FormalPara Key Points for Decision Makers

Different home monitoring services for COVID-19 patients have different costs and results. On average, CO@h services cost slightly less per patient than CVW services.

Using technology to give patients more control over their care can save time for NHS staff and money for health care providers.

Decision makers should consider the amount of health care used and the results it brings when deciding if remote monitoring services are good value for money for different health issues.

1 Introduction

The COVID-19 pandemic originated in China in December 2019, with the UK’s first positive case reported in late January 2020, and person-to-person transmission within the country confirmed by late February [1, 2]. Late lockdown activities, rapid spread of the virus and critical capacity challenges pre-dating the pandemic created unprecedented pressure within the NHS. Hospital attendance was restricted to urgent and emergency care only. The urgent need to free up 30,000 beds by early discharge of clinically ready patients and manage avoidable hospital admission at home led to the establishment of alternative health care services [3].

During the first wave of the pandemic (March to July 2020), some areas in England developed ad hoc services that provided remote home monitoring using pulse oximetry for COVID-19. This was done to reduce the number of patients arriving at hospitals with very low oxygen saturations (often without breathlessness, also known as ‘silent hypoxia’), to prevent invasive treatments and potential intensive care admissions, leading to improved outcomes for patients [4,5,6]. Learning from these preliminary services, in November 2020, the NHS in England launched a national roll-out of a remote home monitoring service for COVID-19 patients known as ‘COVID Oximetry@home’ (CO@h), which was generally provided by primary care and was designed to allow less acute but vulnerable people’s care to be managed safely at home [7]. In January 2021, an additional patient self-monitoring pathway, operated by secondary care, was introduced, called ‘COVID Virtual Ward’ (CVW) [8]. While CO@h services were focused on COVID-19 patients in the community, CVW was intended to support patients being discharged from hospitals and included patients who were severely ill.

The implementation of both pathways was similar, justifying their joint investigation in the current study, and included pulse oximeters that were given to patients who were asked to routinely record and submit oxygen saturation levels and other symptoms to a team of administrators and/or clinicians via digital technologies or over the phone. Patients were then escalated for further investigation if the clinical teams considered it necessary. Implementation sites adopted two mixed methods for monitoring patients, namely (i) tech-enabled and analogue data submission mode (patients could submit their symptoms using either a tech-enabled option like app, weblink, or automated phone calls, or an analogue method, i.e., phone calls with a health professional), reducing the time spent by health professionals to enter data; (ii) analogue-only data submission mode (patients could record their readings on paper and submit the data through phone calls or face-to-face visits with a health professional) [9].

For a patient to be eligible for either CVW or CO@h, they had to be resident in one of the 106 Clinical Commissioning Groups (CCGs) in England, or in a Clinically Extremely Vulnerable (CEV) group, have a positive PCR COVID-19 test and not be a care home resident at the time of their positive test. The national standard operating procedure established the original eligibility criterion for enrolment as patients over 65 years of age; however, local standards may vary, expanding the age range to include people aged 50 and over [10].

This study was part of a larger evaluation of COVID-19 remote home monitoring services which included two phases. Phase one (July–August 2020) involved a rapid systematic review [11] and an empirical mixed-methods study, which included an exploration of the experiences of staff implementing these models, the use of data for monitoring outcomes and variability in staffing and resource allocation [4]. In phase two (January–June 2021), a large-scale, multi-site, mixed methods study examined clinical effectiveness, cost analysis, implementation and patient/staff experience [6, 12,13,14,15]. Despite cost analyses having been carried out for remote home monitoring of some conditions, there is limited evidence in the literature regarding the effectiveness, costs, resource use and workforce implications associated with remote monitoring for COVID-19, as well as the impact of different data submission modes [11]. Our study evaluates the financial implications, allocation of resources and patient enrolment in home monitoring programs, offering evidence-based insights to guide decision making and health care policy development.

The main aim of this study was to evaluate the costs of implementing remote home monitoring for COVID-19 patients across 26 selected sites during wave 2 of the pandemic in England (October 2020–April 2021). The study also aimed to generalise the findings to the national level, as well as identify potential differences between services and modes of delivering these services. To meet these aims, we have collected and analysed data on patient enrolment and care and staffing involved in providing CO@h and CVW services from October 2020 to April 2021.

2 Methods

Our analysis focused on evaluating the costs of remote home monitoring services for COVID-19 patients in England from October 2020 to April 2021. We assessed the costs of these services from the NHS perspective, considering the above-mentioned period. Follow-up events, such as hospital or intensive care admissions, were not included due to data limitations.

