Abstract:
A method for diagnosing diseases in human subjects and a computer system implementing the method. The novel method uses historical patient medical records to create tables, which are used by a medical center to determine what diagnostic test is associated with confirmation of a disease in question. The method provides nonparametric, retrospective, disease cohort, rank ordered, weighted concatenation tables, which are unique to a disease and specific to the medical center where they are created, with prospective application. The method is more efficient and effective than presently used methods.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to currently pending U.S. Provisional Patent Application 61/162,468, filed Mar. 23, 2009, which is incorporated herein by reference. 
    
    
     FIELD OF INVENTION 
     This invention relates to the field of disease diagnosis. 
     BACKGROUND 
     Efficient orderly resource management of diagnostic tests in human disease is a resistant problem. Many diagnostic tests applied on human patient populations carry an element of risk. The decision matrix by which diagnosticians reduce symptoms to specific diagnostic tests varies due to differences in medical centers&#39; diagnostic capacities. Health Maintenance Organization (HMOs), Preferred Physician Providers Organizations (PPOs), and others&#39; methods introduced to reduce healthcare resource consumption by fiscal means have primarily failed as measured by the gross national product. What is needed in the medical arts is an improved method of disease diagnosis that is more effective and efficient than methods currently employed by diagnostic centers. 
     SUMMARY OF INVENTION 
     The present invention, called the Illness Specific Diagnostic System, includes an innovative scientific method to diagnosis diseases in human beings. System tables, created by the novel method, assist diagnosticians at a specific medical center select the most accurate and precise diagnostic test for a specific disease. The system does not use decision trees, but instead uses DIAGNOSIMETRICS™, the application of quantitative analysis to the art of disease diagnosis, as the method for construction of non-parametric, retrospective, disease-cohort, rank-ordered, weighted concatenation tables unique to a disease and specific to the medical center where they are created. The system destroys the myth of a single medical standard of care, and focuses the diagnostician as the individual most capable of diagnosing disease(s) in human beings. A computer system implementing the novel method is also presented. 
     In an embodiment, the method of determining the suitability of a diagnostic test for confirmation of a disease in humans comprises obtaining disease treatment results for patients grouped by disease in a Diagnostic Related Group (DRG) and obtaining diagnostic tests corresponding to each patient, each diagnostic test having a test result selected from the group comprising a positive result, a negative result, and an equivocal result. The method further comprises retrieving a first result for a first diagnostic test performed for each of the patients and a second result for a second diagnostic test performed for each of the patients, and transforming the first and second results to create a system value by solving for 
                   [   sign   ]     ⁡     [       abs   ⁡     (     log   ⁡     (     X   +     1   N       )       )       -     log   ⁡     (     Y   +   N     )         ]       *     (     1   -     (     N   -   T     )       )     *     (     X   +   T     )       ,         
where X is the number of positive results for the first diagnostic test, Y is the number of negative results for the first diagnostic test, T is the total number of positive results, negative results, and equivocal results for the first diagnostic test, and N is the total number of positive results, negative results, and equivocal results for the first diagnostic test and positive results, negative results, and equivocal results for the second diagnostic test. The system value represents the suitability of the diagnostic test for confirmation of the disease.
 
     In another embodiment, the computer system adapted to determine the suitability of a diagnostic test for confirmation of a disease in humans comprises a processor, and a tangible memory storage including software instructions that cause the computer system to perform: obtaining disease treatment results for patients grouped by disease in a Diagnostic Related Group (DRG) and obtaining diagnostic tests corresponding to each patient, each diagnostic test having a test result selected from the group comprising a positive result, a negative result, and an equivocal result. The software instructions cause the computer system to further perform: retrieving a first result for a first diagnostic test performed for each of the patients and a second result for a second diagnostic test performed for each of the patients, and transforming the first and second results to create a system value by solving for 
                   [   sign   ]     ⁡     [       abs   ⁡     (     log   ⁡     (     X   +     1   N       )       )       -     log   ⁡     (     Y   +   N     )         ]       *     (     1   -     (     N   -   T     )       )     *     (     X   +   T     )       ,         
where X is the number of positive results for the first diagnostic test, Y is the number of negative results for the first diagnostic test, T is the total number of positive results, negative results, and equivocal results for the first diagnostic test, and N is the total number of positive results, negative results, and equivocal results for the first diagnostic test and positive results, negative results, and equivocal results for the second diagnostic test. The system value represents the suitability of the diagnostic test for confirmation of the disease, whereby the computer system determines the suitability of a diagnostic test for confirmation of a disease in humans.
 
