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Background <p>Static scorecards dominate hospital outsourcing evaluation, but fixed weights cannot adapt to shifting operational priorities. We developed a dynamic evaluation system that intentionally re-weights indicators each month to steer contractors toward emerging risk areas.</p> Methods <p>A two-campus, 1350-bed medical group was followed for 24 months. During 2023, baseline performance of six domains (service quality, efficiency &amp; timeliness, cost control, compliance, safety, staffing &amp; satisfaction) was captured with equal weights. In 2024, a G1–CRITIC hybrid supplied the initial weight vector, after which an Long Short-Term Memory (LSTM)+Dropout network forecast next-month scores. Weights were automatically up-regulated for indicators predicted to deteriorate and down-regulated for those expected to improve, then normalised to 1. Impact was quantified with interrupted time-series analysis (ITSA).</p> Result <p>The LSTM+Dropout model yielded test RMSE ≤ 1.60 (scale 0–100) across all indicators. After 12 months of dynamic adjustment, ITSA showed immediate and significant improvements in service quality (+23.2 points, <i>p</i> = 0.001), cost control (+24.6 points, <i>p</i> = 0.004) and the standardised mean score (+8.5 points, <i>p</i> = 0.001). No adverse effects were observed in other domains.</p> Conclusions <p>Converting a static scorecard into a self-adjusting steering lever significantly accelerated contractor quality gains. The system requires only routine administrative data and open-source software, offering a readily replicable tool for proactive, data-driven governance of outsourced hospital services.</p>

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Dynamic evaluation system for improving hospital outsourcing service performance: a G1-Critic and LSTM+Dropout approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月27日

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  • Xiao Zhong,
  • Li-Hua Xiao,
  • Yue-Ming Zhong,
  • Lan-Fang Mo,
  • Zhen-Jie Zhong,
  • Hai-Shan Xie,
  • Xiao-Feng Luo,
  • Gui-Lin Liu

摘要

Background

Static scorecards dominate hospital outsourcing evaluation, but fixed weights cannot adapt to shifting operational priorities. We developed a dynamic evaluation system that intentionally re-weights indicators each month to steer contractors toward emerging risk areas.

Methods

A two-campus, 1350-bed medical group was followed for 24 months. During 2023, baseline performance of six domains (service quality, efficiency & timeliness, cost control, compliance, safety, staffing & satisfaction) was captured with equal weights. In 2024, a G1–CRITIC hybrid supplied the initial weight vector, after which an Long Short-Term Memory (LSTM)+Dropout network forecast next-month scores. Weights were automatically up-regulated for indicators predicted to deteriorate and down-regulated for those expected to improve, then normalised to 1. Impact was quantified with interrupted time-series analysis (ITSA).

Result

The LSTM+Dropout model yielded test RMSE ≤ 1.60 (scale 0–100) across all indicators. After 12 months of dynamic adjustment, ITSA showed immediate and significant improvements in service quality (+23.2 points, p = 0.001), cost control (+24.6 points, p = 0.004) and the standardised mean score (+8.5 points, p = 0.001). No adverse effects were observed in other domains.

Conclusions

Converting a static scorecard into a self-adjusting steering lever significantly accelerated contractor quality gains. The system requires only routine administrative data and open-source software, offering a readily replicable tool for proactive, data-driven governance of outsourced hospital services.