# US 12,217,839 B2 · Systems and methods for medical information data warehouse management > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,217,839 B2 - Document ID: US12217839B2 - Status: Granted - Kind: B2 - Application number: 18/063,834 - Filed: 2022-12-09 - Published: 2023-04-13 - Granted: 2025-02-04 - Earliest claimed: 2010-09-01 - Assignees: Apixio, Inc. - Applicants: Apixio, LLC - Inventors: Vishnuvyas Sethumadhavan; John O. Schneider; Darren Matthew Schulte; Robert Derward Rogers - Web page: https://patentgrep.com/patent/US12217839B2 ## CPC - G06Q 10/10: Office automation; Time management - G16H 10/60: for patient-specific data, e.g. for electronic patient records - G06F 16/313: {Selection or weighting of terms for indexing} - G06F 40/123: Storage facilities - G06F 40/134: Hyperlinking - G06F 40/169: Annotation, e.g. comment data or footnotes - G06F 40/216: using statistical methods - G06F 40/247: Thesauruses; Synonyms - G06Q 10/06398: {Performance of employee with respect to a job function} - G06Q 50/22: Social work or social welfare, e.g. community support activities or counselling services - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G16H 70/00: ICT specially adapted for the handling or processing of medical references - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Systems and methods for data warehouse management for medical information is provided. The system receives a set of medical record documents. These documents include evidence for one or more findings which are identified using natural language processing of evidence. The data set is probabilistically transformed into a structured data set (often as a table). This structured data set includes embedded links that reference the source document where the evidence is located. If the finding is supported by multiple articles of evidence, the link will direct the user to the source document with the highest confidence ranking. Evidence in the source document is highlighted and may include associated annotations. Evidence, findings and annotations may be updated by the user. ## Claims ### Claim 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: receive a plurality of medical record documents; select a model and model weights based on an annotation activity and a user, wherein the selecting includes modifying a dynamic confidence threshold based at least in part on the annotation activity, wherein the dynamic confidence threshold is applied to at least some documents of the plurality of medical record documents; identify a finding using the model; transform the plurality of medical record documents into a structured data set; select a number of the plurality of medical record documents associated with the finding; embed links into the structured data set that reference at least one medical record source document of the number of medical record documents; identify a set of proficient coders by filtering a plurality of coders for proficiency, wherein proficiency is a metric including accuracy in an area associated with the finding and time taken to perform coding in the area: measure a number of risk adjustment factors each proficient coder of the set of proficient coders completes per hour on average, a rate of false positive coding measured as a rate of annotation disagreement with a supervisor, and an accuracy measure comprising a degree that a given coder agrees with other coders when presented the same task; route the structured dataset to a coder of the set of proficient coders based at least in part upon the number of risk adjustment factors; and submit the finding for reimbursement. ### Claim 2 (depends on 1) The health information computer system of claim 1, wherein the instructions further cause the at least one processor to rank the set of proficient coders in a ranked coder list by at least one of a risk adjustment factor of the number of risk adjustment factors, the accuracy measure, or the rate of false positive coding. ### Claim 3 (depends on 2) The health information computer system of claim 2, wherein the instructions further cause the at least one processor to: filter the ranked coder list to identify a best suited coder associated with the finding; and route the structured data set to the coder, wherein the coder comprises the best suited coder. ### Claim 4 (depends on 1) The health information computer system of claim 1, wherein the instructions further cause the at least one processor to identify the finding based on evidence from natural language processing. ### Claim 5 (depends on 1) The health information computer system of claim 1, wherein the instructions further cause the at least one processor to: receive a selection of one of the number of medical record documents; and cause display of the one of the number of medical record documents. ### Claim 6 (depends on 5) The health information computer system of claim 5, wherein the instructions further cause the at least one processor to cause display of an annotated version of the one of the number of medical record documents. ### Claim 7 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: receive a plurality of medical record documents; select a model and model weights based on an annotation activity and a user, wherein the selecting includes modifying a dynamic confidence threshold based at least in part on the annotation activity, wherein the dynamic confidence threshold is applied to at least a portion of the plurality of medical record documents; identify a finding using the model; transform the plurality of medical record documents into a structured data set; select a number of the plurality of medical record documents associated with the finding; embed links into the structured data set that reference at least one medical record source document of the number of medical record documents; identify a set of proficient coders by filtering a plurality of coders for proficiency, wherein proficiency is a metric including accuracy in an area associated with the finding and time taken to perform coding in the area; measure a number of risk adjustment factors each proficient coder of the set of proficient coders completes per hour on average, a rate of false positive coding measured as a rate of annotation disagreement with a supervisor, and an accuracy measure comprising a degree that a given coder agrees with other coders when presented the same task; route the structured data set to a coder of the set of proficient coders based at least in part upon the number of risk adjustment factors; and submit the finding for reimbursement. ### Claim 8 (depends on 7) The at least one non-transitory computer-readable storage medium of claim 7, wherein the instructions further cause the at least one processor to rank the set of proficient coders in a ranked coder list by at least one of a risk adjustment factor of the number of risk adjustment factors, the accuracy measure, or the rate of false positive coding. ### Claim 9 (depends on 8) The at least one non-transitory computer-readable storage medium of claim 8, wherein the instructions further cause the at least one processor to: filter the ranked coder list to identify a best suited coder associated with the finding; and route the structured data set to the coder, wherein the coder comprises the best suited coder. ### Claim 10 (depends on 7) The at least one non-transitory computer-readable storage medium of claim 7, wherein the instructions further cause the at least one processor to identify the finding based on evidence from natural language processing. ### Claim 11 (depends on 7) The at least one non-transitory computer-readable storage medium of claim 7, wherein the instructions further cause the at least one processor to: receive a selection of one of the number of medical record documents; and cause display of the one of the number of medical record documents. ### Claim 12 (depends on 7) The at least one non-transitory computer-readable storage medium of claim 7, wherein the instructions further cause the at least one processor to cause display of an annotated version of the one of the number of medical record documents. ### Claim 13 A method of medical data management implemented by at least one processor in communication with at least one memory, the method comprising: receiving a plurality of medical record documents; selecting a model and model weights based on an annotation activity, wherein the selecting includes modifying a dynamic confidence threshold based at least in part on the annotation activity, wherein the dynamic confidence threshold is applied to at least one document of the plurality of medical record documents; identifying a finding using the model; transforming the plurality of medical record documents into a structured data set; selecting a number of the plurality of medical record documents associated with the finding; embedding links into the structured data set that reference at least one medical record source document of the number of medical record documents; identifying a set of proficient coders by filtering a plurality of coders for proficiency, wherein proficiency is a metric including accuracy in an area associated with the finding and time taken to perform coding in the area; measuring a number of risk adjustment factors each proficient coder of the set of proficient coders completes per hour on average, a rate of false positive coding measured as a rate of annotation disagreement with a supervisor, and an accuracy measure comprising a degree that a given coder agrees with other coders when presented the same task; routing the structured data set to a coder; and submitting the finding for reimbursement. ### Claim 14 (depends on 13) The method of claim 13, further comprising: ranking the set of proficient coders in a ranked coder list by at least one of a risk adjustment factor of the number of risk adjustment factors, the accuracy measure, or the rate of false positive coding; filtering the ranked coder list to identify a best suited coder associated with the finding; and routing the structured data set to the coder, wherein the coder comprises the best suited coder.