Danny Kahneman, the ‘Paul McCartney’ of psychology*

Psykolog og Nobelpris-modtager Daniel Kahneman døde tidligere i år. Han er blandt andet kendt for bogen ”Thinking, Fast and Slow” om psykologien bag menneskers beslutningstagen. Hans tanker har også haft stor betydning i sundhedsvæsenet og for patientsikkerheden.

You may be aware that Daniel Kahneman passed away earlier this year (27th March 2024). You may also be aware that Daniel Kahneman was an eminent psychologist renowned for his ground-breaking work on the psychology of judgment and decision-making, a founder of the field of Behavioural Economics and a Nobel Prize winner (2002). Perhaps you’ve heard of his most famous book: ‘Thinking, Fast and Slow’, which summarizes much of the work he undertook with his research partner Amos Tversky (the John Lennon of this analogy) which focused on human cognition, heuristics, and biases which provided invaluable insights across a wide range of disciplines, most famously economics.

But are you aware of the impact his work has had on quality and safety in healthcare? In this brief article, I aim to highlight just a few examples, with a particular focus on enhancing decision-making, reducing errors, and understanding judgement ‘noise’. I will finish with a shameless plug for a recent article I had the pleasure of co-authoring which applies some tools and methods based on Kahneman’s research, in combination with quality improvement methods, aimed at reducing the use of intravenous antibiotics in a hospital in Copenhagen.

*As described by Cass Sunstein and Richard Thaler in their NewYorker article: The two friends who changed how we think about how we think.

Please, please me

Heuristics and Biases: The Foundation of Decision-Making

Let’s start at the beginning. Kahneman and Tversky introduced the concept of heuristics, which are mental shortcuts that people use to make decisions quickly and efficiently (1). Their research showed that while heuristics can be helpful (saving time and energy), they can lead to systematic biases and errors. In healthcare, these biases can significantly impact patient outcomes, for example:

  • Anchoring Bias: This occurs when clinicians rely too heavily on the initial piece of information (the “anchor”) when making decisions. For instance, an initial diagnosis might unduly influence subsequent clinical judgments, potentially leading to diagnostic errors. Kahneman’s work has highlighted the need for awareness and mitigation strategies to combat anchoring bias in clinical settings.
  • Availability Heuristic: This bias involves making decisions based on readily available information, often influenced by recent experiences. In healthcare, this can lead to overestimating the likelihood of rare conditions if a clinician has recently encountered a similar case. Understanding this heuristic helps in designing better diagnostic protocols and training programs to ensure more accurate and evidence-based decision-making.
System 1 and System 2 Thinking: Balancing Intuition and Deliberation

In ‘Thinking, Fast and Slow’ (2), Kahneman describes two systems of thought: System 1 (fast, intuitive) and System 2 (slow, deliberate). For healthcare staff, both systems play crucial roles but can also lead to different types of errors.

  • System 1 Thinking: Quick and automatic, prone to biases and errors, especially under pressure. In emergency situations, while quick decisions are necessary, reliance solely on System 1 can be dangerous.
  • System 2 Thinking: This mode of thinking is slower and more analytical, often leading to more accurate decisions. However, it requires time and cognitive resources, which are limited in high-stress healthcare environments.

Kahneman’s framework encourages a balanced approach, promoting strategies which are now widely used, such as checklists and decision aids to support System 2 thinking without undermining the efficiency of System 1.

Improving Diagnostic Accuracy and Reducing Medical Errors

Medical errors are a significant concern in healthcare, often resulting from cognitive biases and flawed decision-making processes. Kahneman’s insights have informed several strategies (3) to enhance diagnostic accuracy and reduce errors. For example:

  • Debiasing Techniques: Training programs that focus on recognizing and mitigating cognitive biases can improve diagnostic accuracy. For example, reflective practice and second opinions can counteract biases like anchoring and availability.
  • Decision Support Systems: Incorporating decision support tools and evidence-based guidelines into clinical workflows can aid in reducing errors. These systems can prompt clinicians to consider alternative diagnoses and treatments, facilitating more thorough deliberation.

Post ‘Beatles’ output

Noise: A flaw in human judgement

In recent years, Kahneman focused on the issue of ‘noise’ in human judgement (4), which refers to the undesirable variability in judgments of the same problem. In healthcare, noise can significantly affect quality and safety by leading to inconsistent and unreliable outcomes. Noise manifests in various forms in healthcare, impacting clinical decisions, diagnostic accuracy, and patient treatment plans.

Key areas where noise plays a role include:

  • Diagnostic Decisions: Variability in diagnoses for the same symptoms can lead to different treatment plans and outcomes. This inconsistency, or “diagnostic noise,” can result from differences in clinicians’ training, experience, and cognitive processes.
  • Treatment Decisions: Similar patients receiving different treatments based on which clinician they see exemplifies treatment noise. Such variability can undermine the standardization of care and affect patient safety and outcomes.
  • Administrative Decisions: Noise can also occur in non-clinical settings, such as in decisions about resource allocation, insurance approvals, and patient triage. This variability can lead to inequities in care and inefficiencies in healthcare delivery.

