Automatically Generate Referral and Discharge Adjustment Documents: AI Voice to Eliminate "Document Hell" in Regional Collaboration and Reduce Overtime

Medical AI and Hospital AI PMO Company, MedTech Group Inc. (Headquarters: Minato-ku, Tokyo; President and CEO: Kiyotaka Maeda; hereinafter "MedTech G") has announced its "Medical Information Provision Function" as an enhanced version of its "Voice x Summary Function," which integrates voice input and automatic summarization into its AI platform "AI Hippo Medical Loop." This platform significantly reduces the burden of document creation tasks in administrative departments and regional collaboration offices.

This function streamlines the creation of referral letters, discharge adjustment documents, and collaboration documents, supporting a move away from "tasks consumed by document creation."

▼ Medical AI Platform "AI Hippo Medical Loop"

Background - The "Document-Centric Structure" of Regional Collaboration Operations

Regional collaboration offices and medical administrative departments face a large volume of document creation tasks in addition to patient care.

The following challenges are particularly evident:

Burden of creating referral letters and medical information provision reports

Increase in document work associated with discharge and transfer adjustments

Information organization and re-entry among multiple professionals

Inefficiency in collecting information from doctors and nurses

Time spent on document creation leads to delayed responses

These issues stem from a structure where "information is dispersed and needs to be re-organized into documents."

Overview - From "Creating Documents" to "Documents Being Created"

The "Medical Information Provision Function," which can also be accessed through the "Voice x Summary Function" now offered, was developed with a design that transforms the conventional workflow of information gathering → organization → document creation into information input → automatic organization → document generation.

Medical Information Provision Report Creation Screen

Main Functions

Information gathering via voice and text input

Automatic summarization and organization aligned with medical context

Draft generation of referral letters and medical information provision reports

Support for creating discharge and transfer adjustment documents

Integration and key point extraction of information from multiple professionals

*Includes planned features

Features - Freeing Document Creation from "Re-entry Work"

In conventional operations, the general workflow was to gather information from doctors, nurses, and records to create documents.

With this function, necessary information is automatically extracted from existing records and voice input to generate documents, achieving:

Reduction of re-entry

Shortening of work time

Prevention of information omissions

Usage Example

For instance, referral letters and discharge adjustment documents can be automatically generated as drafts based on doctors' consultation details, nursing records, and handover information, allowing staff to only confirm and revise.

Expected Benefits

The introduction of this function is expected to yield the following benefits:

Significant reduction in document creation time

Increased efficiency in regional collaboration operations

Improvement in response speed

Enhanced consistency and accuracy of information

Reduction of administrative workload

About AI Hippo Medical Loop

AI Hippo Medical Loop is an AI platform designed with the challenges of the medical field as its starting point. It supports "connecting fragmented information and structuring medical care" through five functional groups: Voice/Summary (work compression), Medical Loop (decision support), Chat Hippo (education/search), Drive Loop (knowledge integration), and Incident Loop (learning organization).

Future Developments

MedTech G will continue to strengthen its expansion into regional collaboration and medical administrative domains, collaborate with long-term care and home medical care, and integrate with the Medical Data Exchange (IDX), thereby contributing to the operational efficiency of medical institutions and the advancement of regional medical collaboration.

Message

While document creation is indispensable in regional collaboration operations, it represents a significant burden for the field.

AI Hippo transforms the way work is done by changing "how documents are created," rather than "increasing the number of documents."

Summary

Liberation from work consumed by documents.

Reducing "writing work" to enhance the quality of collaboration.

About MedTech Group Inc.

Name: MedTech Group Inc. Established: February 2019 Capital: 40 million yen

President and CEO: Kiyotaka Maeda

Location: 7th Floor, Toranomon 40MT Building, 5-13-1 Toranomon, Minato-ku, Tokyo

URL: https://www.medtechgroup.co.jp/

TEL: 03-6555-7123

MedTech Group Inc., with the philosophy of "an AI company that turns medical data into value and supports the field and management," was established in 2019 from "Ohisama Group," an integrated medical group operating hospitals, clinics, home medical care, and long-term care.

Our company combines the advanced AI technology foundation of AI Data Inc. with the on-site medical knowledge of Ohisama Group to address structural issues such as physician shortages and information fragmentation in regional and small-to-medium-sized hospitals. As its core product, we develop and provide "AI Hippo," a private AI exclusively for medical use.

AI Hippo is a decision support AI designed for practical use in the medical field, supporting the thinking of medical professionals. It can be implemented within 30 days without modifying electronic health records, and its private environment, where data is kept within the institution, structurally prevents data leakage to the outside. Under the philosophy of "Human in the Loop," it is thoroughly designed so that the final decision is always made by the medical professional.

In pilot facilities, quantitative results in clinical practice have been confirmed, such as a significant reduction in nursing record creation time from approximately 2 hours to 30 minutes.