Effect of Telehealth on Use of Secondary Care and Mortality
Effect of Telehealth on Use of Secondary Care and Mortality
Objective To assess the effect of home based telehealth interventions on the use of secondary healthcare and mortality.
Design Pragmatic, multisite, cluster randomised trial comparing telehealth with usual care, using data from routine administrative datasets. General practice was the unit of randomisation. We allocated practices using a minimisation algorithm, and did analyses by intention to treat.
Setting 179 general practices in three areas in England.
Participants 3230 people with diabetes, chronic obstructive pulmonary disease, or heart failure recruited from practices between May 2008 and November 2009.
Interventions Telehealth involved remote exchange of data between patients and healthcare professionals as part of patients’ diagnosis and management. Usual care reflected the range of services available in the trial sites, excluding telehealth.
Main outcome measure Proportion of patients admitted to hospital during 12 month trial period.
Results Patient characteristics were similar at baseline. Compared with controls, the intervention group had a lower admission proportion within 12 month follow-up (odds ratio 0.82, 95% confidence interval 0.70 to 0.97, P=0.017). Mortality at 12 months was also lower for intervention patients than for controls (4.6% v 8.3%; odds ratio 0.54, 0.39 to 0.75, P<0.001). These differences in admissions and mortality remained significant after adjustment. The mean number of emergency admissions per head also differed between groups (crude rates, intervention 0.54 v control 0.68); these changes were significant in unadjusted comparisons (incidence rate ratio 0.81, 0.65 to 1.00, P=0.046) and after adjusting for a predictive risk score, but not after adjusting for baseline characteristics. Length of hospital stay was shorter for intervention patients than for controls (mean bed days per head 4.87 v 5.68; geometric mean difference -0.64 days, -1.14 to -0.10, P=0.023, which remained significant after adjustment). Observed differences in other forms of hospital use, including notional costs, were not significant in general. Differences in emergency admissions were greatest at the beginning of the trial, during which we observed a particularly large increase for the control group.
Conclusions Telehealth is associated with lower mortality and emergency admission rates. The reasons for the short term increases in admissions for the control group are not clear, but the trial recruitment processes could have had an effect.
Trial registration number International Standard Randomised Controlled Trial Number Register ISRCTN43002091.
Efforts worldwide are dealing with the increasing prevalence of chronic disease among an ageing population. The past decade has seen the growing use of telehealth as one possible approach to this problem. Telehealth involves the remote exchange of data between a patient and healthcare professionals as part of the patient’s diagnosis and healthcare management. Examples include the monitoring of blood pressure and blood glucose. Telehealth may help patients to better understand their health conditions by providing tools for self monitoring, encourage better self management of health problems, and alert professional support if devices signal a problem. As a consequence, telehealth promises better quality and more appropriate care for each patient, as well as more efficient use of healthcare resources by reducing the need for expensive hospital care.
Some research suggests that telehealth can have a positive effect on patients with chronic disease, such as improved patient experiences, clinical indicators, and quality of life, and reduced use of secondary healthcare (including emergency hospital admissions). Yet, other studies have found either no effect or a negative effect. Furthermore, such evidence is usually based on assimilating findings from a number of small trials, which could be difficult to generalise, and with many of these trials not meeting robust evaluation standards. A recent review of self monitoring of blood glucose for people with diabetes concluded that there was a need for large controlled trials.
Investment in telehealth has often been justified partly on the basis that its cost can be recovered by reductions in the use of secondary healthcare. However, assessing the scale of such an effect is complicated. Simple study designs comparing stages before and after an intervention can produce misleading results by not having a control group to compare with, particularly if the patients selected for intervention have a history of emergency care. Such patients have a tendency to show reductions in use of emergency care over time (that is, regression to the mean). Therefore, in the absence of a control group, whether observed reductions are the effect of the intervention is unclear.
Analyses of hospital use are further complicated by the fact that the distribution of admissions across patients can be highly skewed. Some high risk patients account for a very high proportion of admissions. Therefore, small differences in the risk profile of patients receiving the intervention can greatly affect observed outcomes in terms of hospital admission. Several predictive risk models have been developed that use information from a person’s health history to predict future hospital use, and can offer an opportunity for case mix adjustment. A further limitation on the size of previous evaluation studies has been the costs of obtaining information from patients, but it is now possible to extract information from operational administrative systems and use secure data linkage procedures to track resource use.
In 2006, the Department of Health in England published a white paper that included a focus on health and social care for people with long term needs. The strategy proposed a series of demonstrator pilots to drive whole systems redesign, supported by advanced assistive technologies. These technologies included telehealth, along with a system of remote, automatic, and passive monitoring for patients with social care needs (known as telecare). The result was the Whole System Demonstrator project, funded by the Department of Health, which tested the benefits of integrated care supported by telehealth and telecare in three sites in England (Cornwall, Kent, and Newham).
One of the project’s aims was to test the effect of telehealth if delivered at a larger scale than existing pilot schemes, which were often limited to fewer than 100 patients. The resulting trial was pragmatic in design, to recruit and randomise suitably large numbers of patients and assess the effect of a broad class of telehealth and telecare technologies in the context of routine delivery of care provided by the United Kingdom’s health service. The telehealth part of the study included people diagnosed with chronic obstructive pulmonary disease, heart failure, or diabetes. These conditions have high prevalence and associated healthcare costs. The evaluation covered several different dimensions. This article is one of five analyses, and reports on how telehealth affected the use of secondary healthcare and mortality. The other analyses will assess how telehealth affected quality of life and cost effectiveness, and explores the patient, professional, and organisation factors related to implementation.
