Broadly speaking, mannequin simulators are either operator-driven or autonomous. Operator-driven mannequins rely on the instructor, rather than modeling, to drive the simulator. Responses to interventions are controlled by the operator since there is typically limited ability of the mannequin to provide intervention feedback. Autonomous simulators utilize mathematical modeling algorithms to prompt changes in status or physiology based on intervention. For example, giving intravenous fluids to an autonomous simulator will correct signs of hypovolemia automatically (i.e., increased blood pressure with a reduction of heart rate), while the same intervention in an operator-driven mannequin requires changing of the appropriate vital signs by the operator. A learner’s perspective of the simulated scenario is the same regardless of type, but the operator’s ability to control the scenario varies. Operator-driven mannequins are typically less complicated and easy to control but are dependent on the operator to ensure that the physiologic data (e.g., vital signs) are realistic. Realism is less of an issue with autonomous simulators, but the plethora of physiologic parameters that can be manipulated can be overwhelming and generally requires at least a modest understanding of the physiologic mechanisms.

Even though a simulator may be primarily operator-driven, varying degrees of physiologic modeling may be included that allows for autonomous changes. For example, some operator-driven mannequins may respond to the application of oxygen or use of bag-valve mask by increasing the simulator’s oxygen saturation without input from the operator. Additionally, some operator-driven mannequins detect electrical current and can respond to cardioversion, defibrillation, or pacing.

Complex mathematical models are crucial to the development of today’s autonomous mannequin simulators to predict physiological and pharmacological responses. These models first appeared in screen-based simulators before being integrated into mannequin simulators. The models use mathematical terms to describe the relationship between two or more physiologic parameters and behave similarly to real-life processes. The model acts as an “engine” for the simulator and is affected by learner intervention as well as events scripted by the operator. By accurately generating physiologic responses to learner actions, these models can reduce, for instance, the need for operators to specify moment-to-moment vital signs, significantly reducing the operator’s workload to allow more time for observing learner activity.

Not all patient conditions can be easily modeled. Because of inter-patient variability and the large number of contributing factors, there is no way to predict exactly when, for example, a patient will have a cardiac event. A model can, however, calculate the likelihood of such an event based on the information available.

Fidelity refers to the degree a simulator is able to reproduce a real-world environment. Though levels of fidelity are not formally defined, it is generally accepted that the advanced ­mannequin-based simulators discussed in this chapter are in the higher-fidelity ranges. These “full-scale” or “high-fidelity” simulators are usually found in high-fidelity environments and designed to interact with healthcare providers on both the psychomotor and cognitive domains of learning [1]. Fidelity ultimately depends on the intended application. A mannequin designed for neurologic surgery would not be considered high fidelity in a simulated labor and delivery setting where the objectives focus on techniques of vaginal delivery.

Mannequin features such as realistic heart sounds, palpable pulses, and the ability to speak add to simulation fidelity. Though these advanced features come at a higher cost [2], there are many studies that indicate training with realistic mannequins improves clinical performance (ACLS [3], detection of murmurs [4]), and improves skills with full-mannequin simulators (airway management [5], medical emergencies [6], PALS [7], ACLS [8], trauma management [9]). Conversely, some studies indicate that learners training with higher-fidelity mannequins have no advantage in skills or knowledge compared to learners training with lower-fidelity modalities [10–12]. There appears to be insufficient evidence to make a final determination on how fidelity correlates with effectiveness or clinical outcomes. It is important to note, however, that learners consistently report greater satisfaction with more realistic high-fidelity mannequins [12, 13]. In some regards, the most appropriate level of fidelity depends on the learners and the learning objectives. It is important to note that even the most advanced simulators have important limitations and none rival the sophistication of, for instance, flight simulators used by pilots.

Commonly the simulation scenario will last approximately one-third of the allotted time allowing two-thirds of the time for debriefing (Chaps. 6 and 7). Depending on the complexity of the case, the types of learners, and the learning objectives, the simulation scenario may only last a matter of minutes. Due to this relatively short time frame, clinical treatments such as administering a fluid bolus, which may typically occur over 5 min, often occur over 1 min or even instantly in simulation. These time-scaling properties are generally built into the commonly available programming platforms. When programming these treatment effects on the simulator and monitor, it is important that they do not occur too fast to be unrealistic but not too slow that the intensity of the session is lost.

