Practice of GIM
Patient Safety and Acute Care Medicine: Engineering the Future
Peter G. Brindley, MD
About the Author
Peter Brindley is an associate professor in the Division of Critical Care, University of Alberta, Edmonton, Alberta. He is the medical lead for patient simulation for Capital Health, Alberta, and vice-president for the nonprofit Canadian Resuscitation Institute. Correspondence may be directed to peterbrindley@cha.ab.ca.
An estimated 40,000–100,000 Americans die annually as a consequence of medical error.1 Many thousands more suffer harm from medical errors, and still others are exposed to errors but are lucky to suffer no obvious harm.2 In fact, medical errors are now the eighth leading cause of death, and Canadian data are just as worrisome.3 This highlights a significant shortfall in patient safety that demands urgent action. This article is a call to arms, a perspective on current practices, and a guide on how we might mitigate patient risk.
The Missing Curriculum
Albert Einstein said, “You can never solve a problem by using the same kind of thinking that created it.”4 We need to recognize that medical errors are not simply the consequence of individual negligence, sloppiness, incompetence, or poor motivation. The health care industry is one of the most complex social systems in the world.2 When one considers the complexity of medical decision making, often done under conditions of time pressure, one can understand how these errors happen.
“Safety is no accident”2; that is, errors in health care are seldom random, unpredictable events. They occur in the setting of established organization against a backdrop of tradition and predictable obsolescence. One might ask how professionals working in such a system can do so without formal training in communication, coordination, and outcome appraisal: yet these skills are rarely taught to, or sought after by, applicants.5 While medical trainees are versed in the science of medicine and taught how to look after individual patients, few are qualified to tackle systemic safety issues or to understand how human beings work in large groups and interact with complex systems.
Engineering and Medicine
Physicians debate whether medicine is more science or art. However, the practice of safe patient care might be better understood as engineering. After all, engineering means “applying the best current technical, scientific and other knowledge to design and implement structures, machines, devices, systems, and processes to safely realize a desired objective.”6 Other high-risk industries, such as aviation, have achieved a log reduction in fatalities by applying engineering principles. There is now one fatal crash per 4.5 million takeoffs, and the most dangerous part of a pilot’s day is the drive to the airport, not the impending flight.7 Sadly, the same cannot be said for patients entering a health care institution.
An engineering approach also means promoting standard operating procedures (e.g., protocols and checklists) and encouraging redundancies (e.g., double-checks, fail-safes, second opinions, and time outs). Engineering philosophy also acknowledges that such a complex system cannot be understood or managed by any one individual.2 Engineering practice continually updates and utilizes the best current information, however incomplete. In the engineering model, near misses represent an opportunity to improve the system, especially if openly discussed – with the goal that all can contribute and learn. One hopes that this approach fosters a sense of responsibility and empowerment, rather than resignation and defeat.
The goal of aviation is safe, efficient, and predictable travel from A to B. There is little reason why we should not similarly strive for safe, efficient, and predictable care from A to D (admission to discharge). Hospital care consists of input, throughout, and output in the same way that air travel involves takeoff, flight, and landing. Maybe the role of the physician should be akin to a product safety engineer, coordinating the overall safe transit of a patient through the system, rather than being responsible for all aspects of care.
Engineering and Error Prevention
Continuing with our engineering analogy, errors are generated when a system allows us to cause them. We can address these faults by identifying problems with personnel, technology, training, and administration.2,5 However, the most important defence against error is culture: the collection of attitudes, beliefs, and values held by the organization.2 Ideally, the combined defensive layers are impermeable. In reality, there are weaknesses in any system. The layers of defence are like slices of Swiss cheese that contain holes: Because there are multiple layers, the presence of single holes does not normally lead to a bad outcome. In contrast, when mishaps occur, the holes in layers have lined up, at least momentarily.2,8 When an adverse event occurs, the important question is not who made the error but how did it happen, why did the defences fail, and what can be done to prevent future errors? This contrasts with the traditional medical approach in which the focus is on assigning responsibility (“name, blame, shame”). Efforts to reduce error, however well intentioned, thus emphasize discipline and retraining but ignore the context in which the error occurred.2
Process-engineering principles could also change how we educate.5,8 Rather than relying upon teachers to simply cover the standard topics, without attention to their relevance, curricula could focus on the goal of safe care. Routine audits would establish major problem areas (e.g., common shortfalls or steps that require particular precision or the coordination of many people), which would then be widely shared. A curriculum would then be drafted and alpha-tested. Finally, widespread dissemination would occur using the optimized material (i.e., beta-tested), and the process would begin again. In this way, educators would not be merely passing facts from one generation to another but, rather, running a patient safety laboratory (or crash-test site) for the modern hospital.5,8 As such, educators would become agents of change and valued as highly as good researchers and clinicians.
