Remote monitoring of oxygen saturation in individuals with COVID-19 pneumonia

Barriers to and Facilitators of Key Stakeholders Influencing Successful Digital Implementation of Remote Monitoring Solutions: Mixed Methods Analysis

BACKGROUND

Implementation of remote monitoring solutions and digital alerting tools in health care has historically been challenging, despite the impetus provided by the COVID-19 pandemic. To date, a health systems-based approach to systematically describe barriers and facilitators across multiple domains has not been undertaken.

OBJECTIVE

We aimed to undertake a comprehensive mixed methods analysis of barriers and facilitators for successful implementation of remote monitoring and digital alerting tools in complex health organizations.

METHODS

A mixed methods approach using a modified Technology Acceptance Model questionnaire and semistructured interviews mapped to the validated fit among humans, organizations, and technology (HOT-fit) framework was undertaken. Likert frequency responses and deductive thematic analyses were performed.

RESULTS

A total of 11 participants responded to the questionnaire and 18 participants to the interviews. Key barriers and facilitators could be mapped onto 6 dimensions, which incorporated aspects of digitization: system use (human), user satisfaction (human), environment (organization), structure (organization), information and service quality (technology), and system quality (technology).

CONCLUSIONS

The recommendations proposed can enhance the potential for future remote sensing solutions to be more successfully integrated in health care practice, resulting in more successful use of "virtual wards."

TRIAL REGISTRATION

ClinicalTrials.gov NCT05321004; https://www.clinicaltrials.gov/study/NCT05321004.

Acceptance and User Experiences of a Wearable Device for the Management of Hospitalized Patients in COVID-19-Designated Wards in Ho Chi Minh City, Vietnam: Action Learning Project

BACKGROUND

Wearable devices have been used extensively both inside and outside of the hospital setting. During the COVID-19 pandemic, in some contexts, there was an increased need to remotely monitor pulse and saturated oxygen for patients due to the lack of staff and bedside monitors.

OBJECTIVE

A prototype of a remote monitoring system using wearable pulse oximeter devices was implemented at the Hospital for Tropical Diseases in Ho Chi Minh City, Vietnam, from August to December 2021. The aim of this work was to support the ongoing implementation of the remote monitoring system.

METHODS

We used an action learning approach with rapid pragmatic methods, including informal discussions and observations as well as a feedback survey form designed based on the technology acceptance model to assess the use and acceptability of the system. Based on these results, we facilitated a meeting using user-centered design principles to explore user needs and ideas about its development in more detail.

RESULTS

In total, 21 users filled in the feedback form. The mean technology acceptance model scores ranged from 3.5 (for perceived ease of use) to 4.4 (for attitude) with behavioral intention (3.8) and perceived usefulness (4.2) scoring in between. Those working as nurses scored higher on perceived usefulness, attitude, and behavioral intention than did physicians. Based on informal discussions, we realized there was a mismatch between how we (ie, the research team) and the ward teams perceived the use and wider purpose of the technology.

CONCLUSIONS

Designing and implementing the devices to be more nurse-centric from their introduction could have helped to increase their efficiency and use during the complex pandemic period.

Validation of oxygen saturations measured in the community by emergency medical services as a marker of clinical deterioration in patients with confirmed COVID-19: a retrospective cohort study

OBJECTIVES

To evaluate oxygen saturation and vital signs measured in the community by emergency medical services (EMS) as clinical markers of COVID-19-positive patient deterioration.

DESIGN

A retrospective data analysis.

SETTING

Patients were conveyed by EMS to two hospitals in Hampshire, UK, between 1 March 2020 and 31 July 2020.

PARTICIPANTS

A total of 1080 patients aged ≥18 years with a COVID-19 diagnosis were conveyed by EMS to the hospital.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary study outcome was admission to the intensive care unit (ICU) within 30 days of conveyance, with a secondary outcome representing mortality within 30 days of conveyance. Receiver operating characteristic (ROC) analysis was performed to evaluate, in a retrospective fashion, the efficacy of different variables in predicting patient outcomes.

RESULTS

Vital signs measured by EMS staff at the first point of contact in the community correlated with patient 30-day ICU admission and mortality. Oxygen saturation was comparably predictive of 30-day ICU admission (area under ROC (AUROC) 0.753; 95% CI 0.668 to 0.826) to the National Early Warning Score 2 (AUROC 0.731; 95% CI 0.655 to 0.800), followed by temperature (AUROC 0.720; 95% CI 0.640 to 0.793) and respiration rate (AUROC 0.672; 95% CI 0.586 to 0.756).

CONCLUSIONS

Initial oxygen saturation measurements (on air) for confirmed COVID-19 patients conveyed by EMS correlated with short-term patient outcomes, demonstrating an AUROC of 0.753 (95% CI 0.668 to 0.826) in predicting 30-day ICU admission. We found that the threshold of 93% oxygen saturation is prognostic of adverse events and of value for clinician decision-making with sensitivity (74.2% CI 0.642 to 0.840) and specificity (70.6% CI 0.678 to 0.734).

Patients' and GPs' views and expectations of home monitoring with a pulse oximeter: a mixed-methods process evaluation of a pilot randomised controlled trial

BACKGROUND

Research on how home monitoring with a pulse oximeter is executed and experienced by patients with an acute illness such as COVID-19 and their GPs is scarce.

AIM

To examine the process of structured home monitoring with a pulse oximeter for patients with COVID-19, their caregivers, and their GPs.

DESIGN AND SETTING

This was a mixed-method process evaluation alongside a pilot feasibility randomised controlled trial. Patients drawn from a general practice setting, with COVID-19, and aged ≥40 years with cardiovascular comorbidities were included.

METHOD

Quantitative trial data from 21 intervention group participants (age 63.2 years) were used, plus qualitative data from semi-structured interviews with 15 patients (age 62.9 years), eight informal caregivers, and 10 GPs.

