Sarang Deo

Governments are increasingly using digital unique identification numbers (UIDs), often linked to citizens' biometrics, to deliver social welfare payments. On the one hand, UID-linked payments reduce frictions in cash access through an additional channel of Banking Agents (BAs), typically nano-store owners with biometric-enabled micro-ATMs. On the other hand, implementation complexity and low financial awareness of citizens can increase frictions in cash access through incorrect linkages in the account mapper and redirection of payments to accounts that citizens are unaware of. Methodology: We examine the impact of UID-linked systems in the context of India's workfare program under which citizens in rural areas have a legal right to demand up to 100 days of work. We hypothesize that the increased (reduced) friction in cash access for citizens will reduce (increase) their confidence in the workfare program and, consequently, their future uptake of work. We test our hypothesis using 22.84 million payments to 1.91 million citizens, of which 57% transition to UID-linked payments. Using a unique cohort-wise difference-indifferences design, we find that average quarterly earnings per citizen rise by INR 281.07, a 41.84% increase after transition. Citizens with a proximate BA (within three kilometers of their village) see a 44.03% quarterly wage increase, vis-à-vis 37.76% for those without. Among transitioned citizens, 26% experience wage redirection. For them, the impact of UID-linked system is insignificant, compared to a 49.98% increase for those whose accounts were mapped correctly. BA presence slightly mitigates this: redirected citizens with a proximate BA see a 4.85% increase, while the impact remains insignificant for those without a proximate BA. Implications: As governments increasingly adopt UID-linked systems, we present the first long-term evidence that the impact of these systems is shaped by the broader institutional ecosystem of public programs, financial institutions, and BAs, within which they are implemented.

Understanding how genomic surveillance capacity translates into population health outcomes is critical for designing effective pandemic response systems, yet the interaction between operational design and epidemiological dynamics remains insufficiently characterized. We develop an integrated analytical framework that links a whole-genome sequencing (WGS) – based surveillance network with a two – variant epidemiological transmission model to evaluate how surveillance operations influence variant detection, intervention timing, and health outcomes. The framework combines a modified susceptible – exposed – infectious – recovered – susceptible (SEIRS) model with a detailed operational representation of a centralized WGS surveillance network in India, incorporating sample collection, transport, batching, sequencing capacity, and reporting delays. We simulate 54 scenario combinations defined by three sequencing capacity levels, three sampling proportions, three variant emergence timings, and two variant profiles (high severity – high immune escape and low severity – low immune escape). Detection of a novel variant triggers a modeled intervention consisting of isolation of some diagnosed individuals, increased testing rates across disease states, and expanded access to hospitalization. Across simulations, the time from variant emergence to intervention implementation ranged from 73 to 351 days, depending on operational and epidemiological conditions. Increasing sampling proportion reduced detection time only when sequencing capacity was sufficient; under constrained capacity, higher sampling increased congestion and delayed detection. Expanding capacity from low to nominal levels substantially reduced turnaround times, with diminishing returns at higher capacity. Earlier detection consistently improved intervention effectiveness, with deaths averted ranging from 0.06% to 14.49% across scenarios. The cost per life – year saved ranged from INR 9,137 to INR 326,714 across all configurations, remaining below one to three times India ′ s GDP per capita, consistent with established cost – effectiveness thresholds. These results demonstrate that the performance of genomic surveillance systems is jointly determined by operational and epidemiological dynamics. Effective surveillance design, therefore, requires coordinated optimization of sampling strategies and sequencing capacity to enable timely intervention and maximize population health benefits.

Nanostores, the backbone of the retail sector in many emerging economies, face unique operational challenges. These include liquidity and shelf-space constraints, and lack of IT infrastructure such as point-of-sale devices. Consequently, upstream partners responsible for replenishment (such as a manufacturer or its agent) do not have access to sales data and must rely on historical replenishment data to estimate customer demand for replenishment planning. However, replenishment data are subject to two distortions: (i) demand-to-sales distortion due to stockouts, i.e., censoring, and (ii) sales-to-replenishment distortion caused by the retailer's inventory policy. The latter is amplified because nanostores operate at a small scale, while inventory is replenished in relatively large batch sizes. We develop a parsimonious model of the nanostore's inventory dynamics using a modified base-stock policy: the nanostore receives replenishments in batches to reach a target inventory level. Using this model as the data generating process for the replenishment data, we construct a distribution of the unobserved inventory and sales data. We employ the Expectation-Maximization (EM) algorithm to jointly estimate the parameters of the demand distribution and the target inventory level. We find that the EM algorithm misattributes stockouts in some periods involving the maximum observed replenishment quantity and therefore systematically underestimates the target inventory level and correspondingly overestimates the demand. We further show that augmenting the replenishment data with a binary stockout indicator can almost eliminate the upward bias in the demand estimate. Finally, we show that this bias correction can help the upstream partners significantly reduce their visit frequency to the nanostore without adversely impacting the cycle service levels. Our results suggest that manufacturers and distributors can significantly reduce the cost of replenishment to nanostores by more accurately estimating the retail demand with little additional information (i.e., stockout indicators) without making heavy investments in digitization of nanostores with sophisticated POS or inventory management tools.

