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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. 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 .

Background: Many public health programs and interventions across the world increasingly rely on using Information and Communications Technology (ICT) tools to train and sensitize health professionals. However, the effects of such programs on provider knowledge, practice, or patient health outcomes have been inconsistent. One of the reasons for the varied effectiveness is low and varying levels of provider engagement, which, in turn, could be because of the form and mode of content used. Tailoring instructional content could improve engagement, but it is expensive and logistically demanding to do so in traditional training methods. ICT-based training interventions make it feasible to customize content to cater to providers’ preferences. Objective: This study aimed to discover preferences among providers over form (articles or videos), mode (featuring peers or experts), and length (short or long) of the instructional content; to quantify the extent to which differences in these preferences can explain variation in provider engagement with ICT-based training interventions; and to compare the power of content preferences to explain provider engagement against that of demographic variables. Methods: We used data from a mobile phone–based intervention focused on improving tuberculosis diagnostic practices among 24,949 private providers from 5 specialties and 1734 cities over 1 year. Engagement time was used as the primary outcome to assess provider engagement. A k-mean clustering was conducted to segment providers based on the proportion of engagement time spent on content formats, modes, and lengths to discover their content preferences. The identified clusters were used to predict engagement time using a linear regression model. Then, we compared the accuracy of the cluster-based prediction model with that based on demographic variables of providers (e.g., specialty and geographic location). Results: The average engagement time across all providers was 7.5 min (median 0, IQR 0-1.58). A total of 69.75% (17,401/24,949) of providers did not consume any content. The average engagement time for providers with nonzero engagement time was 24.8 min (median 4.9, IQR 2.2-10.1). We identified 4 clusters of providers with distinct preferences for the form, mode, and length of content. These clusters explained a substantially higher proportion of the variation in engagement time compared with demographic variables (32.9% vs 1.0%) and yielded a more accurate prediction for the engagement time (root mean square error: 4.29 vs 5.21 and mean absolute error: 3.30 vs 4.26). Conclusions: Providers participating in a mobile phone–based digital campaign have inherent preferences for instructional content. Targeting providers based on individual content preferences could result in higher provider engagement when compared with targeting providers based on demographic variables.

Public Distribution System (PDS) in India is a major instrument for achieving the goal of “Zero Hunger”. Despite the vast amounts of resources spent, PDS suffers from several inefficiencies largely attributable to the monopoly of agents involved in last-mile delivery of grains. To address this issue, several state governments in India have started implementing a novel intervention called portability. This intervention offers beneficiaries the choice of when and where they can avail of their food entitlements while the government controls what and how much. We use detailed and large-scale program data from Andhra Pradesh to analyze the uptake of portability among beneficiaries and identify its underlying drivers. We find that a sizeable fraction (~28%) of beneficiaries utilize this choice. Primary factors influencing the uptake are the number of agents a beneficiary has access to and the number of days in a month an agent is open for distributing food entitlements. We find that usage levels among the vulnerable populations such as the rural, the poor, the elderly and the socially disadvantaged, to be ~24%, ~29%, ~24% and ~16% lesser in comparison to their non-vulnerable counterparts, respectively.

In many emerging economies, callers may abandon ambulance requests due to a combination of operational (small fleet size), infrastructural (long travel times) and behavioral factors (low trust in the ambulance system). As a result, ambulance capacity, which is already scarce, is wasted in serving calls that are likely to be abandoned later. In this paper, we investigate the design of an ambulance system in the presence of abandonment behavior, using a two-step approach. First, because the callers' actual willingness to wait for ambulances is censored, we adopt a Maximum Likelihood Estimator estimation approach suitable for interval censored data. Second, we employ a simulation-based optimization approach to explicitly incorporate customers' willingness to wait in: (a) tactical short-term decisions such as modification of dispatch policies and ambulance allocations at existing base locations; and (b) strategic long-term network design decisions of increasing fleet size and re-designing base locations. We calibrate our models using data from a major metropolitan city in India where historically 81.3% of calls were successfully served without being abandoned. We find that modifying dispatch policies or reallocating ambulances provide relatively small gains in successfully served calls (around 1%). By contrast, increasing fleet size and network re-design can more significantly increase the fraction of successfully served calls with the latter being particularly more effective. Redesigning bases with the current fleet size is equivalent to increasing the fleet size by 8.6% at current base locations. Similarly, adding 29% more ambulances and redesigning the base locations is equivalent to doubling the fleet size at the current base locations and adding 34% more ambulances and redesigning base locations is equivalent to a three-fold increase. Our results indicate that in the absence of changes in behavioral factors, significant investment is required to modify operational factors by increasing fleet size, and to modify infrastructural factors by redesigning base locations.