RESULTS

Adherence to the intervention was very high; 97.6% of protocolised peripheral oxygen saturation (SpO2) measurements in the first 14 days until admission to hospital were recorded (677/694, median daily per patient 2.7). Three identified themes from the interviews were: (a) user-friendliness of home monitoring: easy use of the pulse oximeter and patient preference of a three times daily measurement scheme; (b) patient empowerment: pulse oximeter use enhanced patient self-assurance and empowered patients and informal caregivers in disease management; and (c) added value to current clinical decision making. GPs perceived the pulse oximeter as a useful diagnostic tool and did not experience any additional workload. They felt more secure with remote monitoring with a pulse oximeter than only phone-based monitoring, but emphasised the need to keep an overall view on the patient's condition.

CONCLUSION

Structured home monitoring by pulse oximetry supports patients and their informal caregivers in managing, and GPs in monitoring, acute COVID-19 disease. It appears suitable for use in acutely ill patients in general practice.

Does Telerehabilitation for Coronavirus Disease 2019 Patients Discharged with Oxygen Shorten the Time of Weaning from Oxygen?

OBJECTIVE

Early pulmonary rehabilitation (PR) and acute and post-acute mobilization with telemonitoring and telerehabilitation (TR) have been recommended for coronavirus disease 2019 (COVID-19) patients. We aimed to compare the duration of weaning from oxygen in patients with hypoxemic COVID-19 who received PR and those who did not.

MATERIAL AND METHODS

This study was designed as a quasi-experimental study and was conducted on patients discharged with oxygen supplementation between December 2021 and May 2022. They were compared with patients who received PR and those who did not in terms of the duration of oxygen use.

RESULTS

A total of 61 patients (9 women in each group) completed the study. The mean age was 65 ± 12. Thirty patients underwent PR (group 1) and the remaining 31 patients were classified as control group (group 2). When the groups were compared in terms of duration of oxygen use, patients who performed PR were statistically significant shorter duration than those who did not (P = .012). In addition, PR improved their quality of life compared to group 2.

CONCLUSION

It was concluded that although PR has many indications, it is also effective, feasible, and safe in prolonged infections and it was thought that TR may also be effective as supervised PR.

Remote patient monitoring for COVID-19 patients: comparisons and framework for reporting

BACKGROUND

COVID-19 has challenged health services throughout the world in terms of hospital capacity and put staff and vulnerable populations at risk of infection. In the face of these challenges, many health providers have implemented remote patient monitoring (RPM) of COVID-19 patients in their own homes. However systematic reviews of the literature on these implementations have revealed wide variations in how RPM is implemented; along with variations in particulars of RPM reported on, making comparison and evaluation difficult. A review of reported items is warranted to develop a framework of key items to enhance reporting consistency. The aims of this review of remote monitoring for COVID-19 patients are twofold: (1) to facilitate comparison between RPM implementations by tabulating information and values under common domains. (2) to develop a reporting framework to enhance reporting consistency.

METHOD

A review of the literature for RPM for COVID-19 patients was conducted following PRISMA guidelines. The Medline database was searched for articles published between 2020 to February 2023 and studies reporting on items with sufficient detail to compare one with another were included. Relevant data was extracted and synthesized by the lead author. Quality appraisal was not conducted as the the articles considered were evaluated as informational reports of clinical implementations rather than as studies designed to answer a research question.

RESULTS

From 305 studies retrieved, 23 studies were included in the review: fourteen from the US, two from the UK and one each from Africa, Ireland, China, the Netherlands, Belgium, Australia and Italy. Sixteen generally reported items were identified, shown with the percentage of studies reporting in brackets: Reporting Period (82%), Rationale (100%), Patients (100%), Medical Team (91%) Provider / Infrastructure (91%), Communications Platform (100%), Patient Equipment (100%), Training (48%), Markers (96%), Frequency of prompt / Input (96%),Thresholds (82%), Discharge (61%), Enrolled (96%), Alerts/Escalated (78%), Patient acceptance (43%), and Patient Adherence (52%). Whilst some studies reported on patient training and acceptance, just one reported on staff training and none on staff acceptance.

CONCLUSIONS

Variations in reported items were found. Pending the establishment of a robust set of reporting guidelines, we propose a reporting framework consisting of eighteen reporting items under the following four domains: Context, Technology, Process and Metrics.

Non-invasive arterial blood pressure measurement and SpO2 estimation using PPG signal: a deep learning framework

BACKGROUND

Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection.

METHOD

Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers' interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct a personalized estimation of arterial systolic blood pressure, arterial diastolic blood pressure, and oxygen saturation.

RESULTS

The proposed method was evaluated with a subset of 1,732 subjects from the publicly available ICU dataset MIMIC III. The mean absolute error is 2.52 ± 2.43 mmHg for systolic blood pressure, 1.37 ± 1.89 mmHg for diastolic blood pressure, and 0.58 ± 0.79% for oxygen saturation, which satisfies the requirements of the Association of Advancement of Medical Instrumentation standard and achieve grades A for the British Hypertension Society standard.

CONCLUSIONS

The results indicate that our model meets clinical standards and could potentially boost the accuracy of blood pressure and oxygen saturation measurement to deliver high-quality healthcare.

Non-contact oxygen saturation monitoring for wound healing process using dual-wavelength simultaneous acquisition imaging system

Here we report the fabrication of a noncontact pulse oximeter system based on a dual-wavelength imaging system and its oxygen saturation monitoring performance during wound healing. The dual-wavelength imaging system consists of 660 nm and 940 nm light-emitting diodes and a multi-spectral camera that simultaneously accepts visible and near-infrared images. Using the proposed system, images were acquired at 30 fps at both wavelengths, and photoplethysmography signals were extracted by specifying a specific region in the images. We removed the signals caused by small movements and smoothed them using the discrete wavelet transform and moving average filter. To confirm the feasibility of the proposed noncontact oxygen saturation system, a wound model was created using a hairless mouse and oxygen saturation was measured during wound healing. The measured values were compared and analyzed using a reflective animal pulse oximeter. Through a comparative analysis of these two devices, the error of the proposed system was evaluated and the possibility of its clinical application and wound healing monitoring through oxygen saturation measurement confirmed.

