# US 10,482,999 B2 · Systems and methods for efficient handling of medical documentation

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- Number: US 10,482,999 B2
- Document ID: US10482999B2
- Status: Granted
- Kind: B2
- Application number: 14/543,738
- Filed: 2014-11-17
- Published: 2015-05-21
- Granted: 2019-11-19
- Earliest claimed: 2013-11-18
- Assignees: Apixio, Inc.
- Applicants: Apixio, Inc.
- Inventors: Vishnuvyas Sethumadhavan; Robert Derward Rogers; John O. Schneider; Erin Michelle Faverty; Austin Robert Rogers; Shahram Shawn Dastmalchi
- Web page: https://patentgrep.com/patent/US10482999B2

## CPC

- G06Q 40/08: Insurance
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- G16H 70/60: relating to pathologies
- 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

## Abstract

Systems and methods for efficient medical chart review are provided. In some embodiments, medical records are received. The admissibility of each record is then determined. Next, a condition and MEAT assessment is generated for the medical records. The condition and MEAT determination each have a corresponding confidence. A determination may be made whether human quality assurance is required. If so, the medical records may be routed to one or more coders for human review. In addition, the systems and methods may also perform an audit analysis on the records, which identifies codes which have been submitted and have insufficient evidence. Lastly, a cost metric for the patient based upon the condition and MEAT determination may be generated.

## Claims

### Claim 1

In a health information management system, a computerized method for processing medical records, comprising:
receiving at least one medical record;
processing the at least one medical record by indexing and semantic meta-tagging to alter the at least one medical record format, embedding information in metadata;
providing the processed at least one medical record to an analytics layer of a data storage architecture;
generating, using a processor, a condition based upon at least one medical record of the processed at least one medical record, wherein the condition includes a first confidence value;
determining admissibility of each medical record by confirming that an encounter associated with the medical record was face-to-face, signed, and by an admissible specialist, wherein the confirming the encounter is face-to-face includes contextual analysis of confirmation statements, presence of a procedure that requires patient attendance, and metadata, and wherein the confirming the medical record is signed includes an image recognition algorithm, and wherein the confirming the admissible specialist includes cross referencing the condition against a physician performing the diagnosis responsive to a table of allowable diagnoses for the specialty of the physician;
contacting the physician, a healthcare provider or insurance company when the medical record is inadmissible with a suggestion for correction;
scheduling an examination of the patient to correct the inadmissible record; and
generating a MEAT determination for each medical record, wherein the MEAT determination includes a second confidence value, and further wherein the MEAT determination includes identifying a monitor component, and evaluation component, an assessment component and a treatment component.

### Claim 2 (depends on 1)

The method of claim 1, further comprising determining if human quality assurance is required.

### Claim 3 (depends on 2)

The method of claim 2, wherein the determination of human quality assurance is based upon at least one of the first confidence value, the second confidence value, and medical record attributes.

### Claim 4 (depends on 3)

The method of claim 3, wherein the medical record attributes include at least one of the condition, geography of the medical record, physician who generated the medical record, insurance type, and patient history.

### Claim 5 (depends on 2)

The method of claim 2, further comprising routing the medical record to a coder for user behavior analysis.

### Claim 6 (depends on 5)

The method of claim 5, wherein the routing compares coder attributes to goals and optimizes for outcome.

### Claim 7 (depends on 1)

The method of claim 1, wherein generating the condition and MEAT determination includes keyword and contextual analysis.

### Claim 8 (depends on 5)

The method of claim 5, further comprising generating a summary of the condition and MEAT determination for the coder, wherein the summary includes graphically highlighting evidence within each medical record for the coder.

### Claim 9 (depends on 1)

The method of claim 1, further comprising generating an audit analysis by identifying codes which have been submitted and have insufficient evidence, and analyzing specific medical records with submitted codes to determine if sufficient evidence is present.

### Claim 10 (depends on 9)

The method of claim 9, further comprising determining if there is time to amend the medical records, and if so providing a suggestion to amend the medical records to cure deficiencies.

### Claim 11 (depends on 10)

The method of claim 10, further comprising providing a suggestion for a follow-up with the patient when the medical records cannot be amended.

### Claim 12 (depends on 1)

The method of claim 1, further comprising generating cost metric for the patient based upon the condition and MEAT determination.

### Claim 13

A health information management system for processing medical records comprising:
an interface configured to receive at least one medical record;
a processor for processing the at least one medical record by indexing and semantic meta-tagging to alter the at least one medical record format, embedding information in metadata;
a database including an analytics layer for receiving the processed at least one medical record;
the processor further configured to generate a condition based upon at least one medical record of the processed at least one medical record, wherein the condition includes a first confidence value, and
determine admissibility of each medical record by confirming that an encounter associated with the medical record was face-to-face, signed, and by an admissible specialist, wherein the confirming the encounter is face-to-face includes contextual analysis of confirmation statements, presence of a procedure that requires patient attendance, and metadata, and wherein the confirming the medical record is signed includes an image recognition algorithm, and wherein the confirming the admissible specialist includes cross referencing the condition against a physician performing the diagnosis responsive to a table of allowable diagnoses for the specialty of the physician;
the interface for contacting the physician, a healthcare provider or insurance company when the medical record is inadmissible with a suggestion for correction; and
the processor further configured for scheduling an examination of the patient to correct the inadmissible record, and
generate a MEAT determination for each medical record, wherein the MEAT determination includes a second confidence value, and further wherein the MEAT determination includes identifying a monitor component, and evaluation component, an assessment component and a treatment component.

### Claim 14 (depends on 13)

The system of claim 13, further comprising the processor configured to determine if human quality assurance is required.

### Claim 15 (depends on 14)

The system of claim 14, wherein the determination of human quality assurance is based upon at least one of the first confidence value, the second confidence value, and medical record attributes.

### Claim 16 (depends on 15)

The system of claim 15, wherein the medical record attributes include at least one of the condition, geography of the medical record, physician who generated the medical record, insurance type, and patient history.

### Claim 17 (depends on 14)

The system of claim 14, wherein the processor is further configured to route the medical record to a coder for user behavior analysis.

### Claim 18 (depends on 17)

The system of claim 17, wherein the routing compares coder attributes to goals and optimizes for outcome.

### Claim 19 (depends on 13)

The system of claim 13, wherein generating the condition and MEAT determination includes keyword and contextual analysis.

### Claim 20 (depends on 17)

The system of claim 17, wherein the processor is further configured to generate a summary of the condition and MEAT determination for the coder, wherein the summary includes graphically highlighting evidence within each medical record for the coder.

### Claim 21 (depends on 13)

The system of claim 13, further comprising the processor configured to generate an audit analysis by identifying codes which have been submitted and have insufficient evidence, and analyzing specific medical records with submitted codes to determine if sufficient evidence is present.

### Claim 22 (depends on 21)

The system of claim 21, wherein the processor is further configured to determine if there is time to amend the medical records, and if so providing a suggestion to amend the medical records to cure deficiencies.

### Claim 23 (depends on 22)

The system of claim 22, wherein the processor is further configured to provide a suggestion for a follow-up with the patient when the medical records cannot be amended.

### Claim 24 (depends on 13)

The system of claim 13, further comprising the processor configured to generate a cost metric for the patient based upon the condition and MEAT determination.