     In an additional embodiment, the method of determining the suitability of a diagnostic test for confirmation of a disease in humans comprises obtaining disease treatment results for patients grouped by disease in a Diagnostic Related Group (DRG) and obtaining diagnostic tests corresponding to each patient, each diagnostic test having a test result selected from the group comprising a positive result, a negative result, and an equivocal result. The method further comprises retrieving a first result for a first diagnostic test performed for each of the patients and a second result for a second diagnostic test performed for each of the patients, and transforming the first and second results to create a system value by solving for 
                   [   sign   ]     ⁡     [       abs   ⁡     (     log   ⁡     (     X   +     1   N       )       )       -     log   ⁡     (     Y   +     1   N       )         ]       ⁢     (     1   -     Z   N       )       ,         
where X is the number of positive results for the first diagnostic test, Y is the number of negative results for the first diagnostic test, T is the total number of positive results, negative results, and equivocal results for the first diagnostic test, N is the total number of positive results, negative results, and equivocal results for the first diagnostic test and positive results, negative results, and equivocal results for the second diagnostic test, and Z is the number of patients not receiving the diagnostic test. The system value represents the suitability of the diagnostic test for confirmation of the disease.
 
     In a further embodiment, the computer system adapted to determine the suitability of a diagnostic test for confirmation of a disease in humans comprises a processor, and a tangible memory storage including software instructions that cause the computer system to perform: obtaining disease treatment results for patients grouped by disease in a Diagnostic Related Group (DRG) and obtaining diagnostic tests corresponding to each patient, each diagnostic test having a test result selected from the group comprising a positive result, a negative result, and an equivocal result. The software instructions cause the computer system to further perform: retrieving a first result for a first diagnostic test performed for each of the patients and a second result for a second diagnostic test performed for each of the patients, and transforming the first and second results to create a system value by solving for 
                   [   sign   ]     ⁡     [       abs   ⁡     (     log   ⁡     (     X   +     1   N       )       )       -     log   ⁡     (     Y   +     1   N       )         ]       ⁢     (     1   -     Z   N       )       ,         
where X is the number of positive results for the first diagnostic test, Y is the number of negative results for the first diagnostic test, T is the total number of positive results, negative results, and equivocal results for the first diagnostic test, N is the total number of positive results, negative results, and equivocal results for the first diagnostic test and positive results, negative results, and equivocal results for the second diagnostic test, and Z is the number of patients not receiving the diagnostic test. The system value represents the suitability of the diagnostic test for confirmation of the disease, whereby the computer system determines the suitability of a diagnostic test for confirmation of a disease in humans.
 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a fuller understanding of the invention, reference should be made to the following detailed description, taken in connection with the accompanying drawings, in which: 
         FIG. 1  is a flowchart showing an overview of the system. 
         FIG. 2  is a flowchart showing an overview of the system&#39;s quality control feedback loop. 
         FIG. 3  is a graph illustrating the transformation of the positive system value (Dp), or system value, into the first quadrant Cartesian system. The ordinate axis is the system value, and the abscissa is the number of disease specific cohort tests results. 
         FIGS. 4A-4D  are four continuous parts of an example system table in accordance with an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     In the following detailed description of the preferred embodiments, reference is made to the accompanying drawings, which form a part hereof, and within which are shown by way of illustration specific embodiments by which the invention may be practiced. It is to be understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the invention. 
     The present invention, called the Illness Specific Diagnostic System, provides a system for the diagnosis of diseases in humans that is more effective and efficient than methods currently employed by diagnostic centers. Currently, physicians write prescriptions for diagnostic tests in response to patient symptoms. Then, the patient presents the prescriptions to a medical facility to perform the diagnostic test. 
     Under the present system, physicians perform a clinical assessment based on the patient&#39;s symptoms, and the diagnosticians arrive at one or more provincial disease diagnosis. The system creates system tables, which are used by the medical facility to determine what diagnostic test is associated with confirmation of the disease. The medical facility performs the diagnostic test, and the presence of the disease is confirmed or denied. Examples of medical facilities include but are not limited to hospitals, outpatient diagnostic centers, and clinics. 
     The system applies quantitative analysis to the art of disease diagnosis, as the method for construction of nonparametric, retrospective, disease cohort, rank ordered, weighted concatenation tables, unique to a disease and specific to the medical center where they are created, with prospective application. 
       FIG. 1  is a flowchart showing an overview of an embodiment of system  50 . As shown in  FIG. 1 , a medical facility&#39;s records are gathered and received as input in operation  100 . In the preprocessing of operation  200 , the medical records are manually, or digitally, sorted by Diagnostic Related Groups (DRGs) and diagnostic test values are recorded. DRGs are used to classify medical records cases into cohorts, with each cohort expected to have similar hospital resource use. Diagnostic test values may be one of three possible values: positive, negative, or equivocal. In the main processing of operation  300 , DIAGNOSIMETRICAL™ transformations, explained in detail below, are applied to the medical record cohort specific DRGs. Then, in the output processing of operation  400 , system values, also referred to as DIAGNOSIMETRIC™ values, are created and may then be used to generate system tables. System values (and/or the corresponding system tables) are stored in digital or hard copy format in operation  500 . Then, in the diagnostic capacity temporal stabilizer stage (operation  600 ), the medical facility&#39;s diagnostic capacity is measured at a known date and time. Then, in operation  700 , system values (and/or the corresponding system tables) are checked for quality control based on the diagnostic capacity. 
       FIG. 2  is a flowchart showing an overview of the quality control feedback loop in an embodiment of system  50 , which includes the diagnostic capacity temporal stabilizer (operation  600 ) and the quality control stage (operation  700 ). In operation  600 , a number of factors are checked to determine if a change has occurred that requires changes to the system values (and the corresponding system tables). These factors may include changes in diagnostic equipment, changes in diagnosticians, and changes in technology. Operation  600  is time sensitive. In operation  700 , it is determined if the diagnostic capacity of the medical facility has changed. If a change has occurred as determined in operation  750 , then the system returns to operation  200  so that the new information can be processed and included in the creation of updated system values. This quality control feedback loop dynamically customizes the system table results to the target facility. 
     In an embodiment, referring back to  FIG. 1 , system  50  begins by establishing a target group of patients to be analyzed for disease treatment results. Next, in operation  100 , data from the group of target patients&#39; medical records is input into the system database. At the time of input, the system database software assigns a unique “Patient Key” to each patient record in order to maintain medical privacy. The following is an exemplary list of data taken from each patient&#39;s medical records:
         A. Hospital Demographics
           1. Name   2. Type   3. Region   4. Number of beds   
           B. Patient Demographics
           1. Case Number   2. Date of Birth   3. Religion   4. Insurance Type   5. Sex   6. Occupation (pre-retirement)   7. Tobacco use   8. Admission Date   9. Discharge Date   10. Ethnicity   
           C. Diagnostic Procedure Characteristics
           1. DRG Number   2. DRG Name   3. DRG Modality   4. Diagnostic Test Name (where the Diagnostic Test is the first use of a test)   5. Diagnostic Test CPT   6. Diagnostic Test Result—Positive, Negative, or Equivocal   7. Physician Ordering the test   8. Physician Generating the test result   9. Evaluative Test Date (where the Evaluative Test is the second application of the same Diagnostic Test)   10. Evaluative Test Result—Positive, Negative, or Equivocal   
               

     Once the medical records have been received by system  50 , the medical records are then sorted by DRGs in operation  200 . The system database is scanned with the system analyzer in operation  300 . The system analyzer works by creating and processing internal variables to track mathematical weighting ratios for the probabilities of a particular diagnostic test producing a desired diagnostic result. 
     To do this, the system analyzer generates a system value for each diagnostic test in the database. The system value (D x ) is conceptually based on an odds ratio given by ad/bc, where a is the number of patients with a positive test that have the disease in question (true positive), b is the number of patients with a positive test that do not have the disease in question (false positive), c is the number of patients with a negative test that have the disease in question (false negative), and d is the number of patients with a negative test that do not have the disease in question (true negative). 
     Specifically, the variables from the odds ratio are derived from a 2×2 contingency table as follows: 
     
       
         
               
               
               
             
               
               
               
               
             
           
               
                   
                   
               
               
                   
                 True Positive 
                 True Negative 
               
               
                   
                 (Patients with Disease) 
                 (Patients without Disease) 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 Positive 
                 a 
                 b 
               
               
                   
                 Negative 
                 c 
                 d 
               
               
                   
                   
               
             
          
         
       
     
     The conceptual basis is modified, and the positive odds ratio given by a/c. The odds are 100% for confirmation of the disease in question. 
     It is also possible to develop system values through the complementary opposite of a/c, which is the negative odds ratio given by b/d. This negative odds ratio is 100% for confirmation of no disease being present. As such, a/c represents the true positive values and b/d represents the true negative values. The example below generates positive system values, or DIAGNOSIMETRIC™ values, D p  based on a/c. It is also possible to derive the same system value based on the use of b/d, which gives the negative system values D n  Therefore, D p =D n  in so far as the system table generated is for disease confirmation D p , and D n  is for disease absence. Therefore, D p  is the same system as D n . There is a spectrum of permutation between D p  and D n  all of which are the same system, D p , D n , . . . D x  . . . D nth . The use of D p  is illustrated below. 
     The system value D p  may be found by solving for 
                 D   p     =       a   c     =         [   sign   ]     ⁡     [       abs   ⁢     (     log   ⁡     (     X   +     1   N       )       )       -     log   ⁡     (     Y   +   N     )         ]       *     (     1   -     (     N   -   T     )       )     *     (     X   +   T     )           ,         
where X is the number of positive test results for a specific diagnostic test, Y is the number of negative test results for the specific diagnostic test, T is the total number of test results for the specific diagnostic test (positive, negative, and equivocal), and N is the sum total number of test results for all diagnostic tests performed for this DRG. The system value may also be divided by factors of 10 to produce numbers that are easier to read and analyze. For example, for a divisor of 10,000, the system value would be given as
 
     
       
         
           
             
               
                 
                   
                     
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     Each of the components of the above formulas are defined as follows:
         1. D p  is the composite weighted value given to each diagnostic test in the DRG cohort, and reflects the diagnostic test under investigation strength to identify a specific disease,   2.       

     
       
         
           
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              is a weighted (1/N=weight value) number sum of positive test values (X) the log of which inverses and moves the number (X+1/N) to travel in the same Cartesian quadrant and in the same direction as Y, 
             3. log(Y+N) is a weighted (N=weight value) number sum of negative test values (Y) the log of which inverses and moves the number (Y+N) to travel in the same Cartesian quadrant and in the same direction as X, 
             4. abs insures the result difference number of 
           
         
       
    
     
       
         
           
             
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              is a positive integer, 
             5. (1−(N−T)) is the weight value of occurrence of the diagnostic test under analysis, 
             6. (X+T) is the weight value of positive strength of the diagnostic test under analysis, and 
             7. [sign] moves and inverts the periodic occurrence of the function 
           
         
       
    
     
       
         
           
             
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              to the first quadrant of the Cartesian coordinate system, as illustrated in the graph shown in  FIG. 3 . 
           
         
       
    
     Alternatively, the system value may be found by solving for 
                 (       Z   N     -   1     )     *     (     1   -     (     N   -   T     )       )     *     (     X   +   T     )       ,         
where Z is the number of patients not receiving the diagnostic test. The expression
 
             (       Z   N     -   1     )         
is equivalent to
 
               [   sign   ]     ⁡     [       abs   ⁡     (     log   ⁡     (     X   +     1   N       )       )       -     log   ⁡     (     Y   +     1   N       )         ]           
and is an example of the algebraic manipulation of numbers. Ultimately, both expressions reveal the weighted strength of the diagnostic test in question to identify the presences, or absence of the disease under investigation.
 
