目に見えない洞察:眼科における安全な患者メッセージのAI駆動の探索

Jiyeong Y Kim, Zoha Z Fazal, Sophia Y Wang

medRxiv : the preprint server for health sciences

|February 12, 2026

まとめ

安全な患者メッセージは,眼科における一般的な管理および臨床上の懸念を明らかにし,患者の人口統計に基づいたトピックの有意な違いがあります. 分析は,ケアにおける公平性と効率性を向上させることができます.

科学分野:

オフタルモロジック (眼科)
医療情報工学医療情報工学
医療サービス研究医療サービス

背景:

安全なメッセージングプラットフォームは,医療における患者と医療従事者のコミュニケーションのためにますます使用されています.
これらのメッセージの内容を理解することは,臨床ワークフローと患者のケアを最適化するために不可欠です.
眼科の患者のメッセージには,管理上の問い合わせと臨床上の問い合わせの両方が含まれていることが多い.

研究の目的:

安全な眼科メッセージングを通じて伝達された臨床的および管理上の懸念を特徴づける.
メッセージの内容がさまざまな患者社会人口統計グループでどのように異なるかを評価する.

主な方法:

患者主導の安全なメッセージの非識別を分析した横断的な研究が行われました.
自然言語処理と大型言語モデル支援のトピック分類が採用されました.
メッセージの内容は,患者の人口統計 (年齢,性別,人種,民族,婚姻状態) によって層分かれました.

主要な成果:

メッセージのほぼ半分は,管理上の問題 (スケジュール設定,補給,保険) に関するものだった.
主な臨床的懸念は,視力障害,緑内障,イメージング/腫瘍に関する質問,手術後の問題などでした.
非白人患者は,リフィール/保険/グラウコマの問題をより多く提起し,白人患者は,より多くの外科的懸念を報告した. 高齢の患者は緑内障/外科手術/腫瘍について話し,女性患者は合併症/腫れ/感染症について話し合った.

結論:

安全な患者メッセージには,トリアージとケアに影響を与える臨床的に重要な情報が頻繁に含まれています.
メッセージの内容における人口格差は,介護を求める行動の違いを強調しています.
体系的な分析は,より安全なトリアージ,効率的なワークフロー,公平な眼科医療の提供をサポートします.

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