Challenges in Rolling out mHealth & Access-to-Medicine projects

This article is the first in a series on Change Management in mHealth and Access-to-Medicine projects. This one addresses the challenges facing the adoption of these types of projects.

Rolling out new technology for workers is a challenge at the best of times, but it is even more-so when dealing with project in low-resource settings, such as with mHealth programs or Access-to-Medicine projects.

There are a number of reasons for this. The first is human nature and is worth explaining in some depth. This point was best explained by the prominent American sociologist and theorist Everett Rogers in his 1962 book Diffusion of Innovations. In that book which was based on research, he plotted the population on a bell-curve and proposed that people could be divided into five categories:

  • Innovators: Innovators are willing to take risks, have the highest social status, have financial liquidity, are social and have closest contact to scientific sources and interaction with other innovators. They have a high risk tolerance allows them to adopt technologies that may ultimately fail. Financial resources help absorb these failures.
  • Early Adopters: These individuals have the highest degree of opinion leadership among the adopter categories. Early adopters have a higher social status, financial liquidity, advanced education and are more socially forward than late adopters. They are more discreet in adoption choices than innovators. They use judicious choice of adoption to help them maintain a central communication position.
  • Early Majority: They adopt an innovation after a varying degree of time that is significantly longer than the innovators and early adopters. Early Majority have above average social status, contact with early adopters and seldom hold positions of opinion leadership in a system.
  • Late Majority: They adopt an innovation after the average participant. These individuals approach an innovation with a high degree of skepticism and after the majority of society has adopted the innovation. Late Majority are typically skeptical about an innovation, have below average social status, little financial liquidity, in contact with others in late majority and early majority and little opinion leadership.
  • Laggards: They are the last to adopt an innovation. Unlike some of the previous categories, individuals in this category show little to no opinion leadership. These individuals typically have an aversion to change-agents. Laggards typically tend to be focused on "traditions", lowest social status, lowest financial liquidity, oldest among adopters, and in contact with only family and close friends.

If the human population will fit into the bell-curve more-or-less as depicted below, it is likely that some groups will skew towards one side or other of the curve. As you plan a program, it can be worth asking yourself where the Health Care Workers (HCWs) or Community Care Workers (CCWs) on your project will fit, and if there is a skew one way or the other. It is certainly possible that field workers who are based in low-resource communities might skew slightly towards the right-hand side of the curve. There are a number of reasons for this, notably that the younger more innovative people who are most open to new technologies have a tendency to move to the big cities rather than stay in sometimes impoverished rural communities. In any case, even without a skew, each project will have to contend with the very human and normal indifference, resistance and sometimes open hostility to change that we find in any population.

 

There are several other reasons why rolling out mHealth and Access-to-Medicine projects can be a challenge, compared to say rolling out a new CRM app to sales people in a large corporation. These include:

  • Poor mobile infrastructure: mHealth itself was developed as a response to the limited infrastructure in the developing world, however it does have limits. While there are now over 420 million unique mobile subscribers in Sub-Sahara Africa, many parts of the continent still have either poor or patchy mobile coverage, so those subscribers can’t always connect even to send and receive SMS. It is also much harder and more expensive to get access to two-way SMS in to USSD (that we can connect to our servers) in most African countries.
  • Poor internet infrastructure: Internet connectivity is increasing steadily and is expected to grow from approximately 33% of total mobile users in Sub-Sahara Africa in 2016 to 60% by 2020. Smartphones are also becoming increasingly available, driven by low-cost brands such as China’s Techno and India’s Gionee. However all the trends are going in the right direction and there are some major initiatives in the planning, which should they come to fruition, will eventually bring a step-change in the quality of the developing world’s internet infrastructure (for example SpaceX is working on plans to build a low-orbit satellite constellation to bring internet to less populated areas).
  • Poor transport infrastructure: The costs of travel for field workers makes it difficult to bring people together for training. In fact its not all just a problem of infrastructure – Africa is huge and distances between the provincial towns and the capital city can be great indeed!
  • Education: some workers in the poorer communities in Africa are less-well educated which can causes a challenge for them to use mobile devices or to adopt to using an mHealth app.
  • Crime: higher crime is correlated with poverty which creates the situation that some field staff are afraid to be seen with smartphones in the very low-resource communities where they are most needed.

These are just some of the many challenges facing mHealth and Access-to-Medicine projects in the developing world. The next article in the series will look at ways to address some of these challenges.