Patent Issued for Systems and methods for determination of patient true state for personalized medicine (USPTO 11955238): Apixio LLC

2024 MAY 01 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Rogers, Robert Derward (Oakland, CA, US), Schneider, John O. (Los Gatos, CA, US), Schulte, Darren Matthew (San Francisco, CA, US), Sethumadhavan, Vishnuvyas (Mountain View, CA, US), filed on October 17, 2022, was published online on April 9, 2024, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11955238 is assigned to Apixio LLC (San Mateo, California, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “The present invention relates generally to systems and methods for determination of patient true state using automated first pass review of patient medical records. Knowledge of the true state of a patient (determination of patient condition) enables management of coding risks, as well as enhanced patient management and record retention abilities. Some embodiments of the present systems and methods enable more accurate and rapid capture of MediCare eligible conditions, thereby ensuring providers are more fairly compensated, and ensure that medical records more accurately reflect a patient’s condition.

“Despite rapid growth of innovation in other fields in recent decades, the world of medical information, including patient medical records, billing, referrals, and a host of other information, has enjoyed little to no useful consolidation, reliability, or ease-of-access, leaving medical professionals, hospitals, clinics, and even insurance companies with many issues, such as unreliability of medical information, uncertainty of diagnosis, lack of standard, and a slew of other related problems.

“One common problem with the analysis of medical records is that identification of clinically pertinent conditions is often not properly identified, and further, even when identified, the evidence in the patient records to support such a finding is not always properly referenced. Moreover, the process for verifying a condition is often time consuming and labor intensive. This results in a few issues, including: MediCare compensation difficulties, missing of important health conditions and/or misdiagnosis, and lastly the clouding of medical analytics with incomplete or incorrect data.

“The first issue, compensation by MediCare, results in providers being underpaid for work performed. This may cause many providers to shy away from MediCare patients, increases cost on other patients, and generally leads to inefficiencies in the administration of government backed medical coverage. Additionally, miss-coding of MediCare claim opens providers to potential audit risk.

“The second issue, improper or incomplete diagnosis, can be extremely detrimental to the patient. Often early treatment of a condition results in a far better prognosis for the patient. In the extreme, delays of treatment may reduce the patient’s life expectancy. As such, there is a very compelling reason to ensure the medical information of a patient is properly documented, with a high degree of accuracy.

“In addition to these direct health impacts to the patient, improper or incomplete diagnosis of the patient can lead to unnecessary tests or follow-ups, which can be financially taxing as well as a drain on the resources of the medical community. Thus there are also tangible financial implications to proper diagnosis with supporting evidence.

“Lastly, incorrect or missing data may result in the skewing of analytics performed using the medical records. The medical community is entering into an age of big data analysis. These analyses of large data sets of aggregated medical records generated best practices and means for refining a medical practice. It also enables early detection of health trends and patient behavior. Using these results, medical professionals have the opportunity to greatly increase the efficiency of the administration of medical services. This translates directly into improved patient care at reduced costs. However, such analysis relies upon datasets that are accurate. When the input data is flawed, or incomplete, the analysis suffers.

“It is therefore apparent that an urgent need exists for improved means for recordation and analysis of medical records. In particular, the clinical state of patients may be determined using a computerized system, which then enables a host of subsequent activities, including: 1) enhanced personalized medicine, 2) coding audit risk management, 3) more complete and accurate record keeping for providers, and 4) MediCare reimbursement optimization via the identification of coding opportunities.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “To achieve the foregoing and in accordance with the present invention, systems and methods for personalizing medical care are provided. In some embodiments, the true state of the patient is utilized in order to tailor the personalized care to a given patient.

“Initially a number of medical records for a patient are received. These records are subjected to predictive modeling for various conditions (known as patient ‘true state’). The true state is utilized to identify medical resources that may be useful for the patient. Additionally, a confidence interval may be determined for the true state finding.

“In some embodiments, the patient personal information, previous care, and true state may be provided into a state machine in order to determine the resources needed for the patient. The medical resources may be any of laboratory services, diagnostics, therapies and medications.

“Using the true state information, and number of activities may be performed for the patient based upon the patient’s needs. These activities include scheduling lab or diagnostic procedures in advance of an appointment, filling in documentation gaps, identifying items that require additional documentation using the true state, and tracking follow-up.

“In some embodiments it may also be beneficial to validate the true state. The validation process updates the predictive model and updates the confidence level for the inferred true state. Optimized routing on the evidence used for validation may be employed. This routing ensures that evidence that has the greatest impact on the true state inference is presented first.”

The claims supplied by the inventors are:

“1. A health information computer system comprising: at least one memory with instructions stored thereon; and at least one processor in communication with the at least one memory, wherein the instructions, when executed by the at least one processor, cause the at least one processor to: perform natural language processing on a plurality of machine-readable medical records to extract terms from the plurality of machine-readable medical records; convert the terms extracted from the plurality of machine-readable medical records into a standardized and structured data set, wherein the data set comprises hyperlinks to at least a portion of the plurality of machine-readable medical records to facilitate real-time reference from the data set to the plurality of machine-readable medical records; cluster the terms into medical concept data; determine a condition of a patient based on the medical concept data and at least one probabilistic model; identify evidence associated with the condition of the patient that comprises a largest impact in the determination of the condition with respect to other evidence; generate an output comprising an indication of the condition and at least one hyperlink of the hyperlinks associated with the evidence comprising the largest impact; and validate the condition of the patient based on validating the evidence comprising the largest impact.

