Patent Application Titled “Approaches To Learning, Documenting, And Surfacing Missed Diagnostic Insights On A Per-Patient Basis In An Automated Manner And Associated Systems” Published Online (USPTO 20230352187): Patent Application

2023 NOV 22 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors Adam, Jr., George F. (Denver, CO, US); Schaper, Justin D. (Plano, TX, US); Stimson, Nancy S. (Golden, CO, US), filed on April 27, 2023, was made available online on November 2, 2023.

No assignee for this patent application has been made.

Reporters obtained the following quote from the background information supplied by the inventors: “In the United States healthcare system, the term “medical billing” is commonly used to refer to a process in which a healthcare provider (or simply “provider”) obtains insurance information from a patient and then files a claim with an insurer in order to receive payment for services rendered. The process may be comparable regardless of whether those services relate to low-cost testing procedures-like blood draws and biopsies-or high-cost operating procedures. Moreover, the same process is generally used for most insurers, whether private companies or government-sponsored programs, namely, coding reports are compiled to indicate diagnoses made and procedures performed and then prices are applied accordingly.

“The interaction generally begins with a visit to a healthcare facility-a healthcare professional (or simply “professional”) will typically meet with a patient and then create or update her health record as necessary. Then, diagnosis and procedure codes (or simply “codes”) are normally assigned to the visit (and therefore, the patient) by a clinical coder. Clinical coders (also called “diagnostic coders” or “medical coders”) are professionals-usually employed by providers-whose main duties are to analyze clinical statements included in the health record and assign codes using a classification scheme. These codes assist insurers in determining coverage and medical necessity of the services rendered by providers to patients. Accordingly, the classification scheme can be used to transform descriptions of diagnoses and procedures into standardized statistical codes.

“The classification scheme may include a list of diagnostic codes that are associated with different diseases, ailments, and conditions. Moreover, the classification scheme may include a list of procedure codes that can be used to capture interventional information. Together, the diagnosis and procedure codes are used by providers to document the care provided to patients, as well as seek reimbursement for the care. Claims are usually electronically formatted as American National Standards Institute (“ANSI”) 837 files and submitted using the Electronic Data Interchange, so as to ensure some degree of consistency across different providers and insurers.

“Various features of the technology described herein will become more apparent to those skilled in the art from a study of the Detailed Description in conjunction with the drawings. Various embodiments are depicted for the purpose of illustration. However, those skilled in the art will recognize that alternative embodiments may be employed without departing from the principles of the technology. Accordingly, although specific embodiments are shown in the drawings, the technology is amenable to various modifications.”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors’ summary information for this patent application: “Several entities have developed computer-aided diagnostic support systems in an effort to facilitate the process by which patients are diagnosed and those diagnoses are documented. As an example, imaging has historically been an effective means for detecting cancer, and diagnostic support systems have become a key part of identifying the pathologically relevant features in digital images that are indicative of cancer. As another example, diagnostic support systems have been developed and then trained to identify indicators of cardiopulmonary disease through analysis of echocardiograms and corresponding reports that summarize the findings of professionals.

“Diagnostic support systems have proven useful in detecting a variety of different ailments. However, the analysis performed by these diagnostic support systems is highly dependent on the quality and quantity of information that is available for patients of interest. Simply put, if sufficient information-regardless of form-is not available for analysis, a diagnostic support system may not be able to determine whether to predict a positive diagnosis or a negative diagnosis. The term “positive diagnosis” may be used to refer to a scenario where a patient is determined to have a given disease, while the term “negative diagnosis” may be used to refer to a scenario where a patient is determined to not have a given disease.

“Moreover, fully relying on patient-specific information to predict diagnoses may result in patients being improperly diagnosed. Assume, for example, that a diagnostic support system predicts a negative diagnosis for a patient based on an analysis of her information. There is a chance-especially if the amount of information that is available to the diagnostic support system is small-that the negative diagnosis is representative of a false negative. The term “false negative” refers to a prediction that indicates the patient does not have the disease when the patient actually does have the disease. Because the prediction is based entirely on the information provided to the diagnostic support system as input, there are few options for reliably lowering the likelihood of false negatives.

