We present Operations Management work on overcoming healthcare delivery challenges in remote settings in two chapters. We begin with work on inventory management of a last-mile supply chain. It is motivated by the need for a reliable and cost-effective delivery system to resupply new mosquito-repellent technologies to combat malaria in the Lake Tanganyika region of The Democratic Republic of the Congo. The primary delivery methods used around Lake Tanganyika can become inoperable during periods of floods, machinery breakdowns, and armed conflict. To ensure continuous coverage, our model allows for emergency delivery methods, with high variable costs, to be used during outages. In normal operating times, we use a large-capacity fixed-cost delivery method. We model the changes in the operating environment between normal and outage states using a Markov chain with disruption-specific parameters. We use an infinite-horizon discounted objective, which consists of ordering costs and a linear holding cost. We show that a novel hybrid policy is optimal and we provide the means to calculate the policy parameters. Additionally, for managerial considerations, we propose two heuristic policies and show their relative effectiveness through a series of numerical studies. In the second chapter, we present a constrained optimization model to aid in selecting maternal and newborn community health interventions in previously underserved rural and remote populations through Community Health Worker (CHW) programs. The first application of the model is in Dhusamareeb, Somalia, and the surrounding area within the state of Galmudug. After establishing the resource requirements (financial, time, commodity, and policy) for 25 maternal and neonatal interventions with local stakeholders in Somalia, we used the Lives Saved Tool (LiST) to calculate the number of projected lives saved as a function of services delivered. We then used optimization techniques to sort through the feasible combinations of interventions satisfying the resource constraints to determine the package of care that leads to the most projected lives saved in Galmudug. With a cadre of 1,450 Female Health Workers and a budget of $435,000 for health commodities, we calculate that the optimized set of interventions for Galmudug could avert 15 percent of the 4,132 projected maternal and neonatal deaths in 2023. Sensitivity analysis shows how the optimal combination of interventions, and the number of lives saved, change as the constraints and parameters used in the model change. We focus on Somalia for the first application of this model; however, the combination of LiST's database with a simple framework for sourcing data on local health system resources, allows the application to other country health programs. The model provides practitioners with a new tool and accompanying approach to evaluate possible packages of community health interventions with competing resource requirements.