2.1 Costs Identification

When evaluating new interventions, the National Institute for Health and Care Excellence (NICE) recommends taking into account all relevant NHS costs that could be affected as a result [16]. These include changes in staff numbers and time spent on different activities, training and education requirements, support services (like laboratory tests) and overall service capacity or facilities (including hospital beds, diagnostic services, etc) [17]. Careful consideration of these costs and identification of any constraints on the resources needed to support implementation may significantly impact decision making during prospective evaluations and service planning [18, 19].

We took an NHS perspective to measure costs. All costs were calculated in 2021 UK£ (November 2021: 1GBP = $1.368USD; 1GBP = 1.184EUR). Therefore, we first identified the following items of resource use to include in our analysis: staff; utilisation and maintenance of remote monitoring methods (including digital remote monitoring systems); oximeters; other medical equipment (e.g., thermometers, defibrillators); and other non-medical equipment (e.g., office and IT equipment, stationery). This aligns with NICE methodological guidelines, which recommend breaking down costs into appropriate generic organisational categories and budgetary categories [17] (see also Fig. 1).

Fig. 1
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The description and categorisation of costs required in setting up and running the CO@h and CVW services

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The costs were separated into (i) those related to setting up CO@h and CVW services and (ii) those related to running the service delivery. This distinction was made in line with previous methods used to analyse the costs of telemedicine interventions [20]. The setting-up costs encompassed investments in the remote technology-enabled monitoring platform, procurement of other medical and non-medical equipment required for setting up, as well as staff-related costs (including hours worked and staff involved in setting up the services). The running costs refer to regular and ongoing expenses incurred during the operation of services, including costs related to medical equipment provided to patients, staff involved in service delivery and the maintenance of the technology-enabled platforms. All costs and resource use items were additionally categorised by the mode used to monitor patients (technology-enabled with analogue mode or analogue-only mode).

We quantified costs using a detailed bottom-up approach [21] (see also Fig. 1). This entailed gathering data on resource utilisation and costs for each. Resources used were collected in the appropriate physical units (e.g., hours spent per nurse, physician or other staff by employment band or seniority, etc.) in line with current guidelines and best practices for health economic evaluations [17].

2.2 Cost Valuation

In this section, we explore how the costs of the intervention were determined, using unit costs from reliable sources and data collected from questionnaires. Unit costs extracted from routinely published sources or collected questionnaires were multiplied by the volume of resource use for each item [22]. They were then summed across all items to calculate costs. Due to short-term follow-up (< 1 year), costs were not discounted.

Staff-related costs were calculated using published unit costs per hour and the information collected from study sites about staff hours and salary bands [23]. Clinical staff were divided into three sub-groups based on their salary band (Band 5 or below, Bands 6–7, or Band 8 or above), while non-clinical staff were divided into two sub-groups (Band 5 or below, Band 6 and above). The unit cost per pulse oximeter was reported as varying between £20 and £25 (US$27.36–S$34.2) and we used the mid-point of this range (£22.5; US$30.78) unless a specific unit cost was reported by the site. Unit costs for other non-staff-related resources used (e.g., unit cost of thermometers that might have been given to enrolled patients) were reported through the collected questionnaires.

The costs for similar implementation sites were aggregated (we took the weighted mean value, weighted by the number of patients), enabling us to evaluate the overall financial impact of the intervention and understand how site-specific complexities may influence these costs.

2.3 Data Collection

We gathered information retrospectively using an electronic data collection form. The form was sent to 28 purposively selected implementation sites (COVID‐19 remote home monitoring services delivered in NHS trusts or primary care providers) that were representative of a range of regions, in terms of levels of deprivation, population size and urban/rural mix and ethnic group make-up [6]. Data were collected for the period from 1 October 2020 to 30 April 2021. The form distinguished between the setting-up and running stage of the COVID-19 remote home monitoring services, considering the information on the staff and resources used (Fig. 1).

The information on the number of patients triaged and monitored, those whose health deteriorated and were escalated for treatment (following health deterioration) and the number who died was also collected and used to calculate the mean costs per patient.

2.4 Data Analysis

We calculated the costs incurred for setting up and running the CO@h and CVW services, also examining the variation by data submission mode. We adjusted the resources used and costs incurred per patient by applying weights based on the number of patients monitored by each service (CO@h and CVW) and the total number of patients at each site.

The setting-up costs were only reported as a mean cost per site (as the duration of the running period and the lifespan of digital platforms may exceed our study period). The running costs were calculated as mean costs per patient for the period October 2020 to April 2021. The mean costs per patient were calculated by dividing the total costs of each service by the number of patients for each site. We adjusted the resources used and costs incurred per patient by applying weights based on the number of patients monitored by each service (CO@h and CVW) and the total number at each site. For implementation sites running both CO@h and CVW services, we calculated the mean setting-up and mean per-patient running costs of these services separately.

