The widespread availability of cell phones has enabled non-profits to ship crucial well being info to their beneficiaries in a well timed method. While superior purposes 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 may be efficient in disseminating info to giant communities, significantly these which can be underserved with restricted entry to info and smartphones. ARMMAN1, one non-profit doing simply this, relies in India with the mission of bettering maternal and baby well being outcomes in underserved communities.
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| Overview of ARMMAN |
One of the applications run by them is mMitra, which employs automated voice messaging to ship well timed preventive care info to anticipating and new moms throughout being pregnant and till one yr after delivery. These messages are tailor-made in response to the gestational age of the beneficiary. Regular 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 delivery weight at finish of yr and a 36% improve in ladies figuring out the significance of taking iron tablets.
However, a key problem ARMMAN confronted was that about 40% of girls progressively stopped partaking with this system. While it’s attainable to mitigate this with stay 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 help employees — this highlights the significance of successfully prioritizing who receives such service calls.
In “Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health”, revealed 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 handle 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 examine 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 properly 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 resolution course of (MDP). Each MDP is modeled as a two-state (good or dangerous 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. Further, every MDP has an related reward operate (i.e., the reward accrued at a given state and motion) and a transition operate indicating the likelihood 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 every one arms can change state no matter the motion.
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| State of a beneficiary could transition from good (excessive engagement) to dangerous (low engagement) with instance passive and energetic transition chances proven within the transition matrix. |
Model Development
Finally, the RMAB drawback is modeled such that at any time step, given n whole arms, which okay arms must be acted on (i.e., chosen to obtain a service name), to maximise reward (engagement with this system).
The likelihood of transitioning from one state to a different with (energetic likelihood) or with out (passive likelihood) receiving a service name are due to this fact the underlying mannequin parameters which can be crucial 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, revenue, training, variety of youngsters, and so on., in addition to previous listenership information, all in-line with the NGO’s information privateness requirements (extra under).
However, the restricted quantity of service calls limits the info 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.
In explicit, we carry out clustering on listenership behaviors, after which compute a mapping from the demographic options to every cluster.
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| 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 helpful as a result of when a brand new beneficiary is enrolled, we solely have entry to their demographic info and don’t have any data of their listenership patterns, since they haven’t had an opportunity to hear but. Using the mapping, we are able to 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 combos of coaching information (demographic options solely, options plus passive chances, options plus all chances, passive chances solely) to realize probably the most significant clusters, which can be consultant of the underlying information distribution and have a low variance in particular person cluster sizes.
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| Comparison of passive transition chances obtained from completely different clustering strategies with variety of clusters s = 20 (pink dots) and 40 (inexperienced dots), utilizing floor reality passive transition chances (blue dots). Clustering based mostly on options+passive chances (PPF) captures extra distinct beneficiary behaviors throughout the likelihood house. |
Clustering has the added benefit of lowering computational value for resource-limited NGOs, because the optimization must be solved at a cluster stage moderately than a person stage. Finally, fixing RMAB’s is understood to be P-space onerous, so we select to unravel the optimization utilizing the favored Whittle index method, which in the end offers a rating of beneficiaries based mostly on their possible good thing about receiving a service name.
Results
We evaluated the mannequin in an actual world examine 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 are not 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 thought right here is to have an simply executable coverage that companies sufficient of a cross-section of beneficiaries and may be scaled up or down per week based mostly on obtainable assets (that is the method utilized by the NGO on this explicit case, however the method could fluctuate for various NGOs). The RMAB group receives service calls as predicted by the RMAB mannequin. All the beneficiaries throughout the three teams proceed to obtain the automated voice messages unbiased of the service calls.
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| 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 likelihood of success (blue is excessive and pink is low), RR shows no such strategic choice. |
At the top of seven weeks, RMAB-based service calls resulted within the highest (and statistically important) discount in cumulative engagement drops (32%) in comparison with the CSOC group.
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| 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-value | 0.044 | 0.740 | 0.098 |
Ethical Considerations
An ethics board on the NGO reviewed the examine. We took important measures to make sure participant consent is known and recorded in a language of the group’s alternative at every stage of this system. Data stewardship resides within the fingers of the NGO, and solely the NGO is allowed to share information. The code will quickly be obtainable publicly. The pipeline solely makes use of anonymized information and no personally identifiable info (PII) is made obtainable to the fashions. Sensitive information, equivalent to caste, faith, and so on., are usually not collected by ARMMAN for mMitra. Therefore, in pursuit of making certain equity of the mannequin, we labored with public well being and area consultants to make sure different indicators of socioeconomic standing had been measured and adequately evaluated as proven under.
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| Distribution of highest training obtained (high) and month-to-month household revenue in Indian Rupees (backside) throughout a cohort that obtained service calls in comparison with the entire inhabitants. |
The proportion of beneficiaries that obtained a stay service name inside every revenue bracket moderately matches the proportion within the general inhabitants. However, variations are noticed in decrease revenue classes, the place the RMAB mannequin favors beneficiaries with decrease revenue and beneficiaries with no formal training. Lastly, area consultants at ARMMAN have been deeply concerned within the growth and testing of this method and have offered steady enter and oversight in information interpretation, information consumption, and mannequin design.
Conclusions
After thorough testing, the NGO has at present deployed this method for scheduling of service calls on a weekly foundation. We are hopeful that this may pave the best way for extra deployments of ML algorithms for social affect 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 Research, India. Thanks to all our collaborators at ARMMAN, Google Research India, Google.org, and University Relations: Aparna Hegde, Neha Madhiwalla, Suresh Chaudhary, Aditya Mate, Lovish Madaan, Shresth Verma, Gargi Singh, Divy Thakkar.
1ARMMAN runs a number of applications to offer preventive care info to ladies by means of being pregnant and infancy enabling them to hunt care, in addition to applications to coach and help well being employees for well timed detection and administration of high-risk situations. ↩