Chapter 1: General Introduction to Healthcare Uncertainty

Humans crave certainty (Anderson et al., 2019; Lazarus, 2023) yet we are constantly challenged to balance certainty with uncertainty within and across our personal and professional lives. The manner in which we perceive and respond to uncertainty has received considerable research attention and is often referred to as uncertainty tolerance in contemporary literature (Hillen et al., 2017; Strout et al., 2018). An overview of the uncertainty tolerance construct is provided in Video 1.1 (view the transcript here).

Uncertainty tolerance is a field of great interest within the healthcare sector (Han et al., 2011; Hancock & Mattick, 2020; Strout et al., 2018). Learners may think that healthcare seems highly objective, unambiguous, precise, and certain. The reality is that uncertainty arises frequently in healthcare practice in a variety of forms. For this reason, future healthcare professionals can benefit from learning to effectively and appropriately manage uncertainty in order to optimally care for patients, and to support their own wellbeing.

There is a fundamental challenge when discussing the topic of uncertainty tolerance. The terminology in the literature which describes uncertainty, and how people perceive and respond to it, varies across research disciplines and over time (Budner, 1962; Frenkel-Brunswik, 1949; Hillen et al., 2017; Strout et al., 2018). For example, tolerance of/for ambiguity is another common term used in the literature (Budner, 1962; Furnham & Marks, 2013; Geller et al., 1993). Some of the history of uncertainty tolerance research and the varied terminology used by researchers is described in Video 1.2 (view the transcript here).

There is much still to be learned about the topic of uncertainty tolerance, but there are some key principles that are more ‘settled‘. In other words, there is a large body of longitudinal evidence across multiple fields which illustrates core understandings of the uncertainty tolerance construct. There are also areas, at least at the time of writing this handbook, that are still being investigated. While the settled areas of uncertainty tolerance form the basis of much of this handbook, emerging and debated areas of uncertainty tolerance research will also be mentioned so that educators may gain an understanding of where the field is currently and where it may be headed.

The diverse definitions in the literature (see Video 1.2) drove Hillen et al. (2017) to undertake extensive work to develop clearer distinctions between the terms uncertainty and ambiguity. Hillen et al. (2017) defined uncertainty as a perception of not knowing, and ambiguity as a property of information that can stimulate uncertainty. However, ambiguity is not the only possible cause of uncertainty (see Case Study 1.1).

Case Study 1.1a: Sources of Uncertainty

Case Study 1.1a: Sources of Healthcare Uncertainty, which is hypothetical, introduces some of the ways in which those involved with healthcare (including healthcare professionals and patients) may encounter uncertainty. While this case describes many sources of uncertainty, it does not discuss them all. Subsequent chapters will further describe and expand on the reasons underpinning why a person experiences uncertainty, which can be broadly referred to as sources or stimuli of uncertainty (Han et al., 2011; Hillen et al., 2017). Click through the slides below to learn more.

Case Study 1.1a illustrates how uncertainty can be experienced both by patients and healthcare professionals (Han et al., 2011). In this case, Mei, Cameron, Max, and Priya all experience uncertainty. While there are commonalities across these uncertainties, the underlying reasons for their uncertainty differ.

Researchers and healthcare professionals have classified sources of uncertainty in different ways (Han et al., 2011; Lee et al., 2020; Scott et al., 2023). In their work on uncertainty in healthcare, Han et al. (2011) identify three primary sources of uncertainty:  probability, ambiguity, and complexity.

Probability

For Han et al. (2011, p. 832), the term probability refers to the ‘randomness’ or ‘indeterminacy’ of future outcomes. Others have referred to this as aleatoric uncertainty (Simpkin & Armstrong, 2019). Regardless of the term used, the inability to predict what lies ahead is a source of great uncertainty. An example of probability, or aleatoric uncertainty, related to Mei’s case is the difficulty in predicting how an individual will recover from COVID-19 (Sudre et al., 2021). Some people recover quickly and don’t experience lasting symptoms, whereas others experience persistent symptoms that last for weeks, months, or longer. Although some potential risk factors for developing long COVID have been identified (e.g., severity of the acute infection, age, and sex), at the time of writing this handbook, it is still difficult to predict the likelihood of long COVID on an individual basis.

Note that the word probability is used in different ways across the literature. For example, within the field of sustainability, it refers to quantifiable uncertainty and/or risk and therefore does not include randomness or indeterminacy (Funtowicz, 2020).

