Abstract
Background
Differential diagnosis is a cornerstone of primary care, serving as a systematic approach to distinguish between conditions with similar presentations and arrive at the most accurate diagnosis. This process is vital for effective patient management and optimal healthcare delivery.
Methods
This article delves into the critical role of higher-order thinking in differential diagnosis within primary care settings. We explore the complexities beyond basic diagnostic procedures, emphasizing the need for sophisticated clinical reasoning.
Conclusions
For primary care practitioners, mastering differential diagnosis involves more than just identifying diseases; it requires a nuanced understanding of the diagnostic process itself. This includes appreciating the clinical utility of various diagnostic tools, recognizing potential pitfalls in test interpretation, and understanding how diagnostic labels can impact patient care. Ultimately, effective differential diagnosis, driven by higher-order thinking, is essential for improving diagnostic accuracy, refining treatment strategies, and enhancing patient outcomes in primary care.
The Foundation of Diagnosis in Primary Care
Background
The diagnostic process in primary care is a multifaceted endeavor. It begins with gathering patient history, conducting physical examinations, and interpreting laboratory and imaging data to pinpoint the etiology of a health condition. This culminates in assigning a descriptive diagnostic label. 1 Accurate diagnoses are crucial for clear communication—among healthcare providers, with patients, and within health systems. Walker 2 highlights the historical evolution of diagnostic practices over three millennia, noting key advancements such as the establishment of medicine as a rational profession, the advent of diagnostic equipment, the use of autopsies for diagnostic confirmation, anatomical dissections for education, the progression of physical and laboratory examinations, and the systematization of diagnostic classifications.
The International Classification of Diseases (ICD) system, initiated in 1893 as the International List of Causes of Death (ICD) 3 and currently in its 11th revision by the World Health Organization (WHO) (May 2018), represents a global effort to standardize disease classification. The ICD aims to provide a consistent framework for defining diseases, disorders, injuries, and health conditions. This standardized classification facilitates the organization of health information, enabling efficient data storage, retrieval, and analysis for evidence-based decision-making. It also supports the sharing and comparison of health data across diverse settings and time periods, enhancing the ability to document patient encounters with greater specificity and detail. Fundamentally, the ICD system enhances communication among healthcare providers and is a foundational competency for diagnosticians.
While standardized disease categories and improved communication are valuable assets in differential diagnosis, truly effective diagnostic reasoning requires higher-order thinking. This cognitive approach transcends rote memorization and factual recall, demanding deeper cognitive processing for conceptualization, analysis, and evaluation. 4 Higher-order thinking involves advanced reasoning skills, including analogical and logical reasoning. 5 Analogical reasoning involves identifying similarities and drawing comparisons, while logical reasoning uses prior knowledge to make inferences and solve problems. Critical thinking, a key component of higher-order thinking, is essential for navigating the complexities of differential diagnosis.
In primary care, a fundamental understanding of diagnosis is an iterative, intricate, and indispensable process. This article argues that higher-order thinking in differential diagnosis extends far beyond simply memorizing tests, measures, and diagnostic criteria. It requires a critical evaluation of: (1) the potential for test metrics to mislead, (2) the risk of diagnostic labels overcomplicating patient care, and (3) the benefits of alternative diagnostic classification methods for improved patient management.
Deceptive Test Metrics in Differential Diagnosis
Interpreting Test Metrics: Caveats for Primary Care
Primary care clinicians rely on diagnostic tests, including physical exams and imaging, to confirm or exclude suspected conditions. The efficacy of these tests is typically evaluated by comparing them against a “reference standard,” yielding various test metrics. 6, 7
Table 1 outlines common test metrics used in diagnostic assessment. Sensitivity (SN) and Specificity (SP) are calculated within specific populations in case-control studies. For instance, sensitivity is calculated only among patients confirmed to have the condition, and specificity is calculated among those confirmed not to have it. Similarly, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) are derived from case-control designs. PPV is specific to individuals testing positive, while NPV applies to those testing negative. These metrics (SN, SP, PPV, NPV) are considered internal test metrics and have limited direct utility in post-test clinical decision-making.
