The use of a rational and consistent approach to transforming assessment data into a suitable diagnosis is vital to the practice of every NP. Because diagnosis guides treatment, and hopefully leads to management and resolution of the patient's health problems, diagnosis generation can be considered the most essential stage in patient care.
Evidence-based medicine is a refined application of decision theory; practitioners use statistical knowledge to manage uncertainty and determine a diagnosis in clinical medicine.1 Although it is tempting to think that this evidence-based practice (EBP) can provide a complete set of tools with which to manage patients, the reality is that available EBP can only provide practitioners with guidelines for care, not prescribed formulas for patient diagnosis or management. Consequently, even when using the latest EBP, misdiagnosis or delayed diagnosis of a medical condition, illness, or injury may still occur. Research has estimated that every year 12 million US adults in the ambulatory setting experience a diagnostic error.2 Imprecise diagnosis may result in improper care, delayed treatment, or no treatment. In the worst-case situations, diagnostic errors may lead to worsening of the patient's condition or even death. In a recent 10-year systematic review of research in ambulatory patient safety, the American Medical Association found that the most common error that leads to a malpractice claim was a diagnostic error.3 Research suggests that despite advanced medical technology and modern diagnostic imaging, the rate of diagnostic error remains around 8%.4
Even highly experienced and competent clinicians can make diagnostic errors. The World Health Organization's 2016 report on diagnostic errors found that cognitive errors occurred in over half of cases with identified diagnostic errors.5 Knowledge and experience are the foundations of a clinician's diagnostic skills. Good clinicians continually work to enhance this knowledge through avenues such as pursuing continuing education, reading clinical journals and EBP updates, and reviewing peers' clinical practice. Few clinicians, however, are actively refining their problem-solving skills, the cognitive skillset underlying diagnosing. Research has found that problem solving focuses on three approaches: pattern matching, categorization, and hypothesis testing.6 This article presents a synopsis of the logical reasoning clinicians may use to develop diagnostic accuracy and enhance patient care.
Intuition and heuristics
Clinicians typically use a limited amount of information in the form of prototypical models or illness scripts to rapidly determine a patient's diagnosis.7 As part of this route, intuition seems an instinctive processing of one's prior experience that can produce rapid and impressive results that may be correct but are often totally wrong.8
To illustrate this rapid-diagnostic process, consider a female patient, age 30, with dysuria. Based on this very limited information, the initial intuitive diagnosis that occurs to many clinicians is a urinary tract infection. Although it may be reasonable to proceed to this common conclusion in many cases, the differential list of potential problems with the complaint of dysuria can be long and varied. Dysuria may also suggest urethritis, sexually transmitted infections (STIs), vaginitis, foreign body, dermatologic conditions, medication use, urethral anatomic abnormalities, local trauma, interstitial cystitis/bladder pain syndrome, and several other potentially more complicated and severe issues.9
Instead of applying a strictly intuitive process, many practitioners may use a heuristic technique in evaluating this case patient. Heuristics can be described as decision strategies that are rapid, instinctive, automatic, and driven by networks of associations that are often focused on a few relevant predictors.10 Like intuition, these rapid techniques can be inaccurate and can become a trap for practitioners who seek quick solutions that lead to acceptance of passable judgments. Practitioners may argue that heuristics in the form of an organized mental summary of the knowledge of a disease (for example, illness scripts) are essential in busy practices and that experienced practitioners do not need to employ all the costly diagnostic tools/information available to them (or apply lengthy hypothesis testing deductive analysis) to identify common problems.10
Is there a better approach than heuristics that can avoid the problem of jumping to conclusions or being misled by the programing of education and experience? The answer is yes. Because practitioners work in a science-based field, they need to adopt ways of thinking that serve as the foundation for scientific discovery. Practitioners need to employ a healthy dose of skepticism as they search for empirical evidence that is based on or is verifiable by experiment or observation. Practitioners also need to use evidence to draw conclusions based on logic, not on bias or assumptions.
Thinking like a scientist
To think like a scientist, practitioners must consciously employ the tools of classic logical reasoning. These tools include abductive reasoning to generate an initial likely differential diagnosis list from the available data, deductive reasoning to test generalized propositions of the theorized diagnoses, and inductive reasoning to more meticulously consider the specific observations of the given case to confirm the existing diagnosis or provide alternative diagnoses. (See Types of reasoning.)
