- •Diagnosis is crucial for effective patient communication and documentation, guiding tailored treatment strategies. It fosters clinician collaboration and standardizes care delivery.
- •Beyond rote memorization, higher-order thinking is essential post-diagnosis, demanding complex cognitive processing and diverse learning methodologies.
- •Diagnostic test metrics can be internal, assessing test performance, or external, evaluating their impact on decision-making. Optimal tests effectively guide post-test clinical decisions.
- •Overdiagnosis poses a risk of overtreatment, highlighting a potential downside to excessive diagnostic pursuit and potentially adverse patient outcomes.
- •A single diagnosis can encompass diverse phenotypes, leading to varied patient presentations and treatment responses, underscoring the complexity of diagnostic categories.
Keywords: Differential Diagnosis, Diagnosis, Clinical Reasoning, Diagnostic Accuracy, Phenotyping, Musculoskeletal Disorders
Abstract
Background
Differential diagnosis is a critical, systematic methodology employed to discern the most accurate diagnosis from a spectrum of potential, yet often competing, diagnostic possibilities. This process is foundational in clinical medicine, especially in fields like musculoskeletal care where symptom overlap is common.
Methods
This masterclass aims to explore the intricate, higher-order thinking processes inherent in differential diagnosis. We delve into the cognitive skills and clinical reasoning required to effectively navigate diagnostic complexities and enhance patient care.
Conclusions
For healthcare practitioners, diagnosis, particularly differential diagnosis, stands as a pivotal element within the broader framework of clinical decision-making. It is characterized by the meticulous differentiation between competing conditions to achieve a definitive understanding of a patient’s underlying health issue. The diagnostic journey encompasses a detailed evaluation of patient history, thorough physical examinations, and the judicious review of laboratory findings or diagnostic imaging, culminating in a descriptive diagnosis. While the aptitude for differential diagnosis varies among healthcare professionals, the fundamental concept of diagnosis remains universally relevant across all medical disciplines. Ideally, a robust diagnosis refines classification systems, promotes clear communication, charts a course for treatment, improves prognostic understanding, and, in certain instances, facilitates preventative strategies. Realizing these benefits necessitates a profound grasp of the clinical utility of diagnostic tests and measures in relation to diagnosis, and the optimal application of these insights in clinical settings. This demands a sophisticated level of comprehension – higher-order thinking – regarding the role of diagnosis in comprehensive patient management.
The Essence of Differential Diagnosis: Context, Timing, and Rationale
Background
The diagnostic process fundamentally involves pinpointing and defining the origin of a disease or condition. This is achieved through a comprehensive evaluation of patient narratives, hands-on physical assessments, and the careful interpretation of laboratory results or diagnostic images.1 The outcome is a descriptive diagnosis, a label intended to streamline communication among patients, healthcare providers, payers, and health systems. Walker2 highlights that over three millennia, significant advancements have shaped contemporary diagnostic practices. These include establishing a rational foundation for medicine as a profession, developing diagnostic tools, utilizing autopsies for diagnostic validation, employing human dissection for educational purposes, expanding the scope of physical and laboratory examinations, and categorizing diagnostic commonalities.
The International List of Causes of Death (ICD),3 the inaugural international classification edition, was adopted in 1893 by the International Statistical Institute. The World Health Organization (WHO) launched the 11th ICD revision in May 2018, aiming to standardize the definition of diseases, disorders, injuries, and health conditions globally. The ICD system systematically organizes health information, enabling efficient storage, retrieval, and analysis for evidence-based healthcare decisions. It facilitates health information exchange and comparison across diverse settings and countries, and allows for longitudinal data comparisons within the same location. The ICD coding system offers enhanced specificity and clinical detail, improving documentation of patient encounters and outcome comparisons at a systemic level. At its core, the ICD diagnostic framework enhances communication among healthcare providers and is considered a basic competency for clinical diagnosticians.
While enhanced communication and a shared vocabulary for disease categories are invaluable in differential diagnosis, effectively utilizing these categories and acknowledging the limitations of diagnostic labels necessitates higher-order thinking. This concept suggests that certain learning forms demand greater cognitive engagement, moving beyond simple memorization of facts and concepts. Higher-order cognitive functions include conceptualization, analysis, and evaluation, involving complex reasoning and productive thinking, as opposed to rote learning and reproductive thinking.4 Foundational skills in higher-order thinking encompass analogical and logical reasoning.5 Analogical reasoning involves thinking through analogies and analyzing similarities, while logical reasoning uses prior knowledge to infer and solve problems. Critical thinking is a key component of higher-order thinking.
