Diagnostic Effectiveness Tool

Abstract

A system is disclosed for evaluating diagnostic effectiveness of one or more diagnostic tests or additional findings from a set of known findings as to a patient. The system includes a computing device containing a software application which is used by a healthcare provider to review the patient's medical history and enter findings as to the patient's condition or symptoms, a system storing a validated probabilistic health model including a database of medical knowledge informed from aggregated electronic medical records or other sources of medical knowledge; and a medical knowledge-based inference engine operating on the patient's medical history and findings and the validated probabilistic health model. The engine determines a set of the most probable diseases of the patient, suggests a set of one or more tests or additional findings that differentiate the set of most probable diseases, and generates indicia indicating diagnostic effectiveness or relevancy of the one or more tests or additional findings.

We claim: 
     
         1 . A computer-implemented method, comprising:
 providing, by a graphical user interface, an interactive relational tree, wherein the relational tree represents one or more sequences of diagnosis-finding relationships between findings and diagnoses of medical conditions with respective likelihood scores indicative of a relevance of a medical condition to a patient;   detecting, a selection of a node in the interactive relational tree, wherein the selection of the node indicates a selection by a healthcare provider of an additional finding indicative of one or more most probable medical conditions of the patient;   updating a set of known findings by adding the selected additional finding to the set of known findings; and   displaying, by the graphical user interface, an updated interactive relational tree based on the updated set of known findings.   
     
     
         2 . The computer-implemented method of  claim 1 , further comprising:
 retrieving a stored validated probabilistic health model comprising a database of medical knowledge informed from aggregated electronic medical records or other sources of medical knowledge; and   wherein the set of known findings is determined based on the validated probabilistic health model.   
     
     
         3 . The computer-implemented method of  claim 1 , further comprising:
 determining the set of known findings by applying a medical knowledge-based machine learning model operating on medical history and findings associated with the patient.   
     
     
         4 . The computer-implemented method of  claim 3 , further comprising:
 applying the machine learning model to generate one or more additional diagnosis-finding relationships between the additional finding and associated additional diagnoses, and   wherein the updated interactive relational tree is based on the one or more additional diagnosis-finding relationships.   
     
     
         5 . The computer-implemented method of  claim 1 , further comprising:
 providing, by the graphical user interface and to the healthcare provider, a set of one or more diagnostic tests that differentiate the one or more most medical conditions, and indicia indicating effectiveness or relevancy of the one or more diagnostic tests.   
     
     
         6 . The computer-implemented method of  claim 5 , further comprising an application residing on a healthcare payer network receiving the indicia from a machine learning model, the application configured to facilitate reimbursement or authorization decisions regarding the one or more diagnostic tests. 
     
     
         7 . The computer-implemented method of  claim 5 , wherein the indicia comprise at least one of delta probabilities, likelihood ratios, or costs associated with the set of one or more diagnostic tests. 
     
     
         8 . The computer-implemented method of  claim 5 , further comprising:
 receiving, by the graphical user interface, a selection of a diagnostic test of the one or more diagnostic tests; and   updating, based on the selection, the indicia indicating diagnostic effectiveness or relevancy of the one or more diagnostic tests.   
     
     
         9 . The computer-implemented method of  claim 1 , further comprising:
 updating, based on a confirmation or a denial of a finding from the set of known findings by the healthcare provider, one or more of the set of known findings, or the one or more sequences of diagnosis-finding relationships.   
     
     
         10 . The computer-implemented method of  claim 1 , further comprising:
 storing raw medical information from at least one of document sources, medical records, payment data or input from healthcare providers in a medical database, the medical database having been configured to preserve one or more relationships between one or more of: a diagnosis, a diagnosis attribute, a finding, a finding attribute, a treatment, or a treatment attribute.   
     
     
         11 . The computer-implemented method of  claim 10 , wherein the storing comprises storing effectiveness scores for the relationships. 
     
     
         12 . The computer-implemented method of  claim 10 , wherein the finding attribute includes a cost attribute, and wherein the diagnosis attribute includes a severity attribute. 
     