Ambiguity

Ambiguity is generally used to imply a sense of vagueness and of being open to multiple interpretations. Han et al. (2011, p. 833) provide a definition for ambiguity which differs slightly from the term’s general use, defining ambiguity as a property of information that can stimulate uncertainty due to a ‘lack of reliability, credibility or adequacy’. Healthcare ambiguity can arise in many different ways, including from the scientific basis of healthcare (e.g., insufficient evidence), as well as patients, health professionals, and their interactions. Related to patients, ambiguity may arise from imprecise or conflicting information (e.g., difficulty recalling the onset of subtle symptoms or differences between their own descriptions and the reports of their family) or from missing information, such as may occur when patients move practices or when handover between services or professionals is incomplete. Lack of information related to gaps in a healthcare professionals’ knowledge and experience, and how this shapes their patient assessment, also aligns with Han et al.’s (2011) definition of ambiguity.

A further form of ambiguity that may arise during communication in healthcare is semantic ambiguity, or where words have more than one possible meaning (Degani & Tokowicz, 2010). For example, ‘vagina’ is defined anatomically as the part of the genital tract that connects to the uterus to the vulva, sometimes known as the ‘birth canal’. However, ‘vagina’ is commonly used in lay speech to refer to the vulva (i.e., external genitalia). Hence a patient describing a problem with their ‘vagina’ could be referring to the anatomical vagina, or part of their vulva.

In relation to Case Study 1.1a, both long COVID and fibromyalgia are conditions that are currently relatively poorly understood, and have been associated with stigma (Clauw, 2014; Michelen et al., 2021; Quintner, 2020). COVID-19 is a relatively new condition, and thus the time to build an evidence base has been limited. Although guidelines exist for diagnosing fibromyalgia, there are currently no laboratory or imaging studies which can do so definitively. So rather than, for example, tissue diagnoses used to definitively diagnose types of cancers or imaging studies used to visualise damaged arteries, diagnosis of fibromyalgia relies on the healthcare professional and patient to analyse the types, locations, and frequency of symptoms against a checklist, as well as the patient’s experiences. Research into optimal management of fibromyalgia is ongoing (Clauw, 2014), so although the present management of fibromyalgia may stimulate ambiguity, this may change if more specific treatments are identified. In addition, ambiguity may arise when research studies provide seemingly contradictory results (e.g., one study identifying a statistically significant finding supporting one intervention over placebo, but another study not identifying any difference) or when researchers interpret results in different ways.

Aspects of Han et al.’s (2011) definition correspond with elements of epistemic uncertainty, which Simpkin & Armstrong (2019, p. 2586) describe as ‘relating to incomplete knowledge’. This may be caused by limitations in scientific evidence or in an individual’s capacity to ‘access and process’ knowledge. Simpkin & Armstrong (2019) describe that uncertain scenarios in healthcare are typically combinations of both aleatoric uncertainty and epistemic uncertainty, suggesting that healthcare uncertainty is a complex phenomenon.

Complexity

Han et al. (2011) include complexity as a third source of uncertainty in their classification. The term complexity refers to ‘aspects of the phenomenon… [perceived as uncertain] that make it difficult to comprehend’, and is caused by multiple elements interacting in a non-linear fashion that impact a person’s condition or their overall health (Han et al., 2011, p. 833). It may be particularly prevalent in healthcare because of the inherent complexity of the human body, and the interrelated nature of the healthcare system.

The interconnectedness of the human body means that many health conditions, and their management, can impact multiple bodily systems. Furthermore, many parts of the world are experiencing population ageing (World Health Organization, 2022), which increases the likelihood of individuals simultaneously developing multiple health conditions that, in combination, generate unique challenges when compared to each condition on its own. The presence of multiple conditions raises the possibility that the optimal treatment for one condition may interact with or negatively impact the optimal treatment for another condition in the context of the individual. For example, some classes of medications used in the management of mental health conditions have metabolic side effects (e.g., weight gain and impaired glucose tolerance), which can impact or even predispose to the development of diabetes mellitus. The complexity stemming from multiple conditions can make clinical research more challenging. Historically, research supporting evidence-based medicine typically focusses on single conditions and often excludes groups of people with concomitant health conditions. Therefore, the evidence for caring for people with multiple conditions, and how the treatments interact, is often less established.