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 .

States across the country are taking steps towards providing digital identities to beneficiaries of their public distribution systems. In doing so, the use of Aadhaar-based biometrics seems to be the preferred choice of method. However, several other methods exist for the same and have been adopted by different states at different points in time. States currently embarking on the journey of providing digital identities to their beneficiaries might benefit from evaluating all available alternatives before adopting a suitable method.

This case relates the genesis and evolution of vision centres (VCs) for primary eye care at Aravind Eye Care System (AECS). AECS, based in the south Indian state of Tamil Nadu, is the world's largest eye care provider. The case is based in 2014, exactly a decade after the first vision centre was opened, and evaluates the role of VCs in Aravind's outreach ecosystem. Community outreach programmes were an integral part of Aravind's model from the start and formed a central part of its vision of taking eye care to the community's doorstep. For many years, community outreach at Aravind was done through eye camps held in remote rural locations. Eye camps had worked extremely well for a long time. However, eye camps were not a perfect solution to Aravind's outreach goals for many reasons. Vision centres evolved as a way to address some of the shortcomings of eye camps, and both these outreach methods were employed in parallel. This case focuses on Aravind's vision centres and how they evolved over a decade, how they compare with the eye camps as a vehicle to attain Aravind's goals, and the opportunities and challenges ahead.

There is increasing evidence that workers in many service settings regulate their work speed in response to operating conditions. However, in service episodes of finite duration characterized by time-varying and non-stationary dynamics (e.g., outpatient departments), operating variables that can affect work speed have not been rigorously studied. Understanding the impact of these drivers on work speed can help managers improve resource and appointment scheduling. We employ the trade-off faced by workers between cost of providing service and cost of customer wait in such environments to identify two previously unexplored drivers of work speed - time within the episode and anticipated remaining workload. We empirically test our predictions using data from a high volume, tertiary care outpatient department. We find that workers start a typical service episode at slower work speed and progressively speed-up toward the end of the service episode. As a result, incoming patients experience 23% shorter (50 minutes smaller) average length of stay at the middle, and 60% shorter (130 minutes smaller) toward the end of the service episode. Further, consistent with our conceptual framework, we find that the temporal pattern of speed-up is influenced by the anticipated workload during the service episode. Specifically, a higher anticipated workload leads to a larger increase in work speed earlier in the service episode than later. An increase in anticipated workload by 1 patient per hour leads to 9% (20 minutes) reduction in average length of stay at the beginning, only 4% (8 minutes) reduction during the middle and negligible change toward the end of the service episode. We also identify the role of reduction in discretionary task (ordering diagnostic tests) as one mechanism of achieving this speed-up, which itself is influenced by time within the service episode and anticipated remaining workload. These findings provide managers with several levers to improve efficiency and quality of service provision, e.g. overall shorter service episodes, increasing the staffing level earlier in the service episode and decreasing it later, and scheduling simpler patients earlier in the service episode.

Several Emergency Medical Service (EMS) agencies across the US have demonstrated that the time spent by EDs on ambulance diversion can be reduced by implementing community-wide policies that restrict the duration and frequency of diversion episodes. However, the mechanisms through which these reductions materialize are not well understood. EDs can respond to such restrictions by improving their patient flow processes to reduce crowding and, thereby reducing the need for frequent and prolonged diversion episodes. Alternatively, they can raise the diversion crowding-threshold, thereby tolerating a higher level of crowding. Paramedics -- who decide whether to comply with an ED's diversion signal by diverting the ambulance or not -- are likely to respond differently to these two strategies. We use the framework of strategic communication between a service provider and customers arriving to a queuing system and obtain differential hypotheses on two outcome variables (diversion probability and ambulance waiting time) depending on which of the above mechanisms are actually operative. We test these hypotheses using evidence from a community-wide intervention to reduce diversion in LA County, California. We estimate a binary choice model for the paramedics' diversion decision as well as a two-part model for ambulance waiting time using data on more than 45000 ambulance transports to a network of seven neighboring EDs for a period of seven years (2003-2009). Our results uncover a multifaceted impact of the policy intervention on relevant operational measures. Specifically, a relatively hands-off intervention that restricts the amount of time spent by EDs on diversion can induce them to improve patient flow processes. However, the intervention is likely to be less effective in reducing the fraction of diverted ambulances and the ambulance waiting time due to the complex network dynamics of ambulance diversion and the strategic signaling between paramedics and EDs.

