Ometeotl y el origen primario del universo

Although eHealth readiness increased over the course of the study, this change could not be explained by the interventions. Singly, the interventions were not effective at increasing eHealth readiness and reducing eHealth inequalities. Further analysis was conducted to explore any potential combined effect of the interventions, which also showed to be non-significant. As previous literature highlighted, individuals often experience multiple barriers to eHealth use. It was hypothesised that a combination of interventions, which addressed multiple barriers, would show the most significant improvements in eHealth readiness. Adopting this approach would suggest that a failure in one intervention could limit the effectiveness of the other interventions.

Arguably eHealth readiness could be considered in a ‘tri-pod model’. The use of eHealth relies on three separate but supporting dependencies, essentially the legs supporting the tri-pod. EHealth requires (i) the personal ability to use it, (ii) the presence of systems to provide it and (iii) the infrastructure available to support it. In this setting, a weakness in one of these areas would prevent the increase of eHealth readiness regardless of improvements in other areas. It could be argued that, despite the lack of conclusive evidence, Cornwall is more eHealth ready because of the superfast project. Noticeable changes have occurred which have been detailed above, and Cornwall is now structurally able

to adopt improved services moving forward. However due to the

ineffectiveness of personal and provider side interventions, this potential has yet to be realised. In the context of a tri-pod structure, superfast Cornwall has strengthened one leg but weaknesses remain in the other two supporting legs. Until these are also addressed no significant change is likely to be shown.

7.3 Health related travel

Health related travel measured in trips to GPs and hospitals, remained similar during the 18 months of the study. This was consistent across the all arms of the study, which indicated that the interventions separately and in combination did not significantly impact on health-related travel. As the interventions were not specifically targeted to reduce travel this result is not unexpected, travel was measured as a secondary explorative investigation. The study was not able to show a correlation between an individual’s eHealth readiness and the number of trips to taken to visit a healthcare facility. Without this link being identified it was highly unlikely that the interventions would have shown any significant impact on travel, had they altered eHealth readiness.

The predicted link between health-related travel and eHealth readiness was based on two main assumptions. Firstly, the argument that an individual who is more eHealth ready can avoid unnecessary travel to both hospitals and GP surgeries. This individual may use the internet regularly to look up health related topics and manage any pre-existing conditions, potentially avoiding travel for minor health concerns. They may use forums to discuss health topics with fellow patients, or contact a health professional directly via e-mail.

However, the opposite may also be true, one significant predictor of eHealth use is an individual’s health. People with pre-existing or long term conditions

have an increased interest in finding out more about their condition and

monitoring their own health through internet services [228]. Many responders, 11%, listed that “they had no need for health information” as a barrier to

eHealth, should these individuals become ill their use may increase but also their required travel to health institutes would increase. In this example, the correlation would show the opposite of the original assumption, in that increased eHealth use would be linked with an increase in travel.

A second assumption was that an individual who is more eHealth ready, in that they are computer literate and a regular user of existing eHealth services, would be more ready to adopt a new service such as video consultations. This

assumption has good face validity, and if true, would show a reduction in travel correlated with an increase of eHealth readiness. However, this is reliant on such a service being implemented and available to be used. As previously discussed, no such service was, or became, available within Cornwall during the 18-month study and was not provided by any of the interventions.

Therefore, the potential to measure this impact was not in place. There are certainly examples from existing research that show the potential for eHealth services to reduce car travel [33-38]. To use many of the discussed systems users would require a fast and reliable broadband infrastructure. Such an infrastructure is now in place within Cornwall, therefore it can be argued that Cornwall has much more potential to ‘realise’ these reductions. This is particularly important within Cornwall due to its rurality and strained transport links with healthcare services.

The first step of this process requires a link between eHealth readiness and travel to health services to be established. Currently this is purely ‘face value’ and has not been shown decisively in research. It is apparent that someone

with home internet has more possibilities to adopt a service which could reduce their health-related travel, compared to a non-using counterpart. However just because an individual uses and has access to the internet does not mean they will choose to use the internet for health [92]. It is reasonable to hypothesise that an individual’s eHealth readiness plays a role in their likelihood to adopt, the magnitude of this role needs to be assessed.

7.4 Choice of PERQ as outcome measure