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Effect of telehealth on use of secondary care and mortality: findings from the Whole System Demonstrator cluster randomised trial

 

BMJ Research 2012:

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.1 2 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).1 2 Yet, other studies have found either no effect or a negative effect.3 4 Furthermore, such evidence is usually based on assimilating findings from a number of small trials, which could be difficult to generalise,3 and with many of these trials not meeting robust evaluation standards.4 5 A recent review of self monitoring of blood glucose for people with diabetes concluded that there was a need for large controlled trials.6

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.7 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).8 Therefore, in the absence of a control group, whether observed reductions are the effect of the intervention is unclear.

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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.9 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,1011 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.  Read more

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