Prediction of low pulse oxygen saturation in COVID-19 using remote monitoring post hospital discharge

BACKGROUND

Monitoring systems have been developed during the COVID-19 pandemic enabling clinicians to remotely monitor physiological measures including pulse oxygen saturation (SpO₂), heart rate (HR), and breathlessness in patients after discharge from hospital. These data may be leveraged to understand how symptoms vary over time in COVID-19 patients. There is also potential to use remote monitoring systems to predict clinical deterioration allowing early identification of patients in need of intervention.

METHODS

A remote monitoring system was used to monitor 209 patients diagnosed with COVID-19 in the period following hospital discharge. This system consisted of a patient-facing app paired with a Bluetooth-enabled pulse oximeter (measuring SpO₂ and HR) linked to a secure portal where data were available for clinical review. Breathlessness score was entered manually to the app. Clinical teams were alerted automatically when SpO₂ < 94 %. In this study, data recorded during the initial ten days of monitoring were retrospectively examined, and a random forest model was developed to predict SpO₂ < 94 % on a given day using SpO₂ and HR data from the two previous days and day of discharge.

RESULTS

Over the 10-day monitoring period, mean SpO₂ and HR increased significantly, while breathlessness decreased. The coefficient of variation in SpO₂, HR and breathlessness also decreased over the monitoring period. The model predicted SpO₂ alerts (SpO₂ < 94 %) with a mean cross-validated. sensitivity of 66 ± 18.57 %, specificity of 88.31 ± 10.97 % and area under the receiver operating characteristic of 0.80 ± 0.11. Patient age and sex were not significantly associated with the occurrence of asymptomatic SpO₂ alerts.

CONCLUSION

Results indicate that SpO₂ alerts (SpO₂ < 94 %) on a given day can be predicted using SpO₂ and heart rate data captured on the two preceding days via remote monitoring. The methods presented may help early identification of patients with COVID-19 at risk of clinical deterioration using remote monitoring.

The utility of telemedicine in managing patients after COVID-19

Despite growing knowledge about transmission and relatively wide access to prophylaxis, the world is still facing a severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) global pandemic. Under these circumstances telemedicine emerges as a powerful tool for safe at-home surveillance after a hospital discharge; the data on when to safely release a patient after acute COVID-19 is scarce. Reckoning an urgent need for improving outpatient management and possibly fatal complications of the post-COVID period, we performed the pilot telemonitoring program described below. The study aimed to assess the usefulness of parameters and surveys remotely obtained from COVID-19 convalescents in their individual prognosis prediction. Patients were involved in the study between December 2020 and May 2021. Recruitment was performed either during the hospital discharge (those hospitalized in a Barlicki Memorial Hospital in Lodz) or the first outpatient visit up to 6 weeks after discharge from another center. Every participant received equipment for daily saturation and heart rate measurement coupled with a tablet for remote data transmission. The measurements were made after at least fifteen minutes of rest in a sitting position without oxygen supplementation. Along with the measurements, the cough and dyspnea daily surveys (1-5 points) and Fatigue Assessment Scale weekly surveys were filled. We expected a saturation decrease during thromboembolic events, infectious complications, etc. A total of 30 patients were monitored for a minimum period of 45 days, at least 2 weeks after spontaneous saturation normalization. The mean age was 55 (mean 55.23; SD ± 10.64 years). The group was divided according to clinical improvement defined as the ≥ 10% functional vital capacity (FVC) raise or ≥ 15% lung transfer for carbon monoxide (T_L,CO) rise. Our findings suggest that at-rest home saturation measurements below 94% (p = 0.03) correspond with the lack of clinical improvement in post-COVID observation (p = 0.03). The non-improvement group presented with a lower mean-94 (93-96)% versus 96 (95-97)%, p = 0.01 and minimum saturation-89 (86-92)% versus 92 (90-94)%, p = 0.04. They also presented higher variations in saturation measurements; saturation amplitude was 9 (7-11)% versus 7 (4-8)%, p = 0.03; up to day 22 most of the saturation differences reached statistical significance. Last but not least, we discovered that participants missing 2 or more measurements during the observation were more often ranked into the clinical improvement group (p = 0.01). Heart rate day-to-day measurements did not differ between both groups; gathered data about dyspnea and cough intensity did not reach statistical significance either. A better understanding of the disease's natural history will ultimately lead us to a better understanding of long COVID symptoms and corresponding threats. In this paper, we have found home oxygen saturation telemonitoring to be useful in the prediction of the trajectory of the disease course. Our findings suggest that detection of at-rest home saturation measurement equal to or below 94% corresponds with the lack of clinical improvement at the time of observation and this group of patients presented higher variability of day-to-day oxygen saturation measurements. The determination of which patient should be involved in telemedicine programs after discharge requests further research.

Modeling Energy Aware Photoplethysmography for Personalized Healthcare Applications

The rise of wearable health monitoring has largely incorporated photoplethysmography (PPG), an optical sensing modality, to determine heart rate and blood oxygen saturation metrics by reflecting light through a user's skin. Due to its optical nature, this sensing method is strongly impacted by the skin type, body mass index (BMI), and general physiological composition of the user. In the context of self-powering, there is a need for these devices to consume ultra-low power, to not be dependent on batteries and regular charging, enabling continuous monitoring. This paper presents a novel PPG sensing model for both a custom, ultra-low power (ULP) AFE and the Texas Instruments (TI) AFE4404 which is used to demonstrate the design tradeoffs between system power and SNR. The models also incorporate a novel human skin reflectance component to analyze the effect of the user's skin phototype and BMI on these tradeoffs with the goal of demonstrating inclusive, accurate ULP PPG sensing. Measured results on both devices from 23 participants are included to emphasize the limited design space for enabling self-powered, continuous monitoring wearables.

