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Embracing the Certainty of Uncertainty
Implications for Health Care and Research

“Uncertainty” is the ongoing realization that we cannot predict the future, and “surprise” reminds us lest we forget. Despite the fact that uncertainty is an undeniable fact of everyday experiences, in particular when providing care to patients, it is ignored and under-evaluated scientifically. Understandably and appropriately, medical science seeks knowledge, certainty, and prediction; however, the fundamental truth of intrinsic irreducible uncertainty remains neglected. The principal hypothesis of this article is that greater acceptance and understanding of intrinsic uncertainty offers valuable insights towards improving the delivery and management of health care, as well as the performance of clinical and basic science research. This review highlights the ubiquitous presence and acceptance of irreducible uncertainty in diverse domains of science, defines and classifies uncertainty arising from this awareness, and explores the insights and implications of this understanding with regard to health-care practice, health-care management, physician-patient communication, basic science research, and clinical research. It offers specific recommendations in each area of focus that are proposed to stimulate deliberation and investigation.

Centuries of scientific progress have been devoted to reducing uncertainty. Newtonian physics, introduced over 300 years ago, allowed for precise prediction of planetary and tidal motion, falling bodies and infinitely more, in addition to allowing the construction of the material world. The 20th century [End Page 65] witnessed a revolution in our understanding of organ and cellular function and dysfunction, elucidation of pathways, mediators, receptors, and molecular interactions, and breakthroughs in the characterization of replication, transcription, and translation, all of which has been integral to our understanding of human physiology and pathophysiology. Clinical epidemiology has had a revolutionary impact on our understanding of risk factors for illness, prognostic factors, optimal therapies, and so much more.

Scientists yearn for increasingly detailed knowledge of the present in the hope of predicting the future, and therein lies an insurmountable challenge. Starting with Claude Bernard, applying the laws of chemistry and physics has been a means to predict the future. However, as will be discussed, not even pure physical systems governed by Newtonian mechanics allow for prediction with certainty. In fact, physicist Sir James Lighthill (1986) made a formal public apology on behalf of his colleagues, acknowledging that physicists had long ignored the truth of irreducible uncertainty. While incontrovertible evidence demonstrates that increased knowledge does not imply increased ability to predict, it does not diminish the enormous successes and impact of increased knowledge through basic science and epidemiology.

Ubiquity of Intrinsic Uncertainty and Surprise

The ubiquitous presence of uncertainty is clear even within the “core” sciences of physics, chemistry, and mathematics. In quantum mechanics, the Heisenberg uncertainty principle acknowledges the intrinsic uncertainty inherent in the measurements of position and momentum of subatomic particles; it is impossible to know both with unlimited precision. Similarly, the second law of thermodynamics (an isolated system displays increasing entropy or disorder as useful energy is converted to heat) describes change from statistically improbable configurations to configurations with greater probability, and consequently describes tendencies, not certainties. In 1900, Henry Poincaré demonstrated conclusively in a “simple” model of three gravitationally bound objects governed by physical laws that very small perturbations in initial conditions could lead to dramatic changes in future trajectories, and in so doing, uncovered the scientific concept now known as “chaos.” In chaotic systems, inevitable inaccuracies in the descriptions of the present lead to limitations on the accuracy of the predictions of the future, referred to as the “predictability horizon.” Chaotic behavior may be described with simple nonlinear differential equations that in turn can be applied to numerous biologic, geologic, economic, physical, and ecologic systems (Griffiths and Byrne 1998). Last, within the field of mathematical logic, Gödel’s incompleteness theorems, published in 1931, demonstrated that mathematical systems that are internally consistent must be incomplete, and if a system were complete, it would have to be inconsistent. As accepted by basic scientists, uncertainty [End Page 66] is not a product of our inadequacies, rather it is written into the fabric of our existence from the quantum to the complex.

Definition and Classification of Uncertainty and Surprise

Uncertainty is defined as the “relative degree of our inability to predict the future.” It may be viewed as a dynamic and variable function of time, capable of stable behavior or erratic variation. Given the indisputable evidence of irreducible, fundamental, or innate uncertainty within the core of our basic sciences, it may be reasonable to consider the function of overall uncertainty as a sum of informational uncertainty, secondary to imprecise knowledge of the present or past, and intrinsic uncertainty, which is wholly independent of knowledge. Although both informational and intrinsic uncertainties will vary over time, the former will decrease with increasing knowledge and the latter will vary unrelated to knowledge. Theoretically, as knowledge and understanding approach infinity, informational uncertainty will approach zero. However, even in the presence of infinite knowledge, intrinsic uncertainty is neither altered nor eliminated. This distinction provides the conceptual framework upon which we can embrace the presence of intrinsic uncertainty within health care.

