Diagnosis is fundamental in healthcare, acting as the cornerstone for effective communication, precise documentation of a patient’s condition, and the refinement of treatment strategies. A clear diagnosis facilitates effective “cross-talk” among clinicians, minimizing variability in patient care. Moving beyond simple identification, higher-order thinking becomes crucial after establishing a diagnosis. This advanced cognitive processing moves beyond rote memorization of facts and concepts, enabling deeper understanding and application of knowledge. Diagnostic metrics, whether internal to the test itself or external in influencing post-test decisions, are most valuable when they effectively guide subsequent clinical actions. It’s also critical to recognize the potential downside of overdiagnosis, which can paradoxically lead to overtreatment and potentially worse patient outcomes. Furthermore, within a single diagnosis, diverse phenotypes exist, meaning patients sharing the same diagnosis can present with significant variations and respond differently to treatments.
Keywords: Differential Diagnosis, Diagnosis, Clinical Reasoning, Diagnostic Process, Phenotyping, Prognosis
Abstract
Background
Differential diagnosis is a systematic approach employed to discern the most accurate diagnosis from a range of possible, competing diagnoses. This process is essential for effective clinical decision-making and patient management.
Methods
This article aims to explore the critical role of higher-order thinking within the framework of differential diagnosis. It delves into the nuances of diagnostic processes, highlighting potential pitfalls and advanced strategies for improved accuracy and patient care.
Conclusions
For healthcare professionals, diagnosis is a vital component of the clinical decision-making journey. It is characterized by the differentiation of competing possibilities to achieve a definitive understanding of a patient’s underlying condition. The diagnostic process encompasses the identification of the cause of a disease or condition through meticulous evaluation of patient history, thorough physical examinations, and careful review of laboratory and imaging data. Differential diagnosis represents a sophisticated skill set applicable across all healthcare disciplines, while the core concept of diagnosis remains universally relevant. Ideally, a robust diagnosis enhances classification accuracy, promotes clear communication, guides treatment pathways, improves prognostic understanding, and informs preventative strategies. Realizing these benefits necessitates a deep comprehension of the clinical utility of diagnostic tests and measures, and the optimal integration of these findings into clinical practice. This requires higher-order thinking to truly grasp the role of diagnosis in comprehensive patient management.
The Foundation of Diagnosis: Purpose and Evolution
Background
The diagnostic process is fundamentally about identifying the etiology of a disease or condition. This is achieved through a structured approach involving patient history, physical examination, and the interpretation of laboratory and diagnostic imaging findings, culminating in a descriptive diagnostic label.1 Diagnoses are instrumental in enhancing communication among healthcare providers, with patients, within healthcare systems, and with payers. Walker 2 notes that over millennia, several key advancements have shaped modern diagnostic practices. These include establishing rational foundations for medicine as a profession, developing diagnostic equipment, utilizing autopsy for diagnostic confirmation, employing human dissection for education, expanding physical and laboratory examination techniques, and systematically classifying diagnostic commonalities.
The International Classification of Diseases (ICD) system emerged as a standardized tool for categorizing diseases globally. The first edition, the International List of Causes of Death (ICD), was adopted in 1893.3 The World Health Organization (WHO) released the 11th Revision of the ICD in May 2018, designed to provide a uniform system for defining diseases, disorders, injuries, and health conditions. The ICD classification organizes health information into standardized disease groupings, facilitating: efficient storage, retrieval, and analysis of health data for evidence-based decisions; seamless sharing and comparison of health information across diverse healthcare settings and countries; and robust data comparisons over time within the same location. Furthermore, the ICD coding system allows for greater specificity and clinical detail, enhancing the ability to document patient encounters comprehensively and compare outcomes at a system-wide level. At its core, the ICD diagnostic system strengthens communication among healthcare providers and is a foundational competency for all diagnosticians in healthcare.