A fundamental grasp of diagnosis is a complex, iterative, and essential process. This masterclass posits that higher-order thinking transcends mere memorization of tests, measures, sensitivity, specificity, and ICD-based diagnoses. We contend that advanced differential diagnostic reasoning requires clinicians to focus on: (1) the potential for test metrics to mislead; (2) how diagnostic labels can complicate care unnecessarily; and (3) the potential of alternative diagnostic classification methods to improve patient management.
The Deceptive Nature of Test Metrics in Differential Diagnosis
Interpreting Test Metrics: Unveiling Potential Misinterpretations
In differential diagnosis, clinicians utilize diagnostic tests, such as physical examinations or imaging, to ascertain the presence or absence of a specific disorder. Diagnostic study validity hinges on comparing an “index test” against a definitive “reference standard.” This comparison yields crucial test metrics.6, 7
Table 1 outlines common metrics in diagnostic assessment. Sensitivity (SN) and Specificity (SP) are calculated within specific populations in case-control studies. SN is exclusive to individuals confirmed to have the condition, while SP is calculated only for those without it. Similarly, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) are population-proportionate metrics within case-control designs. PPV is derived from those testing “positive,” and NPV from “negative” results. SN, SP, PPV, and NPV are internal metrics, less suited for guiding post-test decisions.
Likelihood ratios (LR), calculated from the entire case-control study population, are more indicative of clinical utility, influencing diagnostic decisions. An LR+ above 1.0 increases post-test probability given a positive result, while a low LR− (close to 0) increases it with a negative finding.8 These are linked to pretest probability and determine post-test diagnostic probability, aiding in ruling in or out conditions. Benchmarks exist (e.g., LR+ >5, LR- <0.2), but each LR should be evaluated with pretest probability for informed decision-making.
The current diagnostic paradigm uses these metrics to assign diagnostic labels. However, this interpretation is fraught with challenges. Internal metrics like SN, SP, PPV, and NPV are limited for decision-making as they don’t fully represent clinical populations. The concepts of SPin (ruling in with high specificity) and SNout (ruling out with high sensitivity), while seemingly useful, are oversimplified and can lead to errors.9 For instance, a study on subacromial pain diagnosis showed a 100% SP with three clinical features, but only 9% SN.10 This “perfect scheme” is clinically rare, identifying few actual cases and potentially skewing clinician bias towards seeking this specific combination. Effective SPin and SNout strategies require balanced “other” metrics to minimize decision-making errors.
Likelihood ratios, while informative about post-test probability changes, can also mislead. The Lachman test for ACL tears, validated in primary care and orthopedic settings,11, 12 demonstrates this. ACL tear prevalence varies significantly: ~4% in primary care versus 20–25% in orthopedics, due to different patient populations.11 Even with a high LR+,13 post-test probability differs across prevalence cohorts, impacting decisions on imaging or referrals. Prevalence also affects red flag assessments. The utility of history in ruling out serious low back pain is questioned due to low negative LRs and exceptionally low prevalence.14, 15
The Impact of Study Design and Condition Severity on Diagnostic Outcomes
Test metric interpretation heavily relies on the evidence quality supporting the test. For example, the Thessaly test for meniscal tears, initially based on a flawed study, lacked replication.6, 16, 17 Clinicians must critically assess study designs, reference standards, and test descriptions to mitigate biases in diagnostic accuracy studies.18 Furthermore, population severity affects outcomes. Advanced, high-disability conditions yield more sensitive but less specific results. Conversely, mild conditions show lower sensitivity and higher specificity.
Decision-Making Processes: Balancing Evidence and Intuition
Decision-making models advocate for balancing analytical evidence-based approaches (e.g., test metrics) with intuitive, experience-driven judgments.19 Clinicians face daily challenges in avoiding diagnostic test interpretation pitfalls. All tests possess strengths and weaknesses. Analytical process failures can stem from flawed test accuracy interpretation, probability misunderstandings, and poor-quality evidence.20 Intuitive processes can be undermined by confirmation or verification biases like anchoring or premature closure, where clinicians fixate on a favored diagnosis and halt the diagnostic process prematurely.20
Diagnostic test results guide subsequent decisions about further tests and treatments. Clinical reasoning is thus paramount to link test results to appropriate management within a comprehensive care pathway. Higher-order thinking necessitates moving beyond test metrics, considering misclassification costs, and evaluating how decisions affect healthcare utilization.
Key takeaway: Most test metrics are internal and insufficient for post-test probability determination. Metrics are susceptible to biases from study design and patient severity. Even likelihood ratios, intended for post-test probability, require careful consideration of pretest probability’s influence.