Insights to Accepting Uncertainty

Despite our personal awareness and experience of uncertainty, and its scientific realization in other disciplines, the focus in academic medicine remains on establishing knowledge and certainty, most commonly in establishing a diagnosis, formulating a prognosis, or instituting a therapy. This noble and valuable focus should not subvert a complementary focus on understanding uncertainty, as there is no conflict between seeking to augment certainty while simultaneously acknowledging irreducible uncertainty. More specifically, accepting intrinsic uncertainty is complementary to our desire to reduce and quantify informational uncertainty. This article will examine the thesis that acknowledging and affirming the “certainty of uncertainty” has the potential to improve health-care practice, health-care management, physician-patient communication, basic science, and clinical research for patients. In each of these diverse but related domains within health care, recommendations derived from accepting and embracing irreducible intrinsic uncertainty will be summarized. (See Table 1.)

Uncertainty in Health-Care Practice

Since the Flexner report in the early 1900s (Begun and Kaissi 2005), the evolution of health care was rooted in the principal belief that creating new knowledge will reduce fundamental uncertainty. To this end, innumerable basic science and technology breakthroughs of the past century have led to important [End Page 67]

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Table 1.

Manifestations and Implications of Uncertainty

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advances in our understanding and treatment of illnesses and increased societal trust in the science of medicine. While patients greatly rely on the specialized skills and knowledge of health-care professionals to reduce uncertainty, patients also rely on our experiences and judgments to interpret and manage uncertainty. Navigation of uncertainty regarding illness and treatment is the work of health care professionals. Uncertainty is ubiquitous in medicine (Ghosh 2004). In addition to intrinsic uncertainty, Beresford (1991) classified informational medical uncertainty as related to (1) technical uncertainty due to inadequate scientific data, (2) personal uncertainty due to being unaware of patient’s wishes, and (3) conceptual uncertainty due to an inability to apply abstract criteria to concrete situations. Gerrity et al. (1990) identified uncertainty in history taking, tests and treatment characteristics, and organization characteristics, and described variability in how physicians respond to uncertainty.

Both intrinsic and informational uncertainties exist in all of these domains. As patients and families increasingly educate themselves about illness and try to understand the inherent uncertainties in diagnosis, prognosis, and management, there is a need for greater appreciation, analysis, and admission of irreducible uncertainty in the clinical courses of individual patients, and for strategies to reduce uncertainty due to inadequate knowledge. Tonelli (2006) argues that effective clinical decision-making must take into account empirical evidence, clinician’s experiential knowledge of pathophysiology, facilitators and barriers to care provision, and the goals and values of individual patients. Meanwhile, others note that quantitative approaches alone are insufficient for dealing with uncertainty and that qualitative approaches (such as consensus building and teaching physicians to accept and cope with uncertainty) are critical to promoting effective clinical decision-making. Rizzo (1993) highlights that a broad-based approach, which draws on both scientific evidence and consensus building, may represent the best approach to effective clinical decision-making and managing physician uncertainty. When building a consensus with patients, physicians need to be aware that their personal attitudes toward uncertainty and risk may influence their clinical decision-making. For example, Tubbs and colleagues (2006) found that personality traits strongly affected clinical practice, namely that physicians characterized as “risk-seeking” admitted significantly fewer patients who subsequently did not have acute myocardial infarction compared to “risk-avoiding” physicians (29% versus 47%, respectively). Lastly, Philip and Gold (2005) distinguish between the uncertainty arising from the disease (for example, treatment response, prognosis) and that arising within the physician-patient relationship, where ambiguity in the direction of care may result in wasted opportunity, effort, and time.

As physicians, we should first acknowledge our profession’s paradoxical relationship with uncertainty, which entails seeking to reduce informational uncertainty while simultaneously navigating the minefield of intrinsic uncertainty for our patients. This requires that we develop skills to differentiate informational [End Page 69] from intrinsic uncertainty, as well as communicate this distinction (discussed below). Greater emphasis on differentiating informational from intrinsic uncertainty is required within research and medical education. Last, to better understand and communicate the nature of intrinsic uncertainty with our patients and their families, we must confront our own beliefs and values regarding uncertainty.

