The Journal of mHealth Vol 1 Issue 3 (June 2014) - Page 37

Engaging Patients Using mHealth Engaging Patients Using mHealth Thought leadership article by Mark Brincat of Exco InTouch When it comes to developing effective mHealth solutions, it is essential to incorporate a range of proactive methods that engage with patients, and help to integrate the solution in to their accepted treatment process. Maintaining this level of engagement, is the only real way to ensure that a solution can help to deliver positive outcomes. In a series of discussions Mark Brincat of Exco InTouch considers a variety of methods that can help solutions achieve successful and sustained patient engagement. ADAPTABILITY There are no hard and fast rules as to how mHealth adaptability is defined, but I would suggest there are a number of levels that help us understand the extent to which a product might respond to individual patient needs. Perhaps the highest level to start with is personalisation. This is where a patient can put their own mark on a product and make it feel like something that is unique to them, perhaps including some elements of look and feel, or methods of interaction. Next, a patient could configure a product around their own specific needs, the product might have been already configured with a patient’s clinical specifics by a health care professional, but patients could additionally configure to their own needs and priorities. Once the patient is up and running, the product would be designed to respond to a series of patient inputs and events, some adhoc and some scheduled. It is here that products are really going to differentiate themselves. These pathways are numerous and complex and a product must be flexible enough to respond to different patient profiles, requirements and progressions. Multi-level solutions might include assessment, medications management, lifestyle management and informational content, all of which need to work and adapt in sync with each other. For example, if assessment and medication tracking show a change in condition, then there is a likely need to reflect changes in lifestyle management and informational support. Add on top of this a patient’s state of behaviour acceptance and you have a variable set of parameters that a product needs to interpret and respond to correctly. Looking at the technology involved, this would require a rules engine to manage a dynamic set of interactions. At its simplest level, we can think of a rules engine as software which uses rules that can be applied to data to produce outcomes. It is important that rules are only defined where events and outcomes are sufficiently understood. In the future, expert systems will take findings from the system and dynamically build them back into the rules engine, so that a solution learns and improves interventions based on real world data. For now, we will learn from anonymised patient data findings and refine or build new rules and interventions into the system. Companies will need to develop skills around the analysis of ‘big data’, identifying signals and patterns. This will be an exciting period in advancing our understanding of patient populations and disease anthropology. When patients stop taking medication or stop proactively managing their condition generally, their change of mind did not happen that morning, it started weeks or months ago with a series of smaller issues slowly stacking up. mHealth solutions need to focus on the multitude of issues and support patients with a range of interactions that work in sync with each other and respond to an individual patient’s real world experience. As this level of mHealth product takes hold in the market, it will be remarkable how quickly we can advance disease management. BUILDING BEHAVIOUR CHANGE INTO MHEALTH SOLUTIONS We have seen the successful application of behavioural change theories in patient services such that there is now good recognition of the ability of such methods to encourage patients to pro X