     
         13 . The computer-implemented method of  claim 1 , wherein the one or more sequences of diagnosis-finding relationships is based on a positive likelihood ratio and a negative likelihood ratio, wherein each likelihood ratio is based on (i) a sensitivity indicative of a true positive rate of a diagnosis, and (ii) a specificity indicative of a true negative rate for the diagnosis, and the operations further comprising:
 determining the positive likelihood ratio as a ratio of the sensitivity and a relative specificity, wherein the relative specificity is a difference of the specificity from 1; and   determining a negative likelihood ratio as a ratio of a relative sensitivity and the specificity, wherein the relative sensitivity is a difference of the sensitivity from 1.   
     
     
         14 . The computer-implemented method of  claim 13 , further comprising:
 ranking one or more diagnostic tests based on respective costs and respective likelihood ratios.   
     
     
         15 . The computer-implemented method of  claim 1 , further comprising:
 receiving, by the interactive relational tree, a denial of a second finding from the set of additional findings;   updating the set of additional findings by removing, based on the denial of the second finding, the second finding from the set of additional findings.   
     
     
         16 . The computer-implemented method of  claim 1 , further comprising:
 providing, by a diagnostics model explorer and by the interactive graphical user interface, the set of known findings, wherein the diagnostics model explorer is a tabular representation of the set of known findings, the set of additional findings with associated relevance scores, and the associated diagnoses with associated likelihood ratios.   
     
     
         17 . The computer-implemented method of  claim 1 , further comprising:
 providing, by the graphical user interface, a display comprising one or more suggested new diagnostic tests, with associated likelihood ratios;   receiving, from the healthcare provider, a selection of a new diagnostic test of the one or more suggested new diagnostic tests; and   providing, by the graphical user interface, a delta probability indicating a degree of confidence that a result of the selected new diagnostic test increases an accuracy of a diagnosis for the patient.   
     
     
         18 . The computer-implemented method of  claim 17 , further comprising:
 providing, by the graphical user interface, a cost associated with the selected new diagnostic test.   
     
     
         19 . The computer-implemented method of  claim 17 , further comprising:
 providing, by the graphical user interface, a new medical finding related to the patient;   receiving, from the healthcare provider, a confirmation or denial of the new medical finding; and   updating the one or more suggested new diagnostic tests based on the confirmation or the denial of the new medical finding.   
     
     
         20 . A server to facilitate medical diagnosis for a patient, comprising:
 one or more processors; and   memory storing computer-executable instructions that, when executed by the one or more processors, cause the server to perform operations comprising:
 providing, by a graphical user interface, an interactive relational tree, wherein the relational tree represents one or more sequences of diagnosis-finding relationships between findings and diagnoses of medical conditions with respective likelihood scores indicative of a relevance of a medical condition to the patient; 
 detecting, a selection of a node in the interactive relational tree, wherein the selection of the node indicates a selection by a healthcare provider of an additional finding indicative of one or more most probable medical conditions of the patient; 
 updating a set of known findings by adding the selected additional finding to the set of known findings; and 
 displaying, by the graphical user interface, an updated interactive relational tree based on the updated set of known findings. 
   
     
     
         21 . An article manufacture comprising one or more computer readable media having computer-readable instructions stored thereon that, when executed by one or more processors of a computing device, cause the computing device to perform operations comprising:
 providing, by a graphical user interface of the computing device, an interactive relational tree, wherein the relational tree represents one or more sequences of diagnosis-finding relationships between findings and diagnoses of medical conditions with respective likelihood scores indicative of a relevance of a medical condition to a patient;   detecting, a selection of a node in the interactive relational tree, wherein the selection of the node indicates a selection by a healthcare provider of an additional finding indicative of one or more most probable medical conditions of the patient;   updating a set of known findings by adding the selected additional finding to the set of known findings; and   displaying, by the graphical user interface, an updated interactive relational tree based on the updated set of known findings.