The number of pulse oximeters used during the study was based on the number of patients monitored. An assumption was made that 70% of these pulse oximeters would be returned and reused. This was based on results from a patient and carer survey (n = 1069), which showed that 69% of patients or carers had returned the pulse oximeter (range 21.5–100% by site) [6]. Therefore, only 30% of the costs of these pulse oximeters were accounted for during the study period.

2.5 Robustness Checks and Sensitivity Analysis

The mean running cost per patient could be affected by factors such as the type of service, data submission mode, seniority of staff and the total number of patients monitored. We investigated whether the mean running cost per patient was associated with these factors by using a multivariable regression analysis. This method allows us to quantify the relationship between the number of users and costs, adjusting for other service characteristics. The regression analysis controls for the type of service, data submission mode, seniority of full-time equivalent (FTE) staff and the total number of patients monitored to explore the main determinants of the costs. Bootstrap** with 10,000 iterations was used to estimate the standard errors of the regression model coefficients. We investigated the sensitivity of our findings to alternative specifications of staff seniority. Statistical tests and regression analyses were conducted using Stata v.18.

2.6 Projection of Findings on Costs at the National Level

To extend the analysis to a national level, we estimated the total national costs of the CO@h and CVW services by combining our findings with data from other parts of the study [24]. For example, we utilised data from the number of people who fulfilled the inclusion criteria for CO@h and CVW services and multiplied these by the estimated proportion of patients using the CO@h and CVW services and the mean running cost per patient for each service. Finally, the findings of this study were triangulated with other quantitative and qualitative evaluation studies of the same COVID-19 remote home monitoring services [6].

3 Results

Twenty-six out of the 28 implementation sites (with a survey response rate of 92.8%) returned the data collection form, of which 13 provided CO@h services, four provided CVW services and nine provided both CO@h and CVW services. The data on costs are presented as mean costs per patient using patient engagement as well as clinical and non-clinical workforce utilised throughout various care stages.

3.1 Patients and Outcomes

Detailed information on the number of patients monitored using CO@h and CVW services and using the different submission modes is provided in Table 1. The uptake among those who were triaged was much higher for the CVW service than the CO@h service. The proportion of patients who were escalated and the proportion who died among those monitored was higher for the CO@h services compared with the CVW services (16.6% and 10.8%, respectively, for escalated patients and 0.9% and 0.7%, respectively, for the number of deaths).

Table 1 Patients and outcomes by service and data submission mode across all 26 implementation sites from October 2020 to April 2021
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Of the 22 implementation sites running CO@h services, 16 used tech-enabled and analogue modes and six used analogue-only modes. Of the 13 implementation sites running a CVW service, six used the tech-enabled and analogue data submission modes and seven used the analogue-only mode. From the total number of patients monitored in the CO@h services, 76.8% (i.e., 13,379/17,424 patients) were followed up using the tech-enabled and analogue data submission modes, while for CVW services, 39.2% of patients (i.e., 671/1711 patients) were followed up using the tech-enabled and analogue data submission modes.

3.2 Workforce

The number of FTE staff involved in running the CO@h service was on average 7.4 per site (range 1.0–39.0) and for the CVW service it was 2.1 per site (range 0.1–4.8). Staff involved were a mix of medical consultants, emergency department (ED) staff, GPs, nurses, advanced nurse practitioners and medical students. Table 2 provides summary information on the mean number of FTE staff by role and band and stratified by type of service (detailed information with size of site is provided in Appendix 1). For both CO@h and CVW services, most of the staff involved in monitoring patients were clinical (68.4% for CO@h and 86.7% for CVW). There were no clear differences in the proportion of clinical/non-clinical staff or seniority of the staff between CO@h and CVW services (Table 2).

Table 2 Proportion of clinical and non-clinical staff involved in monitoring patients from October 2020 to April 2021
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Table 2 also shows that across all implementation sites running CO@h services, 53.0% of staff were employed at Band 5 or below (27.1% non-clinical and 25.9% clinical staff). Implementation sites using tech-enabled and analogue modes had a higher proportion of non-clinical staff at Band 5 or below (33.1% non-clinical and 18.6% clinical staff) compared with analogue-only implementation sites (16.8% non-clinical and 38.5% clinical staff).

Across all implementation sites running CVW, 51.3% of all staff involved (50.4% clinical and 0.9% non-clinical) were employed at Band 6 and above. A similar trend was observed for the implementation sites using analogue-only modes where 54.1% of all staff involved (53.6% clinical and 0.5% non-clinical) were employed at Band 6 and above, whereas for the tech-enabled and analogue mode, most staff involved (51.6%; 34.1% clinical and 17.5% non-clinical) were at Band 5 and below.