Uncertainty in Health-Care Management

Health-care management and delivery is executed within complex systems involving a myriad of health-care professionals that need to be coordinated, managed, and regulated (Glouberman and Mintzberg 2001a, 2001b). The traditional approach to uncertainty or the occurrence of surprise in a management organization is to treat it as unwelcome, or as evidence of failure that requires someone or something to manage it, fix it, or control it. Notwithstanding, several authors have evaluated how accepting and embracing intrinsic system uncertainty, without eliminating the quest to decrease uncertainty secondary to inadequate information, offers valuable insight and opportunities (Anderson and McDaniel 2000; Begun and Kassi 2004, 2005; Glouberman and Mintzberg 2001a, 2001b; McDaniel and Driebe 2001; McDaniel, Jordan, and Fleeman, 2003; Montgomery 2003).

McDaniel and Driebe argue that the health-care system is a complex adaptive system consisting of nonlinear dynamics, self-organization, emergence, and co-evolution, where the behaviors of this system are fundamentally enigmatic (Anderson and McDaniel 2000; McDaniel and Driebe 2001). An understanding of the characteristics of complex adaptive systems shifts the managerial focus “from knowing the world to making sense of the world, from forecasting the future to preparing the organization to meet an unknowable future, and from controlling the system to unleashing the systems potential” (McDaniel and Driebe 2001, pp. 24–25). McDaniel and colleagues (2003) clearly articulate how a principal value of a positive outlook of uncertainty provides stimulation to learn and be creative and aids in turning crises into opportunities. Underlying their hypothesis is the observation that traditional negative interpretations of surprise may not only be unhelpful or harmful, but also negate the potential to capitalize on their inherent opportunities.

However, reframing surprise as an opportunity requires risk-taking and trust among patients, physicians, and health-care organizations (Montgomery 2003). Begun and Kaissi (2004, 2005) have identified strategies and policies for management of uncertainty at the organizational level, which include: the importance of complexifying, experimenting, and learning, rather than only simplifying and standardizing; an emphasis on holistic culture, rather than purely on reductionist structure; stimulating rather than controlling information flow; acknowledging that mistakes can be the result of the system, not the individual; allowing variable integration; and managing as a “sense-maker” rather than a [End Page 70] “decision-maker.” This terminology must be interpreted with care, as “decisions” are clearly essential to effective management; the point is they require dynamic reevaluation. A common theme in this evolving literature is the importance of continuous dynamic reevaluation, in order to capitalize on the inherent potential for innovation to emerge from uncertainty and surprise. However, the same authors also caution against the exaggeration or overestimation of the degree of uncertainty within management of health-care organizations, citing the potential for management nihilism or overreaction (Begun and Kaissi 2004). A holistic, measured, and dynamic appreciation of uncertainty is what is optimally helpful.

Managers of health care should strive to embrace both uncertainty and complexity, welcoming surprise as an opportunity for learning and improvement, and encouraging creative experimentation and dynamic reevaluation, while simultaneously avoiding over-estimating or over-reacting to uncertainty. A capacity for continuously reexamining the system and its culture allows for optimal and timely management decisions.

Uncertainty in Patient-Physician Communication

Acknowledging the presence of inherent uncertainty of the future clinical course of an illness allows the identification and discussion of patients’ and families’ apprehension and fear, encourages questioning, assists patients’ and decision makers’ ability to make decisions regarding care and research participation, and is an essential component of informed consent. However, one study reports that uncertainty was made clear only 5% of the time during the informed consent process (Braddock et al. 1999), corroborating previous studies demonstrating that uncertainties in diagnosis, prognosis, and treatment are inadequately disclosed (Atkinson 1984; Katz 1984).

In a review on uncertainty and the physician-patient relationship, Henry (2006) states that “physician failure to disclose uncertainty concerning standard of care has increased the responsibilities of the patient,” whereas disclosure of uncertainty “allows for greater communication and an advanced level of shared decision making,” all leading to an improved physician-patient relationship with greater patient satisfaction (p. 322). In an audiotape study of physician-patient interviews, Gordon and colleagues (2000) found expressions of uncertainty present in 71% of clinic visits, and an increase in patient satisfaction with physician expression of uncertainty. However, physicians who made more uncertainty statements also used more positive talk and partnership building and provided more information to patients. Therefore, clinicians who discuss uncertainty do so in conjunction with communication patterns that patients respond favorably to.

