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采用乐观和悲观基准方法评估葡萄牙公立医院在新冠疫情爆发前及期间的绩效。

Assessing the performance of Portuguese public hospitals before and during COVID-19 outbreak, with optimistic and pessimistic benchmarking approaches.

作者信息

Vara Guilherme Mendes, Gomes Marta Castilho, Ferreira Diogo Cunha

机构信息

CEGIST, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal.

CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal.

出版信息

Health Care Manag Sci. 2025 Mar;28(1):1-27. doi: 10.1007/s10729-024-09693-4. Epub 2024 Nov 28.

DOI:10.1007/s10729-024-09693-4
PMID:39604723
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11976781/
Abstract

The COVID-19 pandemic had a profound impact on the tertiary sector, particularly in healthcare, which faced unprecedented demand despite the existence of limited resources, such as hospital beds, staffing resources, and funding. The magnitude and global scale of this crisis provide a compelling incentive to thoroughly analyse its effects. This study aims to identify best practices within the Portuguese national healthcare service, with the goal of improving preparedness for future crises and informing policy decisions. Using a Benefit-of-the-Doubt (BoD) approach, this research constructs composite indicators to assess the pandemic's impact on the Portuguese public hospitals. The study analyzes monthly data from 2017 to May 2022, highlighting critical trends and performance fluctuations during this period. The findings reveal that each COVID-19 wave led to a decline in hospital performance, with the first wave being the most severe due to a lack of preparedness. Furthermore, the pandemic worsened the disparities among examined hospitals. Pre-pandemic top performers in each group improved their performance and were more consistently recognized as benchmarks, with their average benchmark frequency increasing from 66.5% to 83.5%. These top entities demonstrated greater resilience and adaptability, further distancing themselves from underperforming hospitals, which saw declines in both performance scores and benchmark frequency, widening the performance gap. The superior performance of top entities can be attributed to pre-existing strategic tools and contextual factors that enabled them to withstand the pandemic's challenges more effectively. HIGHLIGHTS: • The pandemic aggravated the differences between the hospitals examined. • The top-performing entities further distanced themselves from the remaining entities after the pandemic • Entities considered benchmarks before the pandemic remained the same, and became even more consistent during the pandemic. • The top-performing entities achieved higher scores than their pre-pandemic performance levels. • Benchmarking models for composite indicators with diverse decision-making preferences, and treatment of imperfect knowledge of data.

摘要

新冠疫情对第三产业产生了深远影响,尤其是医疗保健领域,尽管医院床位、人力资源和资金等资源有限,但该领域仍面临着前所未有的需求。这场危机的规模和全球范围为深入分析其影响提供了强有力的动机。本研究旨在确定葡萄牙国家医疗服务体系中的最佳实践,以提高对未来危机的应对能力并为政策决策提供参考。本研究采用疑利法,构建综合指标来评估疫情对葡萄牙公立医院的影响。该研究分析了2017年至2022年5月的月度数据,突出了这一时期的关键趋势和绩效波动。研究结果表明,每一波新冠疫情都导致医院绩效下降,由于准备不足,第一波疫情最为严重。此外,疫情加剧了所考察医院之间的差距。疫情前每组中的顶尖医院提高了其绩效,并更持续地被视为标杆,其平均标杆频率从66.5%提高到了83.5%。这些顶尖机构表现出了更强的恢复力和适应能力,与表现不佳的医院差距进一步拉大,后者的绩效得分和标杆频率均有所下降,绩效差距不断扩大。顶尖机构的卓越表现可归因于其原有的战略工具和背景因素,这些因素使它们能够更有效地应对疫情挑战。

要点

• 疫情加剧了所考察医院之间的差异。

• 疫情后,表现最佳的机构与其他机构的差距进一步拉大。

• 疫情前被视为标杆的机构保持不变,且在疫情期间更加稳定。

• 表现最佳的机构取得了高于疫情前绩效水平的得分。

• 针对具有不同决策偏好的综合指标的标杆模型,以及对数据不完全了解的处理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d774/11976781/62c74b589c6c/10729_2024_9693_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d774/11976781/5c583296e3cd/10729_2024_9693_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d774/11976781/1d492b08a603/10729_2024_9693_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d774/11976781/62c74b589c6c/10729_2024_9693_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d774/11976781/5c583296e3cd/10729_2024_9693_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d774/11976781/0373aad222e8/10729_2024_9693_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d774/11976781/e488c55792a9/10729_2024_9693_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d774/11976781/db380a48bf29/10729_2024_9693_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d774/11976781/1d492b08a603/10729_2024_9693_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d774/11976781/62c74b589c6c/10729_2024_9693_Fig6_HTML.jpg

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