Effect of Telehealth on Use of Secondary Care and Mortality
Effect of Telehealth on Use of Secondary Care and Mortality
Among a set of patients with chronic obstructive pulmonary disease, diabetes, or heart failure, this study has shown that a smaller proportion of telehealth users than controls were admitted to hospital during a 12 month follow-up. This effect remained significant after adjusting for baseline characteristics and for a predictive risk score. However, the magnitude of the group difference in admission proportion was relatively small (10.8%, 95% confidence interval 3.7% to 18.1%), and smaller than the size that the planned study design was able to detect (17.5%), raising questions about the clinical relevance of the results. The significance of some of the effects reflected the increased power of the study, owing to the higher than assumed baseline level of admissions and to the larger number of small practices (even though the intracluster correlation coefficient was higher than assumed).
Intervention patients were significantly less likely to die within 12 months than controls. We also observed small differences in the mean number of emergency admissions per head between the intervention and control groups (crude rate 0.54 v 0.68; difference 0.14). These changes were significant in the unadjusted comparisons and when we adjusted for a predictive risk score, but not when we adjusted for baseline characteristics. Hospital bed days were significantly lower among intervention patients than controls, which reflected the reduced admission proportion overall.
For the other measures of hospital use (including the number of elective admissions, outpatient attendances, and emergency department visits), group differences were not significant in general. Crude differences in notional hospital costs to commissioners of care were also not significant and were relatively small (£188 per head over 12 months), especially compared with the potentially high costs of these types of telehealth intervention, which we did not take into account. In view of our results showing confidence intervals crossing the line of no difference, we cannot conclude that telehealth reduces secondary care costs over 12 months. A formal cost effectiveness analysis of the Whole System Demonstrator intervention has been undertaken on a subset of participants, using self reported data for hospital use and other services and taking into account the intervention cost, compared with health related quality of life and other outcomes.
Differences in hospital use were at their most marked at the start of the trial, when we observed a distinct increase in admissions for the control group. If we excluded activity from the first three months of the trial, differences in the admission proportion would not have been significant under any of the models. Therefore, this increase has implications for the interpretation. Trial recruitment processes may have led indirectly to changes in service use for control patients; however, the same processes might also have affected intervention patients in the absence of telehealth. In this case, differences in admissions can be attributed to telehealth, but with the limitation that the trial could have affected the context for the delivery of care for both trial groups.
One explanation for the increase in emergency admissions observed for the control group is that professionals may have identified additional health problems and unmet needs during the recruitment process and could have decided to intervene with control patients not allocated to receive telehealth. This explanation assumes that, if professionals found additional relevant health problems among the intervention group, they were content to manage these in a community setting, with the support of the remote monitoring.
The trial recruitment process might have also raised patients’ awareness of their health conditions. Anxiety could have increased as a result of being allocated to the control group, to the extent that these patients were more likely to present at emergency departments and be admitted than intervention patients. The decision to offer telehealth to control patients at the end of the 12 month period, while designed to reduce attrition rates, could have increased anxiety if it encouraged a stronger sense that they were being denied access to support that could be beneficial. A final possible explanation for the increase is that biases could have resulted during patient selection, in which only 35% of patients agreed to the initial “light touch” visit. There could have been a propensity to select controls with a higher risk of short term admission and intervention patients with a lower risk. However, observed differences were limited between intervention and control groups, and case mix adjustment was applied.
This particular analysis is one of a series planned by the Whole System Demonstrator Evaluation Team, and was limited to comparisons of inpatient, outpatient, and emergency department hospital use and mortality. We did not consider the full range of health and social care services, and intervention and control groups could have had differences in the use of primary care, community services, or social care. Telehealth could also have had different effects according to long term condition or other patient characteristics. Although we assigned unit costs to more than 95% of inpatient activity, we did not consider costs for some elements of hospital care, including mental health and critical care, which had no national tariff. Use of national tariffs meant that the analysis was relevant to decisions made by commissioners of care, who align with hospital reimbursement guidance, but the economic costs of providing care will differ from the notional costs shown here, and there are regional differences in the costs of providing care.
Although service use could have resource implications, it does not necessarily correlate with health status. Assessment of the effect of interventions should be multidimensional, and important differences could also exist in health outcomes, cost effectiveness, and patient perceptions. These outcomes are explored in the related theme analyses. Telehealth could also have had knock-on effects in non-study patient groups, by freeing up clinical time and resources to care for non-study patients, or by diverting the attention of community teams towards those patients on the trial.