We study the impact of yield uncertainty (supply side) and self-interested consumers (demand side) on the inefficiency in the influenza vaccine supply chain. Previous economic studies, focusing on demand side, find that the equilibrium demand is always less than the socially optimal demand because self-interested individuals do not internalize the social benefit of protecting others via reduced infectiousness (positive externality). In contrast, we show that the equilibrium demand can be greater than the socially optimal demand after accounting for the limited supply due to yield uncertainty and manufacturer's incentives. The main driver for this result is a second (negative) externality: Self-interested individuals ignore that vaccinating people with high infection costs is more beneficial for the society when supply is limited. We show that the extent of the negative externality can be reduced through more efficient and less uncertain allocation mechanisms. To investigate the relative effectiveness of government interventions on supply and demand sides under various demand and supply characteristics, we construct two partially centralized scenarios where the social planner (i.e., government) intervenes either on the demand side or the supply side, but not both. We conduct an extensive numerical analysis. This paper was accepted by Yossi Aviv, operations management.

In recent years, growth in the demand for emergency medical services, along with decline in the number of hospitals with emergency departments (EDs), has raised concerns about the ability of the EDs to provide adequate service. Many EDs frequently report periods of overcrowding during which they are forced to divert incoming ambulances to neighboring hospitals, a phenomenon known as “ambulance diversion.” The objective of this paper is to study the impact of key operational characteristics of the hospitals such as the number of ED beds, the number of inpatient beds, and the utilization of inpatient beds on the extent to which hospitals go on ambulance diversion. We propose a simple queueing network model to describe the patient flow between the ED and the inpatient department. We analyze this network using two different approximations—diffusion and fluid—to derive two separate sets of measures for inpatient occupancy and ED size. We use these sets of measures to form hypotheses and test them by estimating a sample selection model using data on a cross section of hospitals from California. We find that the measures derived from the diffusion approximation provide better explanation of the data than those derived from the fluid approximation. For this model, we find that the fraction of time that the ED spends on diversion is decreasing in the spare capacity of the inpatient department and in the size of the ED, where both are appropriately normalized for the size of the inpatient department. In addition, controlling for these hospital-specific factors, we find that the fraction of time on diversion at a hospital increases with the number of hospitals in its neighborhood. We also find that certain hospitals, owing to their location, ownership, and trauma center status, are more likely to choose ambulance diversion to mitigate overcrowding than others.

Background Rapid scale up of HIV treatment programs in sub-Saharan Africa has refueled the long-standing health policy debate regarding the merits and drawbacks of vertical and integrated system. Recent pilots of integrating outpatient and HIV services have shown an improvement in some patient outcomes but deterioration in waiting times, which can lead to worse health outcomes in the long run. Methods A pilot intervention involving integration of outpatient and HIV services in an urban primary care facility in Lusaka, Zambia was studied. Data on waiting time of patients during two seven-day periods before and six months after the integration were collected using a time and motion study. Statistical tests were conducted to investigate whether the two observation periods differed in operational details such as staffing, patient arrival rates, mix of patients etc. A discrete event simulation model was constructed to facilitate a fair comparison of waiting times before and after integration. The simulation model was also used to develop alternative configurations of integration and to estimate the resulting waiting times. Results Comparison of raw data showed that waiting times increased by 32% and 36% after integration for OPD and ART patients respectively (p

Firms selling to snobbish (exclusivity-seeking) consumers whose valuation decreases in the fraction of population that buys the product display several differences in their pricing and rationing strategies: some firms offer markdown pricing while others charge a uniform price, some create scarcity while others do not. This paper develops a stylized analytical model to understand the drivers behind these strategies. A monopolist sells a product over two periods to two segments of strategic (forward-looking) and snobbish consumers with high and low functional value of the product, respectively. We find that firm’s pricing and rationing strategies depend critically on the proportion of high-value consumers and on consumers’ sensitivity to consumption. The firm implements strategic rationing, i.e., it creates scarcity intentionally, only if consumers’ sensitivity to consumption is sufficiently high. Further, it is optimal to charge a uniform price when the proportion of high-value consumers is sufficiently high, and to mark the price down when the proportion of highvalue consumers is low. When the proportion of high value consumers is intermediate, the pricing policy changes with consumers’ sensitivity to consumption; higher values of sensitivity to consumption leading to markdown pricing while lower values of sensitivity to consumption leading to uniform pricing. We also show that there are two main drivers which increase the value of consumption in the first period and thereby lead to price markdowns in snob-appeal products, namely, rationing due to snobbish consumer behavior and the desire to purchase the product early in the first period when it is more exclusive, Therefore, with snobbish consumers, the firm may mark the price down even in the absence of rationing. Under certain conditions, contrary to intuition, the product can be more exclusive under markdown pricing than under uniform pricing, and more exclusivity-seeking consumer behavior can actually induce the firm to reduce the extent of exclusivity. As expected, the negative impact of strategic consumer behavior is lower when selling to snobbish consumers, because decreasing capacity to counteract strategic consumer behavior also increases product exclusivity and thus consumers’ willingness to pay. Yet, ignoring strategic consumer behavior can be more costly with snobbish consumers.

