Population-level impact of a pulse oximetry remote monitoring programme on mortality and healthcare utilisation in the people with COVID-19 in England: a national analysis using a stepped wedge design

A Cost Evaluation of COVID-19 Remote Home Monitoring Services in England

BACKGROUND

Remote home monitoring services emerged as critical components of health care delivery from NHS England during the COVID-19 pandemic, aiming to provide timely interventions and reduce health care system burden. Two types of service were offered: referral by community health services to home-based care to ensure the right people were admitted to the hospital at the right time (called COVID Oximetry@home, CO@h); and referral by hospital to support patients' transition from hospital to home (called COVID-19 Virtual Ward, CVW). The information collected for the oxygen levels and other symptoms was provided via digital means (technology-enabled) or over the phone (analogue-only submission mode). This study aimed to evaluate the costs of implementing remote home monitoring for COVID-19 patients across 26 sites in England during wave 2 of the pandemic. Understanding the operational and financial implications of these services from the NHS perspective is essential for effective resource allocation and service planning.

METHODS

We used a bottom-up costing approach at the intervention level to describe the costs of setting up and running the services. Twenty-six implementation sites reported the numbers of patients and staff involved in the service and other resources used. Descriptive statistics and multivariable regression analysis were used to assess cost variations and quantify the relationship between the number of users and costs while adjusting for other service characteristics.

RESULTS

The mean cost per patient monitored was lower in the CO@h service compared with the CVW service (£527 vs £599). The mean cost per patient was lower for implementation sites using technology-enabled and analogue data submission modes compared with implementation sites using analogue-only modes for both CO@h (£515 vs £561) and CVW (£584 vs £612) services. The number of patients enrolled in the services and the service type significantly affected the mean cost per patient.

CONCLUSIONS

Our analysis provides a framework for evaluating the costs of similar services in the future and shows that the implementation of these services benefit from the employment of tech-enabled data submission modes.

Evaluation of a virtual ward model of care and readmission characteristics during the COVID-19 pandemic within an Australian tertiary hospital

BACKGROUND

Virtual ward (VW) models of care established during the coronavirus disease 2019 (COVID-19) pandemic provided safe and equitable provision of ambulatory care for low-risk patients; however, little is known about patients who require escalation of care to hospitals from VWs.

AIM

To assess our VW model of care and describe the characteristics of patients admitted to the hospital from the VW.

METHODS

Observational study of all patients admitted to a tertiary hospital COVID-19 VW between 1 December 2021 and 30 June 2022. Utilisation and epidemiological characteristics were assessed for all patients while additional demographics, assessments, treatments and outcomes were assessed for patients admitted to the hospital from the VW.

RESULTS

Of 9494 patient admissions, 269 (2.83%) patients identified as Aboriginal and Torres Strait Islander and 1774 (18.69%) were unvaccinated. The median length of stay was 5.10 days and the mean Index of Relative Socio-economic Advantage and Disadvantage decile was 5.73. One hundred sixty (1.69%) patients were admitted to the hospital from the VW, of which 25 were adults admitted to medical wards. Of this cohort, prominent comorbidities were obesity, hypertension, asthma and frailty, while the main symptoms on admission to the VW were cough, fatigue, nausea and sore throat. High Pandemic Respiratory Infection Emergency System Triage (PRIEST), Veterans Health Administration COVID-19 (VACO), COVID Home Safely Now (CHOSEN) and 4C mortality scores existed for those readmitted.

CONCLUSIONS

This VW model of care was both safe and effective when applied to a broad socioeconomic population during the COVID-19 pandemic. While readmission to the hospital was low, this study identified key characteristics of such presentations, which may assist future triaging, escalation and resource allocation within VWs during the COVID-19 pandemic and beyond.

The Ethical Obligation for Research During Public Health Emergencies: Insights From the COVID-19 Pandemic