Maximizing the Best of Human and Machine
Modern-day hospital medicine requires that we try to understand both the human and technological factors that impact on care. This interface needs to be explored. With this in mind, the 1997 chess match between world champion Garry Kasparov and IBM’s Deep Blue supercomputer offers provocative insights.9 Kasparov (an example of the human mind) won the first game, and Deep Blue (our example of technology) won the second, proving both capable of impressive performance. However, Deep Blue was capable of evaluating 200 million positions per second, whereas Kasparov could only evaluate a handful of moves and overlooked others when overly focused. The inability to pick up on clues in medicine is known as a fixation error and is a major source of error, even for experienced practitioners.10
Deep Blue never fatigued or succumbed to emotions. Kasparov had to be nourished and rested. Deep Blue possessed a perfect opening and end game. Kasparov could think abstractly and plan long-term strategies. Using pattern recognition, Kasparov recognized fragments of previous games in order to choose the most appropriate game plan. When Kasparov won, he did so by maximizing the middle game, when there were too many pieces (variables) on the chessboard for Deep Blue to compute. When the computer won, it was through consistency, aided by an impeccable memory.
Humans excel at pattern recognition. In contrast, we are often poor at recognizing or responding to gradual deterioration, we are prone to tunnel vision (ignoring additional clues due to excessive focus), and we are weak at calculation (13 × 39 = ?). Computers are worse at pattern recognition but excel with calculation and vigilance. The first lesson for health care from the Kasparov versus Deep Blue match is that we should leverage each participant’s particular strength: humans to recognize the constellations of symptoms, and computers to monitor vital signs and ensure a response to gradual changes or concerning trends. Just as good chess is proactive rather than reactive, the best crisis management begins before a crisis erupts.10
The next lesson is how Kasparov and Deep Blue’s programmers learned to mitigate their respective weaknesses. Kasparov used computer chess engines to objectively analyze positions. Deep Blue’s programmers worked with chess masters who recommended certain strategic decisions and used opening moves based upon their collective wisdom. It was argued that both man and machine were actually cyborgs: functional hybrids of each other.11 Kasparov and Deep Blue’s programmers learned that harnessing the best of the human-technology hybrid created more than the sum of its parts. We should learn that this is not a battle of human independence versus technological dominance but a search for synergies in order to achieve excellence. Realizing the best of human and technological strengths is the optimal strategy: hopefully, the patient will be the ultimate victor.
References
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2. Aron D, Headrick L. Educating physicians prepared to improve care and safety is no accident: it requires a systematic approach. Qual Saf Health Care 2002;11:168–73.
3. Baker GR, Norton PG, Flintoft V, et al. The Canadian Adverse Events study: the incidence of adverse events among hospital patients in Canada. Can Med Assoc J 2004;170:1678–86.
4. Brainy Quote. Definition of Solve: Albert Einstein. BrainyMedia, 2009; http://www.brainyquote.com/words/so/solve221543.html. Accessed May 20, 2009.
5. Dunn W, Murphy J.G. Simulation: about safety, not fantasy. Chest 2008;133:6–9.
6. Wikipedia, the Free Encyclopedia. Engineering. Wikimedia Foundation, 2009; www.en.wikipedia.org/wiki/engineering. Accessed May 20, 2009.
7. Wald, ML. Fatal airlines crashed drop 65%. The New York Times 2007 October 1; http://www.nytimes.com/2007/10/01/business/01safety.html. Accessed May 20, 2009.
8. Barry R, Murcko A, Brubaker C. The Six Sigma Book for Healthcare: Improving Outcomes by Reducing Errors. Chicago (IL): Health Administration Press; 2002.
9. Miah A. A deep blue grasshopper. Playing games with artificial intelligence. In: Hale B, ed. Philosophy Looks at Chess. Chicago (IL): Open Court; 2008:13–24.
10. Gaba DM, Fish KJ, Howard SK. Crisis Management in Anesthesiology. New York: Churchill Livingstone; 1994.
11. Hartmann J. Garry Kasparov is a cyborg, or What ChessBase teaches us about technology. In: Hale B, ed. Philosophy Looks at Chess. Chicago (IL): Open Court; 2008:39–64. |
