using digital tools, virtual work interactions, and at-home care delivery.
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Detect and isolate was a common strategy that was employed to ensure that the spread of the Covid-19 virus is contained. However, in low-and-middle income countries like Rwanda, upon isolation, there was lack of reliable means for continued real time monitoring of patients' condition. Furthermore, for public screening, most of the technologies that were deployed e.g., at entrances to hospitals and schools had no capability to relay information to authorities for further action. This work presents a low cost IoT-based vital signs monitoring system that can be deployed either in crowded environments or in private homes for real time monitoring of Covid-19 related vital signs. The developed system consists of a display module (i.e., 0.96OLED_4P) for displaying measured parameters, including pulse rate, oxygen saturation and temperature. The Arduino ATMEGA328P-PU is used as the central processing unit and the SIM800L GSM module is included to facilitate emergency level communication. The system was verified and tested on 392 human test subjects. The functionality of the system was compared to the commercially available Wellue FS20F Bluetooth Finger Oximeter. The results show that the developed system is able to achieve the same level of accuracy as commercially available devices. The estimated total cost of the hardware components is USD 61.5. This system has potential to ensure a wider technology deployment to detect suspected cases and monitor Covid-19 patients, especially in low-and-middle income countries. The kit can also be used to monitor other noncommunicable disease that share the same symptoms as Covid-19. © 2022 IEEE.
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In this study, we used administrative data of six health insurance companies in the Czech Republic, to analyse the incidence of hospitalization in COVID-19 patients early treated with corticosteroids (CS). Cohort selection was based on index date defined by the date of the PCR test reported with COVID-19-related ICD-10 diagnoses found within a period of March 2020 - November 2020. The CS treated cohort was defined patients treated with CS within the 7 days after index date with no history of antibiotics nor corticosteroids within 30 days before index date. The control groups were defined either with or without a use of codeine within the 7 days after index date but always with no use of antibiotics nor corticosteroids within 30 days before and within the 7 days after index date. In total 590 274 records fulfilled the inclusion criteria, of whom 3 670 (0.62%) were hospitalized and 2 467 (0.42%) used CS within 7 days after ID. The contrast between a studied medication and a control was analysed with the logistic regression analysis with adjustment on age. Treatment with CS as compared with a cohort of patients with no curative treatment was associated with significantly (Chi square test p<.0001) increased rate of hospitalization in those treated with CS with odds ratio calculated as 3.1. Compared with a cohort of patients taking codeine the CS was associated with significantly (Chi square test p=0.001) lower rate of hospitalization, giving a moderate odds ratio 0.6. No beneficial effect of CS can be concluded based on this study. The strong influence of the composition of the control group is also evident from this study, as is evident from the reverse effect of the use of codeine in the control group. © 2022 IEEE.
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It is shown that various symptoms could remain in the stage of post-acute sequelae of SARS-CoV-2 infection (PASC), otherwise known as Long COVID. A number of COVID patients suffer from heterogeneous symptoms, which severely impact recovery from the pandemic. While scientists are trying to give an unambiguous definition of Long COVID, efforts in prediction of Long COVID could play an important role in understanding the characteristic of this new disease. Vital measurements (e.g. oxygen saturation, heart rate, blood pressure) could reflect body's most basic functions and are measured regularly during hospitalization, so among patients diagnosed COVID positive and hospitalized, we analyze the vital measurements of first7 days since the hospitalization start date to study the pattern of the vital measurements and predict Long COVID with the information from vital measurements. © 2022 IEEE.
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- All objects are now connected thanks to the technological advancements in the medical industry using IoT. IoT has been applied in a wide range of fields, including daily life. But the primary impact of IoT in healthcare is simply amazing. The project's suggestion is Notion of a health monitoring system that uses these sensors and a Raspberry Pi board to monitor patient metrics like temperature, heart rate, and needs before sending the data to the cloud. In the event of a problem, both the caregiver and the treating physician are promptly informed via a message on the mobile app. Security must come first and foremost when designing a successful remote monitoring system. Copyright © 2023 Ubiquity Press. All rights reserved.
