Researchers Submit Patent Application, “Assist Selection Of Provider/Facility For Surgical Procedures Based On Frequency Of Procedure, History Of…

Researchers Submit Patent Application, "Assist Selection Of Provider/Facility For Surgical Procedures Based On Frequency Of Procedure, History Of Complications, And Cost", for Approval (USPTO 20180211007)

By a News Reporter-Staff News Editor at Managed Care Weekly Digest -- From Washington, D.C., NewsRx journalists report that a patent application by the inventor Cox, James S. (Plano, TX), filed on January 25, 2017, was made available online on July 26, 2018 (see also International Business Machines Corporation).

The patent's assignee is International Business Machines Corporation (Armonk, New York, United States).

News editors obtained the following quote from the background information supplied by the inventors: "The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for assisting selection of provider/facility for surgical procedures based on the frequency of the procedure, history of complications, and cost.

"Decision-support systems exist in many different industries where human experts require assistance in retrieving and analyzing information. An example that will be used throughout this application is a diagnosis system employed in the healthcare industry. Diagnosis systems can be classified into systems that use structured knowledge, systems that use unstructured knowledge, and systems that use clinical decision formulas, rules, trees, or algorithms. The earliest diagnosis systems used structured knowledge or classical, manually constructed knowledge bases. The Internist-I system developed in the 1970s uses disease-finding relations and disease-disease relations. The MYCIN system for diagnosing infectious diseases, also developed in the 1970s, uses structured knowledge in the form of production rules, stating that if certain facts are true, then one can conclude certain other facts with a given certainty factor. DXplain, developed starting in the 1980s, uses structured knowledge similar to that of Internist-I, but adds a hierarchical lexicon of findings.

"Iliad, developed starting in the 1990s, adds more sophisticated probabilistic reasoning Where each disease has an associated a priori probability of the disease (in the population for which Iliad was designed), and a list of findings along with the fraction of patients with the disease who have the finding (sensitivity), and the fraction of patients without the disease who have the finding (I-specificity).

"In 2000, diagnosis systems using unstructured knowledge started to appear. These systems use some structuring of knowledge such as, for example, entities such as findings and disorders being tagged in documents to facilitate retrieval. ISABEL, for example, uses Autonomy information retrieval software and a database of medical textbooks to retrieve appropriate diagnoses given input findings. Autonomy Auminence uses the Autonomy technology to retrieve diagnoses given findings and organizes the diagnoses by body system. First CONSULT allows one to search a large collection of medical books, journals, and guidelines by chief complaints and age group to arrive at possible diagnoses. PEPID DDX is a diagnosis generator based on PEPID's independent clinical content.

"Clinical decision rules have been developed for a number of medical disorders, and computer systems have been developed to help practitioners and patients apply these rules. The Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) takes clinical and ECG features as input and produces probability of acute cardiac ischemia as output to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia. ACI-TIPI is incorporated into many commercial heart monitors/defibrillators. The CaseWalker system uses a four-item questionnaire to diagnose major depressive disorder. The PKC Advisor provides guidance on 98 patient problems such as abdominal pain and vomiting."

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventor's summary information for this patent application: "This Summary is provided to introduce a selection of concepts in a simplified form that are further described herein in the Detailed Description. This Summary is not intended to identify key factors or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

"in one illustrative embodiment, a method is provided in a data processing system comprising at least one processor and at least one memory. The at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a clinical decision support system. The method comprises receiving, by the clinical decision support system, a set of input data about a plurality of patients. The method further comprises identifying, by the clinical decision support system, a target patient within the plurality of patients seeking guidance for a surgical procedure that has been recommended by a physician. The method further comprises determining, by a cluster analysis component executing within the clinical decision support system, a cluster of patients within the plurality of patents that are similar to the target patient based on the set of input data. The method further comprises grouping, by the cluster analysis component, the cluster of patients into a plurality of sub-clusters of patients each being associated with a different level of complications. The method further comprises generating, by the clinical decision support system, a user interface providing an output of providers or facilities ranked by history of complications and cost based on the sub-clusters of patients and corresponding data in the set of input data.

"In other illustrative embodiments, a computer program product comprising a computer usable or readable medium having a computer readable program is provided. The computer readable program, when executed on a computing device, causes the computing device to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.

"In yet another illustrative embodiment, a system/apparatus is provided, system/apparatus may comprise one or more processors and a memory coupled to the one or more processors. The memory may comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.

"These and other features and advantages of the present invention will be described in, or will become apparent to those of ordinary skill in the art in view of, the following detailed description of the example embodiments of the present invention."

The claims supplied by the inventors are:

"1-10. (canceled)

"11. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program comprises instructions, which when executed on a processor of a computing device causes the computing device to implement a clinical decision support system, wherein the computer readable program causes the computing device to: receive, by the clinical decision support system, a set of input data about a plurality of patients; identify, by the clinical decision support system, a target patient within the plurality of patients seeking guidance for a surgical procedure that has been recommended by a physician; determine, by a cluster analysis component executing within the clinical decision support system, a cluster of patients within the plurality of patents that are similar to the target patient based on the set of input data; group, by the cluster analysis component, the cluster of patients into a plurality of sub-clusters of patients each being associated with a different level of complications; and generate, by the clinical decision support system, a user interface providing an output of providers or facilities ranked by history of complications and cost based on the sub-clusters of patients and corresponding data in the set of input data.

"12. The computer program product of claim 11, wherein the set of input data comprise demographics, biometric data, health insurance claims data, electronic medical record clinical history, genomic data, and health insurance plan data.

"13. The computer program product of claim 11, wherein determining the cluster of patients comprises: determining, by a rules engine executing within the clinical decision support system, a set of patient clustering attributes based on the surgical procedure and the set of input data.

"14. The computer program product of claim 11, wherein grouping the cluster of patients into a plurality of sub-clusters of patients comprises: grouping the cluster of patients based on post-operative clinical data and health insurance claims data.

"15. The computer program product of claim 11, wherein grouping the cluster of patients into a plurality of sub-clusters of patients comprises: grouping the cluster of patients into a first sub-cluster of patients having no complications, a second sub-cluster of patients having minor complications, and a third sub-cluster of patients having major complications.

"16. The computer program product of claim 11, wherein generating the user interface comprises generating histogram data showing likely outcome and estimated cost.

"17. The computer program product of claim 11, wherein the user interface allows a user to view available provider or facilities within a selectable geographic region.

"18. The computer program product of claim 11, wherein the user interface allows a user to view and filter providers or facilities based on risk of complications for the surgical procedure.

"19. The computer program product of claim 11, wherein the user interface provides an overlay of cost estimates and ranges including estimated total cost for the surgical procedure, estimated cost to the insurer, and estimated out-of-pocket cost to the patient.

"20. A computing device comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions, which when executed on a processor of a computing device causes the computing device to implement a clinical decision support system, wherein the instructions cause the processor to: receive, by the clinical decision support system, a set of input data about a plurality of patients; identify, by the clinical decision support system, a target patient within the plurality of patients seeking guidance for a surgical procedure that has been recommended by a physician; determine, by a cluster analysis component executing within the clinical decision support system, a cluster of patients within the plurality of patents that are similar to the target patient based on the set of input data; group, by the cluster analysis component, the cluster of patients into a plurality of sub-clusters of patients each being associated with a different level of complications; and generate, by the clinical decision support system, a user interface providing an output of providers or facilities ranked by history of complications and cost based on the sub-clusters of patients and corresponding data in the set of input data."

For additional information on this patent application, see: Cox, James S. Assist Selection Of Provider/Facility For Surgical Procedures Based On Frequency Of Procedure, History Of Complications, And Cost. Filed January 25, 2017 and posted July 26, 2018. Patent URL: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220180211007%22.PGNR.&OS=DN/20180211007&RS=DN/20180211007

Keywords for this news article include: Armonk, Ischemia, New York, Cardiology, Epidemiology, United States, Cluster Analysis, Health Insurance, Computer Companies, Health and Medicine, Risk and Prevention, Technology Companies, Information Technology, Diagnostics and Screening, North and Central America, Information and Data Processing.

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