3.3 Costs

Table 3 provides detailed information on the resources utilised during the setting-up and running period per patient triaged and monitored for both CO@h and CVW services translated into the costs. To assess costs, we utilise data on the number of triaged and monitored patients, along with the hours allocated for clinical and non-clinical staff across different bands. These data show that during the setting-up phase, the largest component of the costs was for non-staff items (e.g., the technology platform, medical and other equipment). In terms of running costs, the mean cost per patient monitored using the CO@h service was marginally lower compared with the CVW service (£527.5 vs £599.1; US$721.62 vs US$819.57). The mean cost per patient monitored was lower for implementation sites using tech-enabled and analogue data submission modes compared with the implementation sites using analogue-only modes for both CO@h (£515 vs £561; US$704.52 vs US$767.4) and CVW (£584 vs £612; US$798.91 vs US$837.22) services.

Table 3 Costs of CO@h and CVW services from October 2020 to April 2021
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The total number of people who fulfilled the inclusion criteria for the remote home monitoring services for the period from 1 October 2020 to 3 May 2021 has been estimated at the national level as 217,650 [24]. Our study showed that 91% of the patients enrolled on remote home monitoring services were in CO@h services, while the rest were in CVW services (Table 1). Assuming that the ratio between patients in CO@h services and CVW services found in our data was the same at the national level, the number of nationally eligible patients for CVW services was estimated to be 21,366. If these services were fully implemented and all eligible patients received them, the estimated cost for the CO@h service would be £114,810,375 (US$157,060,593), and for the CVW service it would be £12,800,599 (US$17,511,219.43).

We investigated whether the mean running costs per patient at each site were associated with the total number of patients monitored. Figures 2 and 3 show a positive relationship (slope coefficient for the CO@h service 0.54 [95% CI 0.19–0.89] and for CVW service 2.10 [95% CI 0.16–4.03]) between the number of patients monitored and the mean running cost per patient for the CO@h and CVW services, respectively. It appears that the majority of expenses associated with running this operation are variable costs, which should rise in proportion to the number of patients being monitored. Figures 2 and 3 show that variable costs rise faster than the rise in the number of patients, especially for the CVW service, which is something that needs to be monitored if more data are available in the future.

Fig. 2
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Mean cost per patient for CO@h services October 2020–April 2021. Based on data provided by each site. Twenty-two implementation sites used the CO@h services. CO@h COVID Oximetry@home

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Fig. 3
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Mean cost per patient for CVW services October 2020–April 2021. Based on data provided by each site. Thirteen implementation sites used the CVW service. CVW COVID Virtual Wards

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We regressed the mean running cost against the type of service, data submission mode, seniority of FTE staff and the total number of patients monitored. Table 4 provides the multivariable regression analysis results, where the coefficients of this table represent the difference in the predicted mean running cost per patient for each one-unit change in the independent variables, assuming all other independent variables are held constant.

Table 4 Regression analysis on the mean running cost per patient
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Results showed that a key determinant (p < 0.01) of costs was the number of patients monitored, with the mean running cost per patient increasing slightly by £0.62 (US$0.85) for each patient monitored, assuming all other control variables remain constant. The type of service was another statistically significant determinant (p < 0.05) with CO@h associated with £457.99 (US$334.06) lower mean running costs than CVW. Even though the type of data submission mode and the FTE difference between the senior and junior staff involved were not statistically significant predictors of mean running costs per patient, the direction of their effect was consistent with the descriptive findings that indicated a lower mean running cost per patient for the tech-enabled and analogue data submission mode. In a sensitivity analysis, the results remained similar in terms of the direction of coefficients and their significance when we varied the number of patients within the 95% confidence interval values for tech-enabled and analogue and analogue-only sites (Appendix 2). Note that these findings should be considered with caution due to the small sample size (N = 35 sites).

4 Discussion

This study provides insights into the operational and financial aspects of remote home monitoring services for COVID-19 patients, offering possible implications for health care provision and policy development. By examining data collected from 26 health care implementation sites between October 2020 and April 2021, we have identified trends and factors influencing the costs of these services.

Our analysis showed that the average running cost per patient was £527.5 (US$721.62) for CO@h services and £599.1 (US$819.57) for CVW services. While differences in costs between technology-enabled and analogue data submissions were observed, we found that patients were responsible for data entry with technology-enabled and analogue submission mode, requiring less time from NHS staff. This underscores the potential efficiency gains from an NHS perspective associated with empowering patients to actively participate in their care management, with broader implications for health care resource utilisation and workforce allocation.

