病院死亡率のリスク調整を改善するためにクレームデータの強化
Michael Pine1, Harmon S Jordan, Anne Elixhauser
1Michael Pine and Associates Inc, Chicago, Ill, USA. mpine@aol.com
JAMA
|January 4, 2007
まとめ
入院コードと実験室データを追加することで,死亡率に対する病院リスク調整モデルが大幅に改善されます. これにより,病院の品質測定と公的な報告の正確性が向上します.
科学分野:
- 医療サービス 研究 医療サービス
- 医療情報工学 医療情報工学
- 健康上の成果について
背景:
- 正確なリスク調整は,公的な報告や業績報酬プログラムにおける病院のパフォーマンスを評価する上で極めて重要です.
- 既存のリスク調整方程式では,病院の品質を正確に測定するには,十分な臨床的詳細が必要です.
研究 の 目的:
- リスク調整された病院死亡率に関する行政請求データに,入院時の入院 (POA) コードと数値検査データを組み込むことの影響を評価する.
- 病院のリスク調整モデルの正確性を高めるために,さまざまなデータタイプの付加価値を決定する.
主な方法:
- ペンシルベニア州の188の病院 (2000年7月~2003年6月) のデータを用いて,入院患者の死亡率に対するリスク調整方程式の比較分析を行った.
- モデルは,行政データ,POAコード,検査値を含む臨床データを順次追加して開発され,c統計を用いて差別的な力を評価した.
- この研究は,特定の疾患 (急性心筋梗塞,閉塞性心不全,脳血管事故,胃腸道出血,肺炎) と手順 (腹腔大動脈瘤修復,冠動脈バイパス移植手術,頭蓋骨切除) に焦点を当てました.
主要な成果:
- ベースラインの管理モデルは,平均c統計値0.79 (0.02) を達成した.
- POAコードと数値検査データの導入により,リスク調整が大幅に改善され,平均c統計値はそれぞれ0.84 (0.01) と0.86 (0.01) でした.
- さらに複雑な臨床データを追加すると,平均c統計値0.88 (0.01) で,控えめな改善が示されました.
結論:
- 入学コードと数値検査値に記載されているものは,リスク調整のための行政データベースの予測能力を大幅に高めています.
- 複雑な臨床結果の二次抽象化は,POAコードと実験室データと比較して,価値あるものの,最小限の追加の予測上の利点を提供します.
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