While improved communication and a shared language of disease categories are valuable assets in differential diagnosis, truly leveraging these categories and appreciating the limitations of diagnostic labels requires higher-order thinking. Higher-order thinking is predicated on the idea that certain forms of learning necessitate more complex cognitive processing, going beyond simple memorization of facts and concepts. These advanced cognitive skills encompass conceptualization, analysis, evaluation, and involve sophisticated reasoning, contrasting productive thinking with rote, reproductive thinking.4 Essential skills within higher-order thinking include analogical and logical reasoning.5 Analogical reasoning uses analogies to analyze similarities, while logical reasoning applies prior knowledge to infer and solve problems. Critical thinking is a crucial component of this higher-order cognitive process.
A fundamental grasp of diagnoses is a complex, iterative, and essential undertaking. This article argues that higher-order thinking extends significantly beyond memorizing tests, measures, sensitivity, specificity, and ICD codes. Specifically, effective differential diagnostic reasoning requires clinicians to pay close attention to: (1) the potential for test metrics to be misleading; (2) the risk of diagnostic labels overcomplicating patient care; and (3) the benefits of alternative diagnostic classification methods to enhance patient management.
Table 1. Common test metrics used in differential diagnosis, including sensitivity, specificity, predictive values, and likelihood ratios.
The Deceptive Nature of Test Metrics in Diagnosis
Interpreting Test Metrics
To arrive at a diagnosis – determining the presence or absence of a condition – clinicians utilize diagnostic tests, ranging from clinical examinations to advanced imaging. The core of a diagnostic study involves comparing an “index test” (the test being evaluated) against a recognized “reference standard” (the definitive diagnostic method). This comparison yields crucial test metrics.6, 7
Table 1 outlines commonly used test metrics in diagnostic assessment. Sensitivity (SN) and Specificity (SP) are calculated within specific populations in case-control studies. Sensitivity is calculated only in individuals confirmed to have the condition, while specificity is calculated only in those confirmed not to have the condition. Similarly, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) are also population-specific in case-control designs. PPV applies only to those testing “positive,” and NPV to those testing “negative.” SN, SP, PPV, and NPV are considered internal test metrics and are generally not recommended for direct post-test decision-making.
Likelihood ratios (LR), calculated from the entire study population in a case-control design, are metrics that inform clinical utility – the ability to make sound diagnostic decisions. A LR+ greater than 1.0 increases the post-test probability of a condition given a positive test result. Conversely, a low LR− (close to 0) increases post-test probability given a negative test result.8 Both LR+ and LR− are linked to pretest probability and are used to determine the post-test probability of a diagnosis, whether to rule it in or out. Benchmark values exist to guide clinicians; for instance, LR+ >5 and LR− <0.2 are often considered clinically significant.9 However, each likelihood ratio must be interpreted in the context of pretest probability to effectively guide clinical decision-making.
The current diagnostic system often relies on interpreting these metrics to identify the most probable diagnostic label. However, this approach has inherent pitfalls. As mentioned, SN, SP, PPV, and NPV are internal metrics and should not be used in isolation for decision-making, as individual values can be misleading and may not represent the broader clinical population. The concepts of SPin (ruling in with high specificity) and SNout (ruling out with high sensitivity) are outdated and can lead to errors in interpretation.9 For example, a study on subacromial pain diagnosis showed that combining three clinical features achieved 100% specificity but only 9% sensitivity.10 While highly specific, this cluster identifies only a small fraction (9%) of all patients with subacromial pain, limiting its practical clinical utility and potentially creating bias if clinicians over-rely on this combination. For SPin and SNout concepts to be useful, other metrics must be reasonably balanced to minimize decision-making errors.
Likelihood ratios, while useful for indicating the magnitude of change in post-test probability, can also mislead. For example, the Lachman test for anterior cruciate ligament (ACL) tears has established accuracy in both primary care and orthopedic settings.11, 12 However, ACL tear prevalence (pretest probability) is about 4% in primary care versus 20–25% in orthopedic clinics, as primary care settings see a wider range of knee issues (e.g., sprains, contusions) not typically referred to specialists.11 Even with a high LR+ for the Lachman test,13 the post-test probability varies significantly between these settings due to prevalence differences. This impacts diagnostic certainty and decisions about further imaging or specialist referrals. Prevalence also affects the assessment of red flags; recent research questions the utility of history elements for ruling out serious causes of low back pain due to poor negative likelihood ratios,14, 15 which is linked to the exceptionally low prevalence of serious pathology in such cases.
