Utilizing ML to Increase Engagement with a Maternal and Youngster Well being Program in India

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The widespread availability of cell phones has enabled non-profits to ship essential well being data to their beneficiaries in a well timed method. Whereas superior functions on smartphones enable for richer multimedia content material and two-way communication between beneficiaries and well being coaches, less complicated textual content and voice messaging companies might be efficient in disseminating data to giant communities, notably these which are underserved with restricted entry to data and smartphones. ARMMAN1, one non-profit doing simply this, relies in India with the mission of enhancing maternal and baby well being outcomes in underserved communities.

Overview of ARMMAN

One of many packages run by them is mMitra, which employs automated voice messaging to ship well timed preventive care data to anticipating and new moms throughout being pregnant and till one 12 months after beginning. These messages are tailor-made in line with the gestational age of the beneficiary. Common listenership to those messages has been proven to have a excessive correlation with improved behavioral and well being outcomes, equivalent to a 17% improve in infants with tripled beginning weight at finish of 12 months and a 36% improve in ladies realizing the significance of taking iron tablets.

Nonetheless, a key problem ARMMAN confronted was that about 40% of ladies step by step stopped partaking with this system. Whereas it’s attainable to mitigate this with reside service calls to ladies to clarify the benefit of listening to the messages, it’s infeasible to name all of the low listeners in this system due to restricted assist employees — this highlights the significance of successfully prioritizing who receives such service calls.

In “Area Examine in Deploying Stressed Multi-Armed Bandits: Helping Non-Income in Bettering Maternal and Youngster Well being”, printed in AAAI 2022, we describe an ML-based answer that makes use of historic information from the NGO to foretell which beneficiaries will profit most from service calls. We deal with the challenges that include a large-scale actual world deployment of such a system and present the usefulness of deploying this mannequin in an actual research involving over 23,000 contributors. The mannequin confirmed a rise in listenership of 30% in comparison with the present customary of care group.

Background
We mannequin this useful resource optimization drawback utilizing stressed multi-armed bandits (RMABs), which have been effectively studied for utility to such issues in a myriad of domains, together with healthcare. An RMAB consists of n arms the place every arm (representing a beneficiary) is related to a two-state Markov choice course of (MDP). Every MDP is modeled as a two-state (good or unhealthy state, the place the great state corresponds to excessive listenership within the earlier week), two-action (corresponding as to whether the beneficiary was chosen to obtain a service name or not) drawback. Additional, every MDP has an related reward perform (i.e., the reward accrued at a given state and motion) and a transition perform indicating the chance of shifting from one state to the subsequent below a given motion, below the Markov situation that the subsequent state relies upon solely on the earlier state and the motion taken on that arm in that point step. The time period stressed signifies that each one arms can change state no matter the motion.

State of a beneficiary could transition from good (excessive engagement) to unhealthy (low engagement) with instance passive and energetic transition chances proven within the transition matrix.

Mannequin Growth
Lastly, the RMAB drawback is modeled such that at any time step, given n whole arms, which okay arms needs to be acted on (i.e., chosen to obtain a service name), to maximise reward (engagement with this system).

The chance of transitioning from one state to a different with (energetic chance) or with out (passive chance) receiving a service name are due to this fact the underlying mannequin parameters which are essential to fixing the above optimization. To estimate these parameters, we use the demographic information of the beneficiaries collected at time of enrolment by the NGO, equivalent to age, earnings, schooling, variety of kids, and so forth., in addition to previous listenership information, all in-line with the NGO’s information privateness requirements (extra under).

Nonetheless, the restricted quantity of service calls limits the information akin to receiving a service name. To mitigate this, we use clustering strategies to study from the collective observations of beneficiaries inside a cluster and allow overcoming the problem of restricted samples per particular person beneficiary.

Specifically, we carry out clustering on listenership behaviors, after which compute a mapping from the demographic options to every cluster.

Clustering on previous listenership information reveals clusters with beneficiaries that behave equally. We then infer a mapping from demographic options to clusters.

This mapping is beneficial as a result of when a brand new beneficiary is enrolled, we solely have entry to their demographic data and haven’t any data of their listenership patterns, since they haven’t had an opportunity to pay attention but. Utilizing the mapping, we will infer transition chances for any new beneficiary that enrolls into the system.

We used a number of qualitative and quantitative metrics to deduce the optimum set of of clusters and explored completely different mixtures of coaching information (demographic options solely, options plus passive chances, options plus all chances, passive chances solely) to attain essentially the most significant clusters, which are consultant of the underlying information distribution and have a low variance in particular person cluster sizes.