Key Stakeholder Barriers and Facilitators to Implementing Remote Monitoring Technologies: Protocol for a Mixed Methods Analysis

Background

The implementation of novel digital solutions within the National Health Service has historically been challenging. Since the start of the COVID-19 pandemic, there has been a greater push for digitization and for operating remote monitoring solutions. However, the implementation and widespread adoption of this type of innovation have been poorly studied.

Objective

We aim to investigate key stakeholder barriers and facilitators to implementing remote monitoring solutions to identify factors that could affect successful adoption.

Methods

A mixed methods approach will be implemented. Semistructured interviews will be conducted with high-level stakeholders from industry and academia and health care providers who have played an instrumental role in, and have prior experience with, implementing digital solutions, alongside the use of an adapted version of the Technology Acceptance Model questionnaire.

Results

Enrollment is currently underway, having started in February 2022. It is anticipated to end in July 2022, with data analysis scheduled to commence in August 2022.

Conclusions

The results of our study may highlight key barriers and facilitators to implementing digital remote monitoring solutions, thereby allowing for improved widespread adoption within the National Health Service in the future.

Trial Registration

ClinicalTrials.gov NCT05321004; https://clinicaltrials.gov/ct2/show/NCT05321004

Patient Experiences With a Remote Monitoring Pathway for COVID-19

Introduction: Due to the coronavirus disease 2019 (COVID-19) pandemic, a remote monitoring pathway was developed at Michael Garron Hospital to allow individuals with confirmed or presumed COVID-19 infection to successfully manage their illness at home. This study aims to understand patients’ experiences on this remote monitoring pathway and to investigate the effectiveness of the pathway in preventing unnecessary emergency department (ED) visits and detecting severe infection.

Methods: A total of 35 semi-structured interviews were conducted over the phone. Researchers reviewed transcripts to come up with an index of nodes. Two researchers initially coded the same four transcripts to ensure high inter-rater reliability. The remaining 31 transcripts were coded by one researcher.

Results: Of patients, 80% (n = 28) had a positive experience on the pathway. Remote monitoring was effective in reassuring 22.9% of patients (n = 8) with mild-moderate symptoms that their symptoms were not significant enough to go to the ED and they were monitored at home. A total of 8.6% of patients (n = 3) were correctly identified as having severe symptoms while on the pathway and were asked to present to the hospital. For 8.6% of patients (n = 3), remote monitoring did not identify their severe COVID-19 illness. Of patients, 2.9% (n = 1) were incorrectly identified as having severe COVID-19 symptoms when they were clinically well.

Discussion: Remote monitoring is an effective tool to optimize healthcare resources during a pandemic. It reduces ER visits and provides a means for routine follow-up while minimizing virus exposure. Patients generally had a positive experience; however, more research needs to be done on optimizing the detection of severe infection.

Aligning mission to digital health strategy in academic medical centers

The strategies of academic medical centers arise from core values and missions that aim to provide unmatched clinical care, patient experience, research, education, and training. These missions drive nearly all activities. They should also drive digital health activities - and particularly now given the rapid adoption of digital health, marking one of the great transformations of healthcare; increasing pressures on health systems to provide more cost-effective care; the pandemic-accelerated funding and rise of well-funded new entrants and technology giants that provide more convenient forms of care; and a more favorable regulatory and reimbursement landscape to incorporate digital health approaches. As academic medical centers emerge from a pandemic-related reactionary digital health posture, where pressures to adopt more digital health technologies mount, a broad digital health realignment that leverages the strengths of such centers is required to accomplish their missions.

Remote COVID-19 patient monitoring system: a qualitative evaluation

BACKGROUND

Many COVID-19 patients are discharged home from hospital with instructions to self-isolate. This reduces the burden on potentially overwhelmed hospitals. The Royal Melbourne Hospital (RMH) Home Monitoring Programme (HMP) is a model of care for COVID-19 patients which chiefly tracks pulse oximetry and body temperature readings.

OBJECTIVE

To evaluate the feasibility and acceptability of the HMP from a patient perspective.

DESIGN, SETTINGS AND PARTICIPANTS

Of 46 COVID-19 patients who used the HMP through RMH during April to August 2020, 16 were invited to participate in this qualitative evaluation study; all accepted, including 6 healthcare workers. Attempts were made to recruit a gender-balanced sample across a range of COVID-19 severities and comorbidities. Participants completed a brief semistructured phone interview discussing their experience of using the HMP.

OUTCOME MEASURES AND ANALYSIS

A thematic analysis of interview data was conducted. Feasibility was defined as the HMP's reported ease of use. Acceptability was considered holistically by reviewing themes in the interview data.

RESULTS

The HMP allowed clinical deterioration to be recognised as it occurred enabling prompt intervention. All participants reported a positive opinion of the HMP, stating it was highly acceptable and easy to use. Almost all participants said they found using it reassuring. Patients frequently mentioned the importance of the monitoring clinicians as an information conduit. The most suggested improvement was to monitor a broader set of symptoms.

CONCLUSIONS

The HMP is highly feasible and acceptable to patients. This model of care could potentially be implemented on a mass-scale to reduce the burden of COVID-19 on hospitals. A key benefit of the HMP is the ability to reassure patients they will receive suitable intervention should they deteriorate while isolating outside of hospital settings.