Study Design and Condition Severity: Influential Factors
Test metric interpretation is heavily influenced by the quality of evidence supporting the test. For instance, the Thessaly test for meniscal tears was initially developed in a study with design flaws, and subsequent studies failed to replicate the original findings.6, 16, 17 Clinicians must critically evaluate study designs, reference standards, and test descriptions to identify potential biases in diagnostic accuracy studies.18 Furthermore, the severity of the studied population impacts outcomes. Advanced conditions with significant disability and pain tend to yield test results that are more sensitive and less specific. Conversely, conditions with lower severity show lower sensitivity and higher specificity.
Impact on Clinical Decision-Making
Decision-making models suggest a balance between analytical approaches based on evidence (e.g., test metrics) and intuitive approaches grounded in clinical experience.19 Clinicians constantly navigate the challenge of avoiding pitfalls in diagnostic test interpretation. All tests, whether clinical examinations or imaging, have strengths and weaknesses. Flawed test accuracy interpretation, poor understanding of probabilities, and low-quality evidence can disrupt analytical reasoning.20 Intuitive processes can be undermined by verification or confirmation biases, such as anchoring (over-relying on initial information) or premature closure (halting the diagnostic process too early upon finding a seemingly fitting diagnosis).20
Ultimately, diagnostic test results drive decisions about further investigations and treatments. Therefore, clinical reasoning is paramount to connect test results with appropriate management within a complete care pathway. Higher-order thinking necessitates moving beyond mere test metrics and considering misclassification costs and how diagnostic decisions influence downstream healthcare utilization.
Key Takeaway: Most test metrics are internal and not designed for direct post-test probability determination. Metrics are susceptible to bias from study design and patient severity. Even likelihood ratios, intended for post-test probability, require careful consideration of pretest probability to avoid misinterpretation.
The Perils of Over-Diagnosis and Diagnostic Labeling
The prevailing patho-anatomical model for diagnostic coding has led to a focus on tissue-based musculoskeletal disorders. However, diagnosing and classifying patients solely within this model can result in overly complex or clinically insignificant diagnostic labels that do not necessarily translate to improved patient outcomes.
Overuse of Diagnostic Testing and Overdiagnosis in Musculoskeletal Care
While diagnostic tests and metrics are central to decision-making across medicine,21 over-reliance on diagnostic labeling is now recognized as a key driver of diagnostic test overuse and overdiagnosis. Overdiagnosis occurs when a patient receives a diagnostic label for a condition that may never have caused them harm.22 This often happens when tests identify abnormalities or risk factors that are unlikely to manifest as symptoms or impairments.23 Overdiagnosis is fundamentally linked to how diagnostic labels are defined and how test metrics are interpreted.
When patients present with pain in the spine, knee, hip, or shoulder, clinicians often initiate a cascade of assessments—history taking, physical exams, clinical measures, and imaging—to pinpoint symptom sources.24 Musculoskeletal care is particularly vulnerable to diagnostic test overuse. Up to 50% of all imaging referrals in this area are considered inappropriate.25 Musculoskeletal disorders are prone to overdiagnosis due to the high prevalence of asymptomatic structural abnormalities detected on imaging. Common examples include labels like “lumbar degeneration,” “disc bulges,”26 “disc herniation,”27 “degenerative meniscal tears,”28 “degenerative labral tears,”29 “subacromial bursal thickening,”30 or “rotator cuff tendinosis.”30
From a clinical pathway perspective, diagnostic test overuse and overdiagnosis can trigger a sequence of potentially inappropriate interventions. These may include orthopedic surgery, opioid over-prescription, or aggressive early rehabilitation protocols as first-line treatments.24, 31 Differentiating between highly specific patho-anatomic diagnoses might be less critical for selecting appropriate initial treatment options. A crucial question arises: do current diagnostic methods actually improve patient outcomes?