The study used administrative datasets. As a result, person level data were available for 98% of participants. Although these datasets avoided problems of non-response, the quality of data was not directly under the research team’s control. Patients tend to underestimate resource use compared with healthcare providers, but several studies have pointed out potential problems with using administrative data, such as limited insight into the quality and appropriateness of care.
Selection bias is recognised as a risk in cluster randomised trials, in which systematic differences can occur between intervention and control groups at both the cluster and individual level. At the individual level, if the trial recruiters had foreknowledge of the allocation group (as was often the case here), bias can result through the recruitment of different types of participant into the two groups. We designed this trial to minimise the possibility of bias within the context of a complex community based intervention. An independent team randomised allocations of practices and a minimisation algorithm aimed to ensure that intervention and control practices were similar in terms of practice size, disease prevalence, and other characteristics. At the individual level, we found no large differences in the characteristics of control and intervention participants at baseline. However, we saw group differences in the median number of participants per general practice (8.5 for telehealth v 12 for controls). Case mix adjustment controlled for observed differences between intervention and control groups.
We based this analysis on an intention to treat method, which compares patients according to their assigned intervention or control group. Although some patients did not receive their allocated interventions, these numbers were small. A substantial proportion of the intervention group could have stopped using telehealth before the end of the 12 months. This study had conservative estimates because, in other telehealth applications, equipment might be removed from patients who stop using it. The trial design aimed to minimise differential rates of attrition between intervention and control groups, by ensuring that all practices were allocated to receive a telemonitoring intervention (telehealth or telecare), and that control participants were offered a telemonitoring intervention at the end of the trial period, if they were still eligible.
The effect of telehealth should be considered as just one element within the health system in which it was used. All participating practices and patients in the study could have benefited from the wider service redesign associated with these trials, including those assigned to usual care. Therefore, the study assessed the added value of telehealth over and above the effects of this wider service redesign.
The study aimed to review a broad class of telehealth devices and not to compare specific devices and monitoring systems. Our results reflect specific models of the introduction of telehealth, based on a series of decisions by local teams. There were differences in the interventions offered by the three sites. Although this plurality might be seen as problematic for the purposes of replicating and linking specific aspects of the interventions to likely changes in hospital use, in some ways it is the merit of a pragmatic trial. Other sites introducing telehealth will make choices driven by local contexts in the same way as did the sites in the present study, so the ability to reflect real life applications of telehealth will add generalisability to the study findings. However, the sites were specifically chosen for their innovations in these areas of care, and conclusions about the effectiveness of telehealth might depend on the environment in which it is used.
Assessment of the effectiveness of telehealth is usually based on assimilating evidence from several small trials, which could make findings difficult to generalise. By contrast, with over 3100 participants, this study is one of the largest randomised trials of telehealth done so far. The focus on combining three disease groups is novel and allowed us to examine the overall effect of telehealth across populations with chronic disease. However, telehealth could have varying effects in different groups. Other studies have typically examined condition groups separately; thus, comparisons between studies are not straightforward.
Paré and colleagues reviewed 65 empirical studies across four conditions and distinguished the effects of telehealth between different conditions. They suggested that effects on a range of measures (for example, reduced visits to emergency departments, hospital admissions, and average length of hospital stay) were more consistent in pulmonary and cardiac disorders than in diabetes and hypertension.
A systematic review of studies for heart failure reported that telemonitoring reduced all cause mortality, whereas both telemonitoring and structured telephone support reduced admissions for heart failure. However, findings were based on generalising a large number of studies with a mean sample size of 330. A study showed that a telehealth intervention that included portable devices significantly reduced costs and admissions for people with heart failure. This was based on a sample size of 460, although the study was adequately powered for the larger effect size it assumed. A more recent study of 1653 patients with heart failure found no significant effect on hospital use or mortality. In relation to the management of chronic obstructive pulmonary disease, several studies showed lower rates of emergency admissions for patients receiving home monitoring plus telephone support, although one review noted that mortality rates were greater in patients receiving telephone support than those receiving usual care. Evaluations of telehealth interventions for people with diabetes have focused on the achievement of a clinical outcome in terms of glycaemic control, with some reported success.
There is also a question about the effect of interventions that combine telemonitoring with educational and motivational tools, such as those we studied, compared with interventions consisting of telemonitoring only. Our study was not designed to answer this question. In a study by Domingo and colleagues of patients with heart failure receiving multidisciplinary care, those who used motivational support tools combined with telemonitoring of weight, blood pressure, and heart rate, thought that their quality of life had improved. These patients also spent less time in hospital after the intervention than before, although before and after comparisons can be vulnerable to regression to the mean. Researchers randomised patients to groups with and without the telemonitoring element and found no significant group differences.
