The key themes in human-centered design are the following:
Design for the user population: The device should be designed for a carefully identified group of users (not just “experts” or “opinion leaders”). They should be involved at every stage of the design process (including conception), with testing conducted throughout with a chosen sample of those anticipated users. One in ten users will be color-blind. Older users may not have the digital dexterity of younger users.
Designs should be adapted to users, not users to designs. Relying on training, memory, warnings, or instructions as a solution to a design problem is weak, expensive, and error inducing.
Affordances: Designs should reflect intended use. For example, a handle on a door that you pull, or a push-plate on a door that you push.
Consistency: The way users interact with devices should, as far as possible, not vary when using similar functions. For example, changing between numeric keypads with “telephone” type and “calculator” type will predispose a keying error.
Redundancy: There should be multiple failure avoidance mechanisms built in. For example, to make a clear distinction on an important dimension, the color, look, and feel should all be different.
Control and display compatibility: How you change something on a device should reflect how it is being changed in the real world.
Functional grouping: Similar functions, displays, and switches regularly used together should be located together. Some anesthetic machines have the power switch located closer to the suction container than the suction power switch. This predisposes to errors.
Understand contexts of use: Where the device is used needs to be considered within a design. The environment, the physical space, interactions with other devices, people, or tasks all affect usability. If an item is to be used while gloved, this may reduce tactile cues.
Procurement: The people who purchase devices for an organization should be the people using them. For many high-cost purchases, user trials would be highly beneficial and cost effective.
Cognition in Context
Humans make decisions within a broad systems context, and problems with decision making are more common than errors in technical skill [23]. Cognition within work contexts and how it leads to decision making have been of extensive interest in HFE and applied psychology research. Traditional clinical decision making tends to focus on which decision from several is best, often based on comparative evidence-based studies. In contrast, HFE focuses on the mental processes by which an understanding is reached and how a decision is made. It is often focused on process decisions—how we set goals and reach them, or how we navigate a patient through the complex sequence of care required to deliver the appropriate care. In this section we consider three different but dominant paradigms of relevance, situational awareness, naturalistic decision making, and distributed cognition.
Of the three paradigms in this chapter, situational awareness (SA) [24, 25] is perhaps the simplest to understand. As with much HFE work, SA research stems from aviation research, where situational awareness was considered to be a deciding factor in air combat success. Subsequent studies arrived at three levels of perceptual and cognitive processing that can be considered in most dynamic, rapidly changing high-technology tasks. The three levels are the following:
Level 1 SA: Noticing (“What?”): This is the basic perceptual level of SA where important elements in the environment become salient to the observer/operator via the basic senses. They might register a change in blood pressure, or a distinctive smell, a vibration or a touch, or the presence of absence of a sound. Without awareness of these stimuli, the next level of SA cannot be reached.
Level 2 SA: Understanding (“So what?”): This is the interpretative stage, where the operator applies meaning to the data they have become aware of in stage 1. It is one thing to recognize a change in the environment, and another to know what it means for the task at hand. Technical training is often focused at this stage. In air combat, knowing what speed you are at combined with the optimal turning speed for your aircraft helps you to understand how close to an optimal turning state your aircraft is currently in. In healthcare, for example, this would be understanding the hemodynamic implications of different arterial pressure locations and measurements.
Level 3 SA: Projecting (“Now what?”): The highest form of SA is being able to predict future states of the system you are working in. Noticing and understanding what is happening, and applying your previous expertise to make predictions about what will happen next, enable the human to respond in the most appropriate way to move closer to the desired goal. In the original air combat scenario, thinking ahead allowed the pilot to avoid getting into low-energy states that an enemy could take advantage of, and instead allowed the pilot to move into a firing solution position. In cardiac surgery, understanding the trajectory of a patient’s vital signs, and responding early if the predicted outcome is undesirable, yields safer, more responsive care. Projecting is the most challenging level of SA .
The more expertise you have, the better able you are to rise up through the levels of SA; while the higher your workload, the more distractions there are, or the more unpredictable or complex the situation is, the more cognition will reside in the lower levels. The less able we are to project into the future, the more likely we are to arrive at a point that is undesirable, unsafe, or even more error inducing. This is why experienced pilots may tell you that they will always anticipate where their aircraft will be in the future, and never aim to fly in a reactionary way—which means that they can plan more effectively, and will stay out of serious trouble. When they can no longer do this, they know that they are in a risky situation.
A simple example of how the three levels of SA interact can be found in driving. Imagine you are driving along a highway and slower moving traffic is merging from an on ramp. You see a car on the on ramp moving slower than you (Noticing/Level 1 SA). You understand that this means that there is a risk of collision and that you may need to make a decision to alter your course (Understanding/Level 2 SA). You recognize that your car and the merging car will arrive at about the same time at the point where the ramp merges with the highway (Projecting/Level 3 SA). This means that you need to decide to speed up, slow down, or change lanes. You look in your mirrors and check your blind spot seeing, that there are no other cars nearby (Level 1 SA). You realize that this means that you can move into the middle lane (Level 2 SA) and that there is time to execute this move in plenty of time before your paths cross (Level 3 SA). You therefore decide to move into the middle lane. The more cars there are on the road with differing speeds and locations, the more variant your or the speed of the merging car is, or the worse the visibility or shorter the timescale, the more difficult this decision will be, and thus the more risk will be experienced. This is also affected by driver fatigue, experience, distractions, alcohol, automation (which often reduces awareness ), and even the familiarity they have with the vehicle and the road on which they are travelling.
