Pharmaceutical systems are a core part of health systems, and their failure to work adequately severely undermines the performance of the entire health system. The view that medicines are mere commodities managed for technical aspects by the national regulatory authority, disregards the role and need for a comprehensive and multidisciplinary system to ensure access to essential medicines. This has serious consequences, as it leaves key functions underfunded and under-represented in national and international platforms where decisions related to health care systems and for achievement of UHC are made. Key strategies to strengthen pharmaceutical systems have been extensively described, but their effective implementation requires political commitment, institutional oversight and adequate financial allocation to each function, including quality assurance, financing, supply and transparency among others. Assessing pharmaceutical systems is often neglected but essential for promoting an effective health system as well as population health. The Chapter presents some key performance measures that countries should routinely apply to measure and report on the performance of pharmaceutical systems and on their good governance.

Design of a farm irrigation system entails both technical and nontechnical considerations. It is an integration of principles borrowed from agriculture, meteorology, hydrology, hydraulics, irrigation, and drainage engineering as well as economic, environmental, and management sciences. This chapter provides a snapshot of the steps involved in designing a farm irrigation system.

With the full system introduced in Chapter 3, now we are ready to discuss how to evaluate the performance of a pattern recognition system: a task that seems easy at first glance but is in fact quite complex. We introduce core concepts such as error and accuracy rates, under- and overfitting, and parameters and hyperparameters. We pay special attention to imbalanced problems. Finally, we present a brief introduction on how confident we can be of the evaluation outcomes. We establish the fact that errors are inevitable in most pattern recognition systems, and also introduce a decomposition of errors into different terms.