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 .
Status of Application
This office action is in response to the most recent amendments filed by applicants on 02/25/26.
Claims 1-2 and 19-20 are amended
No claims are cancelled
No claims are added
Claims 1-20 are pending
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 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 1-18 is/are directed to a method which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 19 is/are directed to a system which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 20 is/are directed to a non-transitory computer readable medium which is a statutory category.
Step 2A Prong 1: Identify the Abstract Idea(s)
The Alice framework, steps 2A-Prong One (part 1 of Mayo Test), here, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP 2106.04(a). In determining, whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
Under the 2019 PEG, Step 2A under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Further, particular groupings of abstract ideas are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
Independent claims 1, 19 and 20, with respect to the Step 2A, Prong One, when “taken as a whole” the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). For instance, independent Method/ System/ Apparatus Claim 1 is directed to an abstract idea, as evidenced by claim limitations “collecting, a plurality of data associated with one or more entities from one or more sources; selecting, and based on the plurality of data, one or more entities for which performance evaluation is conducted with respect to one or more specialties; for each of the one or more specialties: determining, and using one or more models, an overall score for each of the one or more selected entities based on one or more performance scores determined for one or more performance components, wherein the one or more performance components include one or more of: a structural component, a process / expert opinion component, an outcome component, a patient experience component, or a public transparency component, wherein each component includes one or more performance indicators; and generating, a rank for each of the one or more selected entities based on the overall score determined for each of the one or more selected entities; and causing, a display of the rank for the one or more selected entities in association with the one or more specialties in a user interface of a device.”
These claim limitations belong to the grouping of “certain methods of organizing human activity” because the claims are related to ranking entities (e.g., hospitals) may play a crucial role in providing transparency and aiding informed decision-making for patients, healthcare providers, and policymakers alike. However, current methodologies often face significant technical challenges as they oversimplify complex healthcare metrics or rely on limited datasets that fail to capture the full spectrum of the performance (Spec. [0003]) for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Independent Claims 19 and 20 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2A for similar reasons to claim 1 above.
Step 2A Prong 2: Additional Elements That Integrate the Judicial Exception into a Practical Application
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements: “A computer-implemented method comprising: using the one or more processors and using a set of one or more models comprising a first machine learning model, prior to using the first ML model to determine the overall scores, the method further comprises producing the first ML by performing a process that comprises: obtaining stage inputs; obtaining known outcomes; obtaining comparison results indicating a deviation of a processed result from a documented result; using a feature engineering process to produce transformed data points from the obtained stage inputs, known outcomes, and comparison results; using i) the transformed data points ii) a regression model to train an initial ML model; and performing a cross-validation and a hyperparameter turning on the initial ML model to produce the first ML model, A system comprising: one or more processors of a computing system; and at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: A non-transitory computer readable medium, the non-transitory computer readable medium storing instructions which, when executed by one or more processors of a computing system, cause the one or more processors to perform operations comprising:” at a high level of generality such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea with no significantly more elements.
In the training a machine learning model steps above, the claims are pre-processing data for use by the machine learning model to output the performance scores. The training step is math. To overcome the 101 the output of the model would have to be used in a meaningful way. Further, scoring items also constitutes a mental process, such as an observation, evaluation, judgment, or opinion that can be performed in the human mind. The 2019 Guidance expressly recognizes such mental processes as constituting patent-ineligible abstract ideas. MPEP § 2106.04(a).
Further still, training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. The 2019 Guidance expressly recognizes mathematical concepts including mathematical relationships as constituting an abstract idea. MPEP § 2106.04(a).
Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. As a result, claims 1, 19 and 20 do not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception. See MPEP 2106.05(f).
Similarly dependent claims 2-18 are also directed to an abstract idea under 2A, first and second prong. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. For instance, dependent claims 2 recite “the one or more entities are selected based on one or more of structural characteristics, volume, or discharge characteristics” and dependent claims 3 recite “wherein, if the discharge characteristics for an entity is below a pre-determined threshold, the entity is selected if the entity is nominated by a certain percentage or number of providers and/or provider systems”. Dependent claims 4 recite “wherein at least the performance scores for the structural component and the outcome component are determined by, for each of these performance scores: determining one or more values representative of corresponding one or more performance indicators; normalizing the one or more values; assigning a weight to each of the one or more performance indicators; generating a normalized score for each of the one or more performance indicators based on the corresponding weight and normalized value; and generating a performance score for the corresponding performance component based on the normalized score for the one or more performance indicators.” Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above.
Dependent claims 12 recites “wherein the one or more models include one or more scoring models”. In this claim, “soring models” is an additional element. Similarly, in dependent claim 15 recites “wherein, for the specialty of rehabilitation, the one or more scoring models use the performance scores for the structural component, process component, and outcome component”. In this claim, “soring models” is an additional element. However, the additional elements are still being recited such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). As a result, Examiner asserts that dependent claims, such as dependent claims 2-18 are also directed to the abstract idea identified above.
Step 2B: Determine Whether Any Element, Or Combination, Amount to “Significantly More” Than the Abstract Idea Itself
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of “A computer-implemented method comprising: using the one or more processors and using a set of one or more models comprising a first machine learning model, prior to using the first ML model to determine the overall scores, the method further comprises producing the first ML by performing a process that comprises: obtaining stage inputs; obtaining known outcomes; obtaining comparison results indicating a deviation of a processed result from a documented result; using a feature engineering process to produce transformed data points from the obtained stage inputs, known outcomes, and comparison results; using i) the transformed data points ii) a regression model to train an initial ML model; and performing a cross-validation and a hyperparameter turning on the initial ML model to produce the first ML model, A system comprising: one or more processors of a computing system; and at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: A non-transitory computer readable medium, the non-transitory computer readable medium storing instructions which, when executed by one or more processors of a computing system, cause the one or more processors to perform operations comprising:” are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0062] [0077]-[0085]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.). The additional elements amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II).
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices. All of these additional elements are significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
The computing elements with a computing device is recited at high level of generality (e.g. a generic device performing a generic computer function of processing data). Thus, this step is no more than mere instructions to apply the exception on a generic computer. In addition, using a processor to process data has been well- understood routing, conventional activity in the industry for many years. Generic computer features, such as system or storage, do not amount to significantly more than the abstract idea. These limitations merely describe implementation for the invention using elements of a general-purpose system, which is not sufficient to amount to significantly more. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am. Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1791 (Federal Circuit 2015).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent Claims 19 and 20 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2B for similar reasons to claim 1 above.
Further, it should be noted that additional elements of the claimed invention such as claim limitations when considered individually or as an ordered combination along with the other limitations discussed above in method claim 1 also do not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.
Similarly, dependent claims 2-18 also do not include limitations amounting to significantly more than the abstract idea under the second prong or 2B of the Alice framework. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. Further, it should be noted that the dependent claims do not include limitations that overcome the stated assertions. Here, the dependent claims recite features/limitations that include computer components identified above in part 2B of analysis of independent claims 1, 19 and 20. As a result, Examiner asserts that dependent claims, such as dependent claims 2-18 are also directed to the abstract idea identified above.
Further, Examiner notes that the addition limitations, when considered as an ordered combination, add nothing that is not already present when looking at the additional elements individually.
For more information on 101 rejections, see MPEP 2106, January 2019 Guidance at https://www.govinfo.gov/content/pkg/FR-2019-01 -07/pdf/2018-28282.pdf
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Norris et al. (US 2015/0073943), further in view of Delaney et al. (US 2016/0092641) and Antos et al. (US 2023/0420098).
