India’s maternal health is gradually improving with the use of mobiles and ML
The widespread availability of mobiles has enabled non-profits to deliver critical health information to their beneficiaries promptly. While advanced applications on smartphones allow for richer multimedia content and two-way communication between beneficiaries and health coaches, simpler text and voice messaging services can be effective in disseminating information to large communities, particularly those that are underserved with limited access to information and smartphones. ARMMAN1, one non-profit doing just this, is based in India with the mission of improving maternal and child health outcomes in underserved communities.
One of the programs run by them is mMitra, which employs automated voice messaging to deliver timely preventive care information to expecting and new mothers during pregnancy and until one year after birth. These messages are tailored according to the gestational age of the beneficiary. Regular listenership to these messages has been shown to have a high correlation with improved behavioral and health outcomes, such as a 17% increase in infants with tripled birth weight at end of the year and a 36% increase in women knowing the importance of taking iron tablets. However, a key challenge ARMMAN faced was that about 40% of women gradually stopped engaging with the program. While it’s possible to mitigate this with live service calls to women to explain the advantage of listening to the messages, it is infeasible to call all the low listeners in the program because of limited support staff, this highlights the importance of effectively prioritizing who receives such service calls.
This resource optimization problem is modeled using restless multi-armed bandits (RMABs), which have been well studied for application to such problems in a myriad of domains, including healthcare. An RMAB consists of n arms where each arm (representing a beneficiary) is associated with a two-state Markov decision process (MDP). Each MDP is modeled as a two-state (good or bad state, where the good state corresponds to high listenership in the previous week), two-action (corresponding to whether the beneficiary was chosen to receive a service call or not) problem. Further, each MDP has an associated reward function (i.e., the reward accumulated at a given state and action) and a transition function indicating the probability of moving from one state to the next under a given action, under the Markov condition that the next state depends only on the previous state and the action taken on that arm in that time step. The term restless indicates that all arms can change state irrespective of the action.
Source: analyticsinsight.net