“Introduced here is a computer-aided diagnostic system (or simply “system”) that is able to surface diagnostic insights through analysis of data related to claims submitted to insurers for reimbursement purposes. At a high level, the system enables diagnoses that should be associated with individual patients to be more reliably detected, lessening the likelihood of negative diagnoses being false negatives. The system is distinguishable from conventional diagnostic support systems in that the analysis is retrospective, using claims-related datasets (also called “claims datasets”) as further discussed below. Note that, in some embodiments, the system may examiner, consider, or otherwise incorporate clinical data, though its analysis is primarily focused on claims datasets. Through the retrospective lens, patterns of diagnoses, treatments, and consultations can be surfaced by the system. These patterns can be helpful in suggesting diseases that are not readily apparent in clinical data that is available at the time of treatment. Simply put, the system can surface insights into health that simply are not discoverable by professionals, as those professionals do not have access to comprehensive claims datasets (and would not be able to draw general conclusions regarding patterns of diagnoses, treatments, and consultations even if such claims datasets were available).

“By detecting these missing diagnoses, the system helps to close “gaps” in healthcare by enabling better treatment. Further, the system can help determine patterns of best practice (e.g., on a per-disease, per-provider, or per-professional basis), and the system can ensure that providers are appropriately reimbursed based on the actual health states of patients. As a starting point, the system may use claims datasets that are sourced directly from providers or insurers. Such an approach allows the system to detect smaller scale patterns that may be indetectable in larger datasets. For example, the system may be able to detect regional differences in how diagnoses are assigned (e.g., by different professionals or at facilities associated with different providers), while also generating more generally applicable rules. Specifically, the system may develop “local rules” that are suitable for specific professionals or providers and “national rules” that are suitable for all professionals and providers for which claims datasets are available.

“For the purpose of illustration, embodiments may be described with reference to certain system architectures. However, those skilled in the art will recognize that the features of those embodiments may be similarly applicable to other system architectures. For example, while the system may be described as being implemented using a cloud computing service, those skilled in the art will recognize that the system could be implemented as a standalone computer program.

“Moreover, embodiments may be described in the context of executable instructions for the purpose of illustration. However, those skilled in the art will recognize that the system could be implemented via hardware or firmware instead of, or in addition to, software. As an example, a computer program that is able to perform aspects of the approaches described herein may be executed by the processor of a computing device. This computer program may interface-directly or indirectly-with hardware, firmware, or other software implemented on the computing device or another computing device that is communicatively accessible. For instance, the computer program may access a datastore maintained on another computing device in order to obtain a claims dataset for training purposes or obtain a claims dataset for inferencing purposes.

“Terminology

“References in the present disclosure to “an embodiment” or “some embodiments” mean that the feature, function, structure, or characteristic being described is included in at least one embodiment. Occurrences of such phrases do not necessarily refer to the same embodiment, nor are they necessarily referring to alternative embodiments that are mutually exclusive of one another.

“The term “based on” is to be construed in an inclusive sense rather than an exclusive sense. That is, in the sense of “including but not limited to.” Thus, unless otherwise noted, the term “based on” is intended to mean “based at least in part on.”

“The terms “connected,” “coupled,” and variants thereof are intended to include any connection or coupling between two or more elements, either direct or indirect. The connection or coupling can be physical, logical, or a combination thereof. For example, elements may be electrically or communicatively coupled to one another despite not sharing a physical connection.

“The term “module” may refer broadly to software, firmware, hardware, or combinations thereof. Modules are typically functional components that generate one or more outputs based on one or more inputs. A computer program may include or utilize one or more modules. For example, a computer program may utilize multiple modules that are responsible for completing different tasks, or a computer program may utilize a single module that is responsible for completing all tasks.

“When used in reference to a list of multiple items, the word “or” is intended to cover all of the following interpretations: any of the items in the list, all of the items in the list, and any combination of items in the list.

“Overview of Diagnostic System

“FIG. 1 illustrates a network environment 100 that includes a diagnostic system 102 that is executed by a computing device 104. An individual (also called a “user”) may be able to interact with the diagnostic system 102 via interfaces 106. For example, a user may be able to access an interface through which to select a claims dataset for which a rule set is to be generated as part of a training process. As another example, a user may be able to access an interface through which a claims dataset can be selected, such that the rule set can be applied thereto to produce an output as part of an inferencing process. Moreover, the user may be able to review outputs produced by the rule set upon being applied to the claims dataset. Interfaces may be configured to “guide” users through the training and inferencing processing, so that even individuals without expertise in developing or applying learned rules-like professionals, such as physicians and nurses-can use the diagnostic system 102. Some interfaces are configured to facilitate interactions between patients and professionals, while other interfaces are configured to serve as informative dashboards for either patients or professionals.

“As shown in FIG. 1, the diagnostic system 102 may reside in a network environment 100. Thus, the computing device 104 on which the diagnostic system 102 resides may be connected to one or more networks 108A-B. Depending on its nature, the computing device 104 could be connected to a personal area network (“PAN”), local area network (“LAN”), wide area network (“WAN”), metropolitan area network (“MAN”), or cellular network. For example, if the computing device 104 is a computer server, then the computing device 104 may be accessible to users via respective computing devices that are connected to the Internet via LANs.”