Abductive reasoning is applied in the beginning of an encounter by considering and collecting available data into a pattern or patterns that portray a mental construct that best explains the problem. The relationships within the apparent patterns are considered, and the most likely or probable diagnoses (hypotheses) that can account for the current situation are proposed. Although this process may employ heuristics or illness scripts, it is also deliberate, conscious, and considers competing diagnoses. The diagnoses generated should provide a tentative solution to the problem at hand based on all or most of the primary information; thus, guessing and supposition should be kept to a minimum. For example, abduction will try to identify the explanation for observations A, B, and C by labeling the most probable cause as disease Y after quick consideration of diseases X, Y, and Z that have similar characteristics. (See Abductive reasoning.)
Consider the previously mentioned case example of the patient with dysuria. The data set might include a healthy 30-year-old woman with dysuria, no sexual activity, no traumas, a history of reoccurring pain with bladder filling, and a normal urinalysis. Abductive reasoning would consider this minimal data set and identify patterns to narrow a differential diagnosis list. Although all diagnoses might be kept in the back of the clinician's mind, the observable patterns may well exclude STIs, foreign body, dermatologic conditions, medication use, urethral anatomic abnormalities, and traumatic injury. Diagnoses of acute cystitis, acute pyelonephritis, vaginitis (Candida, bacterial vaginosis, Trichomoniasis herpes simplex), urethritis/cervicitis (chlamydia, gonorrhea), and interstitial cystitis/painful bladder syndrome may be considered likely and retained in an order of decreasing likelihood.
Using the likely diagnosis list from abductive reasoning, deductive reasoning is applied to predict the characteristics derived from the scientific underpinnings of the diagnoses. Medical science specifies the characteristics of given diagnoses based on pathology, physiology, reaction to treatment, and prior experience. These defining characteristics constitute basic rules or laws that apply to all forms of the diagnosis being considered. Deduction starts with a general case or law and deduces specific instances. If the rules of a diagnosis are applied to the specific case, the defining characteristics of that diagnosis should be observable.
When considering any given case, the original data will be reviewed and any additional observation, testing, or treatment responses should reveal the presence of the expected or defining characteristics. If the defining characteristics are present, the diagnosis is strengthened or validated. Deduction will forecast that a patient affected by disease Y will manifest signs or symptoms A, B, C, and D; as confirmation of the disease, data will be collected to test for the presence of the expected signs or symptoms. (See Deductive and inductive reasoning.) If the expected data of disease Y are absent or additional data are present, the conclusion may be considered invalid and ruled out, or at least the legitimacy of the diagnosis must be questioned.
Continuing the case example of the patient with dysuria, the signs and symptoms of the various likely diagnoses can be anticipated by considering the science of the disease processes. Diagnoses of acute cystitis, or acute pyelonephritis can be expected to have abnormalities in the urinalysis or a complete blood cell count. Diagnoses of vaginitis (Candida, bacterial vaginosis, Trichomoniasis herpes simplex), or urethritis/cervicitis (chlamydia, gonorrhea) would present with abnormalities on examination, including discharge, lesions, and inflammation, and have positive culture results. Interstitial cystitis/painful bladder syndrome would be a diagnosis of exclusion but would likely have changes observable on cystoscopy and/or symptoms during hydrodistension of the bladder.
If the scientific evidence in the form of the defining characteristics is present in the data, confidence in the diagnosis is strengthened, the diagnosis may be considered validated, and appropriate treatment may be implemented. However, if the original data, additional observations, test results, and/or treatment responses reveal additional information, missing information, or information inconsistent with the defining characteristics, then the presumed diagnoses may be ruled out or at least questioned. This conscious analysis will help the practitioner avoid mistakes, such as choosing a diagnosis with insufficient evidence, clinging to a diagnosis as contradictory evidence accumulates and favoring the tendency to search for, interpret, favor, and recall information in a way that confirms their opinions or early impressions. Researchers identified the most common cognitive biases, framing effects (individuals react to a particular choice depending on how it is presented) and overconfidence (the belief that personal ability or judgment is greater than the objective impartial accuracy of that ability or judgment), and strongly suggest that these biases affect clinical reasoning processes and may lead to errors in the diagnosis, management, or treatment of medical conditions.11
If a diagnosis seems tenuous or has been ruled out, the next step would be to use inductive analysis to reconsider the possible range of diagnoses. Inductive reasoning supports development of diagnoses by matching specific aspects of the patient's problem (characteristics of the case) with characteristics of probable diagnoses. Using a more complete data set of signs and symptoms, inductive reasoning may propose, with varying degrees of confidence, one or more of the observations. For example, if signs and symptoms A, B, C, D, E, F, and G are present, inductive reasoning will catalog probable causes and propose disease R as most likely, T as probable, and Z as least likely but possible.
