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Strategic Decision Making in Emergency Critical Care Settings
Emergency departments and critical care units face complex patient cases that require timely and effective decision making. Clinicians must weigh various treatment options and potential outcomes under pressure. Strong clinical reasoning skills and consideration of evidence are paramount in such high-stakes environments. This article explores strategic decision making approaches and models that can support optimal patient care in emergency and critical care contexts.
Shared Decision Making

A collaborative approach between clinicians and patients known as shared decision making has gained prominence in recent years (Charles, Gafni, & Whelan, 1997). This model recognizes that patients hold valuable insight into their conditions, values, and priorities that should factor into treatment planning. When feasible given the acute nature of emergency presentations, incorporating patient preferences can lead to decisions aligned with individual goals of care (Stacey et al., 2017).
In shared decision making, the clinician presents treatment options and their potential risks and benefits based on best available evidence. The patient then shares their views, allowing values and context to inform the final choice (Makoul & Clayman, 2006). For emergency cases where patients lack decision-making capacity, involving surrogate decision makers such as family can apply a similar process (White, Braddock, Bereknyei, & Curtin, 2007). Overall, shared decision making has been shown to improve patient satisfaction and engagement when applied judiciously in emergency contexts (Coulter, 2017).
Clinical Reasoning Frameworks
Various clinical reasoning frameworks exist to structure complex decision processes. The analytic model involves systematically gathering and weighing different types of evidence before determining the best course of action (Croskerry, 2009). This approach works well for stable patients where time permits a thorough evaluation. However, the nonlinear nature model may be better suited for emergencies requiring an immediate response (Elstein, 1999).
The nonlinear model recognizes that intuition and experience often guide preliminary hypotheses which are then explored through further testing and reconsideration if needed (Hughes, 2008). Pattern recognition of clinical signs and symptoms allows for rapid diagnosis and treatment initiation in unstable patients (Groves, O’Rourke, & Alexander, 2003). Periodic review incorporating analytic processes ensures the optimal plan continues to be followed. Overall, both models have merit depending on the acuity and stability of the presenting patient.
Clinical Decision Support Tools
Technology has the potential to enhance clinical reasoning through decision support tools. For example, diagnostic algorithms and clinical prediction rules embedded within electronic health records can suggest likely differential diagnoses and guide initial testing and management (O’Connor et al., 2011). Such tools are particularly useful for less experienced clinicians or those managing infrequent conditions.
Artificial intelligence is another emerging area with applications including imaging analysis to detect abnormalities, natural language processing of presenting complaints, and predictive analytics (Topol, 2019). While not a replacement for clinical judgment, such adjuncts could help synthesize complex patient data and point clinicians towards important factors meriting consideration in decision making. Tools must be rigorously evaluated and integrated carefully into clinical workflows to avoid potential pitfalls (Wright & Sittig, 2015). Overall, technology augments but does not replace core clinical reasoning abilities.

Effective decision making underlies quality patient care, especially in emergency and critical care contexts. A strategic approach incorporating shared decision making principles when possible, structured clinical reasoning frameworks, and judicious use of decision support tools can help clinicians optimize complex decisions. Continued focus on evidence-based practice, technology evaluation, and development of clinical reasoning competencies ensures the highest standards of emergency and critical care are maintained.
Charles, C., Gafni, A., & Whelan, T. (1997). Shared decision-making in the medical encounter: What does it mean? (Or it takes at least two to tango). Social Science & Medicine, 44(5), 681–692. https://doi.org/10.1016/S0277-9536(96)00221-3
Coulter, A. (2017). Engaging patients in healthcare. McGraw-Hill Education.
Croskerry, P. (2009). A universal model of diagnostic reasoning. Academic Medicine, 84(8), 1022–1028. https://doi.org/10.1097/ACM.0b013e3181ace703
Elstein, A. S. (1999). Heuristics and biases: Selected errors in clinical reasoning. Academic Medicine, 74(7), 791–794. https://doi.org/10.1097/00001888-199907000-00003
Groves, M., O’Rourke, P., & Alexander, H. (2003). Clinical reasoning: The relative contribution of identification, interpretation and hypothesis errors to misdiagnosis. Medical Teacher, 25(6), 621–625. https://doi.org/10.1080/01421590310001603486
Hughes, R. G. (2008). Patient safety and quality: An evidence-based handbook for nurses. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. https://doi.org/10.1037/e573972012-001
Makoul, G., & Clayman, M. L. (2006). An integrative model of shared decision making in medical encounters. Patient Education and Counseling, 60(3), 301–312. https://doi.org/10.1016/j.pec.2005.06.010
O’Connor, A. M., Bennett, C. L., Stacey, D., Barry, M., Col, N. F., Eden, K. B., Entwistle, V. A., Fiset, V., Holmes-Rovner, M., Khangura, S., Llewellyn-Thomas, H., & Rovner, D. (2009). Decision aids for people facing health treatment or screening decisions. Cochrane Database of Systematic Reviews, (3). https://doi.org/10.1002/14651858.CD001431.pub2
Stacey, D., Légaré, F., Lewis, K., Barry, M. J., Bennett, C. L., Eden, K. B., Holmes-Rovner, M., Llewellyn-Thomas, H., Lyddiatt, A., Thomson, R., & Trevena, L. (2017). Decision aids for people facing health treatment or screening decisions. Cochrane Database of Systematic Reviews, 4, CD001431. https://doi.org/10.1002/14651858.CD001431.pub5
Topol, E. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
White, D. B., Braddock, C. H., Bereknyei, S., & Curtin, C. (2007). Toward shared decision making at the end of life in intensive care units: Opportunities for improvement. Archives of Internal Medicine, 167(5), 461–467. https://doi.org/10.1001/archinte.167.5.461
Wright, A., & Sittig, D. F. (2015). A four-phase model of the evolution of clinical decision support architectures. International Journal of Medical Informatics, 84(10), 841–849. https://doi.org/10.1016/j.ijmedinf.2015.07.012
PowerPoint Presentation on Strategic Decision Making in Emergency Care
Slide 1:
Strategic Decision Making in Emergency Care
Slide 2:

Explore approaches to clinical decision making in emergency contexts
Discuss models for structuring complex decisions
Examine roles of evidence, experience, and technology in reasoning
Strategies for optimizing decision quality under pressure
Slide 3:
Shared Decision Making
Collaborative approach between clinicians and patients/surrogates
Incorporates patient values, context, and preferences when feasible
Aligns treatment plans with individual goals of care
Improves satisfaction and engagement if applied judiciously
Slide 4:
Clinical Reasoning Frameworks
Analytic model: systematic gathering and weighing of evidence
Nonlinear model: intuition and experience guide preliminary hypotheses
Both have merit depending on patient acuity and stability
Slide 5:
Technology Support
Diagnostic algorithms and prediction rules within EHRs
Imaging analysis, natural language processing, predictive analytics
Augment but do not replace clinical judgment
Must be rigorously evaluated and carefully integrated
Slide 6:
Effective decision making is paramount in emergency care
Strategic approach using multiple models can optimize complex decisions
Continued focus on evidence-based practice and clinical reasoning skills
Judicious technology use has potential to enhance decision processes
In conclusion, this article and presentation explored strategic approaches to clinical decision making in emergency care contexts. A collaborative shared decision making model, structured clinical reasoning frameworks, and technology-supported tools all have roles to play when optimized decision processes are required under pressure. Continued focus on evidence-based practice, competency development, and rigorous evaluation of adjuncts will help ensure the highest quality of emergency and critical care decisions and outcomes.

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