“2. The health information computer system of claim 1, wherein the instructions further cause the at least one processor to: identify the terms in the plurality of machine-readable medical records by an analytics module.

“3. The health information computer system of claim 2, wherein the instructions further cause the at least one processor to: analyze the plurality of machine-readable medical records by the analytics module; and assign a confidence score to the condition based at least in part upon the plurality of machine-readable medical records.

“4. The health information computer system of claim 1, wherein the instructions further cause the at least one processor to calculate a respective impact for evidence comprising at least a portion of the medical concept data, wherein the impact is based on at least one of cost per time or audit risk per time.

“5. The health information computer system of claim 1, wherein the instructions further cause the at least one processor to cluster the terms into medical concept data based at least in part upon machine learned rules.

“6. The health information computer system of claim 1, wherein the instructions further cause the at least one processor to identify at least one medical resource for the patient based upon the condition of the patient.

“7. The health information computer system of claim 1, wherein the instructions further cause the at least one processor to, based on the condition of the patient, automatically schedule at least one of an appointment, a lab procedure, or a diagnostic procedure.

“8. The health information computer system of claim 1, wherein the instructions further cause the at least one processor to fill documentation gaps associated with the patient based on the condition of the patient.

“9. The health information computer system of claim 1, wherein the instructions further cause the at least one processor to cause display of the evidence comprising the largest impact in the determination of the condition.

“10. At least one non-transitory computer-readable storage medium with instructions stored thereon that, in response to execution by at least one processor, cause the at least one processor to: perform natural language processing on a plurality of machine-readable medical records to extract terms from the plurality of machine-readable medical records; convert the terms extracted from the plurality of machine-readable medical records into a standardized and structured data set, wherein the data set comprises hyperlinks to at least a portion of the plurality of machine-readable medical records to facilitate real-time reference from the data set to the plurality of machine-readable medical records; cluster the terms into medical concept data; determine a condition of a patient based on the medical concept data and at least one probabilistic model; identify evidence associated with the condition of the patient that comprises a largest impact in the determination of the condition with respect to other evidence; generate an output comprising an indication of the condition and at least one hyperlink of the hyperlinks associated with the evidence comprising the largest impact and validate the condition of the patient based on validating the evidence comprising the largest impact.

“11. The at least one non-transitory computer-readable storage medium of claim 10, wherein the instructions further cause the at least one processor to: identify the terms in the plurality of machine-readable medical records by an analytics module.

“12. The at least one non-transitory computer-readable storage medium of claim 11, wherein the instructions further cause the at least one processor to: analyze the plurality of machine-readable medical records by the analytics module; and assign a confidence score to the condition based at least in part upon the plurality of machine-readable medical records.

“13. The at least one non-transitory computer-readable storage medium of claim 10, wherein the instructions further cause the at least one processor to calculate a respective impact for evidence comprising at least a portion of the medical concept data, wherein the impact is based on at least one of cost per time or audit risk per time.

“14. The at least one non-transitory computer-readable storage medium of claim 10, wherein the instructions further cause the at least one processor to cluster the terms into medical concept data based at least in part upon machine learned rules.

“15. The at least one non-transitory computer-readable storage medium of claim 10, wherein the instructions further cause the at least one processor to identify at least one medical resource for the patient based upon the condition of the patient.

“16. The at least one non-transitory computer-readable storage medium of claim 10, wherein the instructions further cause the at least one processor to, based on the condition of the patient, automatically schedule at least one of an appointment, a lab procedure, or a diagnostic procedure.

“17. The at least one non-transitory computer-readable storage medium of claim 10, wherein the instructions further cause the at least one processor to fill documentation gaps associated with the patient based on the condition of the patient.

“18. The at least one non-transitory computer-readable storage medium of claim 10, wherein the instructions further cause the at least one processor to cause display of the evidence comprising the largest impact in the determination of the condition.

“19. A method for medical care implemented by a health information management system comprising at least one processor in communication with at least one memory, the method comprising: performing natural language processing on a plurality of machine-readable medical records to extract terms from the plurality of machine-readable medical records; converting the terms extracted from the plurality of machine-readable medical records into a standardized and structured data set, wherein the data set comprises hyperlinks to at least a portion of the plurality of machine-readable medical records to facilitate real-time reference from the data set to the plurality of machine-readable medical records; clustering the terms into medical concept data; determining a condition of a patient based on the medical concept data and at least one probabilistic model; identifying evidence associated with the condition of the patient that comprises a largest impact in the determination of the condition with respect to other evidence; generating an output comprising an indication of the condition and at least one hyperlink of the hyperlinks associated with the evidence comprising the largest impact and validating the condition of the patient based on validating the evidence comprising the largest impact.

“20. The method of claim 19, further comprising, based on the condition of the patient, automatically scheduling at least one of an appointment, a lab procedure, or a diagnostic procedure.”

URL and more information on this patent, see: Rogers, Robert Derward. Systems and methods for determination of patient true state for personalized medicine. U.S. Patent Number 11955238, filed October 17, 2022, and published online on April 9, 2024. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11955238)&db=USPAT&type=ids

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