# US 2025/0095809 A1 · Automatic medication order generation

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- Number: US 2025/0095809 A1
- Document ID: US20250095809A1
- Status: Published application
- Kind: A1
- Application number: 18/885,353
- Filed: 2024-09-13
- Published: 2025-03-20
- Earliest claimed: 2023-09-15
- Assignees: Oracle International Corporation
- Applicants: Oracle International Corporation
- Inventors: Yuanxu Wu
- Web page: https://patentgrep.com/patent/US20250095809A1

## CPC

- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G06F 40/284: Lexical analysis, e.g. tokenisation or collocates
- G06F 40/295: Named entity recognition
- G06F 40/30: Semantic analysis
- G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 80/00: ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
- 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

The techniques described herein provide a novel medication order pipeline may be used to facilitate medication orders by identifying the medication ordering intent from a natural language utterance, and using the FHIR-compliance data structure to generate medication order information to fulfill medication orders through an EHR system. The medication order information may be a concise search phrase containing the medical entities extracted from the data structure, or converted EHR system-specific medical codes based on the standard medical codes in the data structure.

## Claims

### Claim 1

A method, comprising:
accessing an utterance, the utterance comprising one or more tokens, wherein the one or more tokens correspond to one or more medical entities;
identifying a medication order intent from the utterance; 4
generating a labeled utterance, wherein generating the labeled utterance comprises:
associating the one or more tokens with a hierarchical entity type comprising a set of sub-entity types, wherein the hierarchical entity type is associated with a first medical coding system;
generating medication order information based on the one or more tokens and the set of sub-entity types; and
providing the medication order information to an electronic health record (EHR) system.

### Claim 2 (depends on 1)

The method of claim 1, wherein the hierarchical entity type is a medication entity type, and the set of sub-entity types comprises seven medication attributes.

### Claim 3 (depends on 1)

The method of claim 1, wherein the first medical coding system is Prescription Normalization (RxNORM).

### Claim 4 (depends on 1)

The method of claim 1, wherein the medication order information is a search phrase enabling the EHR system to process a medication order.

### Claim 5 (depends on 4)

The method of claim 4, wherein the search phrase is generated by:
mapping the one or more tokens to one or more first-type medical codes in the first medical coding system; and
generating a Fast Healthcare Interoperability Resources (FHIR)-compliance data structure based on the one or more first-type medical codes and the first medical coding system.

### Claim 6 (depends on 5)

The method of claim 5, wherein mapping the one or more tokens to the one or more first-type medical codes in the first medical coding system utilizes a Cosine similarity search.

### Claim 7 (depends on 1)

The method of claim 1, wherein the medication order information is one or more EHR system-specific codes.

### Claim 8 (depends on 7)

The method of claim 7, wherein the one or more EHR system-specific codes are generated by:
mapping the one or more tokens to one or more first-type medical codes in the first medical coding system; and
converting the one or more first-type medical codes to the one or more EHR system-specific codes.

### Claim 9 (depends on 1)

The method of claim 1, further comprising performing follow-up tasks based on the medication order information.

### Claim 10 (depends on 9)

The method of claim 9, wherein the follow-up tasks comprise verifying missing data in the medication order information, and requesting a signature.

### Claim 11

One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause a system to perform operations comprising:
accessing an utterance, the utterance comprising one or more tokens, wherein the one or more tokens correspond to one or more medical entities;
identifying a medication order intent from the utterance;
generating a labeled utterance, wherein generating the labeled utterance comprises:
associating the one or more tokens with a hierarchical entity type comprising a set of sub-entity types, wherein the hierarchical entity type is associated with a first medical coding system;
generating medication order information based on the one or more tokens and the set of sub-entity types; and
providing the medication order information to an electronic health record (EHR) system.

### Claim 12 (depends on 11)

The one or more non-transitory computer-readable media of claim 11, wherein the hierarchical entity type is a medication entity type, and the set of sub-entity types comprises seven medication attributes.

