Why is diagnostic test important

Learning curve Patients want and should have a role in the innovative process and in conversations about regulation and access. If we are to have a truly patient-centred health system, this is obvious.

Take research and innovation as an example: While patients have been playing an increasingly proactive role in medicines development — notably through a number of Innovative Medicines Initiative IMI projects — this knowledge and experience will not automatically transfer to medical devices. The European Commission and healthcare stakeholders are in the process of devising a successor to the IMI — the largest healthcare public-private partnership in the world for health research and innovation.

The new public-private partnership will definitely take a much broader view of healthcare -and will seek input from patients and patient Tanja Valentin MedTech Europe. Nicola Bedlington EPF. Predicting a patient's health path and changing their behaviour, through the adoption of Artificial Intelligence AI.

By unleashing the power of machine learning, we can better understand behaviour, empower patients to make smarter decisions — and save billions of euros. Unhealthy lifestyles are driving an explosion in chronic conditions, including obesity, diabetes and cardiovascular disease.

By choosing to smoke, having an inconsistency in maintaining a healthy diet and opting out of exercising, we place ourselves at risk of ill-health. At the same time, some patients are neglecting to take their medicines as prescribed or are misusing antibiotics — with devastating consequences. A patient-centric approach to behaviour change promises not only to improve clinical outcomes, but to address the rising demand for health services.

Better education and awareness can help individuals to make smarter choices. There are a range of interventions available, but the challenge is providing the right patient with the right behaviour change intervention at the right time.

If We Can Predict, We Can Prevent Now we have new tools at our disposal, informed by research from psychology and behavioural economics, and powered by technological advances. As someone with a keen interest in behaviour change and the predictive power of analytics, I believe machine learning can help to make our health systems more sustainable.

Artificial Intelligence AI allows us to evaluate how an individual makes lifestyle decisions and tailor behaviour change programmes to suit their needs. When considering an example of poor medication adherence, if we are aware of who is at risk and The Butterfly Effect: when minute changes to our regulatory and payment systems impact the fragile medtech innovation ecosystem.

An incoming tornado. The decline of investment in medtech. Each of these events could be considered a butterfly effect — the notion that small causes can have broad effects. The medical device industry is undergoing tremendous tectonic shifts, where advances in technology are crossing new boundaries in the medical device space and widening horizons for patients. Internally, our industry has been evolving in response to these advancements.

Internet companies are empowering patients with information that enables them to control their destiny more than ever before. Patient advocacy groups are getting stronger and more influential. With this in mind, you might assume that our industry is growing healthy and that our innovation ecosystem is vibrant.

Tests that are capable of fully discriminating between the presence or absence of a disease are uncommon. Diagnostic testing is generally performed to screen for, detect, and monitor diseases. To optimize the use of diagnostic testing, clinicians should be aware of how the results of testing will affect determination of the probability of the presence of disease. To be useful, diagnostic tests should have the potential to change the pretest probability of disease into a post-test probability that is more definitive.

The process of diagnostic testing should be based on a logical sequence that arrives at a sufficiently high probability of disease to make the diagnosis or a sufficiently low probability to exclude the diagnosis.

These thresholds will vary from disease to disease; when the consequences of missing the disease false-negative have high potential to be disastrous, the threshold of post-test probability should be very low, whereas when the consequences of falsely making a diagnosis of the disease have the potential to be disastrous, the threshold of post-test probability should be very high.

On the other hand, when making a diagnosis of MI, clinicians need a high post-test probability because the consequences of treatment thrombolytic therapy, invasive strategies and on prognosis life expectancy can be serious. The process of considering the diagnosis of a disease is often triggered by components of the history and physical examination, which lead the clinician to consider the presence of the disease.

Other key components in considering a diagnosis include the experience and knowledge base of the diagnostician, the frequency of the disease, and the clinical importance of making or refuting a diagnosis.

The former might be favored by clinicians experienced in the disease of interest eg, cardiologists for diagnosing MI , particularly when there is some subjectivity in aspects of the diagnosis. For pulmonary embolism PE , the challenge becomes the spectrum of disease ranging from clinically silent to multiple clinical presentations often with nonspecific symptoms and signs to hemodynamic collapse , the Your MyAccess profile is currently affiliated with '[InstitutionA]' and is in the process of switching affiliations to '[InstitutionB]'.

