Allm's AI Summary Feature Cuts Visiting Nursing Documentation Time by up to 42%
Allm has launched an AI-powered summary feature for its multidisciplinary collaboration system, 'Team,' designed to reduce the time spent on visiting nursing reports by up to 42%. Developed under a national project, the feature automatically drafts reports using historical data, aiming to enhance both operational efficiency and the quality of patient care.
📋 Article Processing Timeline
- 📰 Published: May 25, 2026 at 19:00
- 🔍 Collected: May 25, 2026 at 10:31
- 🤖 AI Analyzed: May 25, 2026 at 10:56 (24 min after Collected)
Allm, a subsidiary of DeNA (Headquartered in Shibuya-ku, Tokyo; Representative Director and CEO: Shingo Okamura), announced the launch of an AI-powered summary feature (nursing report draft generation) for its multidisciplinary collaboration system, 'Team.' This feature is capable of reducing the time required for visiting nursing documentation by up to 42%.
This new feature was developed under the Strategic Innovation Promotion Program (SIP) Phase 3, "Construction of an Integrated Healthcare System," led by the Cabinet Office and managed by the National Institute of Health Crisis Management (JIHS). Allm acted as the lead research institution for the "B-4: Nursing Support and Improvement of Medical Quality" project. The development of this task was inherited from Theme 3, "Use of Generative AI in the Construction of Integrated Healthcare Systems," under the SIP Phase 3 supplementary budget.
Background:
In visiting nursing, documentation such as records, plans, and reports constitutes a significant portion of a nurse's workload. Guidelines from the Ministry of Health, Labour and Welfare (2026) emphasize the necessity of accurately documenting and storing information on patients' medical history, medication status, living environment, and usage of other services, highlighting the burden of these administrative tasks. Consequently, improving efficiency to ensure high-quality care time has become an urgent challenge.
Features of the New Function:
An LLM (Large Language Model) analyzes data stored in 'Team' to automatically generate highly accurate drafts with a single click.
- Seamless data linkage: Automatically retrieves reports from the previous month and plans/records from the current month.
- One-click generation: No complex prompt input required; drafts are completed with a single button.
- Consistency assurance: Creates coherent text based on past progress, preventing transcription errors.
Overview of Empirical Tests:
Empirical tests were conducted from August to October 2025 at six visiting nursing stations in Nagaoka and Sanjo cities, Niigata Prefecture. The tests involved 15 nurses and 460 instances of functional usage, comparing report generation times with and without the AI summary feature.
Results of Empirical Tests:
Significant time-saving effects were confirmed at the visiting nursing stations:
- Standard cases (report generation time under 10 minutes): 42% time reduction.
- High-load cases (report generation time under 20 minutes): 39% time reduction.
Ongoing use of the AI was suggested to contribute not only to administrative efficiency but also to the reduction of nurses' psychological burden.
This new feature was developed under the Strategic Innovation Promotion Program (SIP) Phase 3, "Construction of an Integrated Healthcare System," led by the Cabinet Office and managed by the National Institute of Health Crisis Management (JIHS). Allm acted as the lead research institution for the "B-4: Nursing Support and Improvement of Medical Quality" project. The development of this task was inherited from Theme 3, "Use of Generative AI in the Construction of Integrated Healthcare Systems," under the SIP Phase 3 supplementary budget.
Background:
In visiting nursing, documentation such as records, plans, and reports constitutes a significant portion of a nurse's workload. Guidelines from the Ministry of Health, Labour and Welfare (2026) emphasize the necessity of accurately documenting and storing information on patients' medical history, medication status, living environment, and usage of other services, highlighting the burden of these administrative tasks. Consequently, improving efficiency to ensure high-quality care time has become an urgent challenge.
Features of the New Function:
An LLM (Large Language Model) analyzes data stored in 'Team' to automatically generate highly accurate drafts with a single click.
- Seamless data linkage: Automatically retrieves reports from the previous month and plans/records from the current month.
- One-click generation: No complex prompt input required; drafts are completed with a single button.
- Consistency assurance: Creates coherent text based on past progress, preventing transcription errors.
Overview of Empirical Tests:
Empirical tests were conducted from August to October 2025 at six visiting nursing stations in Nagaoka and Sanjo cities, Niigata Prefecture. The tests involved 15 nurses and 460 instances of functional usage, comparing report generation times with and without the AI summary feature.
Results of Empirical Tests:
Significant time-saving effects were confirmed at the visiting nursing stations:
- Standard cases (report generation time under 10 minutes): 42% time reduction.
- High-load cases (report generation time under 20 minutes): 39% time reduction.
Ongoing use of the AI was suggested to contribute not only to administrative efficiency but also to the reduction of nurses' psychological burden.
FAQ
アルムが提供を開始したAI要約機能のメリットは何ですか？
訪問看護における報告書作成時間を最大42%削減できるほか、過去の経過を踏まえた整合性のある文章を作成することで転記ミスを防止し、看護師の業務負担を軽減します。
このAI機能はどのように報告書を作成しますか？
「Team」内に蓄積された前月の報告書、当月の計画書・記録紙などのデータをLLMが解析し、ボタン一つで精度の高い報告書の下書きを自動構成します。
どのような研究から開発された機能ですか？
内閣府主導の戦略的イノベーション創造プログラム(SIP)第3期「統合型ヘルスケアシステムの構築」における「B-4：看護師支援・医療の質向上」の研究から開発されました。
実証実験ではどの程度の時短効果がありましたか？
新潟県内の訪問看護ステーションで実施された実証実験の結果、標準的なケースで42%、高負荷なケースで39%の時間削減効果が確認されました。
「Team」とはどのようなシステムですか？
株式会社アルムが提供する、医療・介護サービスを繋ぎ、多職種連携をサポートするソリューションです。タブレットで記録し、クラウド上でリアルタイムな情報共有を実現します。