Evidence Linking Diagnostic Tests to Patient Outcomes
Evidence supporting the notion that diagnostic tests improve patient outcomes in musculoskeletal disorders remains limited. A meta-analysis examining the effect of routine diagnostic imaging on patient-reported outcomes for musculoskeletal conditions 32 reviewed 11 trials focusing on low back pain and knee issues. It found moderate evidence that routine diagnostic imaging did not improve pain outcomes.32 One trial indicated that replacing spine radiographs with early magnetic resonance imaging (MRI) in primary care did not reduce back-related disability but increased costs and possibly the rate of spine surgery based on MRI findings.27 Another trial revealed that patients receiving early MRI for low back pain were more likely to be out of work due to disability one year later.24 Further, a study showed that adding MRI in primary care for younger patients with traumatic knee complaints did not improve knee function after one year.33
These studies suggest that adding imaging tests, known to frequently reveal asymptomatic structural findings, into the musculoskeletal care pathway does not lead to better patient outcomes. In fact, it may contribute to overdiagnosis and the overuse of subsequent treatments like surgery. Future research should investigate whether implementing current and emerging diagnostic methods (e.g., ultrasound), classification and biomechanical systems (e.g., McKenzie,34 movement system35, or prediction algorithms (e.g., clinical prediction rules) improves the overall clinical pathway and patient outcomes without exposing patients to the harms of overdiagnosis. In essence, identifying a precise structural or movement-related diagnosis may not alter the selection of high-quality, first-line treatments essential for improving outcomes.
Prognosis: An Equally Important but Underutilized Tool
Prognosis, a method of classification focused on predicting future events,36 is critically important yet often underemphasized. Prognostic research examines whether a clinical decision will positively influence a patient’s future. It’s argued that prognostic decision-making should be as central as diagnostic research, recognizing that “no care” is often as valid a choice as active intervention. Neglecting prognosis in clinical care can contribute to harmful overtreatment and its associated risks (as discussed earlier).
Much of medical education centers on disease diagnosis and treatment principles. Historically, there has been a strong emphasis on informing patients and the public about new understandings of disease causes and mechanisms, and how to achieve a diagnosis and prescribe effective, diagnosis-linked treatments. We argue that placing equal emphasis on prognosis could mitigate overdiagnosis and overtreatment. For instance, adopting a “watchful waiting” approach for benign conditions that often improve spontaneously can reduce the risk of harm, unnecessary interventions, and increased patient anxiety. By accurately predicting patient trajectories, we can develop personalized rehabilitation approaches more likely to improve outcomes. This allows us to differentiate patients who benefit from watchful waiting from those needing intensive rehabilitation, potentially reallocating resources to enhance rehabilitation access.
Interestingly, studies have shown that both physicians and patients often prefer advanced imaging techniques and report higher satisfaction with care, even when patient outcomes are not improved.27, 33 This presents a significant challenge. Conceptual models suggest that receiving a diagnostic label can have physical, psychosocial, and financial consequences, as well as increase treatment burden, exposure to unnecessary tests and treatments, and adverse events, ultimately leading to dissatisfaction with care.31 Patients are often unaware of the potential harms of diagnostic labeling. Given the self-limiting nature of many common musculoskeletal disorders, research is needed to optimize the integration of watchful waiting approaches.
Key Takeaway: Aggressive pursuit of diagnosis can lead to overdiagnosis and subsequent overtreatment. Emphasizing prognosis for self-limiting conditions is crucial for improving overall patient outcomes and reducing unnecessary interventions.
Diagnostic testing and metrics are crucial for informed clinical decision-making in complex medical scenarios.
Phenotyping: Moving Beyond Diagnostic Labels for Personalized Management
Current diagnostic labels in musculoskeletal disorders can sometimes negatively impact patient outcomes. To bridge the gap between diagnosis and improved outcomes, we must address the complexity and heterogeneity hidden within common diagnostic categories. Phenotyping offers a promising approach to better understand musculoskeletal disorders.
Traditionally, “phenotype” referred to the observable characteristics of an organism resulting from the interaction of its genotype and environment.37 Modern science has broadened phenotyping to include physical, biochemical, and genetic traits, along with environmental interactions, that produce unique, observable characteristics.37 In shoulder injury research, phenotyping considering genetic and psychological interactions has been used to predict persistent shoulder pain. George and colleagues demonstrated that specific single nucleotide polymorphisms [38](#bib0420] interacted with psychological factors to predict six shoulder impairment phenotypes, and pain-related genes interacted with psychological factors to predict four shoulder impairment phenotypes.