This div only appears when the trigger link is hovered over. Otherwise it is hidden from view. Along the entire production chain, industry uses these quality control tools to help ensure the safety of products such as infant formula and vaccines, thus contributing to the protection of consumer health.

They play a decisive role in limiting healthcare costs, since the appropriate diagnostic test performed in a timely manner:. This figure concerns all diagnostic tools: in vitro diagnostic tests and medical imaging exams. Skip to main content. Search form Search. The time pressures often involved in clinical appointments also contribute to challenges in the clinical history and interview. An accurate history facilitates a more productive and efficient physical exam and the appropriate utilization of diagnostic testing Lichstein, The physical exam is a hands-on observational examination of the patient.

If the clinician has seen the patient before, these observations can be weighed against previous interactions with the patient. A careful physical exam can help a clinician refine the next steps in the diagnostic process, can prevent unnecessary diagnostic testing, and can aid in building trust with the patient Verghese, There is no universally agreed upon physical examination checklist; myriad versions exist online and in textbooks.

Due to the growing emphasis on diagnostic testing, there are concerns that physical exam skills have been underemphasized in current. For example, Kugler and Verghese have asserted that there is a high degree in variability in the way that trainees elicit physical signs and that residency programs have not done enough to evaluate and improve physical exam techniques.

Educators observe students and residents performing these 25 maneuvers to ensure that trainees are able to elicit the physical signs reliably Stanford Medicine 25 Team, Over the past years, diagnostic testing has become a critical feature of standard medical practice Berger, ; European Society. Pathology is usually separated into two disciplines: laboratory medicine and anatomic pathology. Laboratory medicine, also referred to as clinical pathology, focuses on the testing of fluid specimens, such as blood or urine.

Anatomic pathology addresses the microscopic examination of tissues, cells, or other solid specimens. Laboratory medicine is a medical subspecialty concerned with the examination of specific analytes in body fluids e. Generally, clinical pathologists, except those with blood banking and coagulation expertise, do not interact directly with patients.

Anatomic pathology is a medical subspecialty concerned with the testing of tissue specimens or bodily fluids, typically by specialists referred to as anatomic pathologists, to interpret results and diagnose diseases or health conditions.

Some anatomic pathologists perform postmortem examinations autopsies. Typically, anatomic pathologists do not interact directly with patients, with the notable exception of the performance of fine needle aspiration biopsies. Laboratory scientists, historically referred to as medical technologists, may contribute to this process by preparing and collecting samples and performing tests.

Especially for laboratory medicine, the ordering of diagnostic tests and the. Diagnostic testing may occur in successive rounds of information gathering, integration, and interpretation, as each round of information refines the working diagnosis. In many cases, diagnostic testing can identify a condition before it is clinically apparent; for example, coronary artery disease can be identified by an imaging study indicating the presence of coronary artery blockage even in the absence of symptoms.

The primary emphasis of this section focuses on laboratory medicine, anatomic pathology, and medical imaging see Box Aditional forms of diagnostic testing include, for example, screening tools used in making mental health diagnoses SAMHSA and HRSA, , sleep apnea testing, neurocognitive assessment, and vision and hearing testing. It is worth mentioning that with the advent of precision medicine, molecular diagnostic testing is not specifically aligned with either clinical or anatomic pathology see Box Medical imaging, also known as radiology, is a medical specialty that uses imaging technologies such as X-ray, ultrasound, computed tomography [CT], magnetic resonance imaging [MRI], and positron emission tomography [PET] to diagnose diseases and health conditions.

For many conditions, it is also used to select and plan treatments, monitor treatment effectiveness, and provide longterm follow-up. Image interpretation is typically performed by radiologists or, for selected tests involving radioactive nuclides, nuclear medicine physicians.

Technologists support the process by carrying out the imaging protocols. Most radiologists today have subspecialty training e. Specialists in other clinical disciplines, such as emergency medicine physicians and cardiologists, may be trained and credentialed to perform and interpret certain types of medical imaging.

This can include imaging such as ultrasound to localize tissue targets during biopsy. Several new molecular imaging probes have recently been approved for clinical use, and a growing number are entering clinical trials. The field of radiology also includes interventional radiology, which offers image-guided biopsy and diagnostic procedures as well as image-guided, minimally invasive treatments.