As per claims 1, 19 and 20: Regarding the claim limitations below, Reference Norris shows:
A computer-implemented method comprising:
collecting, using one or more processors, a plurality of data associated with one or more entities from one or more sources (Reference Norris [0065] In particular embodiments, an episode of care may comprise a collection of all clinically related procedure and diagnosis codes for treating an index disease or condition for a particular patient from the onset of the index disease or condition to closure. An episode of care may comprise a record of all encounters between a patient and a health-care provider. In particular embodiments, an episode of care may be defined over a single calendar year. As an example, and not by way of limitation, if a patient Alice starts visiting a doctor Bob for her Type-2 diabetes on Jul. 1, 2013, and continues to see Dr. Bob for her diabetes with monthly visits through August, 2014, matching-engine system 160 may consider Alice to have two episodes of care with Dr. Bob: one episode of care for diabetes in 2013, and a second episode of care for diabetes in 2014. In particular embodiments, a particular patient may only be associated with a single episode of care at a time. If the patient starts visiting a new doctor, or starts being treated for a new primary condition, a new episode of care may be determined. As an example and not by way of limitation, if Alice has been visiting Dr. Bob for her diabetes through August 2014, but then breaks her leg and starts seeing Dr. Bob for treatment of the broken leg for two months, matching-engine system 160 may determine a new episode of care for Alice and Dr. Bob with a new primary condition of "broken leg" from August 2014 to October 2014. If Alice's leg heals and she continues seeing Dr. Bob for her diabetes, matching-engine system 160 may determine a third episode of care for Alice and Dr. Bob for diabetes. In particular embodiments, matching-engine system 160 may determine a single episode of care for 2014 for Alice and Dr. Bob for a primary condition of diabetes, with a time period of January to August 2014, and October to December 2014.);
Regarding the claim limitations below, Reference Norris shows:
selecting, using the one or more processors and based on the plurality of data, one or more entities for which performance evaluation is conducted with respect to one or more specialties (Abstract: The matching-engine system may identify a set of physicians to be recommended to the user based on the parameters, and a performance-score and experience-score associated with the base-concept for each physician. The matching-engine system may send a search-results page to the user listing the recommended physicians. [0009] FIG. 5 illustrates an example method of calculating a performance score using a performance engine. [0031] In particular embodiments, matching-engine system 160 may be a network-addressable computing system that can host an online healthcare provider search engine. Matching-engine system 160 may generate, store, receive, and send patient data, healthcare provider data, medical insurance data, or other suitable data related to the healthcare provider search engine, subject to laws and regulations regarding patient data. Matching-engine system 160 may identify and rank healthcare providers in general, or according to one or more specified criteria, based on a verity of information. Matching-engine system 160 may be accessed by the other components of network environment 100 either directly or via network 110. In particular embodiments, matching-engine system 160 may receive inputs from one or more of a performance engine or an experience engine (which may be independent systems, or sub-systems of matching-engine system 160). The performance engine may receive data about healthcare provider performance for a particular episode of care (e.g., from the healthcare providers directly, insurance companies, governmental agencies, patients, etc.) and calculate an estimated performance rating for an episode of care compared to a peer group of healthcare providers. The experience engine may receive data about healthcare provider experience (e.g., from public records, surveys, healthcare providers, rating sites, insurance companies, governmental agencies, patients, etc.) and calculate an estimated experience of the healthcare provider in general, or according to one or more specified criteria. In particular embodiments, matching-engine system 160 may include one or more servers 162. Each server 162 may be a unitary server or a distributed server spanning multiple computers or multiple datacenters. Servers 162 may be of various types, such as, for example and without limitation, web server, news server, mail server, message server, advertising server, file server, application server, exchange server, database server, proxy server, another server suitable for performing functions or processes described herein, or any combination thereof. In particular embodiments, each server 162 may include hardware, software, or embedded logic components or a combination of two or more such components for carrying out the appropriate functionalities implemented or supported by server 162. In particular embodiments, matching-engine system 164 may include one or more data stores 164. Data stores 164 may be used to store various types of information. In particular embodiments, the information stored in data stores 164 may be organized according to specific data structures. In particular embodiments, each data store 164 may be a relational, columnar, correlation, or other suitable database. Although this disclosure describes or illustrates particular types of databases, this disclosure contemplates any suitable types of databases. Particular embodiments may provide interfaces that enable a client system 130, a matching-engine system 160, or a third-party system 170 to manage, retrieve, modify, add, or delete, the information stored in data store 164.));
Regarding the claim limitations below, Reference Norris in view of Delaney shows:
for each of the one or more specialties:
determining, using the one or more processors and using a set of one or more models comprising a first machine learning model, an overall score for each of the one or more selected entities based on one or more performance scores determined for one or more performance components, wherein the one or more performance components include one or more of:
Norris shows “determining, … for each of the one or more selected entities based on one or more performance scores determined for one or more performance components”: Abstract: The matching-engine system may identify a set of physicians to be recommended to the user based on the parameters, and a performance-score and experience-score associated with the base-concept for each physician. The matching-engine system may send a search-results page to the user listing the recommended physicians. [0009] FIG. 5 illustrates an example method of calculating a performance score using a performance engine. [0031] In particular embodiments, matching-engine system 160 may be a network-addressable computing system that can host an online healthcare provider search engine. Matching-engine system 160 may generate, store, receive, and send patient data, healthcare provider data, medical insurance data, or other suitable data related to the healthcare provider search engine, subject to laws and regulations regarding patient data. Matching-engine system 160 may identify and rank healthcare providers in general, or according to one or more specified criteria, based on a verity of information. Matching-engine system 160 may be accessed by the other components of network environment 100 either directly or via network 110. In particular embodiments, matching-engine system 160 may receive inputs from one or more of a performance engine or an experience engine (which may be independent systems, or sub-systems of matching-engine system 160). The performance engine may receive data about healthcare provider performance for a particular episode of care (e.g., from the healthcare providers directly, insurance companies, governmental agencies, patients, etc.) and calculate an estimated performance rating for an episode of care compared to a peer group of healthcare providers. The experience engine may receive data about healthcare provider experience (e.g., from public records, surveys, healthcare providers, rating sites, insurance companies, governmental agencies, patients, etc.) and calculate an estimated experience of the healthcare provider in general, or according to one or more specified criteria. In particular embodiments, matching-engine system 160 may include one or more servers 162. Each server 162 may be a unitary server or a distributed server spanning multiple computers or multiple datacenters. Servers 162 may be of various types, such as, for example and without limitation, web server, news server, mail server, message server, advertising server, file server, application server, exchange server, database server, proxy server, another server suitable for performing functions or processes described herein, or any combination thereof. In particular embodiments, each server 162 may include hardware, software, or embedded logic components or a combination of two or more such components for carrying out the appropriate functionalities implemented or supported by server 162. In particular embodiments, matching-engine system 164 may include one or more data stores 164. Data stores 164 may be used to store various types of information. In particular embodiments, the information stored in data stores 164 may be organized according to specific data structures. In particular embodiments, each data store 164 may be a relational, columnar, correlation, or other suitable database. Although this disclosure describes or illustrates particular types of databases, this disclosure contemplates any suitable types of databases. Particular embodiments may provide interfaces that enable a client system 130, a matching-engine system 160, or a third-party system 170 to manage, retrieve, modify, add, or delete, the information stored in data store 164.
Norris shows “using a set of one or more models comprising a first machine learning model”: [0051] In particular embodiments, one or more servers 320 may each include one or more search engines 322. A search engine 322 may include hardware, software, or both for providing the functionality of search engine 322. As an example and not by way of limitation, a search engine 322 may implement one or more search algorithms to identify network resources in response to search queries received at search engine 322, one or more ranking algorithms to rank identified network resources, or one or more summarization algorithms to summarize identified network resources. In particular embodiments, a ranking algorithm implemented by a search engine 322 may use a machine-learned ranking formula, which the ranking algorithm may obtain automatically from a set of training data constructed from pairs of search queries and selected Uniform Resource Locators (URLs), where appropriate.