In times of crises, public health leaders may claim that trials of public health interventions are unethical. One reason for this claim can be that equipoise-i.e. a situation of uncertainty and/or disagreement among experts about the evidence regarding an intervention-has been disturbed by a change of collective expert views. Some might claim that equipoise is disturbed if the majority of experts believe that emergency public health interventions are likely to be more beneficial than harmful. However, such beliefs are not always justified: where high quality research has not been conducted, there is often considerable residual uncertainty about whether interventions offer net benefits. In this essay we argue that high-quality research, namely by means of well-designed randomized trials, is ethically obligatory before, during, and after implementing policies in public health emergencies (PHEs). We contend that this standard applies to both pharmaceutical and non-pharmaceutical interventions, and we elaborate an account of equipoise that captures key features of debates in the recent pandemic. We build our case by analyzing research strategies employed during the COVID-19 pandemic regarding drugs, vaccines, and non-pharmaceutical interventions; and by providing responses to possible objections. Finally, we propose a public health policy reform: whenever a policy implemented during a PHE is not grounded in high-quality evidence that expected benefits outweigh harms, there should be a planned approach to generate high-quality evidence, with review of emerging data at preset time points. These preset timepoints guarantee that policymakers pause to review emerging evidence and consider ceasing ineffective or even harmful policies, thereby improving transparency and accountability, as well as permitting the redirection of resources to more effective or beneficial interventions.

Patient and staff experiences of using technology-enabled and analogue models of remote home monitoring for COVID-19 in England: A mixed-method evaluation

OBJECTIVE

To evaluate patient and staff experiences of using technology-enabled ('tech-enabled') and analogue remote home monitoring models for COVID-19, implemented in England during the pandemic.

METHODS

Twenty-eight sites were selected for diversity in a range of criteria (e.g. pre-hospital or early discharge service, mode of patient data submission). Between February and May 2021, we conducted quantitative surveys with patients, carers and staff delivering the service, and interviewed patients, carers, and staff from 17 of the 28 services. Quantitative data were analysed using descriptive statistics and both univariate and multivariate analyses. Qualitative data were interpreted using thematic analysis.

RESULTS

Twenty-one sites adopted mixed models whereby patients could submit their symptoms using either tech-enabled (app, weblink, or automated phone calls) or analogue (phone calls with a health professional) options; seven sites offered analogue-only data submission (phone calls or face-to-face visits with a health professional). Sixty-two patients and carers were interviewed, and 1069 survey responses were received (18 % response rate). Fifty-eight staff were interviewed, and 292 survey responses were received (39 % response rate). Patients who used tech-enabled modes tended to be younger (p = 0.005), have a higher level of education (p = 0.011), and more likely to identify as White British (p = 0.043). Most patients found relaying symptoms easy, regardless of modality, though many received assistance from family or friends. Staff considered the adoption of mixed delivery models beneficial, enabling them to manage large patient numbers and contact patients for further assessment as needed; however, they suggested improvements to the functionality of systems to better fit clinical and operational needs. Human contact was important in all remote home monitoring options.

CONCLUSIONS

Organisations implementing tech-enabled remote home monitoring at scale should consider adopting mixed models which can accommodate patients with different needs; focus on the usability and interoperability of tech-enabled platforms; and encourage digital inclusivity for patients.

A rapid mixed-methods evaluation of remote home monitoring models during the COVID-19 pandemic in England

Background

Remote home monitoring services were developed and implemented for patients with COVID-19 during the pandemic. Patients monitored blood oxygen saturation and other readings (e.g. temperature) at home and were escalated as necessary.

Objective

To evaluate effectiveness, costs, implementation, and staff and patient experiences (including disparities and mode) of COVID-19 remote home monitoring services in England during the COVID-19 pandemic (waves 1 and 2).

Methods

A rapid mixed-methods evaluation, conducted in two phases. Phase 1 (July-August 2020) comprised a rapid systematic review, implementation and economic analysis study (in eight sites). Phase 2 (January-June 2021) comprised a large-scale, multisite, mixed-methods study of effectiveness, costs, implementation and patient/staff experience, using national data sets, surveys (28 sites) and interviews (17 sites).