There are a number of methods for disclosing uncertainties in primary care that can result in an increase in patient satisfaction (Biehn 1982; Ghosh 2004; Hewson et al. 1996; Johnson et al. 1988; Ogden et al. 2002; Parascandola, Hawkins, [End Page 71] and Danis 2002). For example, Hewson and colleagues (1996) identified nine strategies for effective management and expression of uncertainty in patient care, which include: (1) defining the context of the diagnosis and explaining the signs and symptoms as part of the expected spectrum of the disease; (2) eliminating alternative diagnoses by dealing with patient fears and giving reasons in the context of the patient’s belief system; (3) describing the prognosis in terms of the likely course of the disease and the expectations of treatment; (4) negotiating key issues that are important to both patient and physician; (5) negotiating the plan and ensuring the patient understands, and is willing and able to comply, given his or her particular context; (6) keeping diagnostic options open by making provisional diagnoses while keeping alternatives in mind; (7) being circumspect and taking action to minimize the possibility of missing other critical diagnoses; (8) playing for time by allowing signs and symptoms to develop to help clarify the diagnosis; and (9) planning for contingencies by providing appropriate if/then statements concerning situations requiring further action (Hewson et al. 1996). Acknowledging uncertainty does not mean abandoning patients to their autonomy; it is the physician’s responsibility to manage the decision-making process in a fashion in keeping with each individual patient’s values and beliefs. By acknowledging uncertainty within patient care, the physician-patient relationship can be elevated to one of greater communication and shared decision-making.

In order to ensure an honest and dynamic exchange of information between physicians and patients that improves patient satisfaction, physicians should strive to openly acknowledge and discuss the presence and significance of uncertainty, discuss where tests will be utilized to reduce informational uncertainty, and highlight irreducible uncertainty within the context of diagnosis, prognosis, and treatment.

Uncertainty in Basic Science Research

In response to observing inexplicable unpredictability in clinical practice and research, we endeavor to uncover new knowledge, especially within physiology and pathophysiology. Our culture is to discover and study knowledge in the laboratory. Consequently, we do not embrace uncertainty when conducting medical research. Experiments are controlled; individual variables are isolated and varied to evaluate impact and effect.

In an environment of the pursuit of knowledge, what advantage is there in embracing the undeniable existence of intrinsic uncertainty? First, many scientists, have commented on the need to carefully observe for the unexpected, as chance events only offer value to individuals capable of identifying and pursuing them. Second, observations regarding factors that augment uncertainty are inadequately investigated. For example, what factors lead to an experiment providing [End Page 72] results divergent from those previously performed? Purposeful examination of failed reproducibility under variable conditions may identify unique conditions where unexpected events occur. Third, the intentional exploration of uncertainty may prove useful when attempting to evaluate therapeutic interventions in a highly controlled laboratory setting prior to their translation into a relatively uncontrolled and heterogeneous clinical environment.

Laboratory investigations are frequently initiated following seminal clinical observations (such as increased inflammatory mediators in patients with septic shock). In the lab, both in vitro and in vivo experiments are performed in a controlled manner to describe the association between dependent variables—such as increasing tumor necrosis factor (TNF)-alpha concentration—and their impact on a pathway or process—such as inflammation or mortality in a sepsis model. The controlled process is ideally suited to identify mechanisms and pathways of interaction that may, in turn, suggest avenues for therapeutic intervention. Subsequently, the clinical impact of a therapeutic intervention is also performed—anti-TNF therapy for sepsis—in the laboratory in a controlled fashion, for example, by using genetically identical animals, the same insult, and the same timing. When the same process is repeated in an uncontrolled clinical setting, such as multicenter RCT of anti-TNF therapy for septic shock, it is often unsuccessful.

As opposed to the controlled laboratory setting, there is always uncontrolled heterogeneity of clinical insult, genetic predisposition, and comorbidities, as well as variation of timing of insult and intervention in clinical trials. Thus, given the capacity for chaotic dynamics, cascade behavior, nonlinear interactions, and more, complex systems such as the host response to infection are inherently unpredictable when exposed to systemic insults or interventions (Seely and Christou 2000), leaving clinicians at a loss as to define which patients may benefit from proposed interventions. The assumption that a finding in a controlled laboratory applies to an uncontrolled clinical environment ignores the uncertainty inherent to the uncontrolled conditions, and thus remains limited only to the conditions examined.

