NOVEL CONCEPTS OF OUR DIAGNOSTIC ALGORITHM AND COMPUTER PROGRAM
• Disregarding disease prevalence is doubly advantageous: (1) Prior probabilities of diseases (equivalent to prevalence) are eliminated from Bayes formula, which is transformed into a simplified equation for calculating positive predicting values (PP value) based on statistically established or estimated sensitivities (S) of clinical data. (2) Low prevalence no longer is a cause of ruling out a rare disease, giving this disease a chance to become a final diagnosis, based on merit of supporting clinical data.
• Disregarding subjective qualities of clinical data, which are variable and unreliable, remarkably simplifies diagnostic processing without compromising accuracy.
• PP value best indicates how strongly a clinical datum supports a diagnosis and more accurately than does specificity, true positive value, estimated evoking strength, or any other index attached to a clinical datum.
• A clinical datum present rules in the corresponding diagnosis with strength proportional to its positive predictive value (PP value). A clinical datum absent rules out the corresponding diagnosis with strength proportional to its sensitivity (S).
• The greatest of the PP values of clinical data present that support a diagnosis, equals the probability (P) of this diagnosis; this is more accurate than arithmetically combining values of several redundant supportive clinical data, thereby inappropriately increasing this P.
• Our novel mini-max procedure calculates accurately the probability of each diagnosis, in contrast with some other programs that rely on Bayes formula, inadequate for this purpose, because this formula requires that clinical data (symptom, physical sign, test or procedure results) manifested by a patient be independent, and diagnoses be incompatible and exhaustive, conditions that are not fulfilled by internal medicine and actual clinical cases. Bayes calculation deals only with competing diagnoses and is unable to diagnose concurrent diseases.
• Mini-max procedure discriminates competing and concurrent diagnoses, which in turn enables processing complex clinical presentations.
• At each step of the diagnostic inquiry, we apply a novel method to select and recommend the best cost-benefit clinical datum next to investigate. These probabilistically calculated clinical data to investigate next in the patient, based on greatest PP value, greatest S, and smallest cost, achieve more efficiently and economically a final diagnosis.
• Cost has high priority in our program and refers not only to the dollar price, but also to discomfort and risk involved in obtaining each clinical datum; the maximum of these qualitative levels represents the overall cost. It makes little sense to order expensive, uncomfortable, and risky tests or procedures, before resorting to less costly ones that may suffice to confirm or rule out a diagnosis.
• Recommending a set of best cost-benefit clinical data to be investigated simultaneously in the patient, based on diverse heuristic strategies, is essential in emergency situations, but also important in an outpatient setting, to avoid the need of the physician to contact the patient after each single new test result to order the next one. Because the number of such data may be huge, our program offers diverse reduced output files that facilitate selection of a set of best cost-benefit clinical data to investigate simultaneously, by recommending these data organized by cost category, procedure to obtain them, quantity, diagnosis, and P of diagnoses before and after processing these data whether present or absent. These selected partial lists render the diagnostic task more economic and manageable, without compromising the accuracy of the result.
• Grouping clinical data according to the test or procedure necessary to investigate them facilitates their request.
• Entering in Present Data or Absent Data any best cost-benefit clinical datum supersedes and removes from the same cost category all other recommended data that produce a smaller change of P, reducing considerably the number of data to investigate.
• Parameters that provide diverse strategies to reduce number of best cost-benefit clinical data to investigate, without compromising accuracy of the diagnostic process.
• Abridged output files that display a reduced number of recommended best cost-benefit clinical data to investigate, without compromising accuracy of the diagnostic process.
• Models for complex clinical presentations, listing statistically or pathophysiologically related diagnoses, facilitate the diagnosis of complex cases, detect interactions between concurrent diseases or drugs that mask important clinical data of the primary disease, and preclude overlooking associated diagnoses. The entire diagnostic process is partitioned into two distinct steps: the first step uses probabilities to obtain simple final diagnoses; the second step uses categorical combination, integrating these diagnoses into complex clinical presentations. This partitioning eliminates the computational complexity and even impossibility of managing the entire diagnostic process with probabilistic calculations.
• Artifact of fictitious OTHER DISEASES model enables creation of an efficient program with a limited number of disease models and clinical data in the database, overcoming the condition of exhaustiveness stating that all known diseases and clinical data must be included in the database.
• Iteration of the entire program from start, each time a new clinical datum present or absent is entered in the computer, provides an opportunity to diagnoses ruled out at previous iterations to reenter the competition if new supporting clinical data present increase P of such diagnoses.
• A novel and simple method to handle synonyms of diseases and clinical data.
• The program is ease to update, simply by adding to the database a newly developed clinical datum (test or procedure) for a known disease or a newly discovered disease with the corresponding clinical data and their cost and sensitivities. Finally, including the disease in a complex clinical presentation model, if some relations exist with other diseases.
• Such an algorithm, if successful in medicine, may represent a more general model of reasoning; a paradigm of mental structure and functioning applicable to other inexact disciplines such as law, sociology, politics, defense, or corporate strategy.