Effectiveness and safety of pulse oximetry in remote patient monitoring of patients with COVID-19: a systematic review

The COVID-19 pandemic has led health systems to increase the use of tools for monitoring and triaging patients remotely. In this systematic review, we aim to assess the effectiveness and safety of pulse oximetry in remote patient monitoring (RPM) of patients at home with COVID-19. We searched five databases (MEDLINE, Embase, Global Health, medRxiv, and bioRxiv) from database inception to April 15, 2021, and included feasibility studies, clinical trials, and observational studies, including preprints. We found 561 studies, of which 13 were included in our narrative synthesis. These 13 studies were all observational cohorts and involved a total of 2908 participants. A meta-analysis was not feasible owing to the heterogeneity of the outcomes reported in the included studies. Our systematic review substantiates the safety and potential of pulse oximetry for monitoring patients at home with COVID-19, identifying the risk of deterioration and the need for advanced care. The use of pulse oximetry can potentially save hospital resources for patients who might benefit the most from care escalation; however, we could not identify explicit evidence for the effect of RPM with pulse oximetry on health outcomes compared with other monitoring models such as virtual wards, regular monitoring consultations, and online or paper diaries to monitor changes in symptoms and vital signs. Based on our findings, we make 11 recommendations across the three Donabedian model domains and highlight three specific measurements for setting up an RPM system with pulse oximetry.

COVID-19 Oximetry @home: evaluation of patient outcomes

BACKGROUND

COVID-19 has placed unprecedented demands on hospitals. A clinical service, COVID-19 Oximetry @home (CO@h) was launched in November 2020 to support remote monitoring of COVID-19 patients in the community. Remote monitoring through CO@h aims to identify early patient deterioration and provide timely escalation for cases of silent hypoxia, while reducing the burden on secondary care.

METHODS

We conducted a retrospective service evaluation of COVID-19 patients onboarded to CO@h from November 2020 to March 2021 in the North Hampshire (UK) community led service (a collaboration of 15 General Practitioner (GP) practices covering 230 000 people). We have compared outcomes for patients admitted to Basingstoke and North Hampshire Hospital who were CO@h patients (COVID-19 patients with home monitoring of oxygen saturation (SpO₂; n=115), with non-CO@h patients (those directly admitted without being monitored by CO@h (n=633)). Crude and adjusted OR analysis was performed to evaluate the effects of CO@h on patient outcomes of 30-day mortality, Intensive care unit (ICU) admission and hospital length of stay greater than 3, 7, 14 and 28 days.

RESULTS

Adjusted ORs for CO@h show an association with a reduction for several adverse patient outcome: 30-day hospital mortality (p<0.001, OR 0.21, 95% CI 0.08 to 0.47), hospital length of stay larger than 3 days (p<0.05, OR 0.62, 95% CI 0.39 to 1.00), 7 days (p<0.001, OR 0.35, 95% CI 0.22 to 0.54), 14 days (p<0.001, OR 0.22 95% CI, 0.11 to 0.41), and 28 days (p<0.05, OR 0.21, 95% CI 0.05 to 0.59). No significant reduction ICU admission was observed (p>0.05, OR 0.43, 95% CI 0.15 to 1.04). Within 30 days of hospital admission, there were no hospital readmissions for those on the CO@h service as opposed to 8.7% readmissions for those not on the service.

CONCLUSIONS

We have demonstrated a significant association between CO@h and better patient outcomes; most notably a reduction in the odds of hospital lengths of stays longer than 7, 14 and 28 days and 30-day hospital mortality.

The Use of Wearable Pulse Oximeters in the Prompt Detection of Hypoxemia and During Movement: Diagnostic Accuracy Study

Background

Commercially available wearable (ambulatory) pulse oximeters have been recommended as a method for managing patients at risk of physiological deterioration, such as active patients with COVID-19 disease receiving care in hospital isolation rooms; however, their reliability in usual hospital settings is not known.

Objective

We report the performance of wearable pulse oximeters in a simulated clinical setting when challenged by motion and low levels of arterial blood oxygen saturation (SaO₂).

Methods

The performance of 1 wrist-worn (Wavelet) and 3 finger-worn (CheckMe O2+, AP-20, and WristOx2 3150) wearable, wireless transmission–mode pulse oximeters was evaluated. For this, 7 motion tasks were performed: at rest, sit-to-stand, tapping, rubbing, drinking, turning pages, and using a tablet. Hypoxia exposure followed, in which inspired gases were adjusted to achieve decreasing SaO₂ levels at 100%, 95%, 90%, 87%, 85%, 83%, and 80%. Peripheral oxygen saturation (SpO₂) estimates were compared with simultaneous SaO₂ samples to calculate the root-mean-square error (RMSE). The area under the receiver operating characteristic curve was used to analyze the detection of hypoxemia (ie, SaO₂<90%).

Results

SpO₂ estimates matching 215 SaO₂ samples in both study phases, from 33 participants, were analyzed. Tapping, rubbing, turning pages, and using a tablet degraded SpO₂ estimation (RMSE>4% for at least 1 device). All finger-worn pulse oximeters detected hypoxemia, with an overall sensitivity of ≥0.87 and specificity of ≥0.80, comparable to that of the Philips MX450 pulse oximeter.

Conclusions

The SpO₂ accuracy of wearable finger-worn pulse oximeters was within that required by the International Organization for Standardization guidelines. Performance was degraded by motion, but all pulse oximeters could detect hypoxemia. Our findings support the use of wearable, wireless transmission–mode pulse oximeters to detect the onset of clinical deterioration in hospital settings.

Trial Registration

ISRCTN Registry 61535692; http://www.isrctn.com/ISRCTN61535692

International Registered Report Identifier (IRRID)

RR2-10.1136/bmjopen-2019-034404

Remote patient monitoring in COVID-19: a critical appraisal

With great interest, we read the article by Grutterset al. [1], in which they share their results of the largest patient cohort of coronavirus disease 2019 (COVID-19) home monitoring to date. Based on these results, remote patient monitoring (RPM) was praised and was claimed as a tool to reduce the hospital stay of COVID-19 patients by 5 days.