Implications of Noise on Healthcare Quality and Safety (5)

  • Increased Medical Errors: Variability in clinical judgments can contribute to medical errors. For example, different interpretations of imaging results or laboratory tests can lead to incorrect diagnoses or inappropriate treatments.

NB: Ask a radiologist to review the same image before and after lunch!

  • Equity Issues: Noise can exacerbate disparities in healthcare. Patients from different demographics might receive varied care due to unconscious biases and noise in clinical judgment, leading to inequitable health outcomes.
  • Reduced Trust in Healthcare: Patients expect reliable and consistent care. When they experience variability in diagnoses and treatments, it can erode their trust in healthcare providers and the system as a whole.

Strategies to Mitigate Noise in Healthcare (6)

  • variability in clinical decisions. For example, clinical decision support systems (CDSS) can provide evidence-based recommendations to clinicians, reducing noise in diagnostic and treatment decisions.
  • Training and Calibration: Regular training and calibration exercises can help clinicians align their judgments. For instance, radiologists might participate in calibration sessions to ensure consistency in interpreting imaging results.
  • Structured Decision-Making Tools: Using structured tools, such as checklists and scoring systems, can help reduce subjective variability in clinical judgments. These tools provide a systematic approach to decision-making, minimizing noise.
  • Second Opinions and Peer Reviews: Encouraging second opinions and peer reviews for critical decisions can help identify and reduce noise. Collaborative decision-making ensures that multiple perspectives are considered, reducing individual variability.
  • Feedback Mechanisms: Providing clinicians with feedback on their decisions and outcomes can help them understand and correct for noise. Continuous feedback loops can foster learning and improvement in clinical practice.

Come Together

As a result of the work Kahneman and colleagues have undertaken since the 1960’s, several new fields of psychology, research and practice have developed. Most significantly, the field of Behavioural Economics – lest we forget that Richard Thaler also received a Nobel Prize in 2017 for his work on ‘decision-making’, which explore the consequences of limited rationality, social preferences, and lack of self-control which was based on the foundational work of Kahneman and Tversky. Behavioural economics, and the related field of Behavioural Insights has led to the proliferation of ‘nudging’ related books and ‘Nudge Units’ around the world which have influenced political, social and health related decisions over the last 14 years*.

*The first nudge unit was established in 2010 to support and guide decisions for the UK Government and led to various health relevant policies, not least the ‘opt-out’ model for organ donation.

Behavioral insights (BI), involves applying principles from behavioral science to influence decision-making and behavior in beneficial ways. In healthcare, BI has been used to improve quality and safety by addressing cognitive biases, enhancing patient adherence to treatment plans, and optimizing clinical practices.

  • Medication Adherence: Non-adherence to medication regimens is a significant issue in healthcare, leading to poor health outcomes and increased costs. Behavioral interventions, such as simplifying medication schedules, providing reminders, and using social support mechanisms, have been shown to improve adherence.
  • Appointment Attendance: Missed appointments can disrupt patient care and waste healthcare resources. BI techniques, such as sending reminder messages and emphasizing the importance of appointments, have been effective in reducing no-show rates.
  • Enhancing Clinical Practices: Ensuring proper hand hygiene among healthcare workers is crucial for preventing hospital-acquired infections. Behavioral interventions, such as visual cues, feedback, and incentives, have been employed to increase compliance.

I had the pleasure of collaborating with Rie Johansen (Afdeling for kvalitet og uddannelse, Bispebjerg & Frederiksberg Hospitaler) to explore the possibility of using methods and tools from BI to support a quality improvement program aimed at reducing I/V antibiotic use. This is an innovative approach which led to greater to understanding of the psychological factors influencing clinicians’ decision making, guided our use of appropriate interventions, and subsequently led to a significant and sustained decrease in I/V antibiotic use. The article is available in the Journal of Patient Safety

Across the Universe

The influence of Kahneman’s work is immense—not only in psychology and economics, where it has become part of our core understanding, but in every other field of social science, as well as medicine, law, and, increasingly, business and public policy. Kahneman’s research has provided critical insights into the cognitive processes underlying decision-making. By understanding and addressing cognitive biases, balancing intuitive and analytical thinking, raising awareness of judgement noise, and spawning new and diverse fields of research and practice, Kahneman’s contributions continue to shape healthcare delivery from the frontline to the global strategic level. Just as it’s hard to imagine and world without the songs of Lennon and McCartney, so it is hard to think of a world without Kahneman and Tversky. Altogether now, “Thank you Danny”.

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1) Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207-232.
2) Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
3) Croskerry, P. (2002). Achieving quality in clinical decision making: Cognitive strategies and detection of bias. Academic Emergency Medicine, 9(11), 1184-1204.
4) Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A Flaw in Human Judgment. Little, Brown Spark.
5) Cormac Francis Mullins, J J Coughlan, Noise in medical decision making: a silent epidemic? Postgraduate Medical Journal, Volume 99, Issue 1169, March 2023, Pages 96–100.
6) Hallek M, Ockenfels A, Wiesen D. Behavioral Economics Interventions to Improve Medical Decision-Making. Deutsches Arzteblatt International. 2022 Sep;119(38):633-639.

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