DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Claim of priority to provisional patent application filing date of U.S. provisional application serial number 63/087,449 and PCT/US2021/053257 is acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1, 9, 15 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “generally accepted” in claims 1, 9, and 15 are relatives terms which renders the claim indefinite. The terms “approximate, abnormal, strongest” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Note that claims 2-8, 10-14 and 16-20 dependent of independent claims 1, 9, 15 are rejected too by share the same terms. Refer to specification where also are not enough described in a manner that it render define par. 0009, 0027
Note: Claims 2-8, 10-14 are rejected to inherent the same relative terms rejected above of claims 1, 9 and 14.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed subject matter is directed to a judicial exception (an abstract idea) without reciting elements that integrate the exception into a practical application or provide an inventive concept amounting to significantly more than the exception itself.
Step 1: Statutory Categories Analysis
The claims are directed to statutory subject matter, encompassing the following statutory categories:
Process (Claims 1-8): The language reciting "A method for analyzing medical data..." defines a series of acts or steps, aligning with the definition of a process in MPEP § 2106.03.
Machine (Claims 15-20): The language reciting "A medical data analysis system... comprising: an at least one computing device..." describes a concrete thing consisting of parts, aligning with the definition of a machine in MPEP § 2106.03.
Manufacture (Claims 9-14): The language reciting "A non-transitory computer readable medium containing program instructions..." describes a tangible article given a new form through artificial efforts, aligning with the definition of a manufacture in MPEP § 2106.03.
Having confirmed the claims are directed to statutory subject matter, the analysis proceeds to Step 2A Prong one.
Step 2A, Prong One: Judicial Exception Analysis
Step 2A, Prong One determines whether the claim recites a judicial exception, such as an abstract idea, law of nature, or natural phenomenon (MPEP § 2106.04). This step requires identifying the specific limitations in the claims that recite the exception and determining if the claim as a whole is directed to that exception.
The whole invention, under a broadest reasonable interpretation consistent with the specification, relates to a medical data analysis system that harmonizes disparate records to “identify and address abnormal prescribing behaviors” by mathematically modeling “expected” versus “average” prescribing patterns (Spec., Para. [0009]). The inventive concept drives this analysis by stratifying providers into groups based on demographics to statistically determine which “patient demographic data point” or “practice demographic data point” exerts the “strongest influence” on the identified abnormalities (Spec., Para. [0027]).
More specifically, claims 1-20 are directed to a judicial exception because they recite the abstract idea of collecting information, analyzing it through mathematical modeling and statistical comparison, and identifying results based on that analysis. Under MPEP § 2111, the claims as a whole describe a disembodied analytical process of performance evaluation and statistical association, which remains abstract despite the nominal recitation of a "computing system."
Independent Claims Analysis
1. A method for analyzing medical data to identify and address abnormal prescribing behaviors, the method comprising the steps of:
implementing a central computing in selective system communication with an at least one third-party medical records database, the computing system configured for receiving and processing data related to an at least one patient and an associated at least one medical condition, along with an at least one medical service provider tasked with treating the at least one patient;
the computing system establishing an at least one patient record associated with each of the at least one patient, each patient record containing at least one of a unique patient record identifier, a patient age, a patient gender, a patient ethnicity, a patient location, a patient income, a patient education level, a patient employment status, an at least one patient condition for each medical condition the associated patient has experienced or is experiencing, an associated patient prescription for each of the at least one patient condition, and an associated at least one prescription performance indicator for each of the at least one patient prescription;
the computing system establishing an at least one service provider record associated with each of the at least one medical service provider, each service provider record containing at least one of a unique service provider record identifier, a service provider location, an average income representing the average income of patients treated by the associated medical service provider, an average education level representing the average education level of patients treated by the associated medical service provider, an average hours worked representing the average hours worked by patients treated by the associated medical service provider, an average crime level representing the average crime level in the corresponding service provider location, an average age representing the average age of patients treated by the associated medical service provider, an unemployment rate representing the unemployment rate in the corresponding service provider location, a mortality rate representing the mortality rate in the corresponding service provider location, and a patient table containing links to the corresponding patient record of each patient that has been treated by the associated medical service provider;
and upon a user desiring to obtain an analysis of a given medical condition:
the computing system generating a model of expected prescribing behaviors for said medical condition, organized by an at least one pharmaceutical product being prescribed for treating said medical condition, based on existing and generally accepted clinical guidelines;
the computing system accessing data contained in the at least one service provider record related to said medical condition;
the computing system stratifying the associated medical service providers into a plurality of groups based on at least one of the respective patient demographic data points of the associated medical service providers and the prescription performance indicator of each associated patient that has been treated, or is being treated, by each of the medical service providers;
the computing system generating a model of average prescribing behaviors for said medical condition, organized by the at least one pharmaceutical product being prescribed for treating said medical condition, for each of the stratified groups of medical service providers;
upon the computing system determining that a given stratified group does not approximate the model of expected prescribing behaviors for said medical condition, the computing system identifying said stratified group as containing abnormal prescribing behaviors;
and for any stratified groups identified by the computing system as containing abnormal prescribing behaviors:
the computing system determining which of the associated at least one patient demographic data point had the strongest influence on the abnormal prescribing behaviors for the associated medical service providers;
and the computing system determining whether any practice demographic data points had an influence on the abnormal prescribing behaviors for the associated medical service providers.
Note: The bolded portions represent additional elements evaluated in Prong Two and Step 2B. The non-bolded portions represent the abstract idea.
Claim Abstract Classification & Rationale
Under their Broadest Reasonable Interpretation (MPEP § 2111), the independent claims 1, 9, and 15 abstract idea recite the collection of patient and provider data, the generation of mathematical models based on guidelines, and the statistical comparison of actual behavior against those models to identify anomalies. This process aligns with the following abstract idea categories:
Mental Process (MPEP § 2106.04(a)(2)(III)): This category encompasses concepts performed in the human mind, including observations, evaluations, judgments, and opinions. The independent claims 1, 9, and 15 recite “generating a model,” “stratifying,” “determining that a given stratified group does not approximate,” and “determining which... data point had the strongest influence.” When comparing the MPEP § 2111 interpretation of the selected independent claims limitations that describe the abstract idea, they fit with the definition in MPEP § 2106 because they describe mathematical calculations and the formation of judgments based on data comparison, representing the evaluation of information to reach a conclusion without requiring physical action. The specification supports this, stating: “it is sometimes the case that physicians aren't treating their patient base as expected... for various reasons including but not limited to clinical and socioeconomic factors” (Spec., Para. [0005]). This paragraph is relevant and supports the argument because it establishes that the claimed "determination" of influence is an evaluation of the underlying reasons (clinical/socioeconomic factors) for human behavior, requiring the system to form a judgment on causality—why the behavior happened—which is a cognitive task of evaluation rather than a physical action.
Certain Method of Organizing Human Activity (MPEP § 2106.04(a)(2)(II)): This category includes fundamental economic practices and methods of managing interactions between people. The independent claims 1, 9, and 15 recite “implementing a central computing in selective system communication” and “identifying said stratified group as containing abnormal prescribing behaviors.” This describes a managed workflow of interaction, which falls under the sub-category of Managing Personal Behavior or Relationships or Interactions Between People, because the recited “communication” creates an interaction between entities that cannot be performed mentally and is used to oversee and manage the professional behavior of service providers. The specification supports this, stating: “identifying and addressing abnormal prescribing behaviors, so as to correct such abnormal prescribing behaviors” (Spec., Para. [0005]). This paragraph is relevant and supports the argument because the explicit purpose “to correct” confirms that the invention functions as a management tool designed to oversee, police, and modify the professional interactions of service providers.
Manual Replication Scenario (Human Equivalence)
The abstract nature of the claims is reinforced because the entire process is analogous to fundamental human activities:
"implementing a... system communication... receiving and processing data": An administrator performs this organization of human activity by contacting various clinics (the "third-party database") via telephone or mail to request they send their physical patient and provider files for review.
"establishing... patient record" / "establishing... service provider record": The administrator manually gathers the received files and writes the information into two separate sets of summary ledgers or folders: one set for patients ("patient records") and one set for providers ("service provider records").
"upon a user desiring... obtain an analysis": The administrator receives a work order from a supervisor desiring an analysis of a specific condition (e.g., diabetes).
"generating a model of expected prescribing behaviors... based on... clinical guidelines": The administrator performs a mental process by reading the "existing and generally accepted clinical guidelines" (e.g., ADA manuals) and calculating/writing down an "expected model" (e.g., "85% of patients should be prescribed Metformin").
"accessing data contained in the... service provider record" / "stratifying the associated medical service providers into... groups": The administrator physically sorts the provider folders into different piles ("groups") based on demographic labels on the folders (e.g., a pile for "urban clinics" and a pile for "rural clinics").
"generating a model of average prescribing behaviors": The administrator performs a mental process (calculation) by reviewing the files in each pile and calculating the average prescription rate for that specific group (e.g., calculating that the "rural clinics" pile only prescribes Metformin 60% of the time).
"determining that a given... group does not approximate... identifying said... group as containing abnormal prescribing behaviors": The administrator performs a mental process of comparison and judgment to determine that the "rural" group's 60% does not approximate the expected 85%, and then performs an organizing activity by placing a red flag or "abnormal" sticker on that pile to identify it.
"determining which... patient demographic data point had the strongest influence": The administrator performs a mental process of evaluation by reviewing the patient data within that flagged pile (e.g., income levels, locations) and forming a judgment that "patient income" appears to be the strongest factor influencing the result.
"determining whether any practice demographic data points had an influence": The administrator performs a mental process of evaluation by reviewing the practice data (e.g., physician age) to form a judgment on whether those factors also influenced the result.
Dependent Claims Analysis
The dependent claims 2-8, 10-14, and 16-20 are also directed to an abstract idea.
Claims 2, 3, 10, 11, 16, and 17: These claims recite under BRI the application of mathematical techniques such as "weighting the associated... data point" and assigning a "numerical weight," which is a Mental Process (Mathematical Concept). The addition of basic mathematical weighting to the analysis merely refines the abstract calculation steps without adding a concrete physical application (MPEP § 2106.05(f)). Even though these claims add specific mathematical techniques, they do not recite or specify a new non-abstract idea, but rather inherit the abstract idea of the independent claim.
Claims 4, 5, 6, 12, 13, 14, 18, 19, and 20: These claims recite under BRI the arithmetic steps of "totaling an annual prescribing amount," "calculating a median value," and "comparing... against... values," which is a Mental Process. These limitations specify the type of mathematical algorithms used to perform the abstract analysis (calculation and comparison) found in the independent claim.
Claims 7, 8, and 20: These claims recite activity of "generating a report" and "providing an at least one recommendation," which is a Certain Method of Organizing Human Activity.
Proceeding to Step 2A, Prong Two to determine if the claims recite an inventive concept.
Step 2A, Prong Two: Integration into a Practical Application
The claims fail to integrate the abstract idea into a practical application because the additional elements merely provide a generic technological environment.
Evaluation of Independent Claims 1, 9, and 15 Additional Elements
Group 1: Generic Computer Components & Network Structures Elements: central computing, selective system communication, third-party medical records database, computing system
The recitation of central computing, selective system communication, third-party medical records database, and computing system fails to integrate the abstract idea because it:
Under BRI (MPEP § 2111), these elements are interpreted at a high level of generality as standard hardware and network components performing their basic functions of data storage, transmission, and processing, without any specific implementation details that would limit the claim to a specific technical solution.
(MPEP § 2106.05(f)) - Mere Instructions: The recitation of a “central computing” and “computing system” fails to overcome Prong Two because these elements are invoked solely as a tool to automate the mental process, constituting “mere instructions to implement an abstract idea on a computer” (MPEP § 2106.05(f)). The claim language “configured for receiving and processing data” merely instructs the generic hardware to perform its basic, generalized functions, which “amounts to nothing more than automating an abstract idea” (MPEP § 2106.05(f)) rather than providing a specific technical solution.
(MPEP § 2106.05(a)) - No Tech Improvement: The additional elements do not overcome Prong Two because the claims utilize standard components like a “computing system” without reciting any specific hardware modifications, failing to reflect an “improvement to the functioning of a computer” (MPEP § 2106.05(a)). Instead, the claimed improvement of “identify[ing] and address[ing] abnormal prescribing behaviors” is an improvement to the abstract professional process itself, which “is not an improvement to the functioning of a computer” (MPEP § 2106.05(a)).
(MPEP § 2106.05(h)) - Linking to Environment: The requirement for “selective system communication” with a “third-party medical records database” fails to integrate the abstract idea because it merely constrains the statistical analysis to the specific field of healthcare data. MPEP § 2106.05(h) clarifies that “limiting the use of an abstract idea to a particular technological environment” such as a medical records database “has been held not to amount to an inventive concept,” as it does not transform the abstract steps into a practical application.
When viewed as a whole, the combination of these elements does not integrate the abstract idea. The claim describes a generic arrangement of a standard computing system and database performing the abstract analysis, which does not transform the abstract idea into an eligible application but rather uses the computer as a tool to automate the mental process.
Dependent Claims Analysis
The dependent claims add only minor limitations that fail to provide the necessary integration.
Claims 2-6, 10-14, 16-20: These claims add limitations regarding "weighting," "totaling," "calculating a median," and "comparing," which are Mental Processes (Mathematical Concepts). These claims do not recite new additional elements (hardware or specific software structure); they merely further refine the abstract idea itself by specifying the mathematical algorithms used. They fail to integrate the abstract idea because adding more abstract steps to an abstract idea does not render it eligible.
Claims 7, 8, 20: These claims do not recite new additional elements beyond the generic computer generating the output. It is describe abstract idea explained in prong one.
When viewed as a whole, the combination of these elements in the dependent and independent claims does not integrate the abstract idea. The dependent claims merely add mathematical details to the mental process or specify the format of the data output, neither of which transforms the generic computer components into a specialized machine or a practical application.
Because the claims are directed to an abstract idea without integrating it into a practical application, the analysis proceeds to Step 2B.
Step 2B: Inventive Concept Analysis
Step 2B determines whether the claim, when viewed as a whole, includes an "inventive concept" by reciting additional elements that amount to significantly more than the judicial exception (MPEP § 2106.05). The additional elements identified in Prong Two fail to overcome Step 2B because they merely utilize generic, well-understood, routine, and conventional (WRC) components to automate the abstract idea, without providing a specific technical improvement.
Evaluation of Independent Claims 1, 9, and 15 Additional Elements
Group: Generic Computer Components & Network Structures (central computing, selective system communication, third-party medical records database, computing system)
The additional elements of “central computing” and “selective system communication” fail to provide an inventive concept because they constitute “mere instructions to implement an abstract idea on a computer” (MPEP § 2106.05(f)) using hardware “now known or later developed” (Spec., para. [0020]), which is well-understood, routine, and conventional (MPEP § 2106.05(d)). The requirement for a “third-party medical records database” merely links the abstract idea to the field of “collecting... and analyzing medical data” (Spec., para. [0002]), which is a field-of-use limitation (MPEP § 2106.05(h)) insufficient to transform the claim. Furthermore, the use of generic “computing devices” (Spec., para. [0020]) provides no “improvement to the functioning of a computer” (MPEP § 2106.05(a)).
When viewed as a whole, the combination of these generic additional elements does not provide an inventive concept because arranging standard computing devices, databases, and communication protocols in a high-level manner to automate the abstract mental process adds nothing significantly more to the judicial exception.
Dependent Claims Analysis (Step 2B)
Dependent claims 2-8, 10-14, and 16-20 fail to provide an inventive concept. These claims do not recite any additional elements beyond the WRC components of the independent claims. The limitations added, such as weighting... data point, calculating a median value, and providing... a recommendation, merely narrow the abstract idea identified in Prong One.
The additional limitations of the dependent claims, which merely refine the abstract analysis, fail to provide an inventive concept when combined with the generic hardware and data functions of the independent claims.
The claims are directed to an abstract idea and lack an inventive concept. Therefore, Claims 1-20 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 7,8, 9 and 15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US-20070219824-Rawlings.
Rawlings teaches Claim 1.
A method for analyzing medical data to identify and address abnormal prescribing behaviors, the method comprising the steps of: (Rawlings, abstract, par. 0005, 0006, 0037, 0044, 0045, 0078, 0032, 0051, 0555, 0058 )
Rawlings, describe method that identify a patterns of interest with respect to the patient’s treatment, and example of that interest patterns is excess prescriptions. Rawling defined treatment patterns against actual treatment pattern and display deviation.
implementing a central computing system in selective communication with an at least one third-party medical records database, the computing system configured for receiving and processing data related to an at least one patient and an associated at least one medical condition, along with an at least one medical service provider tasked with treating the at least one patient; (Rawlings, par. 0006, 0007, 0009, 0032-0033, 0041)
Rawlings describes a system including a processor and an analysis engine (implementing a central computing system), which reads and processes data from a database of healthcare claims which may include multiple information Sources (in selective communication with an at least one third-party medical records database). This analysis engine is explicitly configured for receiving and processing data related to multiple patients (an at least one patient) and their treatment of a certain medical condition (an associated at least one medical condition) by a certain healthcare provider (an at least one medical service provider).
the computing system establishing an at least one patient record associated with each of the at least one patient, each patient record containing at least one of a unique patient record identifier, a patient age, a patient gender, a patient ethnicity, a patient location, a patient income, a patient education level, a patient employment status, an at least one patient condition for each medical condition the associated patient has experienced or is experiencing, an associated patient prescription for each of the at least one patient condition, and an associated at least one prescription performance indicator for each of the at least one patient prescription; (Rawlings, paragraphs 0009, 0031, 0032, 0045-0046, 0049, 0068)
Rawlings describes a data processing system that generates individual treatment patterns derived from claims data, linked by a specific identification code, where the data includes identifiers, location, employment, diagnoses, and prescriptions. Since Rawlings describes a processor that uses a claimant identification to generate the individual patients treatment pattern containing data points such as claim ID (unique patient record identifier), employer (patient employment status), diagnosis code (patient condition), and prescriptions (patient prescription), it describes a system establishing a patient record containing at least one of the claimed data fields.
the computing system establishing an at least one service provider record associated with each of the at least one medical service provider, each service provider record containing at least one of a unique service provider record identifier, a service provider location, an average income representing the average income of patients treated by the associated medical service provider, an average education level representing the average education level of patients treated by the associated medical service provider, an average hours worked representing the average hours worked by patients treated by the associated medical service provider, an average crime level representing the average crime level in the corresponding service provider location, an average age representing the average age of patients treated by the associated medical service provider, an unemployment rate representing the unemployment rate in the corresponding service provider location, a mortality rate representing the mortality rate in the corresponding service provider location, and a patient table containing links to the corresponding patient record of each patient that has been treated by the associated medical service provider; (Rawlings, paragraphs 0019, 0032, 0041, 0043, 0064, 0067, 0068).
Rawlings describes a system that generates a profile and detailed report for a single service provider (establishing a service provider record). This record includes the names of providers (unique service provider record identifier), is organized by regions (service provider location), and includes a list of claimant identification with a claimant ID link that allows a user to access the individual patients treatment pattern (patient table containing links to the corresponding patient record).
and upon a user desiring to obtain an analysis of a given medical condition:
the computing system generating a model of expected prescribing behaviors for said medical condition, organized by an at least one pharmaceutical product being prescribed for treating said medical condition, based on existing and generally accepted clinical guidelines; (Rawlings, paragraphs 0006, 0008, 0032, 0041, 0043, 0045, 0053, 0056, 0057, 0077).
Rawlings describes a system where a user selects a medical condition (e.g., condition X) for analysis. The system then retrieves or generates a predefined pattern of treatment (model of expected behaviors) based on evidence-based medicine standards and standard guidelines (clinical guidelines). This pattern includes pharmaceuticals or prescriptions and specific drugs (organized by pharmaceutical product).
the computing system accessing data contained in the at least one service provider record related to said medical condition; (Rawlings, 0041, 0042, 0068)
Rawlings describes a process where the analysis engine (computing system) identifies and processes a group of claim data specifically relating to treatment of a certain medical condition, e.g., condition X, by a certain healthcare provider, e.g., provider A. By isolating the data for "condition X" within the data set of "provider A" (the service provider record), the system is accessing data contained in the provider record related to the medical condition.
the computing system stratifying the associated medical service providers into a plurality of groups based on at least one of the respective patient demographic data points of the associated medical service providers and the prescription performance indicator of each associated patient that has been treated, or is being treated, by each of the medical service providers; (Rawlings, 0019, 0043, 0064, 0075)
Rawlings describes stratifying providers by comparing a provider against other healthcare providers that have a similar casemix. " Furthermore, Rawlings explicitly discloses displaying medical service providers organized by regions and market region. Since "patient location" and "service provider location" are demographic data points, organizing providers by regions constitutes stratifying them into groups based on patient/provider demographic data points. Additionally, the use of scorecards to compare entities within levels further supports the stratification functionality.
the computing system generating a model of average prescribing behaviors for said medical condition, organized by the at least one pharmaceutical product being prescribed for treating said medical condition, for each of the stratified groups of medical service providers; (Rawlings, 0006, 0007, 0043, 0052-0053, 0056-0057)
Rawlings describes a system that uses a computational process to create defined treatment pattern data by compiling historical claim data from a group of other patients. This defined treatment pattern is explicitly shown to incorporate average or typical costs for the group and represents the general treatment program, making it the functional and statistical equivalent of generating a model of average prescribing behaviors for the medical condition. This data is detailed enough to include specific prescriptions of medicine like GLEEVEC, showing it is organized by the at least one pharmaceutical product. This pattern is applied for each of the subsets of providers defined by characteristics like similar casemix or specialty group, which is the functional definition of stratified groups of medical service providers.
upon the computing system determining that a given stratified group does not approximate the model of expected prescribing behaviors for said medical condition, the computing system identifying said stratified group as containing abnormal prescribing behaviors; (Rawlings, paragraphs 0005, 0006, 0044, 0056).
Rawlings describes a system that compares actual treatment pattern data for a provider against a predefined treatment pattern (model) to identify one or more discrepancies or aberrations. This process of detecting discrepancies is functionally identical to determining that a given stratified group does not approximate the model. Furthermore, Rawlings explicitly states that these aberrations are identified as unusual, unnecessary, and/or excessive treatments or fraud, which constitutes identifying said stratified group as containing abnormal prescribing behaviors.
and for any stratified groups identified by the computing system as containing abnormal prescribing behaviors: (Rawlings, 0051, 0038)
the computing system determining which of the associated at least one patient demographic data point had the strongest influence on the abnormal prescribing behaviors for the associated medical service providers; (Rawlings, 0051, 0032)
Rawlings describes a system that analyzes patient demographic data points like gene type, co-morbidity, weight, and diabetes status to identify which specific variable has the influence on an observed abnormal behavior or pattern.
and the computing system determining whether any practice demographic data points had an influence on the abnormal prescribing behaviors for the associated medical service providers (Rawlings, 0032, 0062, 0064)
Rawlings explicitly teaches that for aberrant billing patterns (abnormal behaviors), the system allows users to drill down and identify specific data points including provider, specialty, regions, state, employer. These listed items—specialty, regions, state, employer—are the definition of practice demographic data points. The stated purpose of this drill-down is to target the factors that may be related to such trends, which is a functional description of determining whether any... data points had an influence on the behavior. The system correlates the aberrant pattern with the specialty or region to determine the source of the issue.
Rawlings teaches claim 2.
The method of claim 1, wherein the step of the computing system stratifying the associated medical service providers into a plurality of groups further comprises the step of the computing system weighting the associated at least one patient demographic data point based on the relative strength of said patient demographic data point's potential influence on prescribing behaviors. (Rawlings, paragraphs 0032, 0043, 0060-0061).
Rawlings describes stratifying providers by taking into account factors such as casemix and staging indicators (e.g., severity of illness). "Casemix" is a term of art for a methodology that applies weighting to patient data based on resource intensity (influence). Additionally, Rawlings's use of sensitivity analysis to target factors related to trends allows for the determination of the relative strength of influence of those factors. Therefore, the prior art utilizes a weighted approach based on the influence of patient factors to organize the data.
Rawlings teaches claim 7.
The method of claim 1, further comprising the step of the computing system generating a report for the user outlining the patient demographic data points and/or practice demographic data points having the strongest influence on the identified abnormal prescribing behaviors for a given medical service provider. (Rawlings, paragraphs 0032, 0044, 0062).
Rawlings explicitly discloses generating reports and display screens concerning provider treatment patterns. These reports allow users to drill down into aberrant billing patterns to target the factors (such as specialty or region) that are related to such trends. This directly describes generating a report that outlines the influential demographic data points associated with abnormal behavior.
Rawlings teaches claim 8. The method of claim 7, wherein the step of the computing system generating a report further comprises the step of the computing system providing an at least one recommendation on how to counteract the influential patient demographic data points and/or practice demographic data points in order to reduce or eliminate the abnormal prescribing behaviors for a given medical service provider.(Rawling, 0057, 0059, 0061-0062, 0002)
Rawlings describes identifying aberrations (abnormal behaviors) and using the system to prevent payment (reduce fraud) or refer patients to providers who follow best treatment practices (eliminate suboptimal care). This referral mechanism serves as an actionable recommendation to counteract poor prescribing behaviors (or "aberrations") by redirecting patients to appropriate providers.
Note: Claims 9 and 15 are rejected with the same analysis above for being very similar as following (1,9,15).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 3 and 11, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US20070219824-Rawlings in view of US20120116807-Hane.
Rawlings teaches Claim 3.
The method of claim 2, wherein the step of the computing system weighting the associated at least one patient demographic data point further comprises the steps of:
the computing system assigning a relatively larger numerical weight to patient demographic data points related to the medical condition being analyzed (Rawlings, par. 0043, 0060)
Rawlings discloses stratifying and analyzing providers by considering the applicable casemix and staging indicators such as risk of mortality or severity of illness.
and the computing system assigning a relatively larger numerical weight to the at least one prescription performance indicator associated with each of the at least one patient record having the medical condition (Rawlings, paragraphs 0005, 0043, 0056, 0057, 0060).
Rawlings teaches the step of the computing system weighting the associated at least one patient demographic data point broadly, disclosing that the system compares providers by "considering the applicable casemix" and "considering the applicable staging indicators" (para. [0043]) to account for patient severity, and functions by allowing users to "target the factors that may be related to such trends" (para. [0032]) and "determine whether there is an indication... that may be responsible" for an observed pattern (para. [0051]). However, Rawlings fails to explicitly disclose the specific algorithmic hierarchy of assigning a relatively larger numerical weight to patient demographic data points related to the medical condition... than... general patient demographic data points and assigning a relatively larger numerical weight to the at least one prescription performance indicator... than... patient demographic data points related to the medical condition.
Hane teaches the Missing Element in bold, describing a method for "analyzing the clinical data" which comprises "determining a primary magnitude for each geographic region of a primary measure" and "determining a secondary magnitude for each geographic region of a secondary measure" (para. [0008]). Hane explicitly teaches establishing a hierarchy between these values by "comparing the primary magnitude to the secondary magnitude" (para. [0008], claim 1). Furthermore, Hane discloses that "examples of primary measures include... a measure of the type of prescription drug use" (para. [0093, 0096-0097]), thereby teaching the prioritization of prescription data.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of Rawlings with Hane because both references address the shared purpose of analyzing healthcare data to identify patterns (Rawlings, para. [0006]; Hane, para. [0007]). The combination makes the integration of the missing element obvious because it represents the application of a known technique to a known device ready for improvement. Hane provides the mechanism for assigning a "primary magnitude" (para. [0008]) to the most important variable. A POSITA utilizing Hane's tool in Rawlings' system could reach the specific arrangement recited in the claims because Hane enables the user to select the measures. Specifically, a POSITA would choose to assign the "primary magnitude" (the larger weight/importance) to the prescription performance indicator because detecting "unusual, unnecessary, and/or excessive treatments" is the primary goal of Rawlings (para. [0044]). Conversely, the POSITA would assign the "secondary magnitude" to demographic data to use it as a point of comparison (Hane, para. [0095]). This configuration is the logical design choice to ensure the analysis identifies the correct type of outliers (prescribing vs. demographic), rendering the specific arrangement a predictable use of the prior art elements.
A person of ordinary skill in the art would have been motivated to integrate the primary/secondary magnitude hierarchy from Hane into the system of Rawlings to achieve the benefit of improved analysis, as Hane teaches that comparing the "primary magnitude" to the "secondary magnitude" reveals "which areas... may need further investigation" (para. [0099]).
A PHOSITA would have had a reasonable expectation of success in combining the references because the modification required only ordinary skill and routine experimentation. The references provide sufficient enabling disclosure, as Rawlings already employs a computer system for "receiving and processing data" (para. [0006]) and Hane discloses that the analysis logic is part of a "computer program product" (Claim 19), indicating the integration of these analysis rules into Rawlings' existing software architecture would be a straightforward application of known data processing algorithms.
Note: Claims 11,17 are rejected with the same analysis above for being very similar as following claim 3.
Claim(s) (4,12,18), (5,13,19), (6,14,20) is/are rejected under 35 U.S.C. 103 as being unpatentable over US20070219824-Rawlings in view of US20090048877-Binns.
Rawlings teaches claim 4.
The method of claim 1, wherein the step of the computing system determining that a given stratified group does not approximate the model of expected prescribing behaviors for said medical condition, further comprises the steps of:
the computing system totaling an annual prescribing amount of each pharmaceutical product being prescribed for treating said medical condition; (Rawlings, 0006, 0007, 0045, 0053, 0062, 0063, 0064)
Rawlings discloses a system that tracks treatment dates and a list of treatments including pharmaceuticals. The system explicitly calculates and displays data on a per member per month basis for costs and utilization.
and to the computing system comparing the prescribing behavior of a given medical service provider to the average in the associated stratified group in which said medical service provider is categorized. Rawlings, paragraphs 0007, 0043, 0044, 0052, 0062, 0064)
Rawlings teaches the step of the computing system comparing the prescribing behavior of a given medical service provider to the average in the associated stratified group in which said medical service provider is categorized, explicitly disclosing the comparison of a provider's treatment patterns against a "projected target" or "other healthcare providers that have a similar casemix" (para. [0043]) and displaying data trends on a "per member per month" basis (para. [0062]). However, Rawlings fails to explicitly disclose the specific step of totaling an annual prescribing amount of each pharmaceutical product being prescribed for treating said medical condition.
Binns teaches the Missing Element in bold, describing an insurance forecasting system that calculates costs for a "Policy Period" defined as "Typically a 12 consecutive month period" (para. [0040]). Binns explicitly discloses the step of "producing a group-level forecast... by totaling the person-level actual policy period cost forecasts for the group for the policy period" (para. [0059]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of Rawlings with Binns because both references address the shared purpose of analyzing healthcare data for "pricing and underwriting" (Rawlings, para. [0074]; Binns, para. [0004]). The combination makes the integration of the missing element obvious because it represents the combining of prior art elements according to known methods to yield predictable results (MPEP § 2143(A)). A POSITA utilizing Rawlings' system for its stated purpose of "pricing" (para. [0074]) would look to Binns to adapt the monthly ("per member per month") metrics to the standard annual insurance contract. Binns provides the necessary teaching that the relevant timeframe is a "12 consecutive month period" (para. [0040]) and that the method for determining the total obligation is "totaling" the forecasts for that period (para. [0059]). Therefore, totaling an annual prescribing amount is the predictable mathematical aggregation of Rawlings' monthly data to align with the standard business cycle taught by Binns.
A person of ordinary skill in the art would have been motivated to integrate the annual totaling logic from Binns into the system of Rawlings to achieve the benefit of "more accurate predictions" (Binns, para. [0003]) of total liability, ensuring the "abnormal" determination accounts for cumulative annual volume rather than just monthly fluctuations.
A PHOSITA would have had a reasonable expectation of success in combining the references because the modification required only ordinary skill and routine experimentation. The references provide sufficient enabling disclosure, as Rawlings already employs a computer system for calculating costs (para. [0062]) and Binns discloses that the annual totaling is part of a "computer-implemented process" (Claim 1), indicating the integration of this summation logic into Rawlings' existing processor would be a straightforward application of known arithmetic operations.
Rawlings teaches Claim 5.
The method of claim 1, wherein the step of the computing system determining which of the associated at least one patient demographic data point had the strongest influence on the abnormal prescribing behaviors for the associated medical service providers further comprises the steps of:
the computing system calculating a ; (Rawlings, par. 0006, 0032, 0063)
and the computing system comparing the calculated . (Rawlings, par. 0006, 0032, 0063)
Rawlings teaches the step of the computing system determining which of the associated at least one patient demographic data point had the strongest influence on the abnormal prescribing behaviors... and the computing system comparing... to determine which of the patient demographic data points... fail to track, describing a system where "user interfaces can be used to... target the factors that may be related to such trends" (para. [0032]) and where "provider A's treatment patterns are compared against the predefined treatment pattern" to identify "discrepancies" (para. [0044], [0006]). Rawlings further teaches calculating statistical baselines, such as "averages" (para. [0063]). However, Rawlings fails to disclose calculating a median value for each patient demographic data point to serve as the specific baseline for this comparison.
Binns teaches the Missing Element in bold, describing the calculation of median values for demographic and clinical data models, stating: "The CART software with the median regression tree option has produced the best results to date" and "The key is to capture the interactions between base period charges and both clinical and demographic risk factors" (para. [0287]). Binns further teaches: "For each terminal node, the median payment... is calculated" (para. [0305]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to combine the teachings of Rawlings with Binns because both references address the analysis of healthcare claims and demographic data to model expected behaviors and costs (Rawlings,