In the case example of the patient with dysuria, the data set might now include a healthy 30-year-old woman with dysuria, no sexual activity, no traumas, a history of reoccurring pain with bladder filling, and a normal urinalysis. No foreign body, traumatic injury, dermatologic conditions, or medication use are noted. No discharge or lesions upon examination were noted. Positive results include slight inflammation around the urethral meatus and a slightly elevated white blood cell count. Bedside dipstick urinalysis shows elevated urine pH, and trace amounts of red blood cells and nitrates. Urine culture results are pending. Renal and bladder imaging, cystoscopy, and hydrodistension of the bladder have not been performed.
Inductive reasoning requires consideration of the positive, negative, and uncollected data. Induction consists of observation for patterns and can suggest probabilities of the expected outcomes. Consideration of diagnoses in this case might proceed or conclude as follows:
With the results of the urine dipstick urinalysis, history, and physical exam, a diagnosis of acute cystitis may remain the likely conclusion. Because a culture confirmed uncomplicated cystitis is 70% to 90% probable in women with a compatible history and physical, a positive culture would further support the diagnosis.12 If data presented a pattern that included elevated white blood cells, there would be a reasonable concern for acute pyelonephritis or even possibly urolithiasis, though lack of gross hematuria and history makes it less probable. Diagnoses such as vaginitis (Candida, bacterial vaginosis, Trichomoniasis herpes simplex), urethritis/cervicitis (chlamydia, gonorrhea), and interstitial cystitis/painful bladder syndrome would need to remain on the list as differential diagnoses and even further data may need to be collected.
Practitioners need to use available tools such as documentation of quality measures in electronic health records, checklists, and learning health systems to enhance quality improvement and patient safety. Fundamental to patient safety are the assessment, diagnosis, intervention, and evaluation skills of the practitioner. Of all the practitioner's skills, diagnosis may be the most critical component and most certainly provides the basis of all care that follows. Consequently, practitioners need to strive for the most accurate and precise diagnosis they can possibly make with the available information at hand.
Use of heuristics, illness scripts, intuition, education, and experience can be both valuable and important. Practitioners need to adopt scientifically sound ways of thinking that engage a healthy dose of skepticism and are based on strong empirical evidence that help them to verify conclusions through investigation and observation.
By presuming accurate and effective data collection and using the tools of classic logical reasoning, practitioners can be reasonably confident in their diagnoses. This form of deliberate practice with preparation and reflection includes abductive reasoning to generate an initial likely differential diagnosis list from the available data, deductive reasoning to predict and test for diagnostic characteristics, and inductive reasoning to confirm, clarify, or generate alternative diagnoses. Combined with these methodical diagnostic skills, knowledge and experience can improve diagnostic accuracy and reduce diagnostic errors. With experience and practice, these tools will bring proficiency and speed appropriate to even busy practices.
Types of reasoning1
Abductive reasoning is thinking to determine the best explanation with the information at hand, which often is incomplete.
Deductive reasoning observes for characteristics from a theory or general rule in a particular case. If present, the characteristics support the specific conclusion. Deductive reasoning moves from the general rule to the specific application (for example, from biology or pathology to manifestations of disease).
Inductive reasoning begins with observations that are specific and limited in scope, and a generalized conclusion is made that is likely, but not certain, considering accumulated evidence. Inductive reasoning moves from the specific to the general by gathering evidence, seeking patterns, and forming a theory to explain what is seen (for example, the signs and symptoms that are used to help make a diagnosis).