Is there any evidence for #telemonitoring in #COVID19? Read more:https://bit.ly/3p1YXBi

Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients

The COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.

Mixed methods protocol to examine the acceptability and clinical characteristics of a remote monitoring programme for delivery of COVID-19 care, among healthcare staff and patients

INTRODUCTION

The use of remote monitoring technology to manage the care of patients with COVID-19 has been implemented to help reduce the burden placed on healthcare systems during the pandemic and protect the well-being of both staff and patients. Remote monitoring allows patients to record their signs and symptoms remotely (eg, while self-isolating at home) rather than requiring hospitalisation. Healthcare staff can, therefore, continually monitor their symptoms and be notified when the patient is showing signs of clinical deterioration. However, given the recency of the COVID-19 outbreak, there is a lack of research regarding the acceptance of remote monitoring interventions to manage COVID-19. This study will aim to evaluate the use of remote monitoring for managing COVID-19 cases from the perspective of both the patient and healthcare staff.

METHODS AND ANALYSIS

Discharged patients from a large urban teaching hospital in Ireland, who have undergone remote monitoring for COVID-19, will be recruited to take part in a cross-sectional study consisting of a quantitative survey and a qualitative interview. A mixed methods design will be used to understand the experiences of remote monitoring from the perspective of the patient. Healthcare staff who have been involved in the provision of remote monitoring of patients with COVID-19 will be recruited to take part in a qualitative interview to understand their experiences with the process. Structural equation modelling will be used to examine the acceptance of the remote monitoring technology. Latent class analysis will be used to identify COVID-19 symptom profiles. Interview data will be examined using thematic analysis.

ETHICS AND DISSEMINATION

Ethical approval has been granted by the ethical review boards at University College Dublin and the National Research Ethics Committee for COVID-19-related Research. Findings will be disseminated via publications in scientific journals, policy briefs, short reports and social media.

The Use of Telemonitoring in Managing the COVID-19 Pandemic: Pilot Implementation Study

BACKGROUND

Most people with COVID-19 self-manage at home. However, the condition can deteriorate quickly, and some people may develop serious hypoxia with relatively few symptoms. Early identification of deterioration allows effective management with oxygen and steroids. Telemonitoring of symptoms and physiological signs may facilitate this.

OBJECTIVE

The aim of this study was to design, implement, and evaluate a telemonitoring system for people with COVID-19 who are self-managing at home and are considered at significant risk of deterioration.

METHODS

A multidisciplinary team developed a telemonitoring protocol using a commercial platform to record symptoms, pulse oximetry, and temperature. If symptoms or physiological measures breached targets, patients were alerted and asked to phone for an ambulance (red alert) or for advice (amber alert). Patients attending COVID-19 assessment centers, who were considered fit for discharge but at risk of deterioration, were shown how to use a pulse oximeter and the monitoring system, which they were to use twice daily for 2 weeks. Patients could interact with the system via app, SMS, or touch-tone phone. Written guidance on alerts was also provided. Following consent, patient data on telemonitoring usage and alerts were linked to data on the use of service resources. Subsequently, patients who had either used or not used the telemonitoring service, including those who had not followed advice to seek help, agreed to brief telephone interviews to explore their views on, and how they had interacted with, the telemonitoring system. Interviews were recorded and analyzed thematically. Professionals involved in the implementation were sent an online questionnaire asking them about their perceptions of the service.

RESULTS

We investigated the first 116 patients who used the service. Of these patients, 71 (61.2%) submitted data and the remainder (n=45, 38.8%) chose to self-monitor without electronic support. Of the 71 patients who submitted data, 35 (49%) received 152 alerts during their 2-week observation. A total of 67 red alerts were for oxygen saturation (SpO₂) levels of ≤93%, and 15 red alerts were because patients recorded severe breathlessness. Out of 71 patients, 14 (20%) were admitted to hospital for an average stay of 3.6 (SD 4.5) days. Of the 45 who used written guidance alone, 7 (16%) were admitted to hospital for an average stay of 4.0 (SD 4.2) days and 1 (2%) died. Some patients who were advised to seek help did not do so, some because parameters improved on retesting and others because they felt no worse than before. All patients found self-monitoring to be reassuring. Of the 11 professionals who used the system, most found it to be useful and easy to use. Of these 11 professionals, 5 (45%) considered the system "very safe," 3 (27%) thought it "could be safer," and 3 (27%) wished to have more experience with it before deciding. In total, 2 (18%) felt that SpO₂ trigger thresholds were too high.

CONCLUSIONS

Supported self-monitoring of patients with COVID-19 at home is reassuring to patients, is acceptable to clinicians, and can detect important signs of deterioration. Worryingly, some patients, because they felt well, occasionally ignored important signs of deterioration. It is important, therefore, to emphasize the importance of the early investigation and treatment of asymptomatic hypoxia at the time when patients are initiated and in the warning messages that are sent to patients.

Home-monitoring reduces hospital stay for COVID-19 patients

With great interest we read the article by Dirikgilet al. [1], in which they demonstrate the potential of home monitoring to reduce hospital admissions by safely surveying clinical symptoms and vital parameters. They encourage the further consideration of strategies for home monitoring larger patient groups and, particularly, patients with confirmed coronavirus disease 2019 (COVID-19).

Home monitoring reduces hospital stay of patients with COVID-19, which increases regular healthcare capacityhttps://bit.ly/3CdFp1z

Worldwide experiences and opinions of healthcare providers on eHealth for patients with interstitial lung diseases in the COVID-19 era

Interstitial lung diseases (ILD) comprise a group of pulmonary diseases characterised by diffuse parenchymal abnormalities, which can lead to irreversible pulmonary fibrosis [1].

