Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, 02215, USA.
Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, 97201, USA.
Sci Rep. 2023 May 10;13(1):7624. doi: 10.1038/s41598-023-33937-y.
The Centers for Medicare and Medicaid Services require hospitals to report on quality metrics which are used to financially penalize those that perform in the lowest quartile. Surgical site infections (SSIs) are a critical component of the quality metrics that target healthcare-associated infections. However, the accuracy of such hospital profiling is highly affected by small surgical volumes which lead to a large amount of uncertainty in estimating standardized hospital-specific infection rates. Currently, hospitals with less than one expected SSI are excluded from rankings, but the effectiveness of this exclusion criterion is unknown. Tools that can quantify the classification accuracy and can determine the minimal surgical volume required for a desired level of accuracy are lacking. We investigate the effect of surgical volume on the accuracy of identifying poorly performing hospitals based on the standardized infection ratio and develop simulation-based algorithms for quantifying the classification accuracy. We apply our proposed method to data from HCA Healthcare (2014-2016) on SSIs in colon surgery patients. We estimate that for a procedure like colon surgery with an overall SSI rate of 3%, to rank hospitals in the HCA colon SSI dataset, hospitals that perform less than 200 procedures have a greater than 10% chance of being incorrectly assigned to the worst performing quartile. Minimum surgical volumes and predicted events criteria are required to make evaluating hospitals reliable, and these criteria vary by overall prevalence and between-hospital variability.