### Claim 13 (depends on 11)

The one or more non-transitory computer-readable media of claim 11, wherein the first medical coding system is Prescription Normalization (RxNORM).

### Claim 14 (depends on 11)

The one or more non-transitory computer-readable media of claim 11, the medication order information is a search phrase enabling the EHR system to process a medication order.

### Claim 15 (depends on 14)

The one or more non-transitory computer-readable media of claim 14, wherein the search phrase is generated by:
mapping the one or more tokens to one or more first-type medical codes in the first medical coding system; and
generating a Fast Healthcare Interoperability Resources (FHIR)-compliance data structure based on the one or more first-type medical codes and the first medical coding system.

### Claim 16 (depends on 15)

The one or more non-transitory computer-readable media of claim 15, wherein mapping the one or more tokens to the one or more first-type medical codes in the first medical coding system utilizes a Cosine similarity search.

### Claim 17 (depends on 11)

The one or more non-transitory computer-readable media of claim 11, wherein the medication order information is one or more EHR system-specific codes.

### Claim 18 (depends on 17)

The one or more non-transitory computer-readable media of claim 17, wherein the one or more EHR system-specific codes are generated by:
mapping the one or more tokens to one or more first-type medical codes in the first medical coding system; and
converting the one or more first-type medical codes to the one or more EHR system-specific codes.

### Claim 19 (depends on 11)

The one or more non-transitory computer-readable media of claim 11, further comprising performing follow-up tasks based on the medication order information.

### Claim 20 (depends on 19)

The one or more non-transitory computer-readable media of claim 19, wherein the follow-up tasks comprise verifying missing data in the medication order information, and requesting a signature.

### Claim 21

A system comprising:
one or more processing systems; and
one or more computer-readable media storing instructions which, when executed by the one or more processing systems, cause the system to perform operations comprising:
accessing an utterance, the utterance comprising one or more tokens, wherein the one or more tokens correspond to one or more medical entities;
identifying a medication order intent from the utterance;
generating a labeled utterance, wherein generating the labeled utterance 8 comprises:
associating the one or more tokens with a hierarchical entity type comprising a set of sub-entity types, wherein the hierarchical entity type is associated with a first medical coding system;
generating medication order information based on the one or more tokens and the set of sub-entity types; and
providing the medication order information to an electronic health record (EHR) system.

### Claim 22 (depends on 21)

The system of claim 21, wherein the hierarchical entity type is a medication entity type, and the set of sub-entity types comprises seven medication attributes.

### Claim 23 (depends on 21)

The system of claim 21, wherein the first medical coding system is Prescription Normalization (RxNORM).

### Claim 24 (depends on 21)

The system of claim 21, the medication order information is a search phrase enabling the EHR system to process a medication order.

### Claim 25 (depends on 24)

The system of claim 24, wherein the search phrase is generated by:
mapping the one or more tokens to one or more first-type medical codes in the first medical coding system; and
generating a Fast Healthcare Interoperability Resources (FHIR)-compliance data structure based on the one or more first-type medical codes and the first medical coding system.

### Claim 26 (depends on 25)

The system of claim 25, wherein mapping the one or more tokens to the one or more first-type medical codes in the first medical coding system utilizes a Cosine similarity search.

### Claim 27 (depends on 21)

The system of claim 21, wherein the medication order information is one or more EHR system-specific codes.

### Claim 28 (depends on 27)

The system of claim 27, wherein the one or more EHR system-specific codes are generated by:
mapping the one or more tokens to one or more first-type medical codes in the first medical coding system; and
converting the one or more first-type medical codes to the one or more EHR system-specific codes.

### Claim 29 (depends on 21)

The system of claim 21, further comprising performing follow-up tasks based on the medication order information.

### Claim 30 (depends on 29)

The system of claim 29, wherein the follow-up tasks comprise verifying missing data in the medication order information, and requesting a signature.