The #COVID19 pandemic has led to an increase in the use of eHealth for patients with interstitial lung disease. Healthcare providers worldwide are positive towards further implementation of eHealth for research and clinical practice.https://bit.ly/3h2545M

Home-Based Physiological Monitoring of Patients with COVID-19

The COVID-19 pandemic has necessitated the rise of telehealth modalities to relieve the incredible stress the pandemic has placed on the healthcare system. This rise has seen the emergence of new software, applications, and hardware for home-based physiological monitoring, leading to the promise of innovative predictive and therapeutic practices. This article is a literature-based review of the most promising technologies and advances regarding home-based physiological monitoring of patients with COVID-19. We conclude that the applications currently on the market, while helping stem the flow of patients to the hospital during the pandemic, require additional evidence related to improvement in patient outcomes. However, new devices and technology are a promising and successful venture into home-based monitoring with clinical implications reaching far into the future.

Remote home monitoring (virtual wards) for confirmed or suspected COVID-19 patients: a rapid systematic review

BACKGROUND

the aim of this review was to analyze the implementation and impact of remote home monitoring models (virtual wards) for confirmed or suspected COVID-19 patients, identifying their main components, processes of implementation, target patient populations, impact on outcomes, costs and lessons learnt.

METHODS

we carried out a rapid systematic review on models led by primary and secondary care across seven countries (US, Australia, Canada, The Netherlands, Ireland, China, UK). The main outcomes included in the review were: impact of remote home monitoring on virtual length of stay, escalation, emergency department attendance/reattendance, admission/readmission and mortality. The search was updated on February 2021. We used the PRISMA statement and the review was registered on PROSPERO (CRD: 42020202888).

FINDINGS

the review included 27 articles. The aim of the models was to maintain patients safe in the appropriate setting. Most models were led by secondary care and confirmation of COVID-19 was not required (in most cases). Monitoring was carried via online platforms, paper-based systems with telephone calls or (less frequently) through wearable sensors. Models based on phone calls were considered more inclusive. Patient/career training was identified as a determining factor of success. We could not reach substantive conclusions regarding patient safety and the identification of early deterioration due to lack of standardized reporting and missing data. Economic analysis was not reported for most of the models and did not go beyond reporting resources used and the amount spent per patient monitored.

INTERPRETATION

future research should focus on staff and patient experiences of care and inequalities in patients' access to care. Attention needs to be paid to the cost-effectiveness of the models and their sustainability, evaluation of their impact on patient outcomes by using comparators, and the use of risk-stratification tools.

A predictive model of clinical deterioration among hospitalized COVID-19 patients by harnessing hospital course trajectories

From early March through mid-May 2020, the COVID-19 pandemic overwhelmed hospitals in New York City. In anticipation of ventilator shortages and limited ICU bed capacity, hospital operations prioritized the development of prognostic tools to predict clinical deterioration. However, early experience from frontline physicians observed that some patients developed unanticipated deterioration after having relatively stable periods, attesting to the uncertainty of clinical trajectories among hospitalized patients with COVID-19. Prediction tools that incorporate clinical variables at one time-point, usually on hospital presentation, are suboptimal for patients with dynamic changes and evolving clinical trajectories. Therefore, our study team developed a machine-learning algorithm to predict clinical deterioration among hospitalized COVID-19 patients by extracting clinically meaningful features from complex longitudinal laboratory and vital sign values during the early period of hospitalization with an emphasis on informative missing-ness. To incorporate the evolution of the disease and clinical practice over the course of the pandemic, we utilized a time-dependent cross-validation strategy for model development. Finally, we validated our prediction model on an external validation cohort of COVID-19 patients served in a demographically distinct population from the training cohort. The main finding of our study is the identification of risk profiles of early, late and no clinical deterioration during the course of hospitalization. While risk prediction models that include simple predictors at ED presentation and clinical judgement are able to identify any deterioration vs. no deterioration, our methodology is able to isolate a particular risk group that remain stable initially but deteriorate at a later stage of the course of hospitalization. We demonstrate the superior predictive performance with the utilization of laboratory and vital sign data during the early period of hospitalization compared to the utilization of data at presentation alone. Our results will allow efficient hospital resource allocation and will motivate research in understanding the late deterioration risk group.

Integrating hospital and community care: using a community virtual ward model to deliver combined specialist and generalist care to patients with severe chronic respiratory disease in their homes

Background

Chronic respiratory diseases are responsible for significant patient morbidity, mortality, and healthcare use. Community virtual ward (CVW) models of care have been successfully implemented to manage patients with complex medical conditions.

Aims

To explore the feasibility and clinical outcomes of a CVW model of care in patients with chronic respiratory disease.

Methods

Patients known to specialist respiratory services with Chronic Obstructive Pulmonary Disease (COPD) and/or asthma were admitted to the CVW for disease optimisation and exacerbation management. Individualised management plans were delivered in the patients’ home by hospital-based respiratory and community nursing teams, incorporating remote technology to monitor vital signs. Symptoms and health status at admission and discharge were compared.

Results

Twenty patients were admitted. One-quarter of patients had asthma, 50% COPD, and 25% combined asthma/COPD. Patients had severe disease, mean (SD) FEV₁ 50(20) % predicted, and an average 6.4(5.7) exacerbations of disease in the previous 12 months. Patients received personalised disease and self-management education. All acute exacerbations (n = 11) were successfully treated in the community. The average length of CVW admission was 10(4) days. By discharge, 60% of COPD and 66% of asthma patients recorded improvements in symptoms score exceeding the minimal clinically important difference. Fifty percent had clinically meaningful improvements in health status.