All simulators can be run “on the fly,” meaning without a preexisting program. This works very well when very few changes occur or only one change occurs at a time. For example, if a patient develops supraventricular tachycardia (SVT), one can simply change the rhythm from normal sinus rhythm to SVT. This change can be detected on the patient monitor (increase in heart rate and loss of P waves) as well as the simulator (increases in palpable pulse rate and heart sounds). Clinically speaking, however, many changes occur over time and multiple changes may occur simultaneously. For example, a child in shock will have increased heart rate, decreased blood pressure with either a narrowed or widened pulse pressure, increased respiratory rate, and decreased end-tidal carbon dioxide. If this child is given fluids, there will be multiple simultaneous changes over a period of time. It is very difficult for an operator to program these changes “on the fly” and have each of these changes over simultaneously over time. The common programming platforms, however, allow scenarios to be programmed in a way that by clicking one state such as “improvement” or “fluid administration,” all of these changes begin at once and occur over a specified time (Fig. 15.4).

Operator-driven simulators can be programmed by adjusting vital signs, simulator voice/sounds (i.e., vomiting, grunting, crying), and physical exam findings. The programmer must have a working knowledge of expected changes caused by various treatments. Either too great, small, fast, or slow decreases the believability of the scenario. If the person programming the scenario has limited clinical experience, it is important that they work with someone more familiar with clinical manifestations to carefully adjust magnitudes and speed of treatment effects by someone with clinical experience.

Trending is a helpful programming feature that prevents sudden unbelievable vital sign changes. Trending changes can be programmed to occur over seconds to minutes. Examples of effective changes done through the use of trends are:

As these trends are created, they can be saved and used again for any other scenario. For example, many cases will have a component of hypoxia which is improved with oxygen. You can insert this trend into any of these cases.

Autonomous simulators are programmed by adjusting simulator physiology or by programming events that result in physiologic changes. For instance, if the operator wants to simulate shock, they may simply program the simulated patient to lose 10% of their blood volume over a desired time frame. Unlike operator-driven simulators that require the operator to actually change the vital signs, autonomous simulators will improve by infusing blood. The physiology of autonomous simulators is complex but sometimes needs to be adjusted slightly to make changes occur at a different rate or degree. Complex trending in autonomous simulators may be replaced by the use of scenarios that contain multiple physiologic changes in each state. In addition, if more than one physiologic change is programmed, these changes may interact and affect each other. It is crucial to test these combined physiology changes to ensure a reasonable degree of believability.

CAE Healthcare (formerly METI) has one programming platform that functions autonomously, HPS, and a newer programming platform that functions more as operator-driven, Müse® (Fig. 15.5). Laerdal and Gaumard (Fig. 15.3) platforms are mostly operator-driven but may still respond autonomously to some interventions. All platforms include the ability to log and time stamp most physiological, pharmacological, and event data. Some platforms also automatically log advanced values such as alveolar and blood gases, cardiac output, and hematocrit values. These logs can aid in the programming of scenarios and events based on past simulations that were, perhaps, initially performed “on the fly.”

Nontechnical aspects of scenario programming are important as well. As shown in Fig. 15.1, supporting data such as mock laboratory results, appropriate radiographs improve the fidelity of the scenario. In addition, confederates such as parents, spouses, nurses, doctors, and consultants assist both the reality of the case as well as help move the scenario if learners are frustrated or misinterpret a finding which leads them to a completely different set of differential diagnoses and therapies. An example of this is the use of stridor in the Laerdal infant. This inspiratory sound can be misinterpreted as wheezing. If learning objectives include the differential diagnosis of stridor such as laryngotracheitis and foreign body, this will not be considered if the physical finding misinterpretation finding is not corrected. Confederates can gently redirect learners with statements such as “It sounded more like stridor than wheezing to me” or “I think the noise is occurring during inspiration and not expiration.” Their verbal cues as referenced in Fig. 15.1 are often crucial.

At the writing of this chapter, there are three primary companies that produce programmable mannequin-based ­simulators in North America: Laerdal (Stavanger, Norway), CAE Healthcare (Sarasota, Florida), and Gaumard (Miami, Florida). All three companies manufacture adult and pediatric simulators.