The role of evaluation in decision-making in the management of health services

The management of health services is a complex administrative practice due to the breadth of the field of health and the need to reconcile individual, corporate and collective interests that are not always convergent. In this context, the evaluation needs to have specific characteristics in order to fulfill its role. The scope of this study was to establish the characteristics that the evaluation for the management of health services should have to contribute to decision-making. Usefulness, opportunity, feasibility, reliability, objectivity and directionality represent the set of principles upon which the evaluation should be based. Evaluations should lead to decisions that guarantee not only their efficiency and effectiveness but also their implementation. The evaluation process should ensure that decisions involve all stakeholders in order to render the implementation of decisions feasible, and take into account the health needs of the population and the goals set for the services. The scope of this article is to elicit a debate among different stakeholders in the evaluation in the hope that it can contribute to the reflection on the real usefulness of evaluations in which the political component in management has been increasingly prevalent.

A Simple Artificial Life Model Explains Irrational Behavior in Human Decision-Making

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats' neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments.