Науқастың анамнезі мен физикалық тексеру арқылы диагноз қою әдістемесі

Method of analysis of a patient's history and physical examination

In healthcare, a differential diagnosis (DDx) is a method of analysis that distinguishes a particular disease or condition from others that present with similar clinical features. Differential diagnostic procedures are used by clinicians to diagnose the specific disease in a patient, or, at least, to consider any imminently life threatening conditions. Often, each individual option of a possible disease is called a differential diagnosis (e. g., acute bronchitis could be a differential diagnosis in the evaluation of a cough, even if the final diagnosis is common cold). More generally, a differential diagnostic procedure is a systematic diagnostic method used to identify the presence of a disease entity where multiple alternatives are possible. This method may employ algorithms, akin to the process of elimination, or at least a process of obtaining information that increases the "probabilities" of candidate conditions to negligible levels, by using evidence such as symptoms, patient history, and medical knowledge to adjust epistemic confidences in the mind of the diagnostician (or, for computerized or computer assisted diagnosis, the software of the system). Differential diagnosis can be regarded as implementing aspects of the hypothetico deductive method, in the sense that the potential presence of candidate diseases or conditions can be viewed as hypotheses that clinicians further determine as being true or false. A differential diagnosis is also commonly used within the field of psychiatry/psychology, where two different diagnoses can be attached to a patient who is exhibiting symptoms that could fit into either diagnosis. For example, a patient who has been diagnosed with bipolar disorder may also be given a differential diagnosis of borderline personality disorder, given the similarity in the symptoms of both conditions. Strategies used in preparing a differential diagnosis list vary with the experience of the healthcare provider. While novice providers may work systemically to assess all possible explanations for a patient's concerns, those with more experience often draw on clinical experience and pattern recognition to protect the patient from delays, risks, and cost of inefficient strategies or tests. Effective providers utilize an evidence based approach, complementing their clinical experience with knowledge from clinical research.

There are several methods for differential diagnostic procedures and several variants among those. Furthermore, a differential diagnostic procedure can be used concomitantly or alternately with protocols, guidelines, or other diagnostic procedures (such as pattern recognition or using medical algorithms). For example, in case of medical emergency, there may not be enough time to do any detailed calculations or estimations of different probabilities, in which case the ABC protocol (Airway, Breathing and Circulation) may be more appropriate. Later, when the situation is less acute, a more comprehensive differential diagnostic procedure may be adopted. The differential diagnostic procedure may be simplified if a "pathognomonic" sign or symptom is found (in which case it is almost certain that the target condition is present) or in the absence of a sine qua non sign or symptom (in which case it is almost certain that the target condition is absent). A diagnostician can be selective, considering first those disorders that are more likely (a probabilistic approach), more serious if left undiagnosed and untreated (a prognostic approach), or more responsive to treatment if offered (a pragmatic approach). Since the subjective probability of the presence of a condition is never exactly 100% or 0%, the differential diagnostic procedure may aim at specifying these various probabilities to form indications for further action. The following are two methods of differential diagnosis, being based on epidemiology and likelihood ratios, respectively.

One method of performing a differential diagnosis by epidemiology aims to estimate the probability of each candidate condition by comparing their probabilities to have occurred in the first place in the individual. It is based on probabilities related both to the presentation (such as pain) and probabilities of the various candidate conditions (such as diseases).

The procedure of differential diagnosis can become extremely complex when fully taking additional tests and treatments into consideration. One method that is somewhat a tradeoff between being clinically perfect and being relatively simple to calculate is one that uses likelihood ratios to derive subsequent post test likelihoods.

The validity of both the initial estimation of probabilities by epidemiology and further workup by likelihood ratios are dependent on the inclusion of candidate conditions that are responsible for a large part as possible of the probability of having developed the condition, and it is clinically important to include those where relatively fast initiation of therapy is most likely to result in the greatest benefit. If an important candidate condition is missed, no method of differential diagnosis will supply the correct conclusion. The need to find more candidate conditions for inclusion increases with the increasing severity of the presentation itself. For example, if the only presentation is a deviating laboratory parameter and all common harmful underlying conditions have been ruled out, then it may be acceptable to stop finding more candidate conditions, but this would much more likely be unacceptable if the presentation would have been severe pain.

If two conditions get high post test probabilities, especially if the sum of the probabilities for conditions with known likelihood ratios becomes higher than 100%, then the actual condition is a combination of the two. In such cases, that combined condition can be added to the list of candidate conditions, and the calculations should start over from the beginning. To continue the example used above, let's say that the history and physical examination were indicative of cancer as well, with a likelihood ratio of 3, giving an Odds(PostH&E) of 0.057, corresponding to a P(PostH&E) of 5.4%. This would correspond to a "Sum of known P(PostH&E)" of 101.5%. This is an indication for considering a combination of primary hyperparathyroidism and cancer, such as, in this case, a parathyroid hormone producing parathyroid carcinoma. A recalculation may therefore be needed, with the first two conditions being separated into "primary hyperparathyroidism without cancer", "cancer without primary hyperparathyroidism" as well as "combined primary hyperparathyroidism and cancer", and likelihood ratios being applied to each condition separately. In this case, however, tissue has already been resected, wherein a histopathologic examination can be performed that includes the possibility of parathyroid carcinoma in the examination (which may entail appropriate sample staining). Let's say that the histopathologic examination confirms primary hyperparathyroidism, but also showed a malignant pattern. By an initial method by epidemiology, the incidence of parathyroid carcinoma is estimated at 1 in 6 million people per year, giving a very low probability before taking any tests into consideration. In comparison, the probability that non malignant primary hyperparathyroidism would have occurred at the same time as an unrelated non carcinoma cancer that presents with malignant cells in the parathyroid gland is calculated by multiplying the probabilities of the two. The resultant probability is, however, much smaller than the 1 in 6 million. Therefore, the probability of parathyroid carcinoma may still be close to 100% after histopathologic examination despite the low probability of occurring in the first place.

Machine differential diagnosis is the use of computer software to partly or fully make a differential diagnosis. It may be regarded as an application of artificial intelligence. Alternatively, it may be seen as "Augmented Intelligence" if it meets the FDA criteria, namely that (1) it reveals the underlying data, (2) reveals the underlying logic, and (3) leaves the clinician in charge to shape and make the decision. Machine Learning AI is generally seen as a device by the FDA, whereas Augmented Intelligence applications are not. Many studies demonstrate improvement of quality of care and reduction of medical errors by using such decision support systems. Some of these systems are designed for a specific medical problem such as schizophrenia, Lyme disease or ventilator associated pneumonia. Others are designed to cover all major clinical and diagnostic findings to assist physicians with faster and more accurate diagnosis. However, these tools all still require advanced medical skills to rate symptoms and choose additional tests to deduce the probabilities of different diagnoses. Machine differential diagnosis is also currently unable to diagnose multiple concurrent disorders. Thus, non professionals should still see a health care provider for a proper diagnosis.

The method of differential diagnosis was first suggested for use in the diagnosis of mental disorders by Emil Kraepelin. It is more systematic than the old fashioned method of diagnosis by gestalt (impression).

'Differential diagnosis' is also used more loosely, to refer simply to a list of the most common causes of a given symptom, to a list of disorders similar to a given disorder, or to such lists when they are annotated with advice on how to narrow the list down (French's Index of Differential Diagnosis is an example). Thus, a differential diagnosis in this sense is medical information specially organized to aid in diagnosis.

Methods similar to those of differential diagnostic processes in medicine are also used by biological taxonomists to identify and classify organisms, living and extinct. For example, after finding an unknown species, there can first be a listing of all potential species, followed by ruling out of one by one until, optimally, only one potential choice remains. Similar procedures may be used by plant and maintenance engineers and automotive mechanics and used to be used in diagnosing faulty electronic circuitry.

The American television medical drama House stars Hugh Laurie as the main protagonist Dr. Gregory House. He leads a team of diagnosticians at the fictional Princeton–Plainsboro Teaching Hospital in New Jersey, who regularly use differential diagnostics procedures in a bid to produce the right diagnosis. House uses provocation, humiliation and conflict to hone his team's minds and ignore preconceptions in an effort to extract inventive thinking. Throughout the series, the doctors have diagnosed such diseases as lupus, mastocytosis, Plummer's disease, rabies, Kawasaki's syndrome, smallpox, Rickettsialpox, and dozens of others.