Strategies to Reduce Cognitive Load in Healthcare

HiQuiPs: Managing Crises – What’s going on behind the scenes?

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You are on-call – covering medicine and ICU admissions – while overseeing 20 admitted patients. Your wards are at capacity. You receive 3 new admissions from the ED, but you still have to move 2 sick patients to the ICU. You also have to prepare 3 discharges for the next morning.

You wonder how you can best deal with the situation and help your patients. Where do you begin? How do you prioritize tasks? Is there a cognitive framework to deal with these types of decisions and the stress they cause?

Crisis resource management (CRM) is a framework that can be used to tackle challenging situations, where multiple priorities in complex systems seem to demand attention at the same time.​1​ CRM has its roots in aviation and the military but is also used in medicine. The domains of CRM include situational awareness, triaging and prioritization, cognitive load management, and communication. Today’s blog post will focus on cognitive load management.

What is cognitive load management?

Being aware of one’s cognitive load and using strategies to mitigate it is essential for clinicians to manage stress and decrease the risk of making errors. This is especially important when dealing with complex situations involving medically active or critically ill patients. Cognitive load theory states that there is a limited amount of space in one’s working memory, as opposed to one’s long-term memory.​1​ These limitations in working memory may hinder one’s ability to respond to new information. Instead of multitasking, we end up engaging in rapid task-switching and prioritizing, which prevents us from making thoughtful clinical decisions by pushing us to think faster rather than more profoundly.

In the clinical vignette above, the clinician is struggling with multiple time-sensitive and competing tasks, thus pushing the boundaries of their cognitive load. They may not pause to acknowledge their worsening cognitive load but instead foresee the series of tasks before them – not realizing that rapid task switching can further exacerbate this burden. To reduce cognitive load, clinicians can assign tasks to other team members and use tools that reduce reliance on faulty memory during stressful situations. We discuss some of these strategies under a CRM framework below.

Decision-Making Models: Dual-Process and Recognition-Primed Decision Models

Two commonly used models for clinical decision-making are the Dual Process model and the Recognition-Primed Decision (RPD) model.​1​ The Dual Process model suggests that clinicians use Systems I and II thinking.​1​ System I thinking is intuitive, automatic, and faster, whereas System II thinking is analytical, deliberative, and slower. Although most final decisions are based on the activation of both Systems I and II, up to 50% of decisions are determined by System I thinking, especially amongst experienced providers.​2​

An overreliance on System I thinking, however, has been linked to increased medical errors.​3​ For example, patterns associated with initial presentations may lead one down the wrong diagnostic path, like mistaking acute pericarditis for myocardial infarction. When the reflexive and quick nature of System I thinking leads to undesired effects or errors, clinicians should recognize the need to re-frame their approach into a slower and more detailed form of problem-solving.​4​ Ultimately, both systems are beneficial in different contexts and clinicians should recognize when their decision-making is being influenced by the two systems and their implicit biases; with experience, System I thinking can exhibit improved precision nonetheless.

Similarly, the RPD model posits that decision-makers identify the first possible solution based on previous experiences, rather than trying to find the most optimal solution.​3​ Clinicians may be over-reliant on prior exposures, which can lead to implicit biases in the decision-making process. These biases can increase the risk of making medical errors, so clinicians should try to find ways to focus on recognizing and addressing these biases to improve patient care.

When clinicians are burdened by distractions or interruptions, their cognitive load increases. When cognitive load is high, clinicians tend to over-rely on System I or “first possible solution” (intuitive, automatic) thinking. By reducing the stresses of cognitive load, clinicians may be able to shift to a decision-making process that is more representative of System II thinking to find a more optimal solution to their clinical cases.

Reducing cognitive load – but how?

Although System I thinking may improve with time, it is important to recognize one’s biases in decision-making to ensure clinicians are providing optimal patient care. Cognitive load management can be done at both the decision-making and communication levels. Below are some examples in different domains with strategies to reduce cognitive load:

Improving Decision-Making:

  • Using clinical algorithms to standardize decision-making:
    • Ex: patients coming into the clinic with specific infections (e.g., community-acquired pneumonia) should receive standardized empiric therapy outlined by the recommended guidelines.​5​
  • Using forcing functions that prevent errors from occurring:
    • Ex: pharmacy computers cannot fill out an order unless allergy information, patient weight, and patient height have been entered.​6​
  • Using colour-coding resuscitation equipment to allow for easier identification and classification:
    • Ex: colour-coded pediatric-resuscitation carts correspond with specific age and size-appropriate emergency equipment.​7​

Improving Communication:

  • Using standardized communication tools and language to convey information passed on between individuals and teams:
    • Ex: using the I-PASS mnemonic (illness severity, patient summary, action list, synthesis by receiver, summary by receiver) helps to ensure consistent passing of information between healthcare providers.
  • Using closed-loop communication to avoid misunderstandings or incomplete tasks:
    • Ex: when a clinician asks another member of the team to order medications, the team member should respond that they are doing so as verbal confirmation that the message has been received.​8​
  • Using a buddy system to help team members out
    • Ex: doffing of PPE has a relatively high risk of infection; providers can help to oversee the doffing of a colleague to reduce the stress associated with concerns of exposure, especially in the ED when there are many patients.​9​
Strategies to Reduce Cognitive Load in Healthcare
Strategies to Reduce Cognitive Load in Healthcare (Source: Tiffany Tse).