Other phenotyping approaches rely solely on clinical findings. The field of knee osteoarthritis (OA) has significantly advanced this understanding through longitudinal cohort studies. Two research groups, using the Osteoarthritis Initiative study and the Amsterdam OA cohort (including 3494 and 551 participants with knee OA, respectively), identified up to five knee OA phenotypes.39, 40 These phenotypes were based on radiographic OA grades, knee muscle strength, body mass index, comorbidities, psychological distress, and alterations in pain neurophysiology. These phenotypes, all under the same diagnostic label of knee OA, were categorized as “minimal joint disease,” “strong muscle strength,” “severe radiographic,” “obese,” and “depressive mood.”
Other researchers have identified four pain susceptibility phenotypes in 852 participants from the Multicenter Osteoarthritis Study, a cohort of individuals with or at risk of knee OA.41 These phenotypes were based on clinical measures such as pressure pain threshold and temporal summation. The phenotype characterized by high sensitization showed predictive capacity for developing persistent knee pain over two years.
Another study, examining 689 patients undergoing total knee arthroplasty, identified trajectories of knee pain and function over five years post-surgery.42 Subgroups of patients exhibited persistent pain or functional deficits after surgery, and these trajectories could be predicted by comorbidities and reported psychological or physical measures.
In low back pain research, one group identified five pain trajectories over 12 weeks in 1585 patients seeking care for low back pain (recovery at week 2 or 12, pain reduction without recovery, fluctuating pain, and persistent high pain for 12 weeks).43 Longer pain duration and belief in pain persistence predicted delayed or non-recovery from low back pain. High pain intensity and longer duration were associated with persistent high pain at 12 weeks.
Another study identified up to nine subgroups using 112 characteristics from history and physical examinations of patients with low back pain in primary care.44 While these subgroups showed somewhat improved predictive capacity for pain intensity, frequency, and disability over 12 months compared to simpler subgrouping methods, they were also more complex to implement clinically. The authors suggested further research to determine if these subgroups respond better to targeted treatments.
In a study of 682 participants with non-traumatic arm, neck, and shoulder complaints in primary care, three disability trajectories were identified over two years using the Disabilities of the Arm, Shoulder and Hand questionnaire (DASH).45 Prognostic variables from clinical examination, such as high somatization levels, could predict a continuous high disability trajectory. In a cohort of 127 patients with patellofemoral pain, three subgroups were classified as “strong,” “weak and tighter,” and “weak and pronated foot” based on six clinical measures including flexibility, strength, patellar mobility, and foot posture.46 The authors proposed that these subgroups could guide the development of targeted rehabilitation approaches to improve patient outcomes.
These studies collectively suggest that multiple phenotypes can exist within a “single” diagnosis. This implies that patients with the same diagnosis may have different outcomes even with identical treatments. Furthermore, test results may vary within a single diagnosis depending on the patient’s specific phenotype.
Key Takeaway: Phenotyping, based on patient characteristics, reported outcome measures, and clinical examination, offers a more nuanced understanding of patient profiles and different presentation trajectories within a given diagnostic label. Ongoing research using large longitudinal cohorts will further enhance our ability to identify relevant subgroups within musculoskeletal disorders.
Conclusion
Higher-order thinking, a decision-making process that transcends memorization and basic facts, is indispensable for diagnostic clinicians. This article has highlighted how higher-order thinking can mitigate interpretation errors associated with standard diagnostic metrics, reduce overdiagnosis, and reveal the phenotypic diversity within seemingly singular diagnoses. Moving forward, we need to advance diagnostic approaches beyond traditional metrics and integrate phenotyping and prognosis evidence to refine targeted care strategies, ultimately improving patient outcomes. We are at the forefront of understanding the diverse profiles of patients with musculoskeletal disorders. Large cohorts, databases, and data mining tools like artificial intelligence will accelerate our understanding of the critical link between diagnosis and patient outcomes.
Conflicts of interest
The authors declare no conflicts of interest.
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