Early diagnosis of HIV among infants born to HIV infected mothers is critical because roughly 50% of untreated infected infants die before the age of two years. Yet most countries in sub-Saharan Africa experience significant delays in diagnosis due to operational inefficiencies in early infant diagnosis (EID) networks. We develop a two-part modeling framework relying on optimization and simulation to generate operational improvements in the assignment of clinics to laboratories and the allocation of capacity across laboratories, and to evaluate the associated impact on the number of infants initiating treatment. Applying our methodology to EID program data from Mozambique, we validate our simulation model and estimate that optimally reassigning clinics to labs would decrease the average sample turnaround time (TAT) by 11% and increase the number of infected infants starting treatment by about 4% relative to the current system. Further, consolidating all diagnostic capacity in one centralized lab would decrease average TATs by an estimated 22% and increase the number of infected infants initiating treatment by 7%. Our sensitivity analysis suggests that the consolidation of capacity in a single location would remain near-optimal across a wide range of laboratory utilization levels in Mozambique. However, this full consolidation solution is dominated by configurations with two or more labs for EID networks with average transportation times larger than those currently observed in Mozambique by at least 15%.

In services where teams come together for short collaborations, managers are often advised to strive for high team familiarity so as to improve coordination and consequently, performance. However, inducing high team familiarity by keeping team membership intact can limit workers’ opportunities to acquire useful knowledge and alternative practices from exposure to a broader set of partners. We introduce an empirical measure for prior partner exposure and estimate its impact (along with that of team familiarity) on operational performance using data from the London Ambulance Service. Our analysis focuses on ambulance transports involving new paramedic recruits, where exogenous changes in team membership enable identification of the performance effect. Specifically, we investigate the impact of prior partner exposure on time spent during patient pickup at the scene and patient handover at the hospital. We find that the effect varies with the process characteristics. For the patient pickup process, which is less standardized, greater partner exposure directly improves performance. For the more standardized patient handover process, this beneficial effect is triggered beyond a threshold of sufficient individual experience. In addition, we find some evidence that this beneficial performance impact of prior partner exposure is amplified during periods of high workload, particularly for the patient handover process. Finally, a counterfactual analysis based on our estimates shows that a team formation strategy emphasizing partner exposure outperforms one that emphasizes team familiarity by about 9.2% in our empirical context. This paper was accepted by Jay Swaminathan, operations management.

Background Diagnostics in many low- and middle-income countries are conducted through centralized laboratory networks. Samples are collected from patients at remote point-of-care health facilities, and diagnostic tests are performed at centralized laboratories. Sample transportation systems that deliver diagnostic samples and test results are crucial for timely diagnosis and treatment in such diagnostic networks. However, they often lack the timely and accurate data (eg, the quantity and location of samples prepared for collection) required for efficient operation. Objective This study aims to demonstrate the feasibility, adoption, and accuracy of a distributed data collection system that leverages basic mobile phone technology to gather reports on the quantity and location of patient samples and test results prepared for delivery in the diagnostic network of Malawi. Methods We designed a system that leverages unstructured supplementary service data (USSD) technology to enable health workers to submit daily reports describing the quantity of transportation-ready diagnostic samples and test results at specific health care facilities, free of charge with any mobile phone, and aggregate these data for sample transportation administrators. We then conducted a year-long field trial of this system in 51 health facilities serving 3 districts in Malawi. Between July 2019 and July 2020, the participants submitted daily reports containing the number of patient samples or test results designated for viral load, early infant diagnosis, and tuberculosis testing at each facility. We monitored daily participation and compared the submitted USSD reports with program data to assess system feasibility, adoption, and accuracy. Results The participating facilities submitted 37,771 reports over the duration of the field trial. Daily facility participation increased from an average of 50% (26/51) in the first 2 weeks of the trial to approximately 80% (41/51) by the midpoint of the trial and remained at or above 80% (41/51) until the conclusion of the trial. On average, more than 80% of the reports submitted by a facility for a specific type of sample matched the actual number of patient samples collected from that facility by a courier. Conclusions Our findings suggest that a USSD-based system is a feasible, adoptable, and accurate solution to the challenges of untimely, inaccurate, or incomplete data in diagnostic networks. Certain design characteristics of our system, such as the use of USSD, and implementation characteristics, such as the supportive role of the field team, were necessary to ensure high participation and accuracy rates without any explicit financial incentives.

The expansive learning curve literature in operations management has established how various facets of prior experience improve average performance. In this paper, we explore how increased cumulative experience affects performance variability or consistency. We use a two-stage estimation method of a heteroskedastic learning curve model to examine the relationship between experience and performance variability among paramedics at the London Ambulance Service. We find that, for paramedics with lower experience, an increase in experience of 500 jobs reduces the variance of task completion time by 8.7%, in addition to improving average completion times by 2.7%. Similar to prior results on the average learning curve, we find a diminishing impact of additional experience on the variance learning curve. We provide an evidence base for how to model the learning benefits of cumulative experience on performance in service systems. Our findings imply that the benefits of learning are substantially underestimated if the consistency effect is ignored. Specifically, our estimates indicate that queue lengths (or wait times) might be overestimated by as much as 4% by ignoring the impact of the variance learning curve in service systems. Furthermore, our results suggest that previously established drivers of productivity should be revisited to examine how they affect consistency, in addition to average performance. This paper was accepted by Charles Corbett, operations management.

IntroductionTuberculosis (TB) is a global health emergency and low treatment adherence among patients is a major barrier to ending the TB epidemic. The WHO promotes digital adherence technologies (DATs) as facilitators for improving treatment adherence in resource-limited settings. However, limited research has investigated whether DATs improve outcomes for high-risk patients (ie, those with a high probability of an unsuccessful outcome), leading to concerns that DATs may cause intervention-generated inequality.MethodsWe conducted secondary analyses of data from a completed individual-level randomised controlled trial in Nairobi, Kenya during 2016–2017, which evaluated the average intervention effect of a novel DAT-based behavioural support programme. We trained a causal forest model to answer three research questions: (1) Was the effect of the intervention heterogeneous across individuals? (2) Was the intervention less effective for high-risk patients? nd (3) Can differentiated care improve programme effectiveness and equity in treatment outcomes?ResultsWe found that individual intervention effects—the percentage point reduction in the likelihood of an unsuccessful treatment outcome—ranged from 4.2 to 12.4, with an average of 8.2. The intervention was beneficial for 76% of patients, and most beneficial for high-risk patients. Differentiated enrolment policies, targeted at high-risk patients, have the potential to (1) increase the average intervention effect of DAT services by up to 28.5% and (2) decrease the population average and standard deviation (across patients) of the probability of an unsuccessful treatment outcome by up to 8.5% and 31.5%, respectively.ConclusionThis DAT-based intervention can improve outcomes among high-risk patients, reducing inequity in the likelihood of an unsuccessful treatment outcome. In resource-limited settings where universal provision of the intervention is infeasible, targeting high-risk patients for DAT enrolment is a worthwhile strategy for programmes that involve human support sponsors, enabling them to achieve the highest possible impact for high-risk patients at a substantially improved cost-effectiveness ratio.