Conclusion

A CVW model facilitates the delivery of combined specialist and generalist care to patients with chronic respiratory disease in the community and improves symptoms and health status. The principles of the model are transferable to other conditions to improve overall health and reduce emergency hospital care.

An emergency system for monitoring pulse oximetry, peak expiratory flow, and body temperature of patients with COVID-19 at home: Development and preliminary application

BACKGROUND

COVID-19 is characterized by a rapid change in the patient's condition, with major changes occurring over a few days. We aimed to develop and evaluate an emergency system for monitoring patients with COVID-19, which may be useful in hospitals where more severe patients stay in their homes.

METHODOLOGY/PRINCIPAL FINDINGS

The system consists of the home-based patient unit, which is set up around the patient and the hospital unit, which enables the medical staff to telemonitor the patient's condition and help to send medical recommendations. The home unit allows the data transmission from the patient to the hospital, which is performed using a cell phone application. The hospital unit includes a virtual instrument developed in LabVIEW® environment that can provide a real-time monitoring of the oxygen saturation (SpO2), beats per minute (BPM), body temperature (BT), and peak expiratory flow (PEF). Abnormal events may be fast and automatically identified. After the design details are described, the system is validated by a 30-day home monitoring study in 12 controls and 12 patients with COVID-19 presenting asymptomatic to mild disease. Patients presented reduced SpO2 (p<0.0001) and increased BPM values (p<0.0001). Three patients (25%) presented PEF values between 50 and 80% of the predicted. Three of the 12 monitored patients presented events of desaturation (SpO2<92%). The experimental results were in close agreement with the involved pathophysiology, providing clear evidence that the proposed system can be a useful tool for the remote monitoring of patients with COVID-19.

CONCLUSIONS

An emergency system for home monitoring of patients with COVID-19 was developed in the current study. The proposed system allowed us to quickly respond to early abnormalities in these patients. This system may contribute to conserving hospital resources for those most in need while simultaneously enabling early recognition of patients under acute deterioration, requiring urgent assessment.

A Predictive Model and Risk Factors for Case Fatality of COVID-19

This study aimed to create an individualized analysis model of the risk of intensive care unit (ICU) admission or death for coronavirus disease 2019 (COVID-19) patients as a tool for the rapid clinical management of hospitalized patients in order to achieve a resilience of medical resources. This is an observational, analytical, retrospective cohort study with longitudinal follow-up. Data were collected from the medical records of 3489 patients diagnosed with COVID-19 using RT-qPCR in the period of highest community transmission recorded in Europe to date: February-June 2020. The study was carried out in in two health areas of hospital care in the Madrid region: the central area of the Madrid capital (Hospitales de Madrid del Grupo HM Hospitales (CH-HM), n = 1931) and the metropolitan area of Madrid (Hospital Universitario Príncipe de Asturias (MH-HUPA) n = 1558). By using a regression model, we observed how the different patient variables had unequal importance. Among all the analyzed variables, basal oxygen saturation was found to have the highest relative importance with a value of 20.3%, followed by age (17.7%), lymphocyte/leukocyte ratio (14.4%), CRP value (12.5%), comorbidities (12.5%), and leukocyte count (8.9%). Three levels of risk of ICU/death were established: low-risk level (<5%), medium-risk level (5-20%), and high-risk level (>20%). At the high-risk level, 13% needed ICU admission, 29% died, and 37% had an ICU-death outcome. This predictive model allowed us to individualize the risk for worse outcome for hospitalized patients affected by COVID-19.

Use of the patientMpower App With Home-Based Spirometry to Monitor the Symptoms and Impact of Fibrotic Lung Conditions: Longitudinal Observational Study

Background

Daily home-based spirometry in idiopathic pulmonary fibrosis (IPF) has been shown to be feasible and clinically informative. The patientMpower app facilitates home-based spirometry along with home-based monitoring of IPF-related symptoms. The patientMpower app can be downloaded to the user’s mobile phone or tablet device, enabling the recording of objective and subjective data.

Objective

The aim of this paper is to report on the 1-year experience of using patientMpower with home-based spirometry by 36 participants with self-reported pulmonary fibrosis (PF) treated with usual care.

Methods

Self-selecting participants enrolled in this community-based participatory research program through a patient advocacy group in their country: Irish Lung Fibrosis Association in Ireland and PF Warriors in the United States. Disease severity was comparable with a baseline mean predicted forced vital capacity (FVC) of 64% and 62% in the Irish and US participants, respectively. Both groups of participants were allocated to identical, in-country, open-label, single-group observational studies and were provided with a Bluetooth-active Spirobank Smart spirometer integrated directly with patientMpower. Data collected via patientMpower included seated FVC (daily), breathlessness grade (modified Medical Research Council scale score), step count, medication adherence, and symptoms and impact of IPF on daily life, which were measured by a patient-reported outcome measure (PROM) scale that was specifically developed for IPF. Longitudinal patient-reported data on oximetry and oxygen consumption were also collected.

Results

A large majority of the 36 participants reported that their experience using patientMpower was positive, and they wanted to continue its use after the initial 6-week observation. Out of 36 participants, 21 (58%) recorded home-based spirometry without prompting for ≥180 days, and 9 (25%) participants continued with recording home-based spirometry for ≥360 days.

Conclusions

The patientMpower app with associated Bluetooth-connected devices (eg, spirometer and pulse oximeter) offers an acceptable and accessible approach to collecting patient-reported objective and subjective data in fibrotic lung conditions.

Models of healthcare in respiratory diseases

Increasing healthcare cost and utilisation of health services have sparked an interest in new models of care, especially those that allow provision of traditional inpatient care in a community setting. COVID-19 has accelerated the implementation of telehealth, the delivery of medical care via phone or digital communication.

The September issue of Breathe focuses on models of healthcare in respiratory diseases: read the introductory editorial by Chief Editor @ClaudiaCDoblerhttps://bit.ly/2YTcI8V