If one accepts that host response to a major systemic therapy (such as anti-inflammatory therapy) will always be unpredictable in individual patients, then we should consider the following strategies: (1) deliberately altering lab variables (timing of insult or therapy, genotype, age of animal) to evaluate their impact in a simulated uncontrolled laboratory environment to better simulate real-world uncertainty; and (2) exploring potential biomarkers that identify if an individual patient has positively responded to a therapy, or conversely has been harmed by it. The capacity for individualized feedback regarding effectiveness of therapy provides the capacity to modify therapy in a dynamic, individualized, algorithm-based approach. In an effort to bridge the gap between controlled lab and uncontrolled clinic, these strategies may assist in translating knowledge from the bench to the bedside. [End Page 73]

Accepting the intrinsic uncertainty inherent to systemic insult or intervention in a complex system highlights the challenges and limitations of translating controlled laboratory investigations to effective therapeutic intervention in an uncontrolled clinical environment. Thus, deliberate simulation of uncontrolled environments in the lab, specific exploration of factors that augment uncertainty and instability, and focusing on biomarkers to monitor the presence of absence of effectiveness of therapy in individual subjects are three strategies that embrace intrinsic uncertainty and may help bridge the bench and bedside.

Uncertainty in Clinical Research

The science of epidemiology is a population-based scientific response to address, measure, and thus reduce clinical uncertainty. The greatest appreciation and quantification of the concept of uncertainty in health care exists within clinical epidemiology. Confidence intervals and p-values are meant to precisely quantify the uncertainty that a study sample reflects the population.

However, there is room for greater exploration of the science of evaluating individual patient uncertainty. First, data presentation must highlight individual as well as population responses. For example, when highlighting response to a therapeutic intervention in a cohort of patients, the display of individual patient data in addition to the average response would assist clinicians in identifying what proportion of patients respond therapeutically. An evaluation of why specific patients do not respond to therapy or behave as others do offers additional avenues for investigation. Second, the focus on individual patient uncertainty as a function over time may provide valuable clinical insights. For example, in patients recovering from major surgery, myocardial infarction, or multisystem trauma, what are the periods of maximal clinical uncertainty (risk of arrhythmia, bleeding, infection)? What factors augment or reduce uncertainty? When do diagnostic tests reduce uncertainty, and when do they have no impact? A focus on both informational and intrinsic uncertainty as functions over time in populations may prove useful for management decisions (ICU versus ward) and in timing interventions. Third, if one accepts inherent uncertainty regarding individual response to intervention, then designing clinical trials of protocols rather than individual agents, with continuous evaluation of the patient response, offers a means to evaluate therapeutic impact. Examples of highly successful protocolized goal-directed trials with individualized evaluation of patient response include the resuscitation of early septic shock, the eradication of hepatitis B viral load prior to hepatic transplantation, and more (Rivers et al. 2001; Tchervenkov et al. 2001). A protocolized approach to population science, requiring that individual patient responses be evaluated and dynamically incorporated into the intervention, is a technique that both acknowledges and makes use of individual patient uncertainty. [End Page 74]

Accepting uncertainty regarding individual patient response suggests greater focus on characterizing patterns of individual patient responses within a population, rather than the population as a whole. In trials of therapeutic interventions, we must identify which patients favorably respond to or benefit from an intervention and which patients do not, and why. Accepting that one cannot predict an individual patient’s response to intervention a priori, the ability to determine its effectiveness requires that we identify and monitor favorable responses to intervention within each patient. Once monitoring is defined and validated for a particular intervention, goal-directed, response-directed protocols may be evaluated in controlled trials to determine their effectiveness in patient populations. (See Table 1.)


A recurring theme from the exploration of uncertainty is our paradoxical responses to it. Although we benefit from its existence as a profession, we seek to reduce and control it in health-care management, and minimize it clinically and scientifically. However, by embracing the undeniable existence of irreducible intrinsic uncertainty, we benefit from its opportunities. This complementary approach lends greater credibility to improving management of health-care organizations with increased capacity for learning and creativity. The provision of health care at the bedside, which includes patient-physician communication, in addition to research in both basic and population science, all have significant potential benefit from accepting and embracing uncertainty. Furthermore, the ideas presented in this overview are well suited for medical educational programs, as medical students and residents require an understanding of how what they don’t and will never know affects the care of their patients. Understanding of irreducible uncertainty offers a complementary addition to an education focused on knowledge and certainty.

In summary, over the last three centuries, science has greatly concerned itself with the study of certainty while generally ignoring inescapable and irreducible uncertainty. At present, both the presence of intrinsic uncertainty and the potential to benefit from acceptance of uncertainty have become more recognizable. The evolution of the evaluation and understanding of uncertainty will be an exciting (and unpredictable) voyage of discovery for the future.

Andrew J. E. Seely
Divisions of Thoracic Surgery and Critical Care Medicine, 501 Smyth Road, Box 708, Ottawa Health Research Institute, University of Ottawa, Ottawa, ON, K1H 8L6, Canada.
E-mail: aseely@ohri.ca.

The author is the Founder and Chief Science Officer of Therapeutic Monitoring Systems, Inc.


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