There is additional summary information. Please visit full patent to read further.”

The claims supplied by the inventors are:

“1. A method for developing a rule that is designed to identify information that is suggestive of a disease upon being applied to claims datasets associated with patients, the method comprising: acquiring multiple claims datasets from a source, wherein each of the multiple claims datasets includes codes assigned to a corresponding patient that is known to have the disease; implementing logic that calculates measures that are representative of correlation between datapoints across the multiple claims datasets and the disease; generating rules for datapoints, if any, for which the corresponding measures exceed a threshold; compiling the rules into a set that is associated with the disease; and storing the set of the rules in a storage medium.

“2. The method of claim 1, wherein each measure is representative of a correlation probability metric that indicates the correlation between a corresponding one of the datapoints and the disease, as determined from an analysis of the multiple claims datasets.

“3. The method of claim 2, further comprising: sorting the datapoints into a list that is ordered based on the measures; and applying, to the list, a filter that removes datapoints for which the corresponding measures do not exceed the threshold.

“4. The method of claim 1, further comprising: causing display of the rules on an interface that is accessible to an individual; permitting the individual to (i) edit an existing one of the rules, (ii) cancel an existing one of the rules, or (iii) add a new rule through the interface; and receiving input indicative of a confirmation of the rules by the individual; wherein said compiling is performed in response to said receiving.

“5. The method of claim 1, wherein the codes concern diagnoses of the corresponding patient, procedures involving the corresponding patient, or a combination thereof.

“6. The method of claim 1, wherein the source is associated with a healthcare provider that was responsible for providing care to the multiple patients associated with the multiple claims datasets.

“7. The method of claim 1, wherein the source is associated with an insurer that was responsible for insuring the multiple patients associated with the multiple claims datasets.

“8. The method of claim 1, wherein the datapoints correspond to treatments prescribed to the multiple patients associated with the multiple claims datasets.

“9. The method of claim 1, wherein the datapoints correspond to characteristics of the multiple patients associated with the multiple claims datasets.

“10. The method of claim 1, wherein the set is associated with the disease by identifying the disease in metadata that is appended to a data structure that is representative of the set and in which the rules are stored.

“11. The method of claim 1, wherein the multiple claims datasets are associated with a healthcare professional, and wherein the set is associated with the healthcare professional by identifying the healthcare professional in metadata that is appended to a data structure that is representative of the set and in which the rules are stored.

“12. The method of claim 1, wherein the multiple claims datasets are associated with a healthcare provider, and wherein the set is associated with the healthcare provider by identifying the healthcare provider in metadata that is appended to a data structure that is representative of the set and in which the rules are stored.

“13. A non-transitory medium with instructions stored thereon that, when executed by a processor of a computing device, cause the computing device to perform operations comprising: acquiring a claims dataset that includes codes assigned to a patient to which a service has been rendered by a healthcare professional; identifying a set of rules based on an analysis of the claims dataset or accompanying metadata; applying, to the claims dataset, the set of rules so as to produce a set of outputs, wherein each output in the set of outputs indicates whether a corresponding rule in the set of rules was determined to be a match upon being applied to the claims dataset; and determining an insight into health of the patient based on an analysis of the set of outputs.

“14. The non-transitory medium of claim 13, wherein the insight is a positive predicted diagnosis for a disease.

“15. The non-transitory medium of claim 14, wherein the operations further comprise: causing presentation of a notification to the healthcare professional, wherein the notification recommends that the patient be further examined to render an actual diagnosis for the disease.

“16. The non-transitory medium of claim 13, wherein the insight is further based on information related to the individual that is input by the patient, input by the healthcare professional, extracted from a clinical dataset that is associated with the patient, or derived from an electronic health record that is associated with the patient.

“17. The non-transitory medium of claim 13, wherein the operations further comprise: generating a computer-readable file that includes information related to the insight; and causing transmission of the computer-readable file to a destination.

“18. The non-transitory medium of claim 17, wherein the destination is associated with a healthcare provider that employs the healthcare professional.”

For more information, see this patent application: Adam, Jr., George F.; Schaper, Justin D.; Stimson, Nancy S. Approaches To Learning, Documenting, And Surfacing Missed Diagnostic Insights On A Per-Patient Basis In An Automated Manner And Associated Systems. U.S. Patent Application Number 20230352187, filed April 27, 2023 and posted November 2, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230352187)&db=US-PGPUB&type=ids

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