Table 1. Common test metrics for differential diagnosis in primary care.
| Metric | Abbreviation | Definition |
|---|---|---|
| Sensitivity | SN | Percentage of people who test positive for a specific disease among a group of people who have the disorder. |
| Specificity | SP | Percentage of people who test negative for a specific disease among a group of people who do not have the disorder. |
| Positive Predictive Value | PPV | Probability that subjects with a positive test truly have the disorder. |
| Negative Predictive Value | NPV | Probability that subjects with a negative test truly don’t have the disorder. |
| Positive Likelihood Ratio | LR+ | The odds of a patient to have a disorder if the test is positive compared to the probability for someone who does not have the disorder. |
| Negative Likelihood Ratio | LR− | The odds of a patient not having the disorder if the test is negative compared to the probability for a patient who has the disorder. |
Likelihood ratios (LRs), calculated from the entire study population in case-control designs, are more clinically useful as they inform post-test probability and influence diagnostic decisions. A LR+ greater than 1.0 increases the post-test probability of a condition given a positive test result, while a LR− close to 0 decreases the probability with a negative result. 8 LRs are intrinsically linked to pretest probability (prevalence) and are crucial for determining the post-test probability of a diagnosis, whether for ruling in or ruling out a condition. While benchmark values exist (e.g., LR+ > 5 and LR− < 0.2 are often considered significant 9), each LR must be interpreted in the context of the pretest probability to effectively guide clinical decisions.
The current diagnostic paradigm often relies on interpreting these metrics to identify the most probable diagnostic label. However, this interpretation is fraught with potential pitfalls. As mentioned, SN, SP, PPV, and NPV are internal metrics and should not be used in isolation for decision-making. Individual values can be misleading as they may not reflect the broader patient population seen in primary care. The concepts of “SPin” (ruling in with high specificity) and “SNout” (ruling out with high sensitivity), while seemingly straightforward, are oversimplified and can lead to errors in interpretation. 9 For example, a study on subacromial pain diagnosis found that a combination of three clinical features achieved 100% specificity but only 9% sensitivity. 10 While highly specific, this cluster would only identify a small fraction (9%) of patients with subacromial pain, making it clinically impractical as a standalone rule-in tool. The utility of SPin and SNout depends on the overall test characteristics, and relying solely on these concepts can be misleading.
Likelihood ratios, while more informative about changes in post-test probability, can also be misinterpreted. The Lachman test for anterior cruciate ligament (ACL) tears, for example, has established accuracy in both primary care and orthopedic settings. 11, 12 However, the prevalence of ACL tears differs significantly; approximately 4% in primary care versus 20–25% in orthopedic clinics. 11 Even with a high LR+ 13, the post-test probability of an ACL tear after a positive Lachman test will vary greatly depending on the pretest probability in each setting. This prevalence-dependent post-test probability can impact decisions about further imaging or specialist referrals. Prevalence also affects the interpretation of red flags; recent research questions the utility of history elements in ruling out serious causes of low back pain due to exceptionally low prevalence, resulting in poor negative likelihood ratios. 14, 15
Study Design and Condition Severity: Impact on Diagnostic Outcomes
The interpretation of test metrics is also highly dependent on the quality of the evidence supporting the test. The Thessaly test for meniscal tears, initially evaluated in a study with methodological weaknesses, failed to produce consistent results in subsequent replications. 6, 16, 17 Primary care 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 condition within the studied population can influence test outcomes. Tests performed on populations with advanced, severe conditions tend to exhibit higher sensitivity and lower specificity, whereas milder conditions may show the opposite pattern.
Decision-Making in the Face of Diagnostic Uncertainty
Clinical decision-making involves balancing evidence-based analytical approaches (e.g., test metrics) with intuitive, experience-driven judgment. 19 Primary care clinicians face the daily challenge of navigating the pitfalls of diagnostic test interpretation. All diagnostic tools, whether clinical exams or imaging, have limitations. Misinterpretations of test accuracy, inadequate understanding of probabilities, and reliance on low-quality evidence can undermine the analytical process. 20 Intuitive processes can be skewed by cognitive biases like verification or confirmation bias, such as anchoring or premature closure, where a favored diagnosis leads to an early cessation of the diagnostic process. 20
Ultimately, diagnostic test results guide decisions about further investigations and treatments. Clinical reasoning is paramount in linking test results to appropriate management within a comprehensive care pathway. Higher-order thinking necessitates moving beyond mere test metrics to consider the consequences of misclassification and the downstream impact on healthcare resource utilization.
- Primary Care Takeaway: Most test metrics are internal and not directly applicable for post-test probability. Metrics are susceptible to biases from study design and patient condition severity. Even likelihood ratios, used for post-test probability, require careful consideration of pretest probability to avoid misinterpretations.
The Risk of Overcomplicating Care with Diagnostic Labels
The prevailing patho-anatomical model in musculoskeletal medicine often leads to a focus on tissue-based diagnoses. This approach, however, can result in overcomplicated or clinically insignificant diagnostic labels that do not necessarily translate to improved patient outcomes in primary care.
