Clinical reasoning is a fundamental process that underpins all healthcare decision-making. It refers to the mental strategies and cognitive mechanisms clinicians use to gather information, interpret patient data, and make informed decisions about diagnosis and treatment. Effective clinical reasoning is essential not only for identifying the nature of a patient’s condition but also for determining the most appropriate intervention, making it central to safe, efficient, and patient-centered care.
Clinical reasoning is central to the two core tasks of clinical practice, diagnosis and intervention selection, and both rely on an interplay of analytical and non-analytical cognitive processes (1). In diagnosis, clinical reasoning involves gathering and interpreting patient data to formulate, test, and refine hypotheses based on both pattern recognition and probabilistic thinking (1, 2). For intervention selection, clinicians must integrate diagnostic conclusions with evidence-based treatment options, patient values, and contextual factors to develop individualized care plans, requiring both logical analysis and adaptive decision-making. These reasoning processes are iterative and fluid, often occurring simultaneously rather than sequentially (2).
Expert clinicians shift between intuitive (fast, experience-based) and deliberative (slow, analytical) reasoning depending on the complexity, familiarity, and time constraints of the clinical situation. As such, clinical reasoning is the cognitive engine that enables clinicians to move from uncertainty to action in both identifying a problem and deciding how best to address it (3).
Components of Clinical Reasoning
Clinical reasoning involves a series of interconnected cognitive processes. These typically include:
Data collection: Gathering relevant subjective and objective information through history-taking, observation, and examination.
Data interpretation: Identifying meaningful patterns, inconsistencies, and clinically relevant findings.
Hypothesis generation and testing: Formulating possible diagnoses or clinical explanations and refining them based on further evidence.
Decision-making: Selecting a diagnosis and planning an appropriate intervention based on the best available evidence and patient-specific factors.
Reflection and reassessment: Continuously evaluating outcomes and adapting plans based on the patient’s response.
These components are not strictly linear; rather, they form a dynamic, iterative process that evolves with each clinical encounter.
Types of Clinical Reasoning: Dual-Process Theory
There are two main systems of clinical reasoning used in diagnosis. The first, known as the non-analytical system, relies on intuitive, automatic, and implicit processing. This system functions with minimal use of working memory and is often triggered by clinical stimuli, depending heavily on overlearned associations or implicit learning. For example, a clinician may recognize congenital club foot in a child during a clinical examination based on visual cues alone, or apply heuristics when diagnosing a movement system impairment, such as identifying insufficient scapular posterior tilt due to a coordination/control deficit in a patient with altered scapular mechanics during shoulder flexion (3, 4).
The second system is more analytical and reflective, requiring greater cognitive effort and working memory. It involves constructing mental models to hypothesize about possible causes, weigh evidence, and simulate alternative outcomes. A typical example would be diagnosing lumbar radiculopathy secondary to disc herniation in a patient with low back pain and referred leg symptoms, an approach that involves synthesizing multiple findings and reasoning through pathoanatomical relationships (3, 4).
These two systems often work in tandem. Expert clinicians seamlessly switch between intuitive and analytical reasoning, depending on the complexity, familiarity, and ambiguity of the clinical presentation.
Clinical Reasoning in Diagnosis and Intervention
Both diagnosis and intervention selection rely on the interplay of intuitive and analytical reasoning systems. For diagnosis, clinicians often begin with intuitive pattern recognition, such as noticing a child’s toe-walking gait or recognizing a facial droop as a possible sign of stroke. These fast, experience-based impressions are then refined through more deliberate analysis: reviewing the patient’s history, conducting targeted tests, and systematically ruling in or out differential diagnoses based on evidence and probability.
Similarly, intervention selection engages both systems. A therapist may intuitively sense that a patient is hesitant or guarded during a movement, prompting a spontaneous adjustment in manual technique or exercise cueing. At the same time, more analytical reasoning might be used to plan a graded exposure program based on tissue healing timelines, prognostic indicators, or guideline-based progressions.
For example, choosing whether to mobilize a stiff shoulder may depend on intuitive recognition of capsular restriction during active range, but also on analytical reasoning about contraindications, irritability levels, and patient goals. Likewise, determining whether a patient with radicular pain should begin with nerve gliding versus mechanical traction involves both an understanding of the underlying pathophysiology and a real-time assessment of symptom response.
In expert clinical practice, these two reasoning systems are not sequential but highly integrated, allowing clinicians to flexibly adapt their decisions to the demands of the moment, whether diagnosing a complex presentation or fine-tuning an intervention.
Evolving Perspectives on Clinical Reasoning
The diagnostic process is inherently probabilistic. Clinicians use information from patient history, examination findings, and diagnostic tests to estimate the likelihood of various conditions. Probabilistic thinking enables efficient narrowing of differential diagnoses, supports evidence-based decision-making, and enhances diagnostic accuracy. However, clinical reasoning in diagnosis also depends on recognizing atypical patterns, contextual cues, and subtle inconsistencies, especially when data are incomplete or conflicting. This is where non-analytical, pattern-based reasoning often plays a critical role (5).
Moreover, insights from cognitive science and artificial intelligence further illuminate this process. Like large language models, clinicians often operate under conditions of uncertainty, using previous experience and data patterns to guide inference. As Geoffrey Hinton notes, human thinking is not strictly logical but analogical. We reason through resonance more than deduction. This perspective reflects the blend of probabilistic logic and intuitive judgment that defines clinical expertise (5).
Conclusion
Clinical reasoning is not a single skill but a composite of cognitive processes that are integral to diagnosis and intervention planning. The dual-process theory highlights the balance between intuitive and analytical thinking, each playing a role depending on clinical context. In both diagnostic assessment and treatment selection, clinicians must navigate between structured, evidence-based algorithms and flexible, resonance-based decision-making. As healthcare continues to embrace complexity, uncertainty, and individualized care, the clinician’s ability to reason effectively using both systems is essential for safe, effective, and contextually appropriate practice (5).
Reference
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2. Huesmann L, Sudacka M, Durning SJ, Georg C, Huwendiek S, Kononowicz AA, Schlegel C, Hege I. Clinical reasoning: What do nurses, physicians, and students reason about. J Interprof Care. 2023 Nov 2;37(6):990-998. doi: 10.1080/13561820.2023.2208605. Epub 2023 May 15. PMID: 37190790.
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5. Benner P, Hughes RG, Sutphen M. Clinical Reasoning, Decisionmaking, and Action: Thinking Critically and Clinically. In: Hughes RG, editor. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville (MD): Agency for Healthcare Research and Quality (US); 2008 Apr. Chapter 6. Available from: https://www.ncbi.nlm.nih.gov/books/NBK2643/