Any comparison in costs, patients’ enrolment and workforce between the services comes with questions about their appropriateness and usefulness, given that the two services enrol different groups of patients and run independently from each other. Our findings should be used cautiously; however, they provide useful evidence for the programme, which relies on the rationale that both CO@h and CVW represent remote home monitoring services implemented during the COVID-19 pandemic. By evaluating them together, we can understand the differences in cost structures, resource allocation, and outcomes between these two services. This comparison can provide valuable insights into the efficiency and effectiveness of different approaches to remote home monitoring for COVID-19 patients in England. Also, CO@h and CVW services share common resources, such as digital technologies, clinical staff, administrative support, and infrastructure. Assessing the costs of both services in the same study allows us to conduct a comprehensive analysis of resource utilisation and cost allocation across different settings and patient groups. This information can inform resource planning and optimisation strategies for future pandemic response efforts. Conducting a single study that evaluates multiple remote home monitoring services can also enable policymakers to consider a broader range of options and make informed decisions regarding resource allocation, funding priorities, and service delivery models. Policymakers can gain a better understanding of the economic implications of different remote monitoring approaches and tailor their policies accordingly.

Moreover, our regression analysis highlighted the impact of the number of patients monitored and the type of service on the financial aspects of remote home monitoring initiatives. Variable costs for CVW services increased considerably with the number of patients monitored. However, scaling up services may introduce operational challenges and resource constraints that need careful management to ensure service quality and patient safety. It's important to note that our analysis was based on a small sample size (N = 35 sites), and the findings should be interpreted cautiously.

Our study addresses a gap in the existing literature on remote health care services by focusing on the cost of COVID-19-related remote monitoring services. By providing detailed insights into resource use and costs, our analysis provides further information about the financial implications of remote home monitoring services. This information can assist health care decision makers in making informed decisions about resource allocation and service planning. Additionally, our study identifies important predictors, such as the number of monitored patients and the type of service, which can further aid decision makers in determining financial outcomes. This is in the context of previous literature that often lacks comprehensive economic analysis [11].

Our study findings suggest the possibility of cost-saving benefits associated with technology-enabled interventions compared with analogue-enabled patient monitoring, consistent with prior research indicating that telehealth interventions may yield cost savings for health care services, particularly in reaching remote or underserved populations [25]. Previous literature generally supports the notion that virtual monitoring and screening interventions tailored for COVID-19 patients hold promise for delivering cost savings by reducing downstream expenses, such as decreasing hospital admissions and bed days [26, 27]. Additionally, across various health care settings and from the perspective of health care providers, the adoption of telehealth for health care delivery has been linked to cost savings, especially in reaching geographically isolated areas [25, 28]. Nonetheless, the cost effectiveness of remote patient monitoring could vary depending on multiple factors, including the specific implementation context, characteristics of the patient population, and the integration of monitoring services into existing health care pathways [29,30,31]. Furthermore, the realisation of potential cost savings through technology relies on certain assumptions, such as the notion that digital assessments result in shorter interactions, leading to fewer follow-up contacts and decreased symptom deterioration, thereby potentially averting hospital readmissions. However, it is essential to exercise caution, as these assumptions may not universally apply [32].

Our results offer insights into financial aspects, selected patient outcomes, and staffing implications extending beyond the pandemic. Moreover, the broad geographic coverage of implementation sites across England and primary data collection on CO@h and CVW service usage enhance the study's analytical rigour, filling a gap in COVID-related remote monitoring services literature. The study's findings have implications for health care provision, particularly in deploying remote home monitoring services, which may be useful during future infectious outbreaks like NHS bed crises in peak flu seasons. Remote care availability may reduce hospital admissions, facilitate early discharge, and mitigate health care-associated infections, lowering mortality, morbidity, and costs [33].

One important limitation of our study is that it focuses only on the costs of setting up and running monitoring services. We could not conduct an economic evaluation, such as a cost-utility analysis, due to the lack of evidence linking CO@h and CVW services with mortality or downstream hospital and other NHS utilisation [12, 13, 24]. Additionally, we were only able to obtain aggregate rather than patient-level data from sites and included a relatively small number of sites. All of these are additional limitations of our study and may have affected the accuracy and generalisability of our findings.

5 Conclusions

This study provides evidence about the costs and resource use associated with different types of remote home monitoring during the COVID-19 pandemic. Future interventions in remote monitoring for various health conditions should evaluate their impact on both primary and secondary care utilisation, and health outcomes. Future research should also focus on gathering data on total capacity, which will aid in planning of services.