The Overcomplication of Care Through Diagnostic Labeling
Diagnostic codes, rooted in patho-anatomical perspectives, have overemphasized tissue-based musculoskeletal disorders. We argue that this classification model can lead to overly complex or asymptomatic diagnostic labels, failing to improve patient outcomes.
The Overuse of Diagnostic Tests and Overdiagnosis in Musculoskeletal Disorders
Medicine widely employs diagnostic tests and metrics for clinical decisions.21 However, over-reliance on diagnostic labeling is now recognized as a key driver of diagnostic test overuse and overdiagnosis. Overdiagnosis occurs when a patient is labeled with a condition that would likely never cause harm,22 often from tests identifying abnormalities or risk factors unlikely to manifest as symptoms or impairments.23 Overdiagnosis is fundamentally linked to diagnostic labeling definitions and test metric interpretation.
Patients presenting with spine, knee, hip, or shoulder pain often undergo extensive history taking, physical exams, clinical measures, and imaging to pinpoint symptom sources.24 Musculoskeletal care is particularly prone to diagnostic test overuse, with up to 50% of imaging referrals deemed inappropriate.25 Musculoskeletal disorders are also susceptible to overdiagnosis due to the high prevalence of asymptomatic structural abnormalities found in imaging, such as “lumbar degeneration,” “disc bulges,”26 “disc herniation,”27 “degenerative meniscal tears,”28 “degenerative labral tears,”29 “subacromial bursal thickening,”30 and “rotator cuff tendinosis”.30
From a clinical pathway standpoint, diagnostic test overuse and overdiagnosis can trigger inappropriate treatments, such as orthopedic surgery, opioid over-prescription, or aggressive early rehabilitation as first-line interventions.24, 31 Differentiating specific patho-anatomic diagnoses may be irrelevant for choosing appropriate initial treatments. The real question is whether current diagnostic methods genuinely improve patient outcomes.
Trials Linking Diagnostic Tests to Patient Outcomes: Scarcity of Evidence
Evidence linking diagnostic tests to improved musculoskeletal patient outcomes is limited. A meta-analysis assessing routine diagnostic imaging’s impact on patient-reported outcomes for musculoskeletal disorders found 11 trials for low back pain and knee issues. It concluded there was moderate evidence that routine diagnostic imaging does not improve pain outcomes.32 A study showed that replacing spine radiographs with early MRI in primary care did not improve back-related disability, while increasing costs and potentially spine surgeries based on MRI findings.27 Another trial found that early MRI for low back pain patients correlated with increased disability-related work absence after one year.24 Similarly, a trial adding MRI in primary care for younger patients with traumatic knee complaints showed no improvement in knee function after a year.33
These studies indicate that incorporating imaging tests, known to frequently reveal asymptomatic structural findings, into musculoskeletal care pathways does not translate to better patient outcomes. It may, in fact, contribute to overdiagnosis and subsequent overtreatment. Future research should explore whether current and novel diagnostic methods (e.g., ultrasound), classification and biomechanical systems (e.g., McKenzie,34 movement system35, or prediction algorithms (e.g., clinical prediction rules) enhance the overall clinical pathway, improving patient outcomes without the harms of overdiagnosis. In essence, detailed knowledge of structure or movement patterns may not alter the initial high-quality treatment decisions needed for better outcomes.
The Underutilized Power of Prognosis
Prognosis, a classification method predicting future outcomes,36 is often overlooked but equally vital. Prognostic research assesses if a decision will influence a patient’s future health trajectory. Prognostic decision-making deserves as much focus as diagnostic research, given that “no care” is often as valid as active intervention. Neglecting prognosis in clinical care can lead to adverse effects and overtreatment (as previously discussed).
Medical education heavily emphasizes disease diagnosis and treatment. Historically, informing patients and the public about disease causes, mechanisms, diagnosis, and effective treatments has been prioritized. We argue for an equal emphasis on prognosis to mitigate overdiagnosis and overtreatment. For benign, self-limiting conditions, a “watchful waiting” approach can reduce harm, unnecessary interventions, and patient catastrophizing. Predicting trajectories enables personalized rehabilitation, distinguishing patients who benefit from watchful waiting versus intensive rehabilitation, potentially reallocating costs to improve rehabilitation access.