     Once the system values are calculated, system  50  then generates the system table (operation  400 ). The system table contains the recommended diagnostic tests to be run at the target facility per DRG-diagnosed disease. The system table is stored as a digital value or paper copy in operation  500 . This results in the system table, which lists a number of diagnostic test procedures and the corresponding system value for a DRG disease treated by the target facility. Each DRG disease will have its own system table. 
     An example system table is shown in  FIGS. 4A-4D . In this example, the results for syncope and collapse (DRG  312 ) at a medical facility are shown. At this particular medical facility, DRG  312  generated  144  medical records for a one year period. DRG  312  produced 2,732 diagnostic test results, which averages out to almost 20 diagnostic tests per medical record (case). The 2,732 diagnostic tests results split into 2,132 negative results, 482 positive results, 81 equivocal results, and 37 no-test-performed results, for a gross specificity of 0.18. The 2,732 test results came from 187 different types of diagnostic tests. In the example system table, the first column lists all of the diagnostic tests used at this facility on patients that were eventually diagnosed with syncope and collapse. The second, third, and fourth columns lists all the number of the negative test results, positive test results, and equivocal test results for each of the diagnostic tests, respectively. The fifth column lists the number of patients that were diagnosed with syncope and collapse that did not have any diagnostic test performed. The sixth column lists the total number of each patient having each diagnostic test and the last column lists the system value generated by system  50 . The diagnostic test with the highest system value is listed first and the remaining diagnostic tests are listed by decreasing order of their system value. The higher the diagnostic test is on the list, the more likely the test is to appropriately identify the disease in question. In the example chart, a CPK (Creatine Phosphokinase) blood test is the diagnostic test most likely to appropriately identify syncope and collapse at this particular medical facility and the tests least likely to appropriately identify syncope and collapse at the facility are microalbumin, PETIC, and Uric Acid. 
     The subject medical facility can use this table to reduce drastically the number of diagnostic test performed at the facility. If a patient is thought to have syncope and collapse, the first two (or three) tests listed on the table may be performed. If these tests confirm the patient is free of the disease, then the diagnostician can move on to another potential diagnosis (and another system table for the new potential disease). The medical facility will not need to perform the other 185 (or 184) tests to confirm the presence of the disease, resulting in vast financial savings and a great improvement in the quality of medical care. Clinicians will be free from the burden of association of symptoms to diagnostic testing that proves time consuming and wasteful and physicians will have more time to focus on clinical evaluation and consultation to render a set of disease possibilities that the medical facility is expert to evaluate. 
     Based on existing medical physics evaluation record, a baseline value for each diagnostic instrument is obtained in operation  600 . This baseline value is applied to future system evaluation, and is applied to existing values over time. The effect is to insure that changes in the medical facility&#39;s diagnostic capacity are reflected in the system value. Note that the diagnostic capacity is a measure of a medical facility&#39;s ability to diagnosis a disease. If the baseline value changes, a resort of the preprocessing occurs. Also note that diagnostic capacity is measured as follows: 
     
       
         
               
               
               
             
           
               
                   
                   
               
             
             
               
                   
                 Identify the baseline: 
                 0 value = no change; 1 value = change 
               
               
                   
                 Diagnostic physicians: 
                 0 value = no change; 1 value = change 
               
               
                   
                 Technology: 
                 0 value = no change; 1 value = change 
               
               
                   
                 Diagnostic Equipment: 
                 0 value = no change; 1 value = change 
               
               
                   
                   
               
             
          
         
       
     
     System table values and diagnostic capacity are then checked for stability and accuracy in operation  700 . If both parameters are yes values, the system value is considered constant; if not, the table values are not constant, and a reevaluation is required. 
     It will be seen, that the advantages set forth above, and those made apparent from the foregoing description, are efficiently attained, and since certain changes may be made in the above construction without departing from the scope of the invention, it is intended that all matters contained in the foregoing description or shown in the accompanying drawings shall be interpreted as illustrative and not in a limiting sense. 
     It is also to be understood that the following claims are intended to cover all of the generic and specific features of the invention herein described, and all statements of the scope of the invention, which, as a matter of language, might be said to fall therebetween.