Our results suggest that telehealth helped patients to avoid the need for emergency hospital care. The mechanism for this is not yet clear. Telehealth could help patients manage their conditions better and therefore reduce the incidence of acute exacerbations that need emergency admissions. Telehealth could also change people’s perception of when they need to seek additional support, as well as professionals’ decisions about whether to refer or admit patients. Further analyses will provide insights into the mechanisms by which telehealth can lead to reductions in admission rates.
The reduced mortality observed in the intervention group will be an important motivator to invest in these interventions and similar technologies. Although the observed difference in emergency admissions associated with the intervention indicates some potential to reduce use of secondary care, the findings need to be tempered by the estimated scale of the difference in notional hospital cost savings for commissioners of care and the cost of the intervention. Furthermore, the increases seen in emergency admissions among control participants suggest that the trial recruitment processes had an impact. The effect on quality of life must also be considered as part of a broader cost effectiveness analysis. For commissioners of care services, there are questions about whether any reduction in hospital use for patients receiving telehealth translates to an overall change at the organisational level. Any bed days released as a result could be filled with non-study patients rather than released as cash savings. In turn, this could have meant that health benefits accrued to non-study patients, which were not taken into account here.
The observation of a group effect between intervention and controls could mask differences by subgroups. For local practitioners, it is important to assess whether benefits of telehealth are greater in particular patient types, to inform decisions about prioritising the intervention in specific patient groups. For example, Maclean and colleagues observed that telehealth interventions probably did not result in clinically relevant improvements in health outcomes in patients with relatively mild asthma, but could have a role in patients with more severe disease who are at high risk of admission. The current study was not designed to answer these specific questions.
The effect of telehealth could be intricately linked to wider issues about how health systems operate. It is unclear whether effects are attributable to the technology itself or attributable to how it is implemented, and telehealth could be disruptive because it requires some professional groups to work in different ways.
This analysis is one part of the complete evaluation, and the Whole System Demonstrator trial in its entirety will allow a wider discussion of issues around the effects on cost effectiveness, quality of life, and patients’ and carers’ experiences as well as changes at the organisational level.
Discussion
Principal Findings of the Study
Among a set of patients with chronic obstructive pulmonary disease, diabetes, or heart failure, this study has shown that a smaller proportion of telehealth users than controls were admitted to hospital during a 12 month follow-up. This effect remained significant after adjusting for baseline characteristics and for a predictive risk score. However, the magnitude of the group difference in admission proportion was relatively small (10.8%, 95% confidence interval 3.7% to 18.1%), and smaller than the size that the planned study design was able to detect (17.5%), raising questions about the clinical relevance of the results. The significance of some of the effects reflected the increased power of the study, owing to the higher than assumed baseline level of admissions and to the larger number of small practices (even though the intracluster correlation coefficient was higher than assumed).
Intervention patients were significantly less likely to die within 12 months than controls. We also observed small differences in the mean number of emergency admissions per head between the intervention and control groups (crude rate 0.54 v 0.68; difference 0.14). These changes were significant in the unadjusted comparisons and when we adjusted for a predictive risk score, but not when we adjusted for baseline characteristics. Hospital bed days were significantly lower among intervention patients than controls, which reflected the reduced admission proportion overall.
For the other measures of hospital use (including the number of elective admissions, outpatient attendances, and emergency department visits), group differences were not significant in general. Crude differences in notional hospital costs to commissioners of care were also not significant and were relatively small (£188 per head over 12 months), especially compared with the potentially high costs of these types of telehealth intervention, which we did not take into account. In view of our results showing confidence intervals crossing the line of no difference, we cannot conclude that telehealth reduces secondary care costs over 12 months. A formal cost effectiveness analysis of the Whole System Demonstrator intervention has been undertaken on a subset of participants, using self reported data for hospital use and other services and taking into account the intervention cost, compared with health related quality of life and other outcomes.
Differences in hospital use were at their most marked at the start of the trial, when we observed a distinct increase in admissions for the control group. If we excluded activity from the first three months of the trial, differences in the admission proportion would not have been significant under any of the models. Therefore, this increase has implications for the interpretation. Trial recruitment processes may have led indirectly to changes in service use for control patients; however, the same processes might also have affected intervention patients in the absence of telehealth. In this case, differences in admissions can be attributed to telehealth, but with the limitation that the trial could have affected the context for the delivery of care for both trial groups.