Taken together, these strategies can be implemented to reduce cognitive burden and create a safer environment both for physicians and patients, especially in fast-paced, high-stakes, multi-task environments. By reducing cognitive load, one can minimize stress and distractions, which can help clinicians make more logical decisions to find optimal solutions to problems presented in the clinical environment.

You decide to use different strategies of the CRM framework to help you cope with all the tasks being thrown at you. You pause, prioritize what needs to be completed first, and delegate tasks to other members of the team to help you reduce your cognitive load.

You now have a basic understanding of some principles of the CRM framework and can try to apply these to clinical practice. Let us know what you think on Twitter @Hi_QuI_Ps. If there is anything specific you would like to learn about, email us at [email protected].

The senior editor of this post was Ahmed Taher. This post was copyedited by Noaah Reaume and Tiffany Tse.

References

  1. 1.
    Rajendram P, Notario L, Reid C, et al. Crisis Resource Management and High-Performing Teams in Hyperacute Stroke Care. Neurocrit Care. 2020;33(2):338-346. doi:10.1007/s12028-020-01057-4
  2. 2.
    Djulbegovic B, Hozo I, Beckstead J, Tsalatsanis A, Pauker S. Dual processing model of medical decision-making. BMC Med Inform Decis Mak. 2012;12:94. doi:10.1186/1472-6947-12-94
  3. 3.
    Ji Y, Massanari RM, Ager J, Yen J, Miller RE, Ying H. A fuzzy logic-based computational recognition-primed decision model. Information Sciences. Published online October 2007:4338-4353. doi:10.1016/j.ins.2007.02.026
  4. 4.
    Croskerry P. Clinical cognition and diagnostic error: applications of a dual process model of reasoning. Adv Health Sci Educ Theory Pract. 2009;14 Suppl 1:27-35. doi:10.1007/s10459-009-9182-2
  5. 5.
    Margolis C. Uses of clinical algorithms. JAMA. 1983;249(5):627-632. https://www.ncbi.nlm.nih.gov/pubmed/6336813
  6. 6.
    To Err Is Human. National Academies Press; 2000. doi:10.17226/9728
  7. 7.
    Deboer S, Seaver M, Broselow J. Color coding to reduce errors. Am J Nurs. 2005;105(8):68-71. doi:10.1097/00000446-200508000-00031
  8. 8.
    Diaz M, Dawson K. Impact of Simulation-Based Closed-Loop Communication Training on Medical Errors in a Pediatric Emergency Department. Am J Med Qual. 2020;35(6):474-478. doi:10.1177/1062860620912480
  9. 9.
    Brat G, Hersey S, Chhabra K, Gupta A, Scott J. Protecting Surgical Teams During the COVID-19 Outbreak: A Narrative Review and Clinical Considerations. Ann Surg. 2023;278(5):e957-e959. doi:10.1097/SLA.0000000000003926
Tiffany Tse

Tiffany Tse

Tiffany is currently a first year medical student at the University of Toronto. She has an interest in quality improvement, public health, and increasing accessibility to care. In her free time, she enjoys dancing, playing music, and spending time with friends!
Matthew Hacker Teper

Matthew Hacker Teper

Matthew is a 2nd-year medical student at the University of Toronto, with a strong interest in quality improvement, health systems reform, and emergency medicine. He holds a bachelor’s degree in French Literature and an MSc in Family Medicine. Outside of academia, he is a Special Olympics coach and a triathlete. He is also a part-time culinary student at George Brown College in Toronto.
Ahmed Taher

Ahmed Taher

Ahmed is an Emergency Physician at University Health Network and Mackenzie Health in Toronto. He completed the Toronto FRCPC Emergency program, and a Masters of Public Health program at Johns Hopkins Bloomberg School of Public Health with certificates in Quality Improvement & Patient Safety, as well as Public Health Informatics.
Tiffany Tse

Tiffany Tse

Tiffany is currently a first year medical student at the University of Toronto. She has an interest in quality improvement, public health, and increasing accessibility to care. In her free time, she enjoys dancing, playing music, and spending time with friends!
Tiffany Tse

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