Comparability of passive transition chances obtained from completely different clustering strategies with variety of clusters s = 20 (crimson dots) and 40 (inexperienced dots), utilizing floor reality passive transition chances (blue dots). Clustering primarily based on options+passive chances (PPF) captures extra distinct beneficiary behaviors throughout the chance area.

Clustering has the added benefit of decreasing computational value for resource-limited NGOs, because the optimization must be solved at a cluster degree reasonably than a person degree. Lastly, fixing RMAB’s is understood to be P-space laborious, so we select to resolve the optimization utilizing the favored Whittle index method, which finally offers a rating of beneficiaries primarily based on their seemingly good thing about receiving a service name.

Outcomes
We evaluated the mannequin in an actual world research consisting of roughly 23,000 beneficiaries who had been divided into three teams: the present customary of care (CSOC) group, the “spherical robin” (RR) group, and the RMAB group. The beneficiaries within the CSOC group observe the unique customary of care, the place there aren’t any NGO initiated service calls. The RR group represents the state of affairs the place the NGO usually conducts service calls utilizing some systematic set order — the concept right here is to have an simply executable coverage that companies sufficient of a cross-section of beneficiaries and might be scaled up or down per week primarily based on out there sources (that is the method utilized by the NGO on this specific case, however the method could differ for various NGOs). The RMAB group receives service calls as predicted by the RMAB mannequin. All of the beneficiaries throughout the three teams proceed to obtain the automated voice messages unbiased of the service calls.

Distributions of clusters picked for service calls by RMAB and RR in week 1 (left) and a couple of (proper) are considerably completely different. RMAB could be very strategic in choosing only some clusters with a promising chance of success (blue is excessive and crimson is low), RR shows no such strategic choice.

On the finish of seven weeks, RMAB-based service calls resulted within the highest (and statistically vital) discount in cumulative engagement drops (32%) in comparison with the CSOC group.

The plot exhibits cumulative engagement drops prevented in comparison with the management group.
   RMAB vs CSOC      RR vs CSOC      RMAB vs RR   
% discount in cumulative engagement drops   32.0%5.2%28.3%
p-value0.0440.7400.098

Moral Concerns
An ethics board on the NGO reviewed the research. We took vital measures to make sure participant consent is known and recorded in a language of the neighborhood’s selection at every stage of this system. Information stewardship resides within the fingers of the NGO, and solely the NGO is allowed to share information. The code will quickly be out there publicly. The pipeline solely makes use of anonymized information and no personally identifiable data (PII) is made out there to the fashions. Delicate information, equivalent to caste, faith, and so forth., aren’t collected by ARMMAN for mMitra. Due to this fact, in pursuit of guaranteeing equity of the mannequin, we labored with public well being and discipline specialists to make sure different indicators of socioeconomic standing had been measured and adequately evaluated as proven under.

Distribution of highest schooling obtained (high) and month-to-month household earnings in Indian Rupees (backside) throughout a cohort that obtained service calls in comparison with the entire inhabitants.

The proportion of beneficiaries that obtained a reside service name inside every earnings bracket moderately matches the proportion within the general inhabitants. Nonetheless, variations are noticed in decrease earnings classes, the place the RMAB mannequin favors beneficiaries with decrease earnings and beneficiaries with no formal schooling. Lastly, area specialists at ARMMAN have been deeply concerned within the improvement and testing of this method and have supplied steady enter and oversight in information interpretation, information consumption, and mannequin design.

Conclusions
After thorough testing, the NGO has presently deployed this method for scheduling of service calls on a weekly foundation. We’re hopeful that this can pave the way in which for extra deployments of ML algorithms for social impression in partnerships with non-profits in service of populations which have up to now benefited much less from ML. This work was additionally featured in Google for India 2021.

Acknowledgements
This work is a part of our AI for Social Good efforts and was led by Google Analysis, India. Due to all our collaborators at ARMMAN, Google Analysis India, Google.org, and College Relations: Aparna Hegde, Neha Madhiwalla, Suresh Chaudhary, Aditya Mate, Lovish Madaan, Shresth Verma, Gargi Singh, Divy Thakkar.


1ARMMAN runs a number of packages to supply preventive care data to ladies via being pregnant and infancy enabling them to hunt care, in addition to packages to coach and assist well being staff for well timed detection and administration of high-risk situations. 

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