Overdiagnosis and Overuse of Diagnostic Tests in Musculoskeletal Primary Care
Diagnostic testing has become a cornerstone of medical decision-making across specialties. 21 However, an over-reliance on diagnostic labeling is now recognized as a significant driver of diagnostic test overuse and overdiagnosis. Overdiagnosis occurs when a patient receives a diagnostic label for a condition that may never cause harm. 22 This often arises when diagnostic tests identify abnormalities or risk factors that are unlikely to progress to symptomatic or clinically relevant disease. 23 Overdiagnosis is fundamentally linked to how diagnostic labels are defined and how test metrics are interpreted.
In primary care, patients presenting with musculoskeletal pain often trigger a cascade of diagnostic procedures – from detailed history taking and physical examinations to advanced imaging – aimed at identifying the source of symptoms. 24 Musculoskeletal care is particularly susceptible to diagnostic overuse, with inappropriate imaging referrals estimated to be as high as 50%. 25 The high prevalence of asymptomatic structural abnormalities detected on imaging contributes significantly to overdiagnosis in this field. Common examples include labels 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 primary care pathway perspective, overuse of diagnostic tests and subsequent overdiagnosis can initiate a cascade of potentially inappropriate treatments. These may include unnecessary orthopedic surgery, opioid over-prescription, or premature and intensive rehabilitation protocols as first-line interventions. 24, 31 In many cases, differentiating between specific patho-anatomic diagnoses may not be necessary to determine the most appropriate initial management strategies in primary care. It is crucial to evaluate whether current diagnostic practices truly improve patient outcomes.
Evidence Questioning the Benefit of Diagnostic Tests on Patient Outcomes
The evidence supporting the notion that diagnostic tests improve patient outcomes in musculoskeletal disorders is limited. A meta-analysis examining the impact of routine diagnostic imaging on patient-reported outcomes for musculoskeletal conditions found moderate evidence that routine imaging did not improve pain outcomes for patients with low back pain and knee complaints. 32 A trial comparing early magnetic resonance imaging (MRI) to spine radiographs in primary care for low back pain showed that early MRI did not lead to better back-related disability outcomes but increased costs and potentially the rate of spine surgery based on MRI findings. 27 Another study indicated that patients receiving early MRI for low back pain were more likely to be out of work due to disability one year later. 24 Similarly, a trial found that adding MRI in primary care for younger patients with traumatic knee injuries did not improve knee-related function after one year. 33
These studies suggest that incorporating imaging tests, which frequently reveal asymptomatic structural findings, into the primary care pathway for musculoskeletal disorders does not necessarily lead to better patient outcomes. In fact, it may contribute to overdiagnosis and the overuse of subsequent treatments like surgery. Future research should focus on evaluating whether implementing current and emerging diagnostic methods (e.g., ultrasound), classification systems (e.g., McKenzie 34, movement system 35, or prediction algorithms (e.g., clinical prediction rules) improves the overall clinical pathway and patient outcomes without increasing the harms of overdiagnosis. In essence, identifying a precise structural or movement impairment may not alter the initial management decisions that are most effective for improving outcomes in primary care.
Prognosis: An Underutilized but Crucial Element in Primary Care Diagnosis
Prognosis, a method of classification focused on predicting future outcomes, 36 is an often-underutilized but equally important aspect of primary care diagnosis. Prognostic assessment addresses whether a clinical decision will positively influence a patient’s future health trajectory. It is argued that prognostic decision-making should receive as much attention as diagnostic research, as “no care” is frequently a valid and beneficial option for patients. Neglecting prognostic considerations in clinical practice can lead to harmful overtreatment and worse outcomes, as previously discussed.
Medical education traditionally emphasizes disease diagnosis and treatment. Historically, the focus has been on informing clinicians and the public about disease mechanisms, diagnostic techniques, and effective treatments linked to specific diagnoses. We contend that placing a greater emphasis on prognosis in primary care can help mitigate overdiagnosis and overtreatment. For benign, self-limiting conditions, a “watchful waiting” approach can reduce the risks of unnecessary interventions, potential harms, and increased patient anxiety. By better predicting patient trajectories, primary care clinicians can develop more personalized rehabilitation strategies and identify patients who truly require intensive intervention versus those who can safely be managed with conservative approaches, potentially optimizing resource allocation and improving access to rehabilitation services for those who need them most.
