Some common errors of experimental design, interpretation and inference in agreement studies

We signal and discuss common methodological errors in agreement studies and the use of kappa indices, as found in publications in the medical and behavioural sciences. Our analysis is based on a proposed statistical model that is in line with the typical models employed in metrology and measurement theory. A first cluster of errors is related to nonrandom sampling, which results in a potentially substantial bias in the estimated agreement. Second, when class prevalences are strongly nonuniform, the use of the kappa index becomes precarious, as its large partial derivatives result in typically large standard errors of the estimates. In addition, the index reflects rather one-sidedly in such cases the consistency of the most prevalent class, or the class prevalences themselves. A final cluster of errors concerns interpretation pitfalls, which may lead to incorrect conclusions based on agreement studies. These interpretation issues are clarified on the basis of the proposed statistical modelling. The signalled errors are illustrated from actual studies published in prestigious journals. The analysis results in a number of guidelines and recommendations for agreement studies, including the recommendation to use alternatives to the kappa index in certain situations

A flexible and optimal approach for appointment scheduling in healthcare

Appointment scheduling is generally applied in outpatient clinics and other healthcare services. The challenge in scheduling is to find a strategy for dealing with variability and unpredictability in service duration and patient arrivals. The consequences of an ineffective strategy include long waiting times for patients and idle time for the healthcare provider. In turn, these have implications for the perceived quality, cost-efficiency, and capacity of healthcare services. The generation of optimal schedules is a notoriously intractable problem, and earlier attempts at designing effective strategies for appointment scheduling were based on approximation, simulation, or simplification. We propose a novel strategy for scheduling that exploits three tactical ideas to make the problem manageable. We compare the proposed strategy to other approaches, and show that it matches or outperforms competing methods in terms of flexibility, ease of use, and speed. More importantly, it outperforms competing approaches nearly uniformly in approaching the desired balance between waiting and idle times as specified in a chosen objective function. Therefore, the strategy is a good basis for further enrichments