Abstract and Introduction
Abstract
Objective To assess the effect of home based telehealth interventions on the use of secondary healthcare and mortality.
Design Pragmatic, multisite, cluster randomised trial comparing telehealth with usual care, using data from routine administrative datasets. General practice was the unit of randomisation. We allocated practices using a minimisation algorithm, and did analyses by intention to treat.
Setting 179 general practices in three areas in England.
Participants 3230 people with diabetes, chronic obstructive pulmonary disease, or heart failure recruited from practices between May 2008 and November 2009.
Interventions Telehealth involved remote exchange of data between patients and healthcare professionals as part of patients’ diagnosis and management. Usual care reflected the range of services available in the trial sites, excluding telehealth.
Main outcome measure Proportion of patients admitted to hospital during 12 month trial period.
Results Patient characteristics were similar at baseline. Compared with controls, the intervention group had a lower admission proportion within 12 month follow-up (odds ratio 0.82, 95% confidence interval 0.70 to 0.97, P=0.017). Mortality at 12 months was also lower for intervention patients than for controls (4.6% v 8.3%; odds ratio 0.54, 0.39 to 0.75, P<0.001). These differences in admissions and mortality remained significant after adjustment. The mean number of emergency admissions per head also differed between groups (crude rates, intervention 0.54 v control 0.68); these changes were significant in unadjusted comparisons (incidence rate ratio 0.81, 0.65 to 1.00, P=0.046) and after adjusting for a predictive risk score, but not after adjusting for baseline characteristics. Length of hospital stay was shorter for intervention patients than for controls (mean bed days per head 4.87 v 5.68; geometric mean difference -0.64 days, -1.14 to -0.10, P=0.023, which remained significant after adjustment). Observed differences in other forms of hospital use, including notional costs, were not significant in general. Differences in emergency admissions were greatest at the beginning of the trial, during which we observed a particularly large increase for the control group.
Conclusions Telehealth is associated with lower mortality and emergency admission rates. The reasons for the short term increases in admissions for the control group are not clear, but the trial recruitment processes could have had an effect.
Trial registration number International Standard Randomised Controlled Trial Number Register ISRCTN43002091.
Introduction
Efforts worldwide are dealing with the increasing prevalence of chronic disease among an ageing population. The past decade has seen the growing use of telehealth as one possible approach to this problem. Telehealth involves the remote exchange of data between a patient and healthcare professionals as part of the patient’s diagnosis and healthcare management. Examples include the monitoring of blood pressure and blood glucose. Telehealth may help patients to better understand their health conditions by providing tools for self monitoring, encourage better self management of health problems, and alert professional support if devices signal a problem. As a consequence, telehealth promises better quality and more appropriate care for each patient, as well as more efficient use of healthcare resources by reducing the need for expensive hospital care.
Some research suggests that telehealth can have a positive effect on patients with chronic disease, such as improved patient experiences, clinical indicators, and quality of life, and reduced use of secondary healthcare (including emergency hospital admissions). Yet, other studies have found either no effect or a negative effect. Furthermore, such evidence is usually based on assimilating findings from a number of small trials, which could be difficult to generalise, and with many of these trials not meeting robust evaluation standards. A recent review of self monitoring of blood glucose for people with diabetes concluded that there was a need for large controlled trials.
Investment in telehealth has often been justified partly on the basis that its cost can be recovered by reductions in the use of secondary healthcare. However, assessing the scale of such an effect is complicated. Simple study designs comparing stages before and after an intervention can produce misleading results by not having a control group to compare with, particularly if the patients selected for intervention have a history of emergency care. Such patients have a tendency to show reductions in use of emergency care over time (that is, regression to the mean). Therefore, in the absence of a control group, whether observed reductions are the effect of the intervention is unclear.
Analyses of hospital use are further complicated by the fact that the distribution of admissions across patients can be highly skewed. Some high risk patients account for a very high proportion of admissions. Therefore, small differences in the risk profile of patients receiving the intervention can greatly affect observed outcomes in terms of hospital admission. Several predictive risk models have been developed that use information from a person’s health history to predict future hospital use, and can offer an opportunity for case mix adjustment. A further limitation on the size of previous evaluation studies has been the costs of obtaining information from patients, but it is now possible to extract information from operational administrative systems and use secure data linkage procedures to track resource use.
In 2006, the Department of Health in England published a white paper that included a focus on health and social care for people with long term needs. The strategy proposed a series of demonstrator pilots to drive whole systems redesign, supported by advanced assistive technologies. These technologies included telehealth, along with a system of remote, automatic, and passive monitoring for patients with social care needs (known as telecare). The result was the Whole System Demonstrator project, funded by the Department of Health, which tested the benefits of integrated care supported by telehealth and telecare in three sites in England (Cornwall, Kent, and Newham).
One of the project’s aims was to test the effect of telehealth if delivered at a larger scale than existing pilot schemes, which were often limited to fewer than 100 patients. The resulting trial was pragmatic in design, to recruit and randomise suitably large numbers of patients and assess the effect of a broad class of telehealth and telecare technologies in the context of routine delivery of care provided by the United Kingdom’s health service. The telehealth part of the study included people diagnosed with chronic obstructive pulmonary disease, heart failure, or diabetes. These conditions have high prevalence and associated healthcare costs. The evaluation covered several different dimensions. This article is one of five analyses, and reports on how telehealth affected the use of secondary healthcare and mortality. The other analyses will assess how telehealth affected quality of life and cost effectiveness, and explores the patient, professional, and organisation factors related to implementation.