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BACKGROUND: Pediatric obesity is common and a significant burden. Supplementing pediatric obesity treatment with technology is needed. This manuscript examines the usability and satisfaction, as well as explores initial effectiveness, of a remote patient monitoring system (RPMS) designed for youth presenting for pediatric weight management treatment. METHODS: 47 youth, 10 to 17 years old, with obesity and a caregiver participated. For three months, families received treatment via the RPMS. Usability and satisfaction outcomes were examined. Exploratory analyses were conducted to examine initial effectiveness from baseline and post-treatment (month 3) assessments. RESULTS: More than 80% of patients used the RPMS, and overall, patients completed 27 out of 90 daily sessions (30%). Youth and caregivers reported high satisfaction. Non-parametric tests revealed no significant improvements for youth or caregiver weight status after the RPMS treatment. Significant improvements in other outcomes examined were limited. CONCLUSIONS: Families were satisfied with the RPMS, but use of the system was limited. Initial effectiveness was not able to be determined due to the amount of missing data, which was impacted by the COVID-19 pandemic. Modifications of the RPMS and future evaluation of usability and effectiveness are warranted to determine utility in supplementing pediatric obesity clinical treatment.
Subject(s)
COVID-19 , Pediatric Obesity , Telemedicine , Adolescent , Humans , Child , Pediatric Obesity/therapy , COVID-19/epidemiology , Pandemics , Patient Satisfaction , Monitoring, Physiologic , Personal SatisfactionABSTRACT
BACKGROUND: Virtual care (VC) and remote patient monitoring programs were deployed widely during the COVID-19 pandemic. Deployments were heterogeneous and evolved as the pandemic progressed, complicating subsequent attempts to quantify their impact. The unique arrangement of the US Military Health System (MHS) enabled direct comparison between facilities that did and did not implement a standardized VC program. The VC program enrolled patients symptomatic for COVID-19 or at risk for severe disease. Patients' vital signs were continuously monitored at home with a wearable device (Current Health). A central team monitored vital signs and conducted daily or twice-daily reviews (the nurse-to-patient ratio was 1:30). OBJECTIVE: Our goal was to describe the operational model of a VC program for COVID-19, evaluate its financial impact, and detail its clinical outcomes. METHODS: This was a retrospective difference-in-differences (DiD) evaluation that compared 8 military treatment facilities (MTFs) with and 39 MTFs without a VC program. Tricare Prime beneficiaries diagnosed with COVID-19 (Medicare Severity Diagnosis Related Group 177 or International Classification of Diseases-10 codes U07.1/07.2) who were eligible for care within the MHS and aged 21 years and or older between December 2020 and December 2021 were included. Primary outcomes were length of stay and associated cost savings; secondary outcomes were escalation to physical care from home, 30-day readmissions after VC discharge, adherence to the wearable, and alarms per patient-day. RESULTS: A total of 1838 patients with COVID-19 were admitted to an MTF with a VC program of 3988 admitted to the MHS. Of these patients, 237 (13%) were enrolled in the VC program. The DiD analysis indicated that centers with the program had a 12% lower length of stay averaged across all COVID-19 patients, saving US $2047 per patient. The total cost of equipping, establishing, and staffing the VC program was estimated at US $3816 per day. Total net savings were estimated at US $2.3 million in the first year of the program across the MHS. The wearables were activated by 231 patients (97.5%) and were monitored through the Current Health platform for a total of 3474 (median 7.9, range 3.2-16.5) days. Wearable adherence was 85% (IQR 63%-94%). Patients triggered a median of 1.6 (IQR 0.7-5.2) vital sign alarms per patient per day; 203 (85.7%) were monitored at home and then directly discharged from VC; 27 (11.4%) were escalated to a physical hospital bed as part of their initial admission. There were no increases in 30-day readmissions or emergency department visits. CONCLUSIONS: Monitored patients were adherent to the wearable device and triggered a manageable number of alarms/day for the monitoring-team-to-patient ratio. Despite only enrolling 13% of COVID-19 patients at centers where it was available, the program offered substantial savings averaged across all patients in those centers without adversely affecting clinical outcomes.