Simple Heuristics for Near-Optimal Appointment Scheduling in Primary Care.

In primary care allocating appointments to sequential requests can result in sub-optimal scheduling. Optimal scheduling requires hiring of consultants to analyze historical patterns. Many practices focus their resources on larger problems instead of optimizing appointment schedules. We simulate simple heuristics to compare their performance with optimal schedules uncovered using offline optimization models. We use uncapacitated appointment calendars for a nationally representative heterogeneous primary care panel to meet all patients' requests. The stochastic nature of appointment requests gives a distribution for daily appointments and for the uncovered optimal capacity. The First Minimum heuristic gives near-optimal schedules and can be easily implemented in small practices using pen-and-paper, without any investment in computer-systems.

A Two-Step Markov Processes Approach for Parameterization of Cancer State-Transition Models for Low- and Middle-Income Countries.

Implementation of organized cancer screening and prevention programs in high-income countries (HICs) has considerably decreased cancer-related incidence and mortality. In low- and middle-income countries (LMICs), screening and early diagnosis programs are generally unavailable, and most cancers are diagnosed in late stages when survival is very low. Analyzing the cost-effectiveness of alternative cancer control programs and estimating resource needs will help prioritize interventions in LMICs. However, mathematical models of natural cancer onset and progression needed to conduct the economic analyses are predominantly based on populations in HICs because the longitudinal data on screening and diagnoses required for parameterization are unavailable in LMICs. Models currently used for LMICs mostly concentrate on directly calculating the shift in distribution of cancer diagnosis as an evaluative measure of screening. We present a mathematical methodology for the parameterization of natural cancer onset and progression, specifically for LMICs that do not have longitudinal data. This full onset and progression model can help conduct comprehensive analyses of cancer control programs, including cancer screening, by considering both the positive impact of screening as well as any adverse consequences, such as over-diagnosis and false-positive results. The methodology has been applied to breast, cervical, and colorectal cancers for 2 regions, under the World Health Organization categorization: Eastern Sub-Saharan Africa (AFRE) and Southeast Asia (SEARB). The cancer models have been incorporated into the Spectrum software and interfaced with country-specific demographic data through the demographic projections (DemProj) module and costing data through the OneHealth tool. These software are open-access and can be used by stakeholders to analyze screening strategies specific to their country of interest.