One explanation for the increase in emergency admissions observed for the control group is that professionals may have identified additional health problems and unmet needs during the recruitment process and could have decided to intervene with control patients not allocated to receive telehealth. This explanation assumes that, if professionals found additional relevant health problems among the intervention group, they were content to manage these in a community setting, with the support of the remote monitoring.
The trial recruitment process might have also raised patients’ awareness of their health conditions. Anxiety could have increased as a result of being allocated to the control group, to the extent that these patients were more likely to present at emergency departments and be admitted than intervention patients. The decision to offer telehealth to control patients at the end of the 12 month period, while designed to reduce attrition rates, could have increased anxiety if it encouraged a stronger sense that they were being denied access to support that could be beneficial. A final possible explanation for the increase is that biases could have resulted during patient selection, in which only 35% of patients agreed to the initial “light touch” visit. There could have been a propensity to select controls with a higher risk of short term admission and intervention patients with a lower risk. However, observed differences were limited between intervention and control groups, and case mix adjustment was applied.
Strengths and Weaknesses of the Study
This particular analysis is one of a series planned by the Whole System Demonstrator Evaluation Team, and was limited to comparisons of inpatient, outpatient, and emergency department hospital use and mortality. We did not consider the full range of health and social care services, and intervention and control groups could have had differences in the use of primary care, community services, or social care. Telehealth could also have had different effects according to long term condition or other patient characteristics. Although we assigned unit costs to more than 95% of inpatient activity, we did not consider costs for some elements of hospital care, including mental health and critical care, which had no national tariff. Use of national tariffs meant that the analysis was relevant to decisions made by commissioners of care, who align with hospital reimbursement guidance, but the economic costs of providing care will differ from the notional costs shown here, and there are regional differences in the costs of providing care.
Although service use could have resource implications, it does not necessarily correlate with health status. Assessment of the effect of interventions should be multidimensional, and important differences could also exist in health outcomes, cost effectiveness, and patient perceptions. These outcomes are explored in the related theme analyses. Telehealth could also have had knock-on effects in non-study patient groups, by freeing up clinical time and resources to care for non-study patients, or by diverting the attention of community teams towards those patients on the trial.
The study used administrative datasets. As a result, person level data were available for 98% of participants. Although these datasets avoided problems of non-response, the quality of data was not directly under the research team’s control. Patients tend to underestimate resource use compared with healthcare providers, but several studies have pointed out potential problems with using administrative data, such as limited insight into the quality and appropriateness of care.
Selection bias is recognised as a risk in cluster randomised trials, in which systematic differences can occur between intervention and control groups at both the cluster and individual level. At the individual level, if the trial recruiters had foreknowledge of the allocation group (as was often the case here), bias can result through the recruitment of different types of participant into the two groups. We designed this trial to minimise the possibility of bias within the context of a complex community based intervention. An independent team randomised allocations of practices and a minimisation algorithm aimed to ensure that intervention and control practices were similar in terms of practice size, disease prevalence, and other characteristics. At the individual level, we found no large differences in the characteristics of control and intervention participants at baseline. However, we saw group differences in the median number of participants per general practice (8.5 for telehealth v 12 for controls). Case mix adjustment controlled for observed differences between intervention and control groups.
We based this analysis on an intention to treat method, which compares patients according to their assigned intervention or control group. Although some patients did not receive their allocated interventions, these numbers were small. A substantial proportion of the intervention group could have stopped using telehealth before the end of the 12 months. This study had conservative estimates because, in other telehealth applications, equipment might be removed from patients who stop using it. The trial design aimed to minimise differential rates of attrition between intervention and control groups, by ensuring that all practices were allocated to receive a telemonitoring intervention (telehealth or telecare), and that control participants were offered a telemonitoring intervention at the end of the trial period, if they were still eligible.
The effect of telehealth should be considered as just one element within the health system in which it was used. All participating practices and patients in the study could have benefited from the wider service redesign associated with these trials, including those assigned to usual care. Therefore, the study assessed the added value of telehealth over and above the effects of this wider service redesign.
The study aimed to review a broad class of telehealth devices and not to compare specific devices and monitoring systems. Our results reflect specific models of the introduction of telehealth, based on a series of decisions by local teams. There were differences in the interventions offered by the three sites. Although this plurality might be seen as problematic for the purposes of replicating and linking specific aspects of the interventions to likely changes in hospital use, in some ways it is the merit of a pragmatic trial. Other sites introducing telehealth will make choices driven by local contexts in the same way as did the sites in the present study, so the ability to reflect real life applications of telehealth will add generalisability to the study findings. However, the sites were specifically chosen for their innovations in these areas of care, and conclusions about the effectiveness of telehealth might depend on the environment in which it is used.