Smarter Community Health Visits Improve Diabetes Outcomes Diabetes takes a severe toll in developing countries, where high blood glucose contributes more than half of premature deaths. Community health workers (CHWs) offer a culturally tailored lifeline, but deploying them efficiently requires balancing the screening of new patients against managing those already in treatment. In this issue, researchers introduce an innovative optimization framework that personalizes CHW visit plans to maximize community-wide glycemic control. Uniquely, the model explicitly factors in patients’ motivational states—predicting their likelihood of enrolling in or dropping out of care—to guide intervention strategies and reduce attrition. Applied to operational data from urban slums in India, the approach delivers remarkable results; optimized visit plans reduced fasting blood glucose levels by up to 25% compared with the best baseline methods using identical capacity. The model also proved to be robust under imperfect information, offering a powerful, practical tool for global health resource allocation.

Background: Tuberculosis (TB) is a major global health concern, causing 1.5 million deaths in 2020. Diagnostic tests for TB are often inaccurate, expensive, and inaccessible, making chest x-rays augmented with artificial intelligence (AI) a promising solution. However, whether providers are willing to adopt AI is not apparent. Objective: The study seeks to understand the attitude of Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homoeopathy (AYUSH) and informal health care providers, who we jointly call AIPs, toward adopting AI for TB diagnosis. We chose to study these providers as they are the first point of contact for a majority of TB patients in India. Methods: We conducted a cross-sectional survey of 406 AIPs across the states of Jharkhand (162 participants) and Gujarat (244 participants) in India. We designed the survey questionnaire to assess the AIPs’ confidence in treating presumptive TB patients, their trust in local radiologists’ reading of the chest x-ray images, their beliefs regarding the diagnostic capabilities of AI, and their willingness to adopt AI for TB diagnosis. Results: We found that 93.7% (270/288) of AIPs believed that AI could improve the accuracy of TB diagnosis, and for those who believed in AI, 71.9% (194/270) were willing to try AI. Among all AIPs, 69.4% (200/288) were willing to try AI. However, we found significant differences in AIPs’ willingness to try AI across the 2 states. Specifically, in Gujarat, a state with better and more accessible health care infrastructure, 73.4% (155/211) were willing to try AI, and in Jharkhand, 58.4% (45/77) were willing to try AI. Moreover, AIPs in Gujarat who showed higher trust in the local radiologists were less likely to try AI (odds ratio [OR] 0.15, 95% CI 0.03‐0.69; P=.02). In contrast, in Jharkhand, those who showed higher trust in the local radiologists were more likely to try AI (OR 2.11, 95% CI 0.9‐4.93; P=.09). Conclusions: While most AIPs believed in the potential benefits of AI-based TB diagnoses, many did not intend to try AI, indicating that the expected benefits of AI measured in terms of technological superiority may not directly translate to impact on the ground. Improving beliefs among AIPs with poor access to radiology services or those who are less confident of diagnosing TB is likely to result in a greater impact of AI on the ground. Additionally, tailored interventions addressing regional and infrastructural differences may facilitate AI adoption in India’s informal health care sector.

Problem definition: Beneficiaries of social assistance programs with transfers of undifferentiated commodities often have a designated agent to collect their entitlements from. This gives monopoly power to agents over beneficiaries. When coupled with weak government monitoring, agents do not have incentives to adhere to stipulated operating guidelines, leading to reduced uptake by beneficiaries. Some governments are attempting to break the monopoly by allowing beneficiaries to choose agents. However, the impact of choice on uptake may be limited by lack of alternate agents in beneficiaries’ vicinities, restricted ability of agents to compete with undifferentiated commodities, and collusion among agents. Methodology/results: Using a reverse difference-in-differences framework on data from a food security program in two neighboring states in India, Andhra Pradesh and Telangana, we find that providing agent choice results in a 6.6% increase in the quantity of entitlements collected by the beneficiary households. We also find that increase in uptake is about four times higher in regions with high agent density compared with those with low agent density. This emphasizes the importance of having an alternate agent in the vicinity for choice to be effective. Nearly all of the increase in uptake is attributable to new beneficiaries collecting entitlements from their preassigned agent. This is suggestive of agents improving adherence to operating guidelines in response to choice. We find associative evidence for this response in the number of days agents keep their shops open. Managerial implications: Governments executing in-kind transfers of undifferentiated commodities are piloting interventions to provide choice to their beneficiaries. Replacement of in-kind transfers with cash, an increasingly popular intervention, may be challenging in volatile markets, as the magnitude of the transfer needs to be periodically adjusted. Our results indicate that alternate designs of providing choice even in a limited form, that is, the place where the beneficiaries can collect their entitlements with products and prices fixed, can present a viable alternative. Funding: This research was partially supported by a grant from the Omidyar Network to the Digital Identity Research Initiative at the Indian School of Business. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0528 .

