To our knowledge, this is the first trial of a PCHMS to have shown a statistically significant increase in vaccination rates and health service utilization by consumers. The intervention's effect size (an absolute difference of 6.7% in influenza vaccination rate between PCHMS and waitlist control) is similar to the 5%–20% increase in vaccination rates reported for recall and reminder systems targeted at clinicians.25
It does appear that PCHMS can significantly increase consumer participation in preventive health activities, such as influenza vaccination.
We have previously tested the suitability of our PCHMS to support women undergoing fertility treatment. In that clinical setting, patients were highly motivated to engage in their care, and required complex scheduling of procedures and tests over a relatively short period (eg, see Stone et al15
). In contrast, the present study focuses on a preventive clinical setting which has a much lower intensity of activity, where building motivation for engagement with clinical services is one of the challenges. Demonstrating the effectiveness of the PCHMS across these diverse settings and patient groups provides initial evidence that this approach is indeed generalizable and of broad utility.
A previous study using a web-based PHR incorporating reminders, weekly influenza risk maps, and provision of respiratory illness advice, did not report significant improvements in vaccination rates.9
That study did not include online booking, or a consumer specific health service engagement protocol, which are possible reasons why the present intervention was more effective. Online consumer systems in practice offer a ‘bundle’ of e-health services and features well beyond the PHR, and we now need to focus our efforts to identify the right mix of these features that motivate consumer behavior change in different clinical settings.
One approach to bundling e-health services and features is to recognize the ability of a PCHMS to overcome the barriers consumers experience when accessing primary care services.34
This study provides evidence that a PCHMS which (i) addresses knowledge barriers (eg, via disease and task specific service description) and (ii) reduces system barriers (eg, embedding action tools like online booking within these service descriptions) is a promising approach to engage consumers in health service utilization for preventive health activities.
A one-size-fits-all strategy is thus unlikely to result in sustained change in consumer behavior.12
PCHMS service bundles will need to accommodate varying levels of consumer health literacy, motivation, and willingness to engage in preventive behaviors. For example, effective interventions to influence health behaviors in young people will undoubtedly require the use of social networks, social influences, viral communication, and social recommendations,35
given the ubiquitous nature of such channels in this population.
In this study, the relative simplicity of the PCHMS design, along with the use of immediately actionable tools
(such as the ‘Book now’ button), embedded within consumer specific content (ie, the journey), may account for the improved uptake of influenza vaccination and increased visits to the health service. The Health Belief Model, in particular, discusses how cues in an environment can motivate action.37
Developed in the early 1950s, the HBM remains one of the most widely used and cited conceptual frameworks for understanding why individuals did or did not engage in a variety of health-related actions.37
The HBM uses perceived susceptibility, perceived severity, perceived benefits and perceived barriers to explain and predict individuals' acceptance of health and medical care recommendations.37
With our web-based PCHMS, by providing informational cues that are directly linked to action, we may have overcome some of the perceived barriers that participants experience when deciding to obtain an influenza vaccine, and thus increased the likelihood that consumer intention translated to action.
There are several explanations for the dose–response effect we observed between increased PCHMS access and increased health service utilization. There are two broad causal interpretations—either that increased exposure leads to increased service utilization, or second that those who are high service utilizers are also likely to use the PCHMS more frequently, reflected in higher motivation for engagement.38
If increased exposure indeed leads to higher service utilization, then the combination of increased information through content in journeys and the use of actionable tools
in the PCHMS, are likely explanations for this effect. However, the dose–response effect between use of web interventions and health behaviors is complex.38
Having more features on a website does not necessarily increase participant engagement, and may sometimes create adherence challenges as more features require more effort from participants.39
Research is now at the stage where we have evidence that web interventions can indeed trigger health behavior changes, but the empirical and theoretical basis for e-health service design is still weak, especially concerning which ‘bundles’ of website features would lead to behavior change. Future studies should employ more theoretical approaches to designing e-health services, recognizing that uptake and outcome changes may be highly dependent on population, disease group, and socio-economic factors.
Not much is currently known about the cost effectiveness of PCHMS as preventive health strategies. Using results from this trial to calculate the underlying return on investment in PCHMS to improve vaccination rates is difficult for a number of reasons. First, the return on investment depends upon an assessment of the costs of deploying and maintaining a system in a working clinical setting, and the current trial uses a research grade system rather than one designed for routine use. Second, a system like this is likely to concurrently support multiple clinical conditions and tasks across a variety of settings, meaning that the cost of system operation would be distributed across all these parallel uses. We would not anticipate a system like ours would be used exclusively to improve vaccination rates.
The study relied on self-reports by participants, which has been shown to be acceptably accurate in studies of days of absence,40
influenza symptoms, and vaccination status for diverse patient cohorts.41–46
We minimized the risk of recall bias by conducting short 1-min monthly follow-up surveys during the first week of each month. In addition, we validated influenza vaccination and health service utilization rates by matching self-reports from study participants with their medical records at the UHS (supplementary online appendix table C).
In 2010 the uptake of vaccination and the number of confirmed influenza diagnoses were lower than expected due to a relatively mild winter in Australia,47
and the controversies around the adverse effects of the 2010 seasonal influenza vaccine. It is possible that in a more severe season of influenza, the impact of PCHMS on vaccination rates and health service utilization could be higher than observed in our study. Integrating PCHMS into routine health service delivery systems does appear to be an effective mechanism for enhancing consumer engagement in preventive health measures.