Although it was developed specifically for laboratory medicine, the brain-to-brain loop model is useful for describing the general process of diagnostic testing Lundberg, ; Plebani et al. The model includes nine steps: test selection and ordering, sample collection, patient identification, sample transportation, sample preparation, sample analysis, result reporting, result interpretation, and clinical action Lundberg, These steps occur during five phases of diagnostic testing: prepre-analytic, pre-analytic, analytic, post-analytic, and post-post-analytic phases.

Errors related to diagnostic testing can occur in any of these five phases, but the analytic phase is the least susceptible to errors Eichbaum et al. The pre-pre-analytic phase, which involves clinician test selection and ordering, has been identified as a key point of vulnerability in the work process due to the large number and variety of available tests, which makes it difficult for nonspecialist clinicians to accurately select the correct test or series of tests Hickner et al.

The pre-analytic phase involves sample collection, patient identification, sample transportation, and sample preparation. During the analytic phase, the specimen is tested, examined, or both.

Adequate performance in this phase depends on the correct execution of a chemical analysis or morphological examination Hollensead et al. The post-analytic phase includes the generation of results, reporting, interpretation, and follow-up. Ensuring accurate and timely reporting from the laboratory to the ordering clinician and patient is central to this phase. Possible factors contributing to failure in this phase include an incorrect interpretation of the test result by the ordering clinician or pathologist and the failure by the ordering clinician to act on the test results: for example, not ordering a follow-up test or not providing treatment consistent with the test results Hickner et al.

The medical imaging work process parallels the work process described for pathology. There is a pre-pre-analytic phase the selection and ordering of medical imaging , a pre-analytic phase preparing the patient for imaging , an analytic phase image acquisition and analysis , a post-analytic phase the imaging results are interpreted and reported to the ordering clinician or the patient , and a post-post-analytic phase the integration of results into the patient context and further action.

The rel-. Primary care clinicians order laboratory tests in slightly less than one third of patient visits CDC, ; Hickner et al. There are now thousands of molecular diagnostic tests available, and this number is expected to increase as the mechanisms of disease at the molecular level are better understood CDC, ; Johansen Taber et al.

The task of selecting the appropriate diagnostic testing is challenging for clinicians, in part because of the sheer volume of choices. For example, Hickner and colleagues found that primary care clinicians report uncertainty in ordering laboratory medicine tests in approximately 15 percent of diagnostic encounters.

Advances in molecular diagnostic technologies and new diagnostic tests have introduced another layer of complexity. Many clinicians are struggling to keep up with the growing availability of such tests and have uncertainty about the best application of these tests in screening, diagnosis, and treatment IOM, a; Johansen Taber et al.

Even if a test is performed correctly, there is a chance for a false positive or false negative result. Test interpretation involves reviewing numerical or qualitative yes or no results and combining those results with patient history, symptoms, and pretest disease likelihood. Test interpretation needs to be patient-specific and to consider information learned during the physical exam and the clinical history and interview.

Several studies have highlighted test inter-. BOX Molecular Diagnostics. This initiative hinges on recent advances in molecular and cellular biology, which have provided insights into the mechanisms of disease at the molecular level.

Concurrently, the role of pathology has expanded from morphologic observations into comprehensive analyses using combined histological, immunohistochemical, and molecular evaluations.

The use of molecular diagnostics is a rapidly developing area. Molecular diagnostic testing can identify a variety of specific genetic alterations relevant to diagnosis and treatment; molecular diagnostic techniques are also used to detect the genetic material of organisms causing infection. Panels of biomarkers are being developed into molecular diagnostic tests omics-based tests that are used to assess risk and inform treatment decisions, such as Oncotype DX and MammaPrint in breast cancer IOM, Molecular diagnostic testing is expected to improve patient management and outcomes.

The potential advantages of molecular diagnostics include 1 providing earlier and more accurate diagnostic methods; 2 offering information about disease that will better tailor treatments to patients; 3 reducing the occurrence. In addition, test performance may only be characterized in a limited patient population, leading to challenges with generalizability Whiting et al.

The laboratories that conduct diagnostic testing are some of the most regulated and inspected areas in health care see Table Some of the relevant entities include The Joint Commission and other accreditors, the federal government, and various other organizations, such as the College of American Pathologists CAP and the American Society for Clinical Pathology. There are many ways in which quality is assessed. Examples include proficiency testing of clinical laboratory assays and pathologists e.