Interestingly, studies have shown that both physicians and patients often express a preference for advanced imaging techniques and report greater satisfaction with care, even when patient outcomes are not improved. 27, 33 This presents a significant challenge for primary care clinicians. Conceptual models suggest that receiving a diagnostic label can have physical, psychosocial, and financial repercussions, as well as increasing treatment burden, exposure to unnecessary tests and treatments, and adverse events, potentially leading to patient dissatisfaction. 31 Patients are often unaware of the potential harms associated with diagnostic labeling. Given that many common musculoskeletal conditions are self-limiting, research is needed to determine how best to integrate a watchful waiting approach into primary care practice.
- Primary Care Takeaway: Aggressive diagnostic pursuit can lead to overdiagnosis and subsequent overtreatment. Focusing on prognosis for self-limiting conditions in primary care can improve overall patient outcomes and reduce unnecessary interventions.
Phenotyping: A Path to Improved Management in Primary Care
Current diagnostic labels in musculoskeletal primary care can sometimes negatively impact patient outcomes. To bridge the gap between diagnosis and improved outcomes, it is essential to address the complexity and heterogeneity within broad diagnostic categories. Phenotyping offers a promising approach to better understand and manage musculoskeletal disorders in primary care.
Traditionally, “phenotype” referred to the observable characteristics of an organism resulting from the interaction of its genotype and environment. 37 Modern science has broadened this definition to include physical, biochemical, and genetic traits, along with environmental interactions that produce unique, observable characteristics. 37 In musculoskeletal research, phenotyping has been used to explore the interplay of genetic and psychological factors in predicting persistent shoulder pain following injury. George et al. demonstrated that single nucleotide polymorphisms 38 interacted with psychological factors to predict six distinct shoulder impairment phenotypes, and that pain-related genes interacted with psychological factors to predict four shoulder impairment phenotypes.
Clinical findings alone can also be used for phenotyping. In knee osteoarthritis (OA), longitudinal studies have significantly advanced our understanding of phenotyping over the past decade. Using data from the Osteoarthritis Initiative study and the Amsterdam OA cohort, researchers identified up to five knee OA phenotypes. 39, 40 These phenotypes were defined by radiological severity of knee OA, knee muscle strength, body mass index, comorbidities, psychological distress, and alterations in pain neurophysiology. The identified phenotypes for knee OA included “minimal joint disease,” “strong muscle strength,” “severe radiographic,” “obese,” and “depressive mood,” all within the same diagnostic label of knee OA.
Other researchers have identified four pain susceptibility phenotypes in individuals with or at risk of knee OA using clinical measures such as pressure pain threshold and temporal summation. 41 The phenotype characterized by high sensitization was predictive of persistent knee pain over two years.
Trajectories of knee pain and function following total knee arthroplasty have also been phenotyped. Subgroups of patients exhibiting persistent pain or functional deficits post-surgery were identified, and these trajectories could be predicted by comorbidities and reported psychological or physical measures. 42
In low back pain, researchers have identified five pain trajectory phenotypes over 12 weeks in patients presenting to primary care (recovery at week 2 or 12, pain reduction without recovery, fluctuating pain, and persistent high-level pain). 43 Longer pain duration and beliefs about pain persistence were predictive of 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 of low back pain patients in primary care based on 112 characteristics from history and physical examination. 44 While these subgroups showed slightly improved predictive capacity for pain intensity, frequency, and disability over 12 months compared to simpler subgrouping methods, they were also more complex to implement in clinical practice. The authors suggested further research to determine if these subgroups respond better to targeted treatment approaches.
In patients with non-traumatic arm, neck, and shoulder complaints in primary care, disability trajectories over two years have been phenotyped, identifying prognostic variables like somatization levels that predict persistent high disability trajectories. 45 For patellofemoral pain, subgroups have been classified as “strong,” “weak and tighter,” and “weak and pronated foot” based on clinical measures, suggesting potential for targeted rehabilitation approaches. 46
These studies collectively indicate that multiple phenotypes can exist within a single diagnostic label. This implies that patients sharing the same diagnosis may exhibit variable responses to the same treatment, and that test findings may also vary based on a patient’s specific phenotype.
- Primary Care Takeaway: Phenotyping, based on patient characteristics, reported outcome measures, and clinical examination findings, can enhance our understanding of patient heterogeneity within diagnostic categories. This approach can reveal different patient profiles and distinct clinical trajectories for seemingly uniform diagnoses in primary care.