Results

Phase 1 Findings from the review and empirical study indicated that these services have been implemented worldwide and vary substantially. Empirical findings highlighted that communication, appropriate information and multiple modes of monitoring facilitated implementation; barriers included unclear referral processes, workforce availability and lack of administrative support. Phase 2 We received surveys from 292 staff (39% response rate) and 1069 patients/carers (18% response rate). We conducted interviews with 58 staff, 62 patients/carers and 5 national leads. Despite national roll-out, enrolment to services was lower than expected (average enrolment across 37 clinical commissioning groups judged to have completed data was 8.7%). There was large variability in implementation of services, influenced by patient (e.g. local population needs), workforce (e.g. workload), organisational (e.g. collaboration) and resource (e.g. software) factors. We found that for every 10% increase in enrolment to the programme, mortality was reduced by 2% (95% confidence interval: 4% reduction to 1% increase), admissions increased by 3% (-1% to 7%), in-hospital mortality fell by 3% (-8% to 3%) and lengths of stay increased by 1.8% (-1.2% to 4.9%). None of these results are statistically significant. We found slightly longer hospital lengths of stay associated with virtual ward services (adjusted incidence rate ratio 1.05, 95% confidence interval 1.01 to 1.09), and no statistically significant impact on subsequent COVID-19 readmissions (adjusted odds ratio 0.95, 95% confidence interval 0.89 to 1.02). Low patient enrolment rates and incomplete data may have affected chances of detecting possible impact. The mean running cost per patient varied for different types of service and mode; and was driven by the number and grade of staff. Staff, patients and carers generally reported positive experiences of services. Services were easy to deliver but staff needed additional training. Staff knowledge/confidence, NHS resources/workload, dynamics between multidisciplinary team members and patients' engagement with the service (e.g. using the oximeter to record and submit readings) influenced delivery. Patients and carers felt services and human contact received reassured them and were easy to engage with. Engagement was conditional on patient, support, resource and service factors. Many sites designed services to suit the needs of their local population. Despite adaptations, disparities were reported across some patient groups. For example, older adults and patients from ethnic minorities reported more difficulties engaging with the service. Tech-enabled models helped to manage large patient groups but did not completely replace phone calls.

Limitations

Limitations included data completeness, inability to link data on service use to outcomes at a patient level, low survey response rates and under-representation of some patient groups.

Future work

Further research should consider the long-term impact and cost-effectiveness of these services and the appropriateness of different models for different groups of patients.

Conclusions

We were not able to find quantitative evidence that COVID-19 remote home monitoring services have been effective. However, low enrolment rates, incomplete data and varied implementation reduced our chances of detecting any impact that may have existed. While services were viewed positively by staff and patients, barriers to implementation, delivery and engagement should be considered.

Study registration

This study is registered with the ISRCTN (14962466).

Funding

This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (RSET: 16/138/17; BRACE: 16/138/31) and NHSEI and will be published in full in Health and Social Care Delivery Research; Vol. 11, No. 13. See the NIHR Journals Library website for further project information. The views expressed in this publication are those of the authors and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.

Evaluating the impact of a pulse oximetry remote monitoring programme on mortality and healthcare utilisation in patients with COVID-19 assessed in emergency departments in England: a retrospective matched cohort study

BACKGROUND

To identify the impact of enrolment onto a national pulse oximetry remote monitoring programme for COVID-19 (COVID-19 Oximetry @home; CO@h) on health service use and mortality in patients attending Emergency Departments (EDs).

METHODS

We conducted a retrospective matched cohort study of patients enrolled onto the CO@h pathway from EDs in England. We included all patients with a positive COVID-19 test from 1 October 2020 to 3 May 2021 who attended ED from 3 days before to 10 days after the date of the test. All patients who were admitted or died on the same or following day to the first ED attendance within the time window were excluded. In the primary analysis, participants enrolled onto CO@h were matched using demographic and clinical criteria to participants who were not enrolled. Five outcome measures were examined within 28 days of first ED attendance: (1) Death from any cause; (2) Any subsequent ED attendance; (3) Any emergency hospital admission; (4) Critical care admission; and (5) Length of stay.

RESULTS

15 621 participants were included in the primary analysis, of whom 639 were enrolled onto CO@h and 14 982 were controls. Odds of death were 52% lower in those enrolled (95% CI 7% to 75%) compared with those not enrolled onto CO@h. Odds of any ED attendance or admission were 37% (95% CI 16% to 63%) and 59% (95% CI 32% to 91%) higher, respectively, in those enrolled. Of those admitted, those enrolled had 53% (95% CI 7% to 76%) lower odds of critical care admission. There was no significant impact on length of stay.

CONCLUSIONS

These findings indicate that for patients assessed in ED, pulse oximetry remote monitoring may be a clinically effective and safe model for early detection of hypoxia and escalation. However, possible selection biases might limit the generalisability to other populations.

Role of hospital strain in determining outcomes for people hospitalised with COVID-19 in England

BACKGROUND

In England, reported COVID-19 mortality rates increased during winter 2020/21 relative to earlier summer and autumn months. This study aimed to examine the association between COVID-19-related hospital bed-strain during this time and patient outcomes.

METHODS

This was a retrospective observational study using Hospital Episode Statistics data for England. All unique patients aged ≥18 years in England with a diagnosis of COVID-19 who had a completed (discharged alive or died in hospital) hospital stay with an admission date between 1 July 2020 and 28 February 2021 were included. Bed-strain was calculated as the number of beds occupied by patients with COVID-19 divided by the maximum COVID-19 bed occupancy during the study period. Bed-strain was categorised into quartiles for modelling. In-hospital mortality was the primary outcome of interest and length of stay a secondary outcome.