Strengths and Weaknesses in Relation to Other Studies
Assessment of the effectiveness of telehealth is usually based on assimilating evidence from several small trials, which could make findings difficult to generalise. By contrast, with over 3100 participants, this study is one of the largest randomised trials of telehealth done so far. The focus on combining three disease groups is novel and allowed us to examine the overall effect of telehealth across populations with chronic disease. However, telehealth could have varying effects in different groups. Other studies have typically examined condition groups separately; thus, comparisons between studies are not straightforward.
Paré and colleagues reviewed 65 empirical studies across four conditions and distinguished the effects of telehealth between different conditions. They suggested that effects on a range of measures (for example, reduced visits to emergency departments, hospital admissions, and average length of hospital stay) were more consistent in pulmonary and cardiac disorders than in diabetes and hypertension.
A systematic review of studies for heart failure reported that telemonitoring reduced all cause mortality, whereas both telemonitoring and structured telephone support reduced admissions for heart failure. However, findings were based on generalising a large number of studies with a mean sample size of 330. A study showed that a telehealth intervention that included portable devices significantly reduced costs and admissions for people with heart failure. This was based on a sample size of 460, although the study was adequately powered for the larger effect size it assumed. A more recent study of 1653 patients with heart failure found no significant effect on hospital use or mortality. In relation to the management of chronic obstructive pulmonary disease, several studies showed lower rates of emergency admissions for patients receiving home monitoring plus telephone support, although one review noted that mortality rates were greater in patients receiving telephone support than those receiving usual care. Evaluations of telehealth interventions for people with diabetes have focused on the achievement of a clinical outcome in terms of glycaemic control, with some reported success.
There is also a question about the effect of interventions that combine telemonitoring with educational and motivational tools, such as those we studied, compared with interventions consisting of telemonitoring only. Our study was not designed to answer this question. In a study by Domingo and colleagues of patients with heart failure receiving multidisciplinary care, those who used motivational support tools combined with telemonitoring of weight, blood pressure, and heart rate, thought that their quality of life had improved. These patients also spent less time in hospital after the intervention than before, although before and after comparisons can be vulnerable to regression to the mean. Researchers randomised patients to groups with and without the telemonitoring element and found no significant group differences.
Possible Explanations and Implications for Clinicians and Policymakers and Other Researchers
Our results suggest that telehealth helped patients to avoid the need for emergency hospital care. The mechanism for this is not yet clear. Telehealth could help patients manage their conditions better and therefore reduce the incidence of acute exacerbations that need emergency admissions. Telehealth could also change people’s perception of when they need to seek additional support, as well as professionals’ decisions about whether to refer or admit patients. Further analyses will provide insights into the mechanisms by which telehealth can lead to reductions in admission rates.
The reduced mortality observed in the intervention group will be an important motivator to invest in these interventions and similar technologies. Although the observed difference in emergency admissions associated with the intervention indicates some potential to reduce use of secondary care, the findings need to be tempered by the estimated scale of the difference in notional hospital cost savings for commissioners of care and the cost of the intervention. Furthermore, the increases seen in emergency admissions among control participants suggest that the trial recruitment processes had an impact. The effect on quality of life must also be considered as part of a broader cost effectiveness analysis. For commissioners of care services, there are questions about whether any reduction in hospital use for patients receiving telehealth translates to an overall change at the organisational level. Any bed days released as a result could be filled with non-study patients rather than released as cash savings. In turn, this could have meant that health benefits accrued to non-study patients, which were not taken into account here.
The observation of a group effect between intervention and controls could mask differences by subgroups. For local practitioners, it is important to assess whether benefits of telehealth are greater in particular patient types, to inform decisions about prioritising the intervention in specific patient groups. For example, Maclean and colleagues observed that telehealth interventions probably did not result in clinically relevant improvements in health outcomes in patients with relatively mild asthma, but could have a role in patients with more severe disease who are at high risk of admission. The current study was not designed to answer these specific questions.
The effect of telehealth could be intricately linked to wider issues about how health systems operate. It is unclear whether effects are attributable to the technology itself or attributable to how it is implemented, and telehealth could be disruptive because it requires some professional groups to work in different ways.
This analysis is one part of the complete evaluation, and the Whole System Demonstrator trial in its entirety will allow a wider discussion of issues around the effects on cost effectiveness, quality of life, and patients’ and carers’ experiences as well as changes at the organisational level.
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