Clinical decision support and closed-loop systems are ubiquitous to any modern lifestsyle. From maintaining ambient temperature to flying our airplanes, we have accepted and benefited from these tools. In this chapter, we discuss key concepts in design and clinical goals, as well as the challenges, of these systems in the context of perioperative medicine. We particularly focus on the past and present development of automated and decision support systems for hemodynamic optimization with fluids and vasopressors.

This chapter presentsthe conceptual and analytical basics of feedback in a carefully integrated way. The analytical details presented herecan alsobe found many other places:root locus, Black's formula,the fundamental oscillation condition, and the like are treated in many places. What is often missing in texts for the advanced student are the conceptual insights.Why, for instance, is the phase response of the loop transmission so important?What does it mean for a complex pole pair to "dominate" a system response, and why is such dominanceso common? Why is open-loop control better for speed than closed-loop control? As always, conceptual understanding and analytical mastery are mutually reinforcing.

New technologies permit monitoring of an increasing number of physiological parameters in real time by multiple stakeholders in diverse environments. Combined with dramatic improvements in data analytics, this allows us to glean boundless information from our monitors. Miniaturization and nanotechnology make monitors more portable and also let us measure previously unobtainable parameters. The convergence of these factors moves us toward a seamless connection of providers to patients who are free to ambulate while monitored continuously and accurately. Smartphones, smartwatches, e-textiles, and consumer wearables have expanded the reach of physiological monitoring beyond the hospital and produced an explosion in the quantity of data produced. The convergence of all of these technologies allows tracking, analysis, and the production of predictive analytics. As these technologies improve and become more accurate and reliable for healthcare use, they present a myriad of integration challenges that include privacy, dependability, cost, infrastructure maintenance, and even ethical considerations.