This paper studies a model of community-based healthcare delivery for a chronic disease. In this setting, patients periodically visit the healthcare delivery system, which influences their disease progression and consequently their health outcomes. We investigate how the provider can maximize community-level health outcomes through better operational decisions pertaining to capacity allocation across different patients. To do so, we develop an integrated capacity allocation model that incorporates clinical (disease progression) and operational (capacity constraint) aspects. Specifically, we model the provider's problem as a finite horizon stochastic dynamic program, where the provider decides which patients to schedule at the beginning of each period. Therapy is provided to scheduled patients, which may improve their health states. Patients that are not seen follow their natural disease progression. We derive a quantitative measure for comparison of patients' health states and use it to design an easy-to-implement myopic heuristic that is provably optimal in special cases of the problem. We employ the myopic heuristic in a more general setting and test its performance using operational and clinical data obtained from Mobile C.A.R.E. Foundation, a community-based provider of pediatric asthma care in Chicago. Our extensive computational experiments suggest that the myopic heuristic can improve the health gains at the community level by up to 15% over the current policy. The benefit is driven by the ability of our myopic heuristic to alter the duration between visits for patients with different health states depending on the tightness of the capacity and the health states of the entire patient population.

Early infant diagnosis (EID) programs in many resource-limited settings are aimed at diagnosing infants born to HIV positive mothers. Due to the complexity of the diagnostic technology, EID programs are often highly centralized with few laboratories testing blood samples from a large network of health facilities. This leads to long diagnostic delays and consequent failure of patients to collect results in a timely manner. Several point-of-care (POC) devices that provide rapid diagnosis within the health facilities are being developed to mitigate these drawbacks of centralized EID networks. We study the decision of which facilities should receive the POC device (the placement plan) using the EID program in Mozambique as a case-study. We argue that the choice of an appropriate plan is critical to maximizing the public health impact of POC devices in the presence of tight budget constraints. To formalize this argument, we develop a detailed simulation model to evaluate the impact of a placement plan. It comprises two parts: an operational model that quantifies the impact of a POC placement plan on the diagnostic delay and a behavioral part that quantifies the impact of diagnostic delay on the likelihood of result collection by infants' caregivers. We also develop an approximate version of these operational and patient behavior dynamics and embed them in an optimization model to generate candidate POC placement plans. We find that the optimization based plan can result in up to 30% more patients collecting their results compared to rules of thumb that have practical appeal. Finally, we show that the effectiveness of POC devices is much higher than other operational improvements to the EID network such as increased laboratory capacity, reduced transportation delay, and more regularized transport.

We present a model of dynamic resource allocation in a setting where continuity of service is important and future resource availability is uncertain. The paper is inspired by the challenges faced by HIV clinics in resource-limited settings in the allocation of scarce HIV treatment among a large pool of eligible patients. Many clinics receive insufficient supply to treat all patients and the supply they do receive is highly uncertain. This supply uncertainty, combined with the clinical importance of an uninterrupted treatment throughout patients’ life, requires the clinics to make a trade-off between providing access to treatment for new patients and ensuring continuity of treatment for current patients. Setting aside other aspects of the treatment rationing problem, we model the decisions of a clinic facing this trade-off using stochastic dynamic programming. We derive sufficient conditions under which the optimal policy coincides with the clinically preferred policy of prioritizing previously enrolled patients. We use numerical examples to investigate the impact of supply uncertainty on the performance of enrollment policies used in practice. We also discuss how our model applies to other intertemporal resource allocation decisions such as that faced by non-profit organizations where continuity of service is crucial to meeting the organization’s social objective, or that faced by an entrepreneur who wants to attract new customers without reducing service quality to existing customers.