Where there are no doctors, the proliferation of mobile technology offers to alleviate to some degree the deficit of expertise.
As the unit price of Android phones plummets, we can, for the first time, put decision support systems in the hands of willing but poorly-trained health workers.
We can help them do their job more thoroughly and more reliably, with better health outcomes for their clients.
In substantial parts of the world, particularly rural parts, there are, for all practical purposes, no doctors.
But the potential is greater still: use of decision support apps generates data as a by-product which gets synchroized to a server. We can then present aggregations of these data and provide vital information enabling supervisors and health system managers to make informed decisions.
It is important to note that the technology we are developing and deploying therefore are to be regarded as systems as a whole, of which the apps in use by the health worker is just one (very important) part.
In a remote village in rural Malawi, a Community Health Worker, using an interactive advice-giving application on a low-cost smartphone, gently examines a sick baby to determine whether he can
provide some basic medicine and advice to the mother, or whether the child is so ill that mother needs to take the child to the
He enters information about the symptoms he observes, along with what the mother tells him about the child, and the application advises about the child's condition, and the most appropriate treatment.
The problem we are addressing is one of higher mortality rates concentrated in poor countries.
These higher rates of mortality are identified as being primarily due not, for example, to outbreaks of Ebola, but to a systematic failure to identify and manage problems which are usually straightforward to treat, such as diahrroea.
Hence, the medical algorithms we need to put in the hands of health workers are specific decision-procedures
which deal with these issues.
Though there are a few standardized algorithms published by the WHO, implementation of any of these is always subject to modification due to country context.
D-tree International does a lot of work in looking at national guidelines, for example for ante-natal care, and formalizing algorithms which express their logic.
Well before the emergence of mobile digital technology, WHO's Integrated Management of Chilhood Illnesses (IMCI) strategy was developed with an algorithm at its heart.
ETAT is a simplified triage algorithm developed originally for use in hospitals, which however has been shown to be useful also in busy clinics.
CCM is effectively a simplified version of IMCI for use in the Community: an algorithm for determining the condition of a sick child, including treatment recommendations or referral.
This video illustrates this project in action in a busy clinic. A child is examined by a Community Health Worker interacting with a decision support app and is found to have worrying symptoms, making it a priority case. The CHW brings the mother and child forward in the queue to see the clinician.
Innovative use of technology can help in surprising ways. In a project funded by Vodafone Foundation, D-tree International and Things Prime, using mangologic, implemented a call-centre application which despatchers use to determine the severity of an obstetric emergency.
If needed, and if (as is often the case) no ambulance is available,
the despatcher may be prompted to call drivers in the locality (stored in the app database) to find a car to take the woman to hospital - an action which may well save her life.
Once the despatcher has confirmed that the journey has been completed, and the mangologic app has synchronized with the server, the driver is automatically paid an agreed fee via the Mpesa API
Vodafone Foundation present an overview of the project in this video:
A presentation explaining more detail concerning the project (MMH = Mobilising Maternal Health):