BACKGROUND Tuberculosis (TB) is a major global health concern, causing 1.5 million deaths in 2020. Diagnostic tests for TB are often inaccurate, expensive, and inaccessible, making chest X-rays (CXRs) augmented with Artificial Intelligence (AI) a promising solution. However, whether providers are willing to adopt AI is not apparent. OBJECTIVE The study seeks to understand the attitude of AYUSH (Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homoeopathy) and informal healthcare providers, who we jointly call AIPs, towards adopting AI for TB diagnosis. We chose to study these providers as they are the first point of contact for a majority of TB patients. METHODS We conducted a cross-sectional survey of 406 AIPs across the states of Jharkhand (162 participants) and Gujarat (244 participants) in India. We designed the survey questionnaire to assess the AIPs’ confidence in treating presumptive tuberculosis (TB) patients; their trust in local radiologists’ reading of the CXR images; their beliefs regarding the diagnostic capabilities of AI; and their willingness to adopt AI for TB diagnosis. RESULTS After removing incomplete responses, we found that 93.7% (270/288) of AIPs believed that AI improved the accuracy of TB diagnosis, and 69.4% (200/288) were willing to try AI. AIPs who were more confident in diagnosing TB and had greater trust in the local radiologists were more likely to believe in AI-based TB diagnosis. However, we found significant differences in AIPs’ willingness for AI adoption across the two states. Specifically, in Gujarat, a state with better and more accessible healthcare infrastructure, 73.4% (155/211) were willing to try AI; and in Jharkhand, 58.4.% (45/77) were willing to try AI. Moreover, AIPs in Gujarat who showed higher trust in the local radiologists were less likely to try AI. In contrast, in Jharkhand, those who showed higher trust in the local radiologists were more likely to try AI. CONCLUSIONS While most AIPs believed in the potential benefits of AI-based TB diagnosis, many did not intend to try AI, indicating that the expected benefits of AI measured in terms of technological superiority may not directly translate to impact on the ground. Improving beliefs among AIPs with poor access to radiology services or those who are less confident of diagnosing TB is likely to result in greater impact of AI on the ground.

Problem definition: Healthcare systems in resource-limited settings rely on diagnostic networks in which medical samples (e.g., blood, sputum) and results need to be transported between geographically dispersed healthcare facilities and centralized laboratories. Academic/practical relevance: Existing sample transportation (ST) systems typically operate fixed schedules, which do not account for demand variability and lead to unnecessary transportation visits as well as delays. Methodology: We design an optimized sample transportation (OST) system that comprises two components: (i) a new approach for timely collection of information on transportation demand (samples and results) using low-cost technology based on feature phones, and (ii) an optimization-based solution approach to the problem of routing and scheduling courier trips in a multistage transportation system. Results: Our solution approach performs well in a range of numerical experiments. Furthermore, we implement OST in collaboration with Riders For Health, who operate the national ST system in Malawi. Based on analysis of field data describing over 20,000 samples and results transported during July–October 2019, we show that the implementation of OST routes reduced average ST delays in three districts of Malawi by approximately 25%. In addition, the proportion of unnecessary trips by ST couriers decreased by 55%. Managerial implications: Our approach for improving ST operations is feasible and effective in Malawi and can be applied to other resource-limited settings, particularly in sub-Saharan Africa. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This work was supported by Bill and Melinda Gates Foundation [Grant OPP1182217] and by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health [Grant U54EB027049]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1182 .

Telemedicine is increasingly used across the developing world to expand access to healthcare, to improve outcomes, and to reduce costs. One common model is that of telemedicine centers, which are small primary care facilities run by midlevel (nonphysician) providers who conduct a preliminary examination and then facilitate a telemedicine visit with a remote physician in real time. However, the impact of this channel of care delivery—particularly on existing physical healthcare-delivery channels—has not been thoroughly examined. We use data from one of the largest tele-ophthalmology implementations in the world to examine this issue. Using a quasi-experimental difference-in-differences approach, we find that opening a nearby telemedicine center generates a 31% increase in the overall network visit rate from the population within 10 km of the new center, 62% of which is driven by new patients, suggesting a substantial increase in access. The rate of eyeglasses prescriptions to correct for simple refractive errors increases by 18.5%, whereas the rate of cataract surgery to replace the natural lens in a patient’s eye with an artificial lens remains unchanged. The increase in access and treatment rates does not significantly impact the direct costs incurred by patients, but reduces their indirect costs (measured as travel distance) by 30% (12 km). Finally, we find significant spatial heterogeneity in these effects, which vary with the distance of patients to facilities. These results have important implications for the design of telemedicine networks and the portfolio of healthcare services provided through them. This paper was accepted by Stefan Scholtes, healthcare management. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4488 .