One element of complexity seen in Mei’s case (Case Study 1.1a) is a possible co-occurrence of multiple conditions (i.e., fibromyalgia and long COVID). There is diagnostic uncertainty as to whether Mei has long COVID alongside fibromyalgia and, if so, whether this may have exacerbated her symptoms such as fatigue and brain fog. Elements of Mei’s case also raise the possibility of a mental health condition (e.g., she has been feeling ‘off’ since Briony’s birth, and the fact she needed to recover from a perineal tear could suggest the possibility of post-natal depression), which does not appear to have been explored by the healthcare professionals she has seen. The potential diagnosis of a mental health condition adds further complexity to Mei’s case – especially because such conditions may be related to long COVID, fibromyalgia or distinct from both (Häuser & Fitzcharles, 2018; Davis et al., 2023). Complexity is discussed further in Chapter 3, with the description of conceptual modelling of uncertainty tolerance.

Case Study 1.1b: Issues of Healthcare Uncertainty

In addition to classifying the sources of uncertainty, in terms of probability, ambiguity, and complexity, Han et al. (2011) describe the areas in which specific issues of uncertainty in healthcare can arise, identifying these as ‘scientific (data-centered)’, ‘practical (system-centered)’, and ‘personal (patient-centered)’ [sic]. Click through the slides in Case Study 1.1b to explore these issues further.

Han et al. (2011, p. 835) classify issues (i.e., specific problems) of healthcare uncertainty into a taxonomy, wherein the issues range from those that are ‘disease-centered’ to those that are ‘system- and patient-centered’. At the disease-centred end are issues related to the scientific aspects of healthcare, such as ‘diagnosis’, ‘prognosis’, and ‘treatment recommendations’. Midway along the taxonomy are issues related to healthcare systems, such as ‘structures of care’ and ‘processes of care’. At the other end are ‘psycho-social’ and ‘existential’ issues experienced by patients.

Case Study 1.1b illustrates how multiple issues, or factors, can contribute uncertainty for patients, for their related social network, and for healthcare professionals. Although Mei is recommended treatments for her fibromyalgia, she experiences challenges relating to the healthcare system, because the length of the waiting list for professionals, such as a psychologist, is unknown. Where waiting lists are already long, people seeking care who have more acute or severe (e.g., life-threatening) conditions could be prioritised for such care. Extended waiting lists can result in people with initially less severe problems deteriorating while on a waiting list, necessitating more complex or radical management when they are able to access a healthcare professional (Aitken & Watters, 2022). Beyond waiting lists, access to healthcare services may be impacted by social and economic factors, such as residential location, employment, caring responsibilities, insurance status, and finances in general (Australian Institute of Health and Welfare, 2022), leading to uncertainties for both the patient and the healthcare professionals.

Differences in health beliefs and communication preferences can also introduce uncertainty. When Mei sees Cameron after visiting the long COVID clinic, Cameron’s communication approach focusses on the perceived facts of the situation (i.e., comparing Mei’s symptoms to diagnostic criteria and conveying alternative diagnoses that have been effectively ruled out) and practical next steps (i.e., referrals to a physiotherapist and psychologist). Cameron communicates some of the uncertainty related to Mei’s case (e.g., describing how the effectiveness of management strategies varies for different people). However, Mei’s communication preferences, including how much uncertainty relating to her case she would like to know about, are unexplored. Differences between Mei’s communication preferences and how Cameron decides to communicate could result in Mei feeling more uncertain and influence how she responds to uncertainty about her condition.

This case suggests that both Mei and Cameron understand the potential benefits of cognitive behavioural therapy for treating fibromyalgia, while Mike does not. This could impact Mei’s relationship with Mike and her wellbeing and could lead her to make decisions about her healthcare and returning to her job that she might not make if she and Mike had similar health beliefs. Mei’s interest in trying acupuncture to manage her symptoms suggests an openness to complementary and alternative medicines. Depending on Cameron’s and Mike’s views on such medicines, this could introduce further uncertainties to their interactions.

Summary

From initial presentation and diagnosis through to management and prognosis, uncertainty can arise across all stages of the healthcare pathway and can be experienced by patients and by healthcare professionals alike. When considering people in the context of the broader healthcare system and their social networks, more issues contributing to uncertainty may arise.

Further examples of how uncertainty may be experienced across the health professions are described in later chapters in this handbook. Although healthcare can be highly uncertain, rest assured that it’s not all uncertain. This handbook will teach you to draw upon your experiences and knowledge (i.e., sources of certainty) to help your learners develop skills for managing uncertainty in their future practice. To begin with, this handbook explains principles and conceptual models explaining uncertainty tolerance and, critically, where some certainty can be found to guide healthcare professional learners in their practice.

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