RESULTS

There were 253 768 unique hospitalised patients with a diagnosis of COVID-19 during a hospital stay. Patient admissions peaked in January 2021 (n=89 047), although the crude mortality rate peaked slightly earlier in December 2020 (26.4%). After adjustment for covariates, the mortality rate in the lowest and highest quartile of bed-strain was 23.6% and 25.3%, respectively (OR 1.13, 95% CI 1.09 to 1.17). For the lowest and the highest quartile of bed-strain, adjusted mean length of stay was 13.2 days and 11.6 days, respectively in survivors and was 16.5 days and 12.6 days, respectively in patients who died in hospital.

CONCLUSIONS

High levels of bed-strain were associated with higher in-hospital mortality rates, although the effect was relatively modest and may not fully explain increased mortality rates during winter 2020/21 compared with earlier months. Shorter hospital stay during periods of greater strain may partly reflect changes in patient management over time.

Factors associated with enrolment into a national COVID-19 pulse oximetry remote monitoring programme in England: a retrospective observational study

BACKGROUND

Hypoxaemia is an important predictor of severity in individuals with COVID-19 and can present without symptoms. The COVID Oximetry @home (CO@h) programme was implemented across England in November, 2020, providing pulse oximeters to higher-risk people with COVID-19 to enable early detection of deterioration and the need for escalation of care. We aimed to describe the clinical and demographic characteristics of individuals enrolled onto the programme and to assess whether there were any inequalities in enrolment.

METHODS

This retrospective observational study was based on data from a cohort of people resident in England recorded as having a positive COVID-19 test between Oct 1, 2020, and May 3, 2021. The proportion of participants enrolled onto the CO@h programmes in the 7 days before and 28 days after a positive COVID-19 test was calculated for each clinical commissioning group (CCG) in England. Two-level hierarchical multivariable logistic regression with random intercepts for each CCG was run to identify factors predictive of being enrolled onto the CO@h programme.

FINDINGS

CO@h programme sites were reported by NHS England as becoming operational between Nov 21 and Dec 31, 2020. 1 227 405 people resident in 72 CCGs had a positive COVID-19 test between the date of programme implementation and May 3, 2021, of whom 19 932 (1·6%) were enrolled onto the CO@h programme. Of those enrolled, 14 441 (72·5%) were aged 50 years or older or were identified as clinically extremely vulnerable (ie, having a high-risk medical condition). Higher odds of enrolment onto the CO@h programme were found in older individuals (adjusted odds ratio 2·21 [95% CI 2·19-2·23], p<0·001, for those aged 50-64 years; 3·48 [3·33-3·63], p<0·001, for those aged 65-79 years; and 2·50 [2·34-2·68], p<0·001, for those aged ≥80 years), in individuals of non-White ethnicity (1·35 [1·28-1·43], p<0·001, for Asian individuals; 1·13 [1·04-1·22], p=0·005, for Black individuals; and 1·17 [1·03-1·32], p=0·015, for those of mixed ethnicity), in those who were overweight (1·31 [1·26-1·37], p<0·001) or obese (1·69 [1·63-1·77], p<0·001), or in those identified as clinically extremely vulnerable (1·58 [1·51-1·65], p<0·001), and lower odds were reported in those from the least socioeconomically deprived areas compared with those from the most socioeconomically deprived areas (0·75 [0·69-0·81]; p<0·001).

INTERPRETATION

Nationally, uptake of the CO@h programme was low, with clinical judgment used to determine eligibility. Preferential enrolment onto the pulse oximetry monitoring programme was observed in people known to be at the highest risk of developing severe COVID-19.

FUNDING

NHS England, National Institute for Health Research, and The Wellcome Trust.

Undertaking rapid evaluations during the COVID-19 pandemic: Lessons from evaluating COVID-19 remote home monitoring services in England

Introduction

Rapid evaluations can offer evidence on innovations in health and social care that can be used to inform fast-moving policy and practise, and support their scale-up according to previous research. However, there are few comprehensive accounts of how to plan and conduct large-scale rapid evaluations, ensure scientific rigour, and achieve stakeholder engagement within compressed timeframes.