However, the translation of molecular diagnostic technologies into clinical practice has been a complex and challenging endeavor. One major challenge is the development and rigorous evaluation of molecular diagnostic tests before their implementation in clinical practice. The development pathway is often timeconsuming, expensive, and uncertain. In addition, there are underdeveloped and inconsistent standards of evidence for evaluating the scientific validity of tests and a lack of appropriate study designs and analytical methods for these analyses IOM, , , Ensuring that diagnostic tests have adequate analytical and clinical validity is critical to preventing diagnostic errors.

For example, in the Centers for Disease Control and Prevention and the Food and Drug Administration issued a warning about potential diagnostic errors related to false positives caused by contamination in a Lyme disease test Nelson et al. As molecular diagnostic testing becomes increasingly complex such as the movement from single biomarker tests to omicsbased tests that rely on high-dimensional data and complex algorithms , there is considerable interest in ensuring their appropriate development and use IOM, Molecular diagnostic testing presents many regulatory, clinical practice, and reimbursement challenges; an Institute of Medicine study is looking into these issues and is expected to release a report in IOM, b.

For example, one regulatory issue is the oversight of laboratorydeveloped tests, an area that has been met with considerable controversy see Table Evans and Watson, ; Sharfstein, A clinical practice issue is next generation sequencing, which may frequently identify new genetic variants with unknown implications for health outcomes ACMG Board of Directors, Medical imaging plays a critical role in establishing the diagnoses for innumerable conditions and it is used routinely in nearly every branch of medicine.

The advancement of imaging technologies has improved the ability of clinicians to detect, diagnose, and treat conditions while also allowing patients to avoid more invasive procedures European Society of Radiology, ; Gunderman, For many conditions e. The appropriate choice of imaging modality depends on the disease, organ, and specific clinical questions to be addressed. CT procedures are frequently used to assess and diagnose cancer, circulatory system diseases and conditions, inflammatory diseases, and head and internal organ injuries.

A majority of MRI procedures are performed on the spine, brain, and musculoskeletal system, although usage for the breast, prostate, abdominal, and pelvic regions is rising IMV, Medical imaging is characterized not just by the increasingly precise anatomic detail it offers but also by an increasing capacity to illuminate biology.

For example, magnetic resonance spectroscopic imaging has allowed the assessment of metabolism, and a growing number of other MRI sequences are offering information about functional characteristics, such as blood perfusion or water diffusion.

In addition, several new tracers for. Functional and molecular imaging data may be assessed qualitatively, quantitatively, or both. Although other forms of diagnostic testing can identify a wide array of molecular markers, molecular imaging is unique in its capacity to noninvasively show the locations of molecular processes in patients, and it is expected to play a critical role in advancing precision medicine, particularly for cancers, which often demonstrate both intra- and intertumoral biological heterogeneity Hricak, The growing body of medical knowledge, the variety of imaging options available, and the regular increases in the amounts and kinds of data that can be captured with imaging present tremendous challenges for radiologists, as no individual can be expected to achieve competency in all of the imaging modalities.

General radiologists continue to be essential in certain clinical settings, but extended training and sub-specialization are often necessary for optimal, clinically relevant image interpretation, as is involvement in multidisciplinary disease management teams.

Furthermore, the use of structured reporting templates tailored to specific examinations can help to increase the clarity, thoroughness, and clinical relevance of image interpretation Schwartz et al. Like other forms of diagnostic testing, medical imaging has limitations.

Some studies have found that between 20 and 50 percent of all advanced imaging results fail to provide information that improves patient outcome, although these studies do not account for the value of negative imaging results in influencing decisions about patient management Hendee et al. Imaging may fail to provide useful information because of modality sensitivity and specificity parameters; for example, the spatial resolution of an MRI may not be high enough to detect very small abnormalities.

Inadequate patient education and preparation for an imaging test can also lead to suboptimal imaging quality that results in diagnostic error. Perceptual or cognitive errors made by radiologists are a source of diagnostic error Berlin, ; Krupinski et al. In addition, incomplete or incorrect patient information, as well as insufficient sharing of patient information, may lead to the use of an inadequate imaging protocol, an incorrect interpretation of imaging results, or the selection of an inappropriate imaging test by a referring clinician.

Referring clinicians often struggle with selecting the appropriate imaging test, in part because of the large number of available imaging options and gaps in the teaching of radiology in medical schools.