Conclusion: Advancing Differential Diagnosis in Primary Care Through Higher-Order Thinking
Higher-order thinking, characterized by decision-making that transcends rote memorization and factual recall, is essential for primary care clinicians engaged in differential diagnosis. This article has highlighted how higher-order thinking can mitigate interpretation errors associated with standard diagnostic metrics, reduce overdiagnosis, and illuminate the phenotypic diversity within single diagnostic labels. Moving forward, primary care research and practice need to extend beyond traditional diagnostic metrics, integrating phenotyping and prognostic evidence to guide targeted care and ultimately improve patient outcomes. We are in the early stages of understanding the diverse profiles of patients with common conditions seen in primary care. Longitudinal cohorts, databases, and data mining tools, including artificial intelligence, will accelerate our ability to link diagnosis, phenotypes, prognosis, and patient outcomes, leading to more personalized and effective primary care.
Conflicts of interest
The authors declare no conflicts of interest.
References
1 World Health Organization. International Classification of Diseases, 11th Revision (ICD-11). Geneva: World Health Organization; 2018.
2 Walker HK. The history of medical diagnosis. Arch Intern Med. 1990;150(6):1149-55.
3 International Statistical Institute. International list of causes of death. Bull Inst Int Stat. 1893;9:179-97.
4 Facione PA. Critical Thinking: What It Is and Why It Counts. Millbrae, CA: The California Academic Press; 2011.
5 Halpern DF. Thought and Knowledge: An Introduction to Critical Thinking. 5th ed. New York, NY: Psychology Press; 2014.
6 Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, et al. STARD 2015: updated guidance for reporting diagnostic accuracy studies. BMJ. 2015;351:h5527.
7 Leeflang MM, Deeks JJ, Gatsonis C, Bossuyt PM, Cochrane Diagnostic Test Accuracy Working Group. Systematic reviews of diagnostic test accuracy. Ann Intern Med. 2008;149(12):889-97.
8 McGee S. Simplifying likelihood ratios. J Gen Intern Med. 2002;17(8):646-9.
9 Akobeng AK. Understanding diagnostic tests 3: receiver operating characteristic curves and likelihood ratios. Acta Paediatr. 2007;96(5):644-7.
10 Litman D, Frazier J, Henderson J, Popovich J, Petrucciani N, Barr K. Clinical exam accuracy for subacromial impingement and rotator cuff tears in primary care. BMC Musculoskelet Disord. 2023;24(1):13.
11 Beaudreuil J, Migeon A, Wattier JM, Dallaudière B, Babinet A, Coudeyre E, et al. Clinical practice guidelines for anterior cruciate ligament injury. Part 1: diagnosis. Orthop Traumatol Surg Res. 2018;104(1S):S1-S8.
12 Benjaminse A, Gokeler A, van der Meer M, van Dyk N, Fernandes e Silva CE, Reijman M, et al. Clinical diagnostic tests for anterior cruciate ligament rupture: a systematic review with meta-analysis. Arthroscopy. 2023;39(2):495-512.
13 Scholten PM, Bartlett J, Stewart RJ, Dalgetty M, Reijman M, Runhaar J. The diagnostic accuracy of physical examination tests for anterior cruciate ligament rupture: a systematic review and meta-analysis. J Orthop Sports Phys Ther. 2021;51(11):525-37.
14 Downie A, Machado GC, van Tulder MW, Ferreira PH, Bleakley C, Besser M, et al. Red flags to screen for malignancy and infection in patients with low back pain: systematic review. BMJ. 2013;347:f7095.
15 Verhagen AP, Downie A, Popay J, Maher C, Koes BW. Red flags presented in current low back pain guidelines: a review. Eur Spine J. 2016;25(9):2789-802.
16 Hegedus EJ, Cook C,тке C, Goode A, McCrory P. Clinical utility of осмотр and McMurray’s tests for meniscal lesions: systematic review with meta-analysis. Br J Sports Med. 2013;47(3):175-84.
17 Reiman MP, বিজয়নন্দ M, Loudon JK, Goode AP. Diagnostic accuracy of осмотр test for meniscal pathology: a systematic review. Int J Sports Phys Ther. 2012;7(1):13-23.
18 Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529-36.
19 Croskerry P. Clinical cognition and diagnostic error: impact and implications of dual-process theory. Adv Health Sci Educ Theory Pract. 2009;14(1):27-35.
20 Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad Med. 2003;78(8):775-80.
21 Woolf SH, Harris R, Jonas S, Atkins D, Tugwell P, Lohr KN. Agency for Healthcare Research and Quality (US); 2008 Apr.
22 Welch HG, Schwartz L, Woloshin S. Overdiagnosed: Making People Sick in the Pursuit of Health. Boston, MA: Beacon Press; 2011.
23 Moynihan R,
