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Patient-centered Care01:13

Patient-centered Care

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Patient-centered care involves delivering care beyond inpatient hospitalization. Reflective practice can enhance a patient-centered approach. Reflective practice is a process of reasoning that considers all aspects of the present situation, including practicalities, learning from personal practice, and consideration of patient needs. Patients appreciate care decisions made while considering their input. Involving the patient in their care provides the patient with a sense of contribution rather...
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Healthcare Agencies II01:17

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There are various healthcare agencies in the United States—some of which are managed by religious institutions and others by different government branches.
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Healthcare agencies provide healthcare services to people. In the United States, voluntary agencies are often non-profit centers sponsored by donations, grants, or fundraisers. One such organization is Meals on Wheels, which provides meals to the elderly and homebound. The American Heart Association and the American Lung Association are other non-profit community organizations. Doctors and nurses are frequently active members of these organizations, which offer health checks and educational...
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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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At the different levels of the healthcare system, we see varying methods of healthcare used. These methods include managed care systems, case management, and primary healthcare.
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大規模言語モデル駆動型プロバイダーディレクトリによる価値ベースケアにおけるエピソードレベルの透明性の実現

Amol Kodan1

  • 1Public Health, Monroe University, New York City, USA.

Cureus
|February 25, 2026
PubMed
まとめ

大規模言語モデル（LLM）は、価値ベースケア（VBC）の医療プロバイダーディレクトリを改善できます。LLM搭載チャットボットは、透明性とナビゲーションを強化しますが、広く普及させるにはランキング精度を最適化する必要があります。

科学分野:

  • ヘルスインフォマティクス
  • ヘルスケアにおける人工知能

背景:

  • 従来のプロバイダーディレクトリは、米国医療システムの重要な要素ですが、脆弱でもあります。
  • 現在のディレクトリの欠点（コスト/リスクコンテキストの欠如や不正確なデータなど）は、透明性と価値ベースケア（VBC）の効果を制限します。
  • エピソードベースの支払いモデルは、不十分なプロバイダー選択ツールによって妨げられています。

研究 の 目的:

  • エピソードベースケアナビゲーションのための大規模言語モデル（LLM）駆動型プロバイダーディレクトリチャットボットを評価すること。
  • 構造化された合成データセットを使用して、4つのLLM（GPT-3.5-turbo、GPT-4o-mini、GPT-4o、GPT-5.1）のパフォーマンスを評価すること。
  • 出力の妥当性、エピソード識別、プロバイダーランキング、数値忠実度、および幻覚リスクの観点からLLMのパフォーマンスを調査すること。

主な方法:

  • 厳密に構造化された合成コストおよびパフォーマンスデータセットを使用した87の自然言語テストシナリオを利用しました。
  • 同一の決定論的条件下で、広く使用されている4つのLLMを評価しました。
  • 正確なエピソード識別を優先するランキングの正確さの改訂された定式化を導入しました。

主要な成果:

  • 評価されたすべてのLLMは、91%に迫る高いエピソード識別精度を示しました。
キーワード:
人工知能エピソードベースケア医療の透明性大規模言語モデルプロバイダーディレクトリ価値ベースケア

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  • モデル間では、下流のプロバイダーランキングの信頼性と数値精度に大きなばらつきが見られました。
  • LLM搭載ディレクトリは、VBC設定における透明性とユーザーエクスペリエンスの向上に可能性を示しました。

    • 結論:

    • LLM駆動型プロバイダーディレクトリは、価値ベースケアにおける透明性とナビゲーションの改善に有望です。
    • エピソード識別は強力ですが、大規模展開の前に、プロバイダーランキングの精度と数値忠実度について、さらなる最適化が必要です。
    • これらの予備的な調査結果は、LLMが従来のプロバイダーディレクトリの制限に対処することにより、VBCを意味のある方法で強化できることを示唆しています。