Thus, the concept of situational awareness helps us to understand how information is used to make accurate decisions; and how the clarity of the information, the environment, the training and expertise of the human, and their active involvement in the task over time helps us to make safe and appropriate decisions within complex, unpredictable, changing situations [26]. The best decisions are made when key information is presented clearly and understood by someone with enough expertise and who has been involved in the task long enough to predict what is going to happen next and account for it.
In situations where the goals , and ways to achieve them, may not be as straightforward, the naturalistic decision-making paradigm [27] can be useful. It helps us understand how human decision making is mediated by technological, organizational, and environmental contexts in greater uncertainty, and less dynamic or fluid situations. It has been extremely influential in the science of applied cognition, especially in military operations [28], although it has not been widely applied in healthcare. Decisions are not necessarily logical, linear, and evidence based. Instead, they are based on a wider view of multiple patients, expertise, systems complexity, behavioral intention, individual beliefs, and current understanding of the system. This research has led to a number of conclusions that often run counter to how clinical decision making is usually considered, such as the following [29]:
Experienced decision makers can draw on patterns to handle time pressure and never even compare options.
Expertise in decision making does not depend upon learning rules and procedures but on tacit knowledge.
Problems are not always solved by a clear description of goals at the outset, since many projects involve wicked problems and ill-defined goals.
Humans do not make sense of the world as “information processors” by fusing multiple data streams into eventual understanding—instead, experience and understanding define the important data streams, and most data is ignored.
Uncertainty is not necessarily reduced through more information—too much data reduces performance, while uncertainty can stem from an absence of contextual cues that accompany data.
Decision making is not necessarily improved by understanding assumptions since we may be unaware of our most flawed assumptions.
Moving towards more complex, team-based tasks, studies of human-system relationships in socio-technical environments have also led us to consider that cognition and decision making are not purely the properties of what occurs in the head of one individual. In fact, cognitive processes are often shared between different individuals working together through communication and shared culture; across material environments which aid in recall and action through cognitive artifacts such as computer displays or hand-written notes; and across time, where strategies, approaches, protocols, cultures, and artifacts accumulate over time. This is known as distributed cognition. The classic text by Hutchins (“how a cockpit remembers its speed”) [30] considers the aircraft cockpit as the cognitive unit, and the people, displays, and procedures all components of how cognition is successfully distributed to achieve an understanding of the world that would be impossible for any one component alone. More recently, this approach has been used in anesthesia and other healthcare-related settings [31], considering the following:
How information flows in tasks and between people.
How tools and representations of work (such as protocols or checklists) are structured and how they affect the work.
How the physical layout of a room or environment affects the distribution of information.
How the social structure—roles, relationships, knowledge, and goals—affects the “cognition” of the whole.
How the whole changes over time.
This alternative approach to the reductionism found in more traditional science and engineering approaches has yet to be well recognized within healthcare, but would seem extremely apt for understanding the complex, highly distributed tasks found in cardiac surgery. In particular, perfusion management requires the complex coordination of people, equipment, information, and tasks in order to perform appropriately. No one person has full knowledge of every aspect of this task. Thus, perhaps we should consider “how an operating room manages cardio-pulmonary bypass.”
Performance-Shaping Factors
In this final section, we explore how environmental factors often outside the control of the human can affect human performance. These “performance-shaping factors” include fatigue, noise and vibration, lighting, temperature and humidity, and physical constraints of the workspace. A huge number of experimental studies have explored the effects of these different stressors on a variety of tasks. They can also be considered in terms of staff safety, offering environmental risks. There is a growing interest in these factors and the role they play in patient outcomes. Though there are many models, the general concept is that these factors adapt cognitive capacity downwards, increasing errors. This creates further opportunities for failure that further reduce human capacity, leading to a spiral of increased risk. Fatigue, for example, compromises perceptual abilities, increasing the chances of errors, and decision making, reducing the likelihood of appropriate responses . Noise can mask important communication, and can either reduce or exacerbate fatigue, depending on the types of noise and individuals experiencing it. Interruptions and distractions divert attention from the primary task, which can reduce hand-eye coordination, create task fragmentation, increasing the chances of forgetting or omitting steps, and introduces delays while the human switches away from, and then back to, the primary task. Temperature and humidity increase physiological stress, can lead to dehydration and fatigue, and can also create interruptions, for example, while the human wipes their brow or clears fogging of a lens or goggles (Fig. 4.2).
