ブラジルの公立病院の継続的な改善のためのリーン・DEAアプローチ
Samuel Martins Drei1, Lidia Angulo-Meza2
1Federal Fluminense University , Niteroi, Brazil.
Journal of health organization and management
|September 4, 2025
まとめ
この研究は,病院の効率化と廃棄物の削減のために,データ環境分析 (DEA) との統合です. 新しいアプローチは,医療サービスの継続的な改善のためのベストプラクティスを特定します.
科学分野:
- 医療管理
- 運用研究
- 公衆衛生政策
背景:
- 病院は,増加する需要と予算の制約の中で,効率を改善し,廃棄物を減らすための圧力に直面しています.
- レインヘルスケア (LH) はプロセスの最適化のための方法論を提供していますが,病院全体の効率性への影響は,堅実な評価を必要とします.
- データ環境分析 (DEA) は,意思決定ユニット (DMU) の相対的な効率を測定するための強力なツールです.
研究 の 目的:
- 病院の効率を高めるために,リーン・ヘルスケア (LH) とデータ・エンヴォルメント・アナリスト (DEA) を組み合わせた統合アプローチを提案し,検証する.
- 医療環境における廃棄物の削減とプロセスの改善のためのベストプラクティスを確立する.
- 公共病院のリソース配置とパフォーマンスの向上のための戦略的意思決定支援ツールを提供すること.
主な方法:
- LHとDEAを統合した2段階のアプローチが開発されました.
- 第1段階では,非指向的なスラックベースの測定データ環境分析 (SBM-DEA) を使用して,LH導入後の効率的および非効率的な病院を特定しました.
- 第二段階では,効率的な病院セットに新しいDEAモデルを適用し,将来の効率を予測し,改善戦略を導くために理想的なDMUを組み込みました.
主要な成果:
- 統合されたLean-DEAアプローチは効率的で非効率的な病院を効果的に特定し,パフォーマンスメトリックを提供しました.
- この研究は,医療サービスの改善のための戦略的指針を提供する能力を示しました.
- 理想的なDMUを組み込むことで 動的効率の予測が可能になり 効率の悪い病院を効率的な実践へと導くことができました
結論:
- 提案されたLean-DEAモデルは,特に資源の限られた環境において,病院の効率化と資源配置を改善するための貴重な意思決定支援ツールです.
- 理想的なDMUを含むLHとDEAの統合は,医療の継続的な改善のための総合的かつダイナミックなアプローチを提供します.
- この研究は,病院のプロセスを最適化することで,より公平なアクセスを促進し,より良い公衆衛生結果を促すことで,医療制度の強化に貢献します.
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