Interestingly, trials revealed that physicians and patients often preferred advanced imaging and were more satisfied with care despite unimproved patient outcomes.27, 33 This poses a clinical challenge. Conceptual models suggest diagnostic labeling can have physical, psychosocial, and financial repercussions, alongside increased treatment burden, unnecessary tests, and adverse events, leading to care dissatisfaction.31 Patients are frequently unaware of diagnostic labeling’s potential harms. Given the self-limiting nature of common musculoskeletal disorders, integrating watchful waiting strategies warrants further investigation.
Key takeaway: Overzealous diagnostic efforts can lead to overdiagnosis and subsequent overtreatment. Focusing on prognosis for self-limiting conditions is crucial for improving overall patient care and outcomes.
Phenotyping: A Refined Approach to Differential Diagnosis for Enhanced Management
We have demonstrated that current diagnostic labels in musculoskeletal disorders can sometimes negatively impact patient outcomes. To bridge the gap between diagnosis and outcomes, we must address the complexity and heterogeneity within broad diagnostic categories. Phenotyping offers a potentially superior method for understanding musculoskeletal disorders.
Traditionally, phenotype described observable organism properties resulting from genotype-environment interactions.37 Modern phenotyping encompasses physical, biochemical, and genetic traits, alongside environmental interactions, producing unique observable characteristics.37 In shoulder injury patients, phenotyping has examined genetic and psychological factor interactions to predict persistent shoulder pain. George et al. showed that single nucleotide polymorphisms[38](#bib0420], interacting with psychological factors, predicted six shoulder impairment phenotypes, and pain-related genes interacted with psychological factors to predict four phenotypes.
Phenotyping can also rely solely on clinical findings. Knee osteoarthritis (OA) research has significantly advanced this area. Two groups, using the Osteoarthritis Initiative and the Amsterdam OA cohort (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, BMI, comorbidities, psychological distress, and pain neurophysiology alterations. The phenotypes, within the same knee OA diagnosis, were labeled “minimal joint disease,” “strong muscle strength,” “severe radiographic,” “obese,” and “depressive mood.”
Other researchers identified four pain susceptibility phenotypes in 852 Multicenter Osteoarthritis Study participants (with or at risk of knee OA).41 These phenotypes were based on clinical measures like pressure pain threshold and temporal summation. The high sensitization phenotype predicted persistent knee pain development over two years.
Another study tracked knee pain and function trajectories post-total knee arthroplasty over five years in 689 patients.42 Subgroups showed persistent pain or functional deficits post-surgery, predictable by comorbidities and psychological or physical reported measures.
In low back pain, five pain trajectories over 12 weeks were identified in 1585 patients (recovery at week 2 or 12, pain reduction without recovery, fluctuating pain, and high-level pain for 12 weeks).43 Longer pain duration and belief in persistence predicted delayed or non-recovery. High pain intensity and duration correlated with persistent high pain at 12 weeks.
Another group identified up to nine subgroups using 112 characteristics from history and physical exams of primary care low back pain patients.44 While predictive capacity for pain intensity, frequency, and disability over 12 months was somewhat improved compared to prior subgrouping, clinical application was more complex. Further research should assess if these subgroups improve response to targeted treatments.
In 682 participants with non-traumatic arm, neck, and shoulder complaints in primary care, three disability trajectories at 2 years were identified using the DASH questionnaire.45 High somatization levels predicted continuous high disability trajectories. In 127 patellofemoral pain patients, three subgroups – “strong,” “weak and tighter,” and “weak and pronated foot” – were classified based on six clinical measures (flexibility, strength, patellar mobility, foot posture).46 These subgroups could inform targeted rehabilitation for better patient outcomes.
These studies indicate that a “single” diagnosis may encompass multiple phenotypes, meaning patients within that diagnosis may respond differently to the same treatment. Test findings may also vary within a diagnosis depending on the patient’s phenotype.
Key takeaway: Phenotyping, based on patient characteristics, reported outcomes, and clinical exams, enhances understanding of patient profiles and presentation trajectories within a diagnostic label. Ongoing large longitudinal cohort studies in musculoskeletal disorders will further refine subgroup identification.
Conclusion
Higher-order thinking, surpassing rote memorization of facts and concepts, is vital for diagnostic clinicians. This masterclass highlighted how it can mitigate interpretation errors in diagnostic metrics, reduce overdiagnosis, and address the phenotypic diversity within single diagnoses. Future research must extend beyond standard metrics to link phenotyping and prognosis evidence for targeted care and improved patient outcomes. We are at the cusp of understanding the diverse profiles within musculoskeletal disorders. Cohorts, databases, and AI-driven data mining will accelerate our understanding of the diagnosis-patient outcome linkage.
Conflicts of interest
The authors declare no conflicts of interest.
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