Methods

Using a case study of a national mixed-methods rapid evaluation of COVID-19 remote home monitoring services in England, conducted during the COVID-19 pandemic, this manuscript examines the process of conducting a large-scale rapid evaluation from design to dissemination and impact, and reflects on the key lessons for conducting future large-scale rapid evaluations. In this manuscript, we describe each stage of the rapid evaluation: convening the team (study team and external collaborators), design and planning (scoping, designing protocols, study set up), data collection and analysis, and dissemination.

Results

We reflect on why certain decisions were made and highlight facilitators and challenges. The manuscript concludes with 12 key lessons for conducting large-scale mixed-methods rapid evaluations of healthcare services. We propose that rapid study teams need to: (1) find ways of quickly building trust with external stakeholders, including evidence-users; (2) consider the needs of the rapid evaluation and resources needed; (3) use scoping to ensure the study is highly focused; (4) carefully consider what cannot be completed within a designated timeframe; (5) use structured processes to ensure consistency and rigour; (6) be flexible and responsive to changing needs and circumstances; (7) consider the risks associated with new data collection approaches of quantitative data (and their usability); (8) consider whether it is possible to use aggregated quantitative data, and what that would mean when presenting results, (9) consider using structured processes & layered analysis approaches to rapidly synthesise qualitative findings, (10) consider the balance between speed and the size and skills of the team, (11) ensure all team members know roles and responsibilities and can communicate quickly and clearly; and (12) consider how best to share findings, in discussion with evidence-users, for rapid understanding and use.

Conclusion

These 12 lessons can be used to inform the development and conduct of future rapid evaluations in a range of contexts and settings.

Home oxygen monitoring and therapy: learning from the pandemic

PURPOSE OF REVIEW

Home oxygen monitoring and therapy have been increasingly used in the management of patients with chronic diseases. The COVID-19 pandemic has prompted the rapid uptake of remote monitoring programmes to support people with COVID-19 at home. This review discusses the recent evidence and learning in home oxygen monitoring and therapy from the pandemic.

RECENT FINDINGS

Many home oxygen monitoring programmes were established around the world during the pandemic, mostly in high-income countries to support early detection of hypoxaemia and/or early hospital discharge. The characteristics of these programmes vary widely in the type of monitoring (self-monitoring or clinician-monitoring) and the patient risk groups targeted. There is a lack of evidence for benefits on clinical outcomes, including mortality, and on reductions in healthcare utilisation or cost-effectiveness, but programmes are viewed positively by patients. Recent studies have highlighted the potential bias in pulse oximetry in people with darker skin.

SUMMARY

Recent evidence indicates that home oxygen monitoring therapy programmes are feasible in acute disease, but further research is needed to establish whether they improve patient outcomes, are cost-effective and to understand their equity impact.

The impact of remote home monitoring of people with COVID-19 using pulse oximetry: A national population and observational study

BACKGROUND

Remote home monitoring of people testing positive for COVID-19 using pulse oximetry was implemented across England during the Winter of 2020/21 to identify falling blood oxygen saturation levels at an early stage. This was hypothesised to enable earlier hospital admission, reduce the need for intensive care and improve survival. This study is an evaluation of the clinical effectiveness of the pre-hospital monitoring programme, COVID oximetry @home (CO@h).

METHODS

The setting was all Clinical Commissioning Group (CCG) areas in England where there were complete data on the number of people enrolled onto the programme between 2nd November 2020 and 21st February 2021. We analysed relationships at a geographical area level between the extent to which people aged 65 or over were enrolled onto the programme and outcomes over the period between November 2020 to February 2021.

FINDINGS

For every 10% increase in coverage of the programme, mortality was reduced by 2% (95% confidence interval:4% reduction to 1% increase), admissions increased by 3% (-1% to 7%), in-hospital mortality fell by 3% (-8% to 3%) and lengths of stay increased by 1·8% (-1·2% to 4·9%). None of these results are statistically significant, although the confidence interval indicates that any adverse effect on mortality would be small, but a mortality reduction of up to 4% may have resulted from the programme.

INTERPRETATION

There are several possible explanations for our findings. One is that CO@h did not have the hypothesised impact. Another is that the low rates of enrolment and incomplete data in many areas reduced the chances of detecting any impact that may have existed. Also, CO@h has been implemented in many different ways across the country and these may have had varying levels of effect.

FUNDING

This is independent research funded by the National Institute for Health Research, Health Services & Delivery Research programme (RSET Project no. 16/138/17; BRACE Project no. 16/138/31) and NHSEI. NJF is an NIHR Senior Investigator.