Although consensus-based guidelines e. The use of clinical decision support systems at the point of care as well as direct consultations with radiologists have been proposed by the ACR as methods for improving imaging test selection Allen and Thorwarth, There are several mechanisms for ensuring the quality of medical imaging. The Mammography Quality Standards Act MQSA —overseen by the Food and Drug Administration—was the first government-mandated accreditation program for any type of medical facility; it was focused on X-ray imaging for breast cancer.

MQSA provides a general framework for ensuring national quality standards in facilities that perform screening mammography IOM, MQSA requires all personnel at facilities to meet initial qualifications, to demonstrate continued experience, and to complete continuing education. MQSA addresses protocol selection, image acquisition, interpretation and report generation, and the communication of results and recommendations.

In addition, it provides facilities with data on diagnostic performance that can be used for benchmarking, self-monitoring, and improvement. MQSA has decreased the variability in mammography performed across the United States and improved the quality of care Allen and Thorwarth, However, the ACR noted that MQSA is complex and specified in great detail, which makes it inflexible, leading to administrative burdens and the need for extensive training of staff for implementation Allen and Thorwarth, It also focuses on only one medical imaging modality in one disease area; thus, it does not address newer screening technologies IOM, The requirements include personnel qualifications, image quality, equipment performance, safety standards, and quality assurance and quality control ACR, a.

MIPPA also mandated that, beginning in , ordering clinicians will be required to consult appropriateness criteria to order advanced medical imaging procedures, and the act called for a demonstration project evaluating clinician compliance with appropriateness criteria Timbie et al. The consult may help to confirm or reject the working diagnosis or may provide information on potential treatment options.

Clinicians can also recommend that the patient seek a second opinion from another clinician to verify their impressions of an uncertain diagnosis or if they believe that this would be helpful to the patient. Diagnostic consultations can also be arranged through the use of integrated practice units or diagnostic management teams Govern, ; Porter, ; see Chapter 4. The committee elaborated on several aspects of the diagnostic process which are discussed below, including.

One of the complexities in the diagnostic process is the inherent uncertainty in diagnosis. This does not mean that a diagnosis needs to be absolutely certain in order to initiate treatment. Kassirer concluded that:. Absolute certainty in diagnosis is unattainable, no matter how much information we gather, how many observations we make, or how many tests we perform. As the inferential process unfolds, our confidence as [clinicians] in a given diagnosis is enhanced by the gathering of data that either favor it or argue against competing hypotheses.

Our task is not to attain cer-. Kassirer, , p. Thus, the probability of disease does not have to be equal to one diagnostic certainty in order for treatment to be justified Pauker and Kassirer, The decision to begin treatment based on a working diagnosis is informed by: 1 the degree of certainty about the diagnosis; 2 the harms and benefits of treatment; and 3 the harms and benefits of further information-gathering activities, including the impact of delaying treatment.

The risks associated with diagnostic testing are important considerations when conducting information-gathering activities in the diagnostic process.

While underuse of diagnostic testing has been a long-standing concern, overly aggressive diagnostic strategies have recently been recognized for their risks Zhi et al.

However, there is growing recognition that overly aggressive diagnostic pursuits are putting patients at greater risk for harm, and they are not improving diagnostic certainty Kassirer, ; Welch, When considering diagnostic testing options, the harm from the procedure itself needs to be weighed against the potential information that could be gained. For some patients, the risk of invasive diagnostic testing may be inappropriate due to the risk of mortality or morbidity from the test itself such as cardiac catheterization or invasive biopsies.

In addition, the risk for harm needs to take into account the cascade of diagnostic testing and treatment decisions that could stem from a diagnostic test result. Included in these assessments are the potential for false positives and ambiguous or slightly abnormal test results that lead to further diagnostic testing or unnecessary treatment. There are some cases in which treatment is initiated even though there is limited certainty in a working diagnosis.

For example, an individual who has been exposed to a tick bite or HIV may be treated with prophylactic antibiotics or antivirals, because the risk of treatment may be felt to be smaller than the risk of harm from tick-borne diseases or HIV infection.

However, it is important to note. A treatment that is beneficial for some patients might not be beneficial for others with the same condition Kent and Hayward, , hence the interest in precision medicine, which is hoped to better tailor therapy to maximize efficacy and minimize toxicity Jameson and Longo, In addition, there are isolated cases where the morbidity and the mortality of a diagnostic procedure and the likelihood of disease is sufficiently high that significant therapy has been given empirically.