Even though Norris shows in [0064]: In particular embodiments, using an episode of care as the base point for determining performance scores may be more accurate than determining performance merely based on cost. As an example and not by way of limitation, if Dr. Alan has a lower average cost than Dr. Brad for medical services, but Dr. Alan requires more tests and more office visits to treat the same disease as Dr. Brad, the individual service costs may point to Dr. Alan as having better performance (e.g. lower cost for the same services), but the overall episode of care may point to Dr. Brad as having better overall performance in treating the disease, which may be a better indicator of doctor efficiency. This may be reasonably understood as reading on “an overall score”. Norris does not explicitly show “an overall score”. However, Delaney shows in [0063] The scoring component 106 is configured to evaluate each patient group on the basis of one or more of the following metrics: cost of care delivery, length of stay (LOS), readmission rate, complication rate, financial variance, and complexity of case and/or clinical risk. In an aspect, scoring component 106 can determine a score for each of the individual metrics and an overall operating score that reflects overall performance of the healthcare organization based on all the metrics. Using these performances scores, the healthcare management server 102 and/or clinicians and operators can rapidly develop apples to apples comparisons across patient populations, supporting rapid analysis and root cause investigation of variance between groups of patients or clinical approaches. [0066] In some aspects, for each variable except case complexity, a lower number improves the OP score. F1-F6 are weighted coefficients that the relative importance of the associated variable with respect to overall financial and clinical operating performance of the healthcare organization. [0070] Comparison component 110 can employ the OP scores respectively associated with the different patient groups in each of the different clusters to determine the specific filtering criteria that is associated with high variance in OP scores and/or associated with low OP scores. The specific filtering criteria associated with a group cluster having high variance in OP scores (e.g., with respect to a variance threshold) and/or low OP scores (e.g., with respect to a score value threshold) can be identified as a healthcare service parameter that contributes significantly to the overall OP score for the patient group diagnosed with cancer 1248. For example, the specific healthcare service parameters can be identified as an area in which improvement can be achieved with respect to the overall clinical and financial performance of the healthcare organization.
Reference Norris and Reference Delaney are analogous prior art to the claimed invention because the references generally relate to field of analyzing data to help users make better healthcare decisions. Further, said references are part of the same classification, i.e., G06F. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Delaney, particularly the ability to provide an overall score [0066]-[0070], in the disclosure of Reference Norris, particularly in the ability to evaluate the overall performance of care in [0064], in order to provide for a system that evaluates each patient group on the basis of one or more of the following metrics: cost of care delivery, length of stay (LOS), readmission rate, complication rate, financial variance, and complexity of case and/or clinical risk. In an aspect, scoring component 106 can determine a score for each of the individual metrics and an overall operating score that reflects overall performance of the healthcare organization based on all the metrics as taught by Reference Delaney (see at least in [0063]), where upon the execution of the method and system of Reference Delaney allows using these performance scores, the healthcare management server 102 and/or clinicians and operators can rapidly develop apples to apples comparisons across patient populations, supporting rapid analysis and root cause investigation of variance between groups of patients or clinical approaches so that the process of analyzing data to help users make better healthcare decisions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data to help users make better healthcare decisions field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Norris in view of Reference Delaney, the results of the combination were predictable (MPEP 2143 A);
wherein the one or more performance components include one or more of:
a structural component,
a process / expert opinion component,
an outcome component,
a patient experience component (Norris: [0022]: FIG. 9 illustrates an example embodiment of the matching engine selecting a set of physicians based on parameters for performance scores and experience indices. [0031]: Matching-engine system 160 may be accessed by the other components of network environment 100 either directly or via network 110. In particular embodiments, matching-engine system 160 may receive inputs from one or more of a performance engine or an experience engine (which may be independent systems, or sub-systems of matching-engine system 160). The performance engine may receive data about healthcare provider performance for a particular episode of care (e.g., from the healthcare providers directly, insurance companies, governmental agencies, patients, etc.) and calculate an estimated performance rating for an episode of care compared to a peer group of healthcare providers. The experience engine may receive data about healthcare provider experience (e.g., from public records, surveys, healthcare providers, rating sites, insurance companies, governmental agencies, patients, etc.) and calculate an estimated experience of the healthcare provider in general, or according to one or more specified criteria. [0056]: the administrator may wish to specify a range of acceptable performance scores for physicians, acceptable monetary costs for physicians when conducting particular treatments or procedures, or an acceptable range of experience indices for physicians. In particular embodiments, performance score may represent a physician's efficiency in time and resources in treating a patient for a particular condition or disease, or in applying a particular treatment method. A performance score may be of interest to both a patient and the patient's insurance provider as an indication that a physician will require less time, office visits, resources, etc. In particular embodiments, an experience index for a physician may indicate the physician's experience in dealing with a particular condition or disease, or in performing a particular treatment method. In particular embodiments, the experience index may also represent how many different types of conditions within a condition group a physician has seen. These parameters may allow the administrator to direct users towards physicians that the administrator has determined are cost-effective physicians, or in the alternative, physicians that are above a threshold baseline of competency while remaining affordable for the employer. [0063]: matching-engine system 160 may also receive and use as an input one or more experience indices associated with a particular physician. The experience index for a physician may be an indication of how much experience a physician has in a particular specialty or with a particular disease or condition, based on the number of patients seen with the particular specialty or disease or condition, and the severity of each patient seen. Experience Index: [0077] In particular embodiments, matching-engine system 160 may consider the experience index of physicians in generating or ranking the set of physicians for recommendation to the user. An experience index may represent the overall experience of a physician in dealing with a particular type of conditions or diseases, beyond a mere count of patients who have visited the physician. In particular embodiments, an experience index may also account for relative case volume, case severity, and variety seen by the physician. In particular embodiments, the experience index may be localized to be evaluated against physicians within a given state; within a given MSA; within a specialty class within the MSA; within a specialization within the specialty class; and within a condition group within the specialization based on diagnoses and procedure codes. In particular embodiments, using a localized context for the experience index may allow for a very homogenous context of analysis, may allow for environmental and population health factors to be included in the analysis, and may narrow the context of the analysis. The experience index is calculated using only provider-reported insurance claims data, and thus will be free of any subjective reviews from patients. Another advantage to the experience index may be that because it is calculated by comparing providers to each other, just two providers in the same specialization in the same locality are needed to calculate the experience index. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.), or
a public transparency component, wherein each component includes one or more performance indicators; and
Regarding the claim limitations below, Reference Norris in view of Delaney shows:
generating, using the one or more processors, a rank for each of the one or more selected entities based on the overall score determined for each of the one or more selected entities (Norris: [0031]: Matching-engine system 160 may generate, store, receive, and send patient data, healthcare provider data, medical insurance data, or other suitable data related to the healthcare provider search engine, subject to laws and regulations regarding patient data. Matching-engine system 160 may identify and rank healthcare providers in general, or according to one or more specified criteria, based on a verity of information. Matching-engine system 160 may be accessed by the other components of network environment 100 either directly or via network 110, [0035]: relevance-and-ranking engine, [0051] In particular embodiments, one or more servers 320 may each include one or more search engines 322. A search engine 322 may include hardware, software, or both for providing the functionality of search engine 322. As an example and not by way of limitation, a search engine 322 may implement one or more search algorithms to identify network resources in response to search queries received at search engine 322, one or more ranking algorithms to rank identified network resources, or one or more summarization algorithms to summarize identified network resources. In particular embodiments, a ranking algorithm implemented by a search engine 322 may use a machine-learned ranking formula, which the ranking algorithm may obtain automatically from a set of training data constructed from pairs of search queries and selected Uniform Resource Locators (URLs), where appropriate. [0056]: matching-engine system 160 may include other physicians, but may rank the physicians meeting the administrator's parameters more highly. [0077] In particular embodiments, matching-engine system 160 may consider the experience index of physicians in generating or ranking the set of physicians for recommendation to the user. Claim 13: wherein the set of recommended physicians is ranked for presentation based at least in part on the performance-score and experience-score of each physician.); and
Regarding the claim limitations below, Reference Norris in view of Delaney shows:
causing, using the one or more processors, a display of the rank for the one or more selected entities in association with the one or more specialties in a user interface of a device (Norris: [0088]: the columns 1022 may display information that is not a corresponding value to the experience index or performance score of the physician),
Regarding the claim limitations below, Reference Norris in view of Delaney and Antos shows:
“wherein:
prior to using the first ML model to determine the overall scores, the method further comprises producing the first ML by performing a process that comprises: obtaining stage inputs;
obtaining known outcomes;
obtaining comparison results indicating a deviation of a processed result from a documented result;
using a feature engineering process to produce transformed data points from the obtained stage inputs, known outcomes, and comparison results;
using i) the transformed data points ii) a regression model to train an initial ML model; and
performing a cross-validation and a hyperparameter turning on the initial ML model to produce the first ML model.”
Norris shows “a machine learning model” and training the model: [0051] In particular embodiments, one or more servers 320 may each include one or more search engines 322. A search engine 322 may include hardware, software, or both for providing the functionality of search engine 322. As an example and not by way of limitation, a search engine 322 may implement one or more search algorithms to identify network resources in response to search queries received at search engine 322, one or more ranking algorithms to rank identified network resources, or one or more summarization algorithms to summarize identified network resources. In particular embodiments, a ranking algorithm implemented by a search engine 322 may use a machine-learned ranking formula, which the ranking algorithm may obtain automatically from a set of training data constructed from pairs of search queries and selected Uniform Resource Locators (URLs), where appropriate.
Delaney shows [0016] FIG. 26 presents a flow diagram of an example method for evaluating variance between care paths of similar patient encounters and updating a model care path using machine learning techniques, in accordance with various aspects and embodiments described herein; [0030] The system further includes a comparison component configured to compare the respective groups of patients based on the performances scores respectively associated therewith to facilitate identifying one or more of the uncommon healthcare service parameters that are responsible for variance between at least a subset of performance scores. In an aspect, the comparison component is configured to select a subset of the respective groups of patients for comparison based on association with performance scores that differ beyond a threshold deviation value, and wherein the comparison component is configured to identify one or more of the uncommon healthcare service parameters that are responsible for variance between the performance scores of the respective groups included in the subset.
Even though Norris in view of Delaney shows “a machine learning model” and training the model (Norris [0051], Delaney [0016] and [0030]). Neither References show the details of the training process for the machine learning model as are recited in the claim. As such, Reference Antos is added to show the details of the training process for the machine learning model as are recited in the claim.
Reference Antos shows (Abstract): Examples of a system and methods for quantifying patient improvement via artificial intelligence are disclosed. In general, via at least one processing element, a machine learning model such as a Siamese neural network is trained in view of a cost function to learn on average a maximum difference in outcomes between a patient at different points in time. Given the architecture of the neural network, a plurality of outcome measures generated for a given point in time can be condensed into a single score. [0008] In one specific example, the present inventive concept can take the form of a computer-implemented method, comprising the steps of accessing, by a computing device, a first dataset of input data for one or more outcome measures derived from a patient at a first point in time of rehabilitation; accessing, by the computing device, a second dataset of the input data for the one or more outcome measures derived from the patient at a second point in time of the rehabilitation; and generating, by the computing device applying the first dataset and the second dataset as inputs to a machine learning model, an output including a machine learning score that infers improvement of the patient from the first point in time to the second point in time, the machine learning model trained to map the inputs to the output to minimize a cost function defined by the machine learning model and maximize the dissimilarity of the patient (but may be trained using a plurality of patients) between the first point in time and the second point in time. The machine learning model may be a Siamese neural network trained that minimizes the cost function based on training data defining outcome measures fed to the machine learning model during training. [0027] Described herein are examples of computer-implemented systems and methods that relate to quantification of patient improvement using artificial intelligence. In various instances, machine learning can be implemented by one or more processing elements to train a machine learning model such as a neural network to take any set of numeric outcome measures and biomarkers before and after treatment (and/or at two or more predetermined points in time) and generate a distribution of scores reflecting a computed difference in the patient. More specifically, a first set of outcome measures associated with a first point in time may be fed to the trained machine learning model to compute a first intermediate score, and a second set of outcome measures associated with a second point in time may be fed to the trained machine learning model to compute a second intermediate score; the difference between the second intermediate score and the first intermediate score defining a machine learning (ML) score reflecting a total difference in the patient between the first point in time and the second point in time. While there are infinite ways to combine outcome measures into a single intermediate score, it is the way they are combined according to the novel examples described herein (e.g., trained neural networks) which dictate the properties of the intermediate scores and make it meaningful. [0032] In some examples, the processor 104 of the computing device 102 is operable to execute any number of instructions 130 within the memory 106 to perform operations associated with training a machine learning model 132 and/or conducting machine learning, implementing a cost function 134 that assists with the machine learning, testing or otherwise implementing a trained machine learning (ML) model 136 defining at least one equation 137, and generating a machine learning score 138 by implementing the trained ML model 136 as described herein. In general, the system 100 is configured to compute the trained ML model 136 (including the equation 137 with various configured weights, biases, and parameters) by applying machine learning 132 in view of the cost function 134 to training datasets defined by the data 120 (during a training phase 140), so that the trained ML model 136 when executed by the processor 104 in view of new outcome measures 121 outputs an ML score 138 indicating a difference in a patient over time (during a testing and/or implementation phase 142) based on the new outcome measures 121. Aspects may be rendered via an output 144 to the display 116 (e.g., a graph or report illustrating patient improvement by the computed ML score 138 over time), and aspects may be accessed by the end user device 114 via one or more of an application programming interface (API) 146 or otherwise accessed. [0035] Referring to FIGS. 2A-2B and FIGS. 3A-3B, via one or more processing elements such as the processor 104, one or more machine learning models may be trained, tested, and implemented to quantify patient improvement using artificial intelligence such as neural networks. In general, outcome measures 121, examples in FIG. 3B, are fed to the machine learning model as trained to generate any number of outputs 144 viewable via the display 116 or otherwise. Any number of ML models may be trained and implemented, and ML models may be trained for specific groups of outcome measures 121 or any predetermined rehabilitative procedures. [0036] To illustrate the training phase 140 of FIG. 2A, an exemplary computer-implemented process 200 may be performed by the processor 104 or other processing element to train a machine learning model 132 such as a neural network. Any number or type of the outcome measures 121 can be used to train the ML model 132. By training many or all known/relevant outcome measures across an entire population or predetermined demographic, the machine learning model 132 learns which measurements are most reliable and show the greatest difference for the intervention (i.e., inpatient rehabilitation). Also see [0039]-[0040], [0043]-[0044], [0052]-[0059].
Reference Norris in view of Reference Delaney and Reference Antos are analogous prior art to the claimed invention because the references generally relate to field of analyzing data to help users make better healthcare decisions. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Antos, particularly the details of the training process for the machine learning model [0052]-[0059], in the disclosure of Reference Norris in view of Delaney, particularly in the machine learning model and training of the mode in (Norris [0051], Delaney [0016] and [0030]), in order to provide for a system that utilizes a practical application of machine learning to quantify or estimate improvement that incorporates an assumption that patients are admitted to inpatient rehabilitation at a given ability level and leave inpatient rehabilitation with a new ability level. On average, a patient's ability improves from admission to discharge because inpatient rehabilitation is the best available intervention for that patient. Furthermore, skilled clinicians choose outcome measures that will provide the most relevant data to infer a patient's ability, as taught by Reference Antos (see at least in [0007]), where upon the execution of the method and system of Reference Antos allows using and training the machine learning models such that the process of analyzing data to help users make better healthcare decisions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data to help users make better healthcare decisions field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Norris in view of Delaney and Reference Antos, the results of the combination were predictable (MPEP 2143 A).
As per claim 2: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
Wherein the one or more entities are selected based on one or more of structural characteristics, volume, or discharge characteristics (Norris: [0063]: In particular embodiments, matching-engine system 160 may consider the case volume seen by a particular physician within a particular specialty, which may represent a proportional volume of cases for a particular disease seen by the particular physician versus all other physicians in the same specialty. [0077]: An experience index may represent the overall experience of a physician in dealing with a particular type of conditions or diseases, beyond a mere count of patients who have visited the physician. In particular embodiments, an experience index may also account for relative case volume, case severity, and variety seen by the physician. [0079] At step 740, matching-engine system 160 may calculate a patient-volume for a particular condition group for a provider based on the received patient-diagnosis codes and the corresponding severity factors. In particular embodiments, the volume of patients may be a relative measure of how many patients a particular provider saw compared to the rest of the identified providers).
As per claim 3: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein, if the discharge characteristics for an entity is below a pre-determined threshold, the entity is selected if the entity is nominated by a certain percentage or number of providers and/or provider systems (Norris, [0056]: These parameters may allow the administrator to direct users towards physicians that the administrator has determined are cost-effective physicians, or in the alternative, physicians that are above a threshold baseline of competency while remaining affordable for the employer. In particular embodiments, when a user of the health-care plan run by that administrator submits a query for one or more physicians, matching-engine system 160 may only present physicians meeting the acceptable parameters specified by the administrator. [0093]: Matching-engine system 160 may also be able to identify whether a first physician is making a referral to a second physician who is below-average experience in the type of medical services that the referral comprises, particularly if compared to other physicians referred to by the first physician or the first physician's peers. In particular embodiments, matching-engine system 160 may use a threshold performance score or experience index for referred physicians with respect to a referring physician. [0094]: In particular embodiments, if a referring physician makes referrals to another physician with resulting performance scores below the threshold performance score, matching-engine system 160 may filter out that referring physician from a set of recommended physicians for users requesting treatment in the same base concept or specialization as the unnecessary referrals. [0096]: In particular embodiments, the referral-score may be further based on the experience index of the second physicians, compared to a threshold experience index. In particular embodiments, the threshold experience index may be the average experience index of other physicians referred to by the first physician).
As per claim 4: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein at least the performance scores for the structural component and the outcome component are determined by, for each of these performance scores:
determining one or more values representative of corresponding one or more performance indicators (Norris: [0056]: A performance score may be of interest to both a patient and the patient's insurance provider as an indication that a physician will require less time, office visits, resources, etc. In particular embodiments, an experience index for a physician may indicate the physician's experience in dealing with a particular condition or disease, or in performing a particular treatment method. In particular embodiments, the experience index may also represent how many different types of conditions within a condition group a physician has seen. [0062]: a performance score for a physician may reflect an indication of the amount of healthcare resources used by the physician or health-care provider in delivery of care for treating patients with the same or similar clinical complexity. Complex treatments may result in more resources being consumed than simple cases regardless of physician skills, but there may be a variation in how much resources are used. [0067]: In particular embodiments, matching-engine system 160 may consider physicians having the same primary specialty. As an example and not by way of limitation, matching-engine system 160 may generate a score that indicates the particular physician's efficiency and performance compared to other physicians that the user may also receive recommendations for.);
Regarding the claim limitations below, Reference Norris in view of Delaney shows:
normalizing the one or more values (Norris: [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.);
Regarding the claim limitations below, Reference Norris in view of Delaney shows:
assigning a weight to each of the one or more performance indicators (Norris: [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example, and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.);
Regarding the claim limitations below, Reference Norris in view of Delaney shows:
generating a normalized score for each of the one or more performance indicators based on the corresponding weight and normalized value (Norris: [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.); and
Regarding the claim limitations below, Reference Norris in view of Delaney shows:
generating a performance score for the corresponding performance component based on the normalized score for the one or more performance indicators (Norris: [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.).
As per claim 5: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein determining the overall score for each of the one or more selected entities comprises:
assigning a weight to each of the one or more performance components, wherein the performance score for each of the one or more performance components is based on the corresponding weight (Norris: [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.); and
Regarding the claim limitations below, Reference Norris in view of Delaney shows:
aggregating the one or more performance scores for the one or more performance components (Norris: [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.).
As per claim 6: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein at least the performance score for the process / expert opinion component is determined by:
receiving a plurality of data objects from qualified providers and/or provider systems during a pre-determined time period (Norris: [0065]: As an example and not by way of limitation, if a patient Alice starts visiting a doctor Bob for her Type-2 diabetes on Jul. 1, 2013, and continues to see Dr. Bob for her diabetes with monthly visits through August, 2014, matching-engine system 160 may consider Alice to have two episodes of care with Dr. Bob: one episode of care for diabetes in 2013, and a second episode of care for diabetes in 2014. In particular embodiments, a particular patient may only be associated with a single episode of care at a time. If the patient starts visiting a new doctor, or starts being treated for a new primary condition, a new episode of care may be determined. As an example and not by way of limitation, if Alice has been visiting Dr. Bob for her diabetes through August 2014, but then breaks her leg and starts seeing Dr. Bob for treatment of the broken leg for two months, matching-engine system 160 may determine a new episode of care for Alice and Dr. Bob with a new primary condition of "broken leg" from August 2014 to October 2014. If Alice's leg heals and she continues seeing Dr. Bob for her diabetes, matching-engine system 160 may determine a third episode of care for Alice and Dr. Bob for diabetes. In particular embodiments, matching-engine system 160 may determine a single episode of care for 2014 for Alice and Dr. Bob for a primary condition of diabetes, with a time period of January to August 2014, and October to December 2014. [0073]: The value for each RVU group 662-668 may represent an average value of the individual RVU costs for each patient in the group. In the example of FIG. 6G, the RVU groups 662-668 may be correlated with their respective sub-concept groups 622-628. In particular embodiments, the average RVU cost may be a weighted average, with patients seen more recently being weighted more heavily than patients seen during an earlier period of time. [0084]: Over a predetermined period of time, these physicians 860 may have diagnosed patients with six distinct conditions 861-866. Each of the conditions 861-866 may comprise a different condition group; or matching-engine system 160 may calculate variety based on distinct conditions within a condition group.);
Regarding the claim limitations below, Reference Norris in view of Delaney shows:
determining a score for each data object received from a corresponding provider or provider system (Norris: [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.);
Regarding the claim limitations below, Reference Norris in view of Delaney shows:
generating a weighted score for each data object based on one or more characteristics of the corresponding provider or provider system (Norris: [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.);
Regarding the claim limitations below, Reference Norris in view of Delaney shows:
generating transformed scores by applying log transformation to the weighted scores for the plurality of data objects
Regarding the claim limitations above, Norris does not explicitly show “applying log transformation”. However, Delaney shows the above limitation at least in [0226] FIG. 31 presents an example output interface 3100 presenting the results of an admissions modeling assessment based on input criteria provided in interface 3000. The output provides output results in each category corresponding to expected LOS, expected encounter cost, expected encounter revenue and likelihood of readmission. The results for each category are further assigned a score (e.g., determined by admissions prediction component 2806) indicating the quality of the result (e.g., good, ok, etc.). In other aspects, the scores can include a numerical value. Via interface 3100, the user can select an option to log the results or return to the modeler. [0227] FIG. 32 presents a log explorer interface 3200 that facilitates analyzing logged admissions modeler results for different patients. Interface 3200 for example, includes information comparing admissions models of four selected patients (e.g., one patient corresponding to each bar of the bar graph). Via interface 3200, the user can compare the selected patients with respect to various filtering criteria. For example, an interactive input menu 3204 allows the user to provide information selecting a criteria to chart by (e.g., LOS prediction, readmission rate, cost, etc.), aggregate by and sort by, in association with generating a graphical visualization 3202 representative of the selected data);
Regarding the claim limitations below, Reference Norris in view of Delaney shows:
generating normalized scores by normalizing the transformed scores (Norris: [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.); and
Regarding the claim limitations below, Reference Norris in view of Delaney shows:
generating a performance score for the process / expert opinion component based on the normalized scores for the data objects (Norris: [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.).
As per claim 7: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein the plurality of data objects received from the qualified providers and/or provider systems include survey responses (Norris: [0031] In particular embodiments, matching-engine system 160 may be a network-addressable computing system that can host an online healthcare provider search engine. Matching-engine system 160 may generate, store, receive, and send patient data, healthcare provider data, medical insurance data, or other suitable data related to the healthcare provider search engine, subject to laws and regulations regarding patient data. Matching-engine system 160 may identify and rank healthcare providers in general, or according to one or more specified criteria, based on a verity of information. Matching-engine system 160 may be accessed by the other components of network environment 100 either directly or via network 110. In particular embodiments, matching-engine system 160 may receive inputs from one or more of a performance engine or an experience engine (which may be independent systems, or sub-systems of matching-engine system 160). The performance engine may receive data about healthcare provider performance for a particular episode of care (e.g., from the healthcare providers directly, insurance companies, governmental agencies, patients, etc.) and calculate an estimated performance rating for an episode of care compared to a peer group of healthcare providers. The experience engine may receive data about healthcare provider experience (e.g., from public records, surveys, healthcare providers, rating sites, insurance companies, governmental agencies, patients, etc.) and calculate an estimated experience of the healthcare provider in general, or according to one or more specified criteria. In particular embodiments, matching-engine system 160 may include one or more servers 162. Each server 162 may be a unitary server or a distributed server spanning multiple computers or multiple datacenters. Servers 162 may be of various types, such as, for example and without limitation, web server, news server, mail server, message server, advertising server, file server, application server, exchange server, database server, proxy server, another server suitable for performing functions or processes described herein, or any combination thereof. In particular embodiments, each server 162 may include hardware, software, or embedded logic components or a combination of two or more such components for carrying out the appropriate functionalities implemented or supported by server 162.).
As per claim 8: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein the performance score for the patient experience component is determined based on a plurality of data objects from one or more of patients, entity leaders, or other stakeholders during a pre-determined time period (Norris: [0022]: FIG. 9 illustrates an example embodiment of the matching engine selecting a set of physicians based on parameters for performance scores and experience indices. [0031]: Matching-engine system 160 may be accessed by the other components of network environment 100 either directly or via network 110. In particular embodiments, matching-engine system 160 may receive inputs from one or more of a performance engine or an experience engine (which may be independent systems, or sub-systems of matching-engine system 160). The performance engine may receive data about healthcare provider performance for a particular episode of care (e.g., from the healthcare providers directly, insurance companies, governmental agencies, patients, etc.) and calculate an estimated performance rating for an episode of care compared to a peer group of healthcare providers. The experience engine may receive data about healthcare provider experience (e.g., from public records, surveys, healthcare providers, rating sites, insurance companies, governmental agencies, patients, etc.) and calculate an estimated experience of the healthcare provider in general, or according to one or more specified criteria. [0056]: the administrator may wish to specify a range of acceptable performance scores for physicians, acceptable monetary costs for physicians when conducting particular treatments or procedures, or an acceptable range of experience indices for physicians. In particular embodiments, performance score may represent a physician's efficiency in time and resources in treating a patient for a particular condition or disease, or in applying a particular treatment method. A performance score may be of interest to both a patient and the patient's insurance provider as an indication that a physician will require less time, office visits, resources, etc. In particular embodiments, an experience index for a physician may indicate the physician's experience in dealing with a particular condition or disease, or in performing a particular treatment method. In particular embodiments, the experience index may also represent how many different types of conditions within a condition group a physician has seen. These parameters may allow the administrator to direct users towards physicians that the administrator has determined are cost-effective physicians, or in the alternative, physicians that are above a threshold baseline of competency while remaining affordable for the employer. [0063]: matching-engine system 160 may also receive and use as an input one or more experience indices associated with a particular physician. The experience index for a physician may be an indication of how much experience a physician has in a particular specialty or with a particular disease or condition, based on the number of patients seen with the particular specialty or disease or condition, and the severity of each patient seen. Experience Index: [0077] In particular embodiments, matching-engine system 160 may consider the experience index of physicians in generating or ranking the set of physicians for recommendation to the user. An experience index may represent the overall experience of a physician in dealing with a particular type of conditions or diseases, beyond a mere count of patients who have visited the physician. In particular embodiments, an experience index may also account for relative case volume, case severity, and variety seen by the physician. In particular embodiments, the experience index may be localized to be evaluated against physicians within a given state; within a given MSA; within a specialty class within the MSA; within a specialization within the specialty class; and within a condition group within the specialization based on diagnoses and procedure codes. In particular embodiments, using a localized context for the experience index may allow for a very homogenous context of analysis, may allow for environmental and population health factors to be included in the analysis, and may narrow the context of the analysis. The experience index is calculated using only provider-reported insurance claims data, and thus will be free of any subjective reviews from patients. Another advantage to the experience index may be that because it is calculated by comparing providers to each other, just two providers in the same specialization in the same locality are needed to calculate the experience index. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.).
As per claim 9: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein the plurality of data objects received from one or more of the patients, entity leaders, or other stakeholders include survey results (Norris: [0031] In particular embodiments, matching-engine system 160 may be a network-addressable computing system that can host an online healthcare provider search engine. Matching-engine system 160 may generate, store, receive, and send patient data, healthcare provider data, medical insurance data, or other suitable data related to the healthcare provider search engine, subject to laws and regulations regarding patient data. Matching-engine system 160 may identify and rank healthcare providers in general, or according to one or more specified criteria, based on a verity of information. Matching-engine system 160 may be accessed by the other components of network environment 100 either directly or via network 110. In particular embodiments, matching-engine system 160 may receive inputs from one or more of a performance engine or an experience engine (which may be independent systems, or sub-systems of matching-engine system 160). The performance engine may receive data about healthcare provider performance for a particular episode of care (e.g., from the healthcare providers directly, insurance companies, governmental agencies, patients, etc.) and calculate an estimated performance rating for an episode of care compared to a peer group of healthcare providers. The experience engine may receive data about healthcare provider experience (e.g., from public records, surveys, healthcare providers, rating sites, insurance companies, governmental agencies, patients, etc.) and calculate an estimated experience of the healthcare provider in general, or according to one or more specified criteria. In particular embodiments, matching-engine system 160 may include one or more servers 162. Each server 162 may be a unitary server or a distributed server spanning multiple computers or multiple datacenters. Servers 162 may be of various types, such as, for example and without limitation, web server, news server, mail server, message server, advertising server, file server, application server, exchange server, database server, proxy server, another server suitable for performing functions or processes described herein, or any combination thereof. In particular embodiments, each server 162 may include hardware, software, or embedded logic components or a combination of two or more such components for carrying out the appropriate functionalities implemented or supported by server 162.).
As per claim 10: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein the performance score for the public transparency component is determined based on data voluntarily reported to the public by a corresponding entity (Norris: [0111]: As a result, a new chart 1002 is generated and presented that identifies the average cost of supplies per manufacturer and the volume of cases (from the 145 encounter groups) associated with each manufacture. In this example interface 1000, the actual input values included in the chart 1002 are clarified to facilitate describing an example comparison application initiated based on the data presented in FIGS. 11-13. [0150] Interface component 112 is configured to generate a visualization to the right metrics filtering criteria menu 2302 based on the selected group of patient encounters being evaluated and the numerical ranges set for the performance metrics. The visualization provides clear pictorial representation of the degree different care path events/activities impact different performance metric criteria. Visualization 2304 is generated in response to setting of the performance metrics in the metrics filtering criteria menu 2302 for a selected group of patient encounters.).
As per claim 11: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein the one or more specialties include one or more of:
cancer;
cardiology (Norris: [0078] FIG. 7 illustrates an example method of calculating an experience index for a particular physician. The method may begin at step 710, where matching-engine system 160 may identify all health-care providers or physicians within a particular specialization in a specialty class and geographic area. As an example and not by way of limitation, a specialization may be cardiology within a specialty class of internal medicine. In particular embodiments, the geographic area may be an MSA within a particular state.),
heart and vascular surgery;
diabetes and endocrinology (Norris: [0065] In particular embodiments, an episode of care may comprise a collection of all clinically related procedure and diagnosis codes for treating an index disease or condition for a particular patient from the onset of the index disease or condition to closure. An episode of care may comprise a record of all encounters between a patient and a health-care provider. In particular embodiments, an episode of care may be defined over a single calendar year. As an example and not by way of limitation, if a patient Alice starts visiting a doctor Bob for her Type-2 diabetes on Jul. 1, 2013, and continues to see Dr. Bob for her diabetes with monthly visits through August, 2014, matching-engine system 160 may consider Alice to have two episodes of care with Dr. Bob: one episode of care for diabetes in 2013, and a second episode of care for diabetes in 2014. In particular embodiments, a particular patient may only be associated with a single episode of care at a time. If the patient starts visiting a new doctor, or starts being treated for a new primary condition, a new episode of care may be determined. As an example and not by way of limitation, if Alice has been visiting Dr. Bob for her diabetes through August 2014, but then breaks her leg and starts seeing Dr. Bob for treatment of the broken leg for two months, matching-engine system 160 may determine a new episode of care for Alice and Dr. Bob with a new primary condition of "broken leg" from August 2014 to October 2014. If Alice's leg heals and she continues seeing Dr. Bob for her diabetes, matching-engine system 160 may determine a third episode of care for Alice and Dr. Bob for diabetes. In particular embodiments, matching-engine system 160 may determine a single episode of care for 2014 for Alice and Dr. Bob for a primary condition of diabetes, with a time period of January to August 2014, and October to December 2014.);
ear, nose, and throat;
gastroenterology and GI surgery;
geriatrics, obstetrics and gynecology;
neurology and neurosurgery;
ophthalmology;
pulmonology and lung surgery;
psychiatry;
rehabilitation;
rheumatology; or
urology.
As per claim 12: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein the one or more models include one or more scoring models (Norris: [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.).
As per claim 13: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein, with respect to the specialties of cancer, diabetes and endocrinology, ear, nose, and throat, gastroenterology and GI surgery, geriatrics, ophthalmology, psychiatry, rheumatology, and urology, the one or more scoring models use the performance scores for the structural component, process/expert opinion component, outcome component, and patient experience component.
(Norris: [0078] FIG. 7 illustrates an example method of calculating an experience index for a particular physician. The method may begin at step 710, where matching-engine system 160 may identify all health-care providers or physicians within a particular specialization in a specialty class and geographic area. As an example and not by way of limitation, a specialization may be cardiology within a specialty class of internal medicine. In particular embodiments, the geographic area may be an MSA within a particular state. [0065] In particular embodiments, an episode of care may comprise a collection of all clinically related procedure and diagnosis codes for treating an index disease or condition for a particular patient from the onset of the index disease or condition to closure. An episode of care may comprise a record of all encounters between a patient and a health-care provider. In particular embodiments, an episode of care may be defined over a single calendar year. As an example and not by way of limitation, if a patient Alice starts visiting a doctor Bob for her Type-2 diabetes on Jul. 1, 2013, and continues to see Dr. Bob for her diabetes with monthly visits through August, 2014, matching-engine system 160 may consider Alice to have two episodes of care with Dr. Bob: one episode of care for diabetes in 2013, and a second episode of care for diabetes in 2014. In particular embodiments, a particular patient may only be associated with a single episode of care at a time. If the patient starts visiting a new doctor, or starts being treated for a new primary condition, a new episode of care may be determined. As an example and not by way of limitation, if Alice has been visiting Dr. Bob for her diabetes through August 2014, but then breaks her leg and starts seeing Dr. Bob for treatment of the broken leg for two months, matching-engine system 160 may determine a new episode of care for Alice and Dr. Bob with a new primary condition of "broken leg" from August 2014 to October 2014. If Alice's leg heals and she continues seeing Dr. Bob for her diabetes, matching-engine system 160 may determine a third episode of care for Alice and Dr. Bob for diabetes. In particular embodiments, matching-engine system 160 may determine a single episode of care for 2014 for Alice and Dr. Bob for a primary condition of diabetes, with a time period of January to August 2014, and October to December 2014. [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.)
As per claim 14: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein, for the specialties of cardiology, heart and vascular surgery, obstetrics and gynecology, neurology and neurosurgery, and pulmonology and lung surgery, the one or more scoring models use the performance scores for the structural component, process/expert opinion component, outcome component, patient experience component, and public transparency component.
(Norris: [0078] FIG. 7 illustrates an example method of calculating an experience index for a particular physician. The method may begin at step 710, where matching-engine system 160 may identify all health-care providers or physicians within a particular specialization in a specialty class and geographic area. As an example and not by way of limitation, a specialization may be cardiology within a specialty class of internal medicine. In particular embodiments, the geographic area may be an MSA within a particular state. [0065] In particular embodiments, an episode of care may comprise a collection of all clinically related procedure and diagnosis codes for treating an index disease or condition for a particular patient from the onset of the index disease or condition to closure. An episode of care may comprise a record of all encounters between a patient and a health-care provider. In particular embodiments, an episode of care may be defined over a single calendar year. As an example and not by way of limitation, if a patient Alice starts visiting a doctor Bob for her Type-2 diabetes on Jul. 1, 2013, and continues to see Dr. Bob for her diabetes with monthly visits through August, 2014, matching-engine system 160 may consider Alice to have two episodes of care with Dr. Bob: one episode of care for diabetes in 2013, and a second episode of care for diabetes in 2014. In particular embodiments, a particular patient may only be associated with a single episode of care at a time. If the patient starts visiting a new doctor, or starts being treated for a new primary condition, a new episode of care may be determined. As an example and not by way of limitation, if Alice has been visiting Dr. Bob for her diabetes through August 2014, but then breaks her leg and starts seeing Dr. Bob for treatment of the broken leg for two months, matching-engine system 160 may determine a new episode of care for Alice and Dr. Bob with a new primary condition of "broken leg" from August 2014 to October 2014. If Alice's leg heals and she continues seeing Dr. Bob for her diabetes, matching-engine system 160 may determine a third episode of care for Alice and Dr. Bob for diabetes. In particular embodiments, matching-engine system 160 may determine a single episode of care for 2014 for Alice and Dr. Bob for a primary condition of diabetes, with a time period of January to August 2014, and October to December 2014. [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.)
As per claim 15: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein, for the specialty of rehabilitation, the one or more scoring models use the performance scores for the structural component, process component, and outcome component.
(Norris: [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.
As per claim 16: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein each of the one or more performance indicators associated with a corresponding performance component represents an attribute or a trait of a corresponding entity that is used in evaluating performance of that entity (Norris: [0067] In particular embodiments, the performance score for a particular physician may be represented as a relational score for the particular physician with respect to similarly-situated physicians. In particular embodiments, similarly-situated physicians may additionally be defined as seeing a similar set of patients which are characterized by attributes of age, gender, place of service, MSA, and disease co-morbidity. In particular embodiments, additional attributes may be used to determine similarly-situated physicians. In particular embodiments, matching-engine system 160 may consider physicians having the same primary specialty. As an example and not by way of limitation, matching-engine system 160 may generate a score that indicates the particular physician's efficiency and performance compared to other physicians that the user may also receive recommendations for. For example, if Drs. Irene, John, and Kyle are the three nephrologists in a particular geographic region, any performance score for Dr. John may represent his relative performance when compared to Drs. Irene and Kyle.).
As per claim 17: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein the one or more performance indicators for the structural component include one or more of: advanced technologies; number of patients; outpatient volume; volume of care; nurse staffing; trauma center; patient services; ICU specialists; designated institution; nurse magnet status; or accreditation (Norris: [0078] FIG. 7 illustrates an example method of calculating an experience index for a particular physician. The method may begin at step 710, where matching-engine system 160 may identify all health-care providers or physicians within a particular specialization in a specialty class and geographic area. As an example and not by way of limitation, a specialization may be cardiology within a specialty class of internal medicine. In particular embodiments, the geographic area may be an MSA within a particular state. [0065] In particular embodiments, an episode of care may comprise a collection of all clinically related procedure and diagnosis codes for treating an index disease or condition for a particular patient from the onset of the index disease or condition to closure. An episode of care may comprise a record of all encounters between a patient and a health-care provider. In particular embodiments, an episode of care may be defined over a single calendar year. As an example and not by way of limitation, if a patient Alice starts visiting a doctor Bob for her Type-2 diabetes on Jul. 1, 2013, and continues to see Dr. Bob for her diabetes with monthly visits through August, 2014, matching-engine system 160 may consider Alice to have two episodes of care with Dr. Bob: one episode of care for diabetes in 2013, and a second episode of care for diabetes in 2014. In particular embodiments, a particular patient may only be associated with a single episode of care at a time. If the patient starts visiting a new doctor, or starts being treated for a new primary condition, a new episode of care may be determined. As an example and not by way of limitation, if Alice has been visiting Dr. Bob for her diabetes through August 2014, but then breaks her leg and starts seeing Dr. Bob for treatment of the broken leg for two months, matching-engine system 160 may determine a new episode of care for Alice and Dr. Bob with a new primary condition of "broken leg" from August 2014 to October 2014. If Alice's leg heals and she continues seeing Dr. Bob for her diabetes, matching-engine system 160 may determine a third episode of care for Alice and Dr. Bob for diabetes. In particular embodiments, matching-engine system 160 may determine a single episode of care for 2014 for Alice and Dr. Bob for a primary condition of diabetes, with a time period of January to August 2014, and October to December 2014. [0074]: In particular embodiments, the cost factor may be the monetary cost of the treatments within the episode of care. In particular embodiments, the cost factor may be a normalized value, such as RVU. In particular embodiments, matching-engine system 160 may consider both the absolute monetary costs expended in treating the episode of care, as well as the RVU. [0079]: The two case volumes may be combined by adjusting for case severity. In particular embodiments, chronic cases may be multiplied by an additional factor. As an example and not by way of limitation, chronic cases may be deemed to require three times as many resources as non-chronic cases. Therefore, chronic case volume may be weighted three times as much as non-chronic cases. In the example above, Dr. Alex may have a severity-normalized case volume of (0.20*3+0.15)/(3+1)=0.1875. A higher normalized case volume may indicate that the particular provider has seen more overall cases in the condition group, and/or more severe cases compared to their peers. [0081] At step 760, matching-engine system 160 may calculate an experience index based on the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be the product of the severity-normalized case volume and the variety score. In particular embodiments, the experience index may be a weighted sum or average of the severity-normalized case volume and the variety score. As an example and not by way of limitation, a weighted average may be used if matching-engine system 160 determines that the normalized case volume is more indicative of a physician's experience than the variety of cases the physician has seen.
Further, Delaney shows “ICU specialists” [0080] In an aspect, opportunity assessment component 204 presents opportunity in two ways. LOS and Readmission are presented in aggregate, meaning, this is the total improvement opportunity for the population. Cost, on the other hand, is presented both in aggregate (total opportunity) and at the cost category level (pharmacy, ICU, laboratory, radiology, OR, etc.). This tells the healthcare organization the specific cost category that is responsible for the deviation and the percentage of the total opportunity that is associated with that operating category.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Delaney, particularly the ability to provide an overall score [0066]-[0070], in the disclosure of Reference Norris, particularly in the ability to evaluate the overall performance of care in [0064], in order to provide for a system that evaluates each patient group on the basis of one or more of the following metrics: cost of care delivery, length of stay (LOS), readmission rate, complication rate, financial variance, and complexity of case and/or clinical risk. In an aspect, scoring component 106 can determine a score for each of the individual metrics and an overall operating score that reflects overall performance of the healthcare organization based on all the metrics as taught by Reference Delaney (see at least in [0063]), where upon the execution of the method and system of Reference Delaney allows using these performance scores, the healthcare management server 102 and/or clinicians and operators can rapidly develop apples to apples comparisons across patient populations, supporting rapid analysis and root cause investigation of variance between groups of patients or clinical approaches so that the process of analyzing data to help users make better healthcare decisions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data to help users make better healthcare decisions field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Norris in view of Reference Delaney, the results of the combination were predictable (MPEP 2143 A).
As per claim 18: Regarding the claim limitations below, Reference Norris in view of Delaney shows:
wherein the one or more performance indicators for the outcome component include one or more of: mortality rate; discharge rate; or measure of outpatient complication.
Regarding the claim limitations above, Norris does not explicitly show the above limitations. However, Delaney shows the above limitations in [0041] Billing information associated with patient records can include an account number for a patient that represents a course of care for the patients. For example, an account number can represent a single inpatient stay, or a cluster of related outpatient visits. In one embodiment, financial information extracted from cost management/information (discussed hereafter) is used to identify a course of care of a patient and a length of stay of the patient at a hospital (or other medical institution) where the patient was admitted in association with the course of care. In an aspect, billing information for a patient record can include information identifying the patient, information on admission and discharge dates, admitting provider, discharging provider, primary care physician, referring physician, and the like.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Delaney, particularly the ability to provide an overall score [0066]-[0070], in the disclosure of Reference Norris, particularly in the ability to evaluate the overall performance of care in [0064], in order to provide for a system that evaluates each patient group on the basis of one or more of the following metrics: cost of care delivery, length of stay (LOS), readmission rate, complication rate, financial variance, and complexity of case and/or clinical risk. In an aspect, scoring component 106 can determine a score for each of the individual metrics and an overall operating score that reflects overall performance of the healthcare organization based on all the metrics as taught by Reference Delaney (see at least in [0063]), where upon the execution of the method and system of Reference Delaney allows using these performance scores, the healthcare management server 102 and/or clinicians and operators can rapidly develop apples to apples comparisons across patient populations, supporting rapid analysis and root cause investigation of variance between groups of patients or clinical approaches so that the process of analyzing data to help users make better healthcare decisions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data to help users make better healthcare decisions field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Norris in view of Reference Delaney, the results of the combination were predictable (MPEP 2143 A).
Response to Arguments
Applicants’ arguments are moot in view of the new grounds of rejection necessitated by the amendments made to previously presented claims.
Applicant’s Argument #1
Applicants argue on page(s) 10-11 of applicants remarks that the amended claims overcome previously made rejection under 35 U.S.C. 101 (see applicants remarks for more details).
Response to Argument #1
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
These claim limitations belong to the grouping of “certain methods of organizing human activity” because the claims are related to ranking entities (e.g., hospitals) may play a crucial role in providing transparency and aiding informed decision-making for patients, healthcare providers, and policymakers alike. However, current methodologies often face significant technical challenges as they oversimplify complex healthcare metrics or rely on limited datasets that fail to capture the full spectrum of the performance (Spec. [0003]) for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The claims merely use a computer to improve the performance of that determination—not the performance of a computer. (See MPEP 2106.05(a)(II)(i); A commonplace business method or mathematical algorithm being applied on a general-purpose computer, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)).
MPEP 2106.05f - Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363
MPEP 2106.05(f) iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016).
Additionally, applicants’ arguments that the claims are a practical application are not persuasive. As demonstrated by use of language "using a feature engineering process", "using the transformed data points” and other similar limitations, without much technological details following the limitation are simply the preparation steps and subsequent use of machine to represent mere invocation of machinery per MPEP 2106.05(f)(2) possibly an example of a mathematical algorithm [here machine learning preparation] being applied on a general-purpose computer per MPEP 2106.05(f)(2)(i).
Here, the claims are pre-processing data for use by the machine learning model to output the performance scores. The training step is math. To overcome the 101 the output of the model would have to be used in a meaningful way.
Further, scoring items also constitutes a mental process, such as an observation, evaluation, judgment, or opinion that can be performed in the human mind. The 2019 Guidance expressly recognizes such mental processes as constituting patent-ineligible abstract ideas. MPEP § 2106.04(a).
Further still, training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. The 2019 Guidance expressly recognizes mathematical concepts including mathematical relationships as constituting an abstract idea. MPEP § 2106.04(a).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference:
S. Raza and C. Ding, "Improving Clinical Decision Making With a Two-Stage Recommender System," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 21, no. 5, pp. 1180-1190, Sept.-Oct. 2024, doi: 10.1109/TCBB.2023.3318209.
Clinical decision-making is complex and time-intensive. To help in this effort, clinical recommender systems (RS) have been designed to facilitate healthcare practitioners with personalized advice. However, designing an effective clinical RS poses challenges due to the multifaceted nature of clinical data and the demand for tailored recommendations. In this article, we introduce a 2-Stage Recommendation framework for clinical decision-making, which leverages a publicly accessible dataset of electronic health records. In the first stage, a deep neural network-based model is employed to extract a set of candidate items, such as diagnoses, medications, and prescriptions, from a patient's electronic health records. Subsequently, the second stage utilizes a deep learning model to rank and pinpoint the most relevant items for healthcare providers. Both retriever and ranker are based on pre-trained transformer models that are stacked together as a pipeline. To validate our model, we compared its performance against several baseline models using different evaluation metrics. The results reveal that our proposed model attains a performance gain of approximately 12.3% macro-average F1 compared to the second-best performing baseline. Qualitative analysis across various dimensions also confirms the model's high performance. Furthermore, we discuss challenges like data availability, privacy concerns, and shed light on future exploration in this domain.
Foreign Reference:
(CN 112639995 A) Ray et al. Multi-factor Priority Frame Based On Machine Learning For Optimizing Patient Placement.
The invention claims techniques for optimizing the placement of a patient in a medical facility based on a machine-learning system and a priority framework using a multi-factor. In one embodiment, providing a computer-implemented method, the method comprises operatively coupled to the processor of the system receiving request to the patient is set to the patient of the medical care facility of the patient placement request, wherein the request is associated with the identification of the patient medical service and bed type information. the method further comprises the system based on the medical service and the bed type from a set of setting priority model selecting setting priority model, and by the system using the priority model and the state information about the current state of the medical care facility to determine the priority score reflecting the priority of the patient placement request.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.N.P/Examiner, Art Unit 3624
/PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624