Moroff and Pauker described a decision analysis in which a year-old practicing lawyer with a new 1. Of major importance in the diagnostic process is the element of time. Some diagnoses can be determined in a very short time frame, while months may elapse before other diagnoses can be made.

This is partially due to the growing recognition of the variability and complexity of disease presentation. Similar symptoms may be related to a number of different diagnoses, and symptoms may evolve in different ways as a disease progresses; for example, a disease affecting multiple organs may initially involve symptoms or signs from a single organ. The thousands of different diseases and health conditions do not present in thousands of unique ways; there are only a finite number of symptoms with which a patient may present.

At the outset, it can be very difficult to determine which particular diagnosis is indicated by a particular combination of symptoms, especially if symptoms are nonspecific, such as fatigue. Diseases may also present atypically, with an unusual and unexpected constellation of symptoms Emmett, Adding to the complexity of the time-dependent nature of the diagnostic process are the numerous settings of care in which diagnosis occurs and the potential involvement of multiple settings of care within a single diagnostic process.

Some diagnoses may be more important to establish immediately than others. These include diagnoses that can lead to significant patient harm if not recognized, diagnosed, and treated early, such as anthrax, aortic dissection, and pulmonary embolism. Sometimes making a timely diagnosis relies on the fast recognition of symptoms outside of the health care setting e.

In these cases, the benefit of treating the disease promptly can greatly exceed the potential harm from unnecessary treatment. Consequently, the threshold for ordering diagnostic testing or for initiating treatment becomes quite low for such health problems Pauker and Kassirer, , In other cases, the potential harm from rapidly and unnecessarily treating a diagnosed condition can lead to a more conservative or higher-threshold approach in the diagnostic process.

Population trends, such as the aging of the population, are adding significant complexity to the diagnostic process and require clinicians to consider such complicating factors in diagnosis as comorbidity, polypharmacy and attendant medication side effects, as well as disease and medication interactions IOM, , b. Diagnosis can be especially challenging in older patients because classic presentations of disease are less common in older adults Jarrett et al.

For example, infections such as pneumonia or urinary tract infections often do not present in older patients with fever, cough, and pain but rather with symptoms such as lethargy, incontinence, loss of appetite, or disruption of cognitive function Mouton et al. Acute myocardial infarction MI may present with fatigue and confusion rather than with typical symptoms such as chest pain or radiating arm pain Bayer et al. Sensory limitations in older adults, such as hearing and vision impairments, can also contribute to challenges in making diagnoses Campbell et al.

Physical illnesses often present with a change in cognitive status in older individuals without dementia Mouton et al. In older adults with mild to moderate dementia, such illnesses can manifest with worsening cognition.

Older patients who have multiple comorbidities, medications, or cognitive and functional impairments are more likely to have atypical disease presentations, which may increase the risk of experiencing diagnostic errors Gray-Miceli, Communicating with diverse populations can also contribute to the complexity of the diagnostic process.

Language, health literacy, and cultural barriers can affect clinician—patient encounters and increase the potential for challenges in the diagnostic process Flores, ; IOM, ; The Joint Commission, There are indications that biases influence diagnosis; one well-known example is the differential referral of patients for cardiac catheterization by race and gender Schulman et al.

In addition, women are more likely than men to experience a missed diagnosis of heart attack, a situation that has been partly attributed to real and perceived gender biases, but which may also be the result of physiologic differences, as women have a higher likelihood of presenting with atypical symptoms, including abdominal pain, shortness of breath, and congestive heart failure Pope et al.

Mental health diagnoses can be particularly challenging. Mental health diagnoses rely on the Diagnostic and Statistical Manual of Mental Disorders DSM ; each diagnosis in the DSM includes a set of diagnostic criteria that indicate the type and length of symptoms that need to be present, as well as the symptoms, disorders, and conditions that cannot be present, in order to be considered for a particular diagnosis APA, Compared to physical diagnoses, many mental health diagnoses rely on patient reports and observation; there are few biological tests that are used in such diagnoses Pincus, A key challenge can be distinguishing physical diagnoses from mental health diagnoses; sometimes physical conditions manifest as psychiatric ones, and vice versa Croskerry, a; Hope et al.

In addition, there are concerns about missing psychiatric diagnoses, as well as overtreatment concerns Bor, ; Meyer and Meyer, ; Pincus, For example, clinician biases toward older adults can contribute to missed diagnoses of depression, because it may be perceived that older adults are likely to be depressed, lethargic, or have little interest in interactions. Patients with mental health—related symptoms may also be more vulnerable to diagnostic errors, a situation that is attributed partly to clinician biases; for example, clinicians may disregard symptoms in patients with previous diagnoses of mental illness or substance abuse and attribute new physical symptoms to a psychological cause Croskerry, a.

Individuals with health problems that are difficult to diagnose or those who have chronic pain may also be more likely to receive psychiatric diagnoses erroneously. Understanding the clinical reasoning process and the factors that can impact it are important to improving diagnosis, given that clinical reasoning processes contribute to diagnostic errors Croskerry, a; Graber, The current understanding of clinical reasoning is based on the dual process theory, a widely accepted paradigm of decision making.

The dual process theory integrates analytical and non-analytical models of decision making see Box Analytical models slow system 2 involve a conscious, deliberate process guided by critical thinking Kahneman, Nonanalytical models fast system 1 involve unconscious, intuitive, and automatic pattern recognition Kahneman, Fast system 1 nonanalytical, intuitive automatic processes require very little working memory capacity.

They are often triggered by stimuli or result from overlearned associations or implicitly learned activities. In contrast, slow system 2 reflective, analytical processing places a heavy load on working memory and involves hypothetical and counterfactual reasoning Evans and Stanovich, ; Stanovich and Toplak, System 2 processing requires individuals to generate mental models.

Analytical models slow system 2. Hypotheticodeductivism is an analytical reasoning model that describes clinical reasoning as hypothesis testing Elstein et al. The steps involved in hypothesis testing include. Analytical reasoning models have several additional characteristics. First, the generation of a set of hypotheses that occurs after cue acquisition facilitates the construction of a differential diagnosis, with evidence suggesting that the consideration of potential hypotheses prior to gathering information can improve diagnostic accuracy Kostopoulou et al.

Second, in order to supplement hypotheses retrieved from memory, some clinicians may employ clinical decision support tools. Third, the evolving list of diagnostic hypotheses determines subsequent information-gathering activities Kassirer et al.

Fourth, the entire process involves, either explicitly or implicitly, clinicians assigning and updating the probability of each potential diagnosis, given the available data Kassirer et al. These models hold that clinical problem-solving tasks, such as diagnosis, require deliberate, logically sound reasoning by clinicians.

Thus, clinical reasoning can be improved by developing the critical thinking skills Papp et al. They also imply that clinical reasoning uses the presence or absence of specific signs or symptoms to be evidence that either confirms or disproves a diagnosis.

Counterfactual reasoning occurs when one thinks about what should occur if the situation differed from how it actually is. The deliberate,. However, studies also suggest that experience is crucial to the development of expertise and that general problemsolving skills, such as hypothesis testing, cannot account for differences in clinical reasoning skills between experts and novices Elstein and Schwarz, ; Groen and Patel, ; Neufeld et al.

These findings support a role for nonanalytical models of clinical reasoning and the importance of content knowledge and clinical experience. Nonanalytical models fast system 1. Broadly construed through a pattern-recognition framework, nonanalytical models attempt to understand clinical reasoning through human categorization and classification practices.

These models suggest that clinicians make diagnoses and choose treatments by matching presenting patients to their mental models of diseases or information about diseases that is stored in memory. Although the nature of these mental models remain under debate, most assume that they are either exemplars specific patients seen previously and stored in memory as concrete examples or prototypes an abstract disease conceptualization that weighs disease features according to their frequency Bordage and Zacks, ; Norman, ; Rosch and Mervis, ; Schmidt et al.

Expert pattern matching by experienced clinicians may involve illness scripts, in which elaborated disease knowledge includes enabling conditions or risk factors e. After encountering a patient, a clinician may activate a single illness script or multiple scripts.

Illness scripts differ from exemplars and prototypes by having more extensive knowledge stored for each disease.

As the diagnostic process evolves, the clinician matches the activated scripts against the presenting signs and symptoms, with the best matching script offered as the most likely diagnosis. While exemplars, prototypes, and illness scripts are assumed to encode different types of information about disease conditions—that is, actual instances versus typical presentation versus multidimensional information—pattern recognition models assume them to play the same role in diagnosis.

Heuristics—mental shortcuts or cognitive strategies that are automatically and unconsciously employed—are particularly important for decision making Gigerenzer and Goldstein, Heuristics can facilitate decision making but can also lead to errors, especially when patients present with atypical symptoms Cosmides and Tooby, ; Gigerenzer, ; Kahneman, ; Klein, ; Lipshitz et al.

When a heuristic fails, it is referred to as a cognitive bias. Cognitive biases, or predispositions to think in a way that leads to failures in judgment, can also be caused by affect and motivation Kahneman, Prolonged learning in a regular and predictable environment increases the success-fulness of heuristics, whereas uncertain and unpredictable environments are a chief cause of heuristic failure Kahneman, ; Kahneman and Klein, There are many heuristics and biases that affect clinical reasoning and decision making see Table for medical and nonmedical examples.

Additional examples of heuristics and biases that affect decision making and the potential for diagnostic errors are described below Croskerry, b :. In addition to cognitive biases, research suggests that fallacies in reasoning, ethical violations, and financial and nonfinancial conflicts of interest can influence medical decision making Seshia et al.

The interaction between fast system 1 and slow system 2 remains controversial. Some hold that these processes are constantly occurring in parallel and that any conflicts are resolved as they arise. When system 2 overrides system 1, this can lead to improved decision making, because engaging in analytical reasoning may correct for inaccuracies. It is important to note that slow system 2 processing does not guarantee correct decision making. For instance, clinicians with an inadequate knowledge base may not have the information necessary to make a correct decision.

There are some instances when system 1 processing is correct, and the override from system 2 can contribute to incorrect decision making. However, when system 1 overrides system 2 processing, this can also result in irrational decision making. Intervention by system 2 is likely to occur in novel situations when the task at hand is difficult; when an individual has minimal knowledge or experience Evans and Stanovich, ; Kahneman, ; or when an individual deliberately employs strategies to overcome known biases Croskerry et al.

Monitoring and intervention by system 2 on system 1 is unlikely to catch every failure because it is inefficient and would require sustained vigilance, given that system 1 processing often leads to correct solutions Kahneman, Factors that affect working memory can impede the ability of system 2 to monitor and, when necessary, intervene on system 1 processes Croskerry, b.

For example, if clinicians are tired or distracted by elements in the work system, they may fail to recognize when a decison provided by system 1 processing needs to be reconsidered Croskerry, b.

System 1 and system 2 perform optimally in different types of clinical practice settings. System 1 performs best in highly reliable and predictable environments but falls short in uncertain and irregular settings Kahneman and Klein, ; Stanovich, System 2 performs best in relaxed and unhurried environments. This section applies the dual process theory of clinical reasoning to the diagnostic process Croskerry, a,b; Norman and Eva, ; Pelaccia et al.

Croskerry and colleagues provide a framework for understanding the cognitive activities that occur in clinicians as they iterate through information gathering, information integration and interpretation, and determining a working diagnosis Croskerry et al. When patients present, clinicians gather information and compare that information with their knowledge about various diseases. This can. When a patient presents to a clinician, the initial data include symptoms and signs of disease, which can range from single characteristics of disease to illness scripts.

If the symptoms and signs of illness are recognized, system 1 processes are used. If they are not recognized, system 2 processes are used. Repetition of data to system 2 processes may eventually be recognized as a new pattern and subsequently processed through system 1.

Multiple arrows stem from system 1 processes to depict intuitive, fast, parallel decision making. Because system 2 processes are slow and serial, only one arrow stems from system 2 processes, depicting analytical decision making. The executive override pathway shows that system 2 surveillance has the potential to overrule system 1 decision making.

The irrational override pathway shows the capability for system 1 processes to overrule system 2 analytical decision making. The toggle arrow T illustrates how the decision maker may employ both fast system 1 and slow system 2 processes throughout the decision-making process. Cognitive debiasing 1: Origins of bias and theory of debiasing. Croskerry, G. Singhal, and S. This initial pattern matching is an instance of fast system 1 processing.

If a sufficiently unique match occurs, then a diagnosis may be made without involvement of slow system 2. However, some symptoms or signs may not be recognized or they may trigger mental models for several diseases at once. When this happens, slow system 2 processing may be engaged, and the clinician will continue to gather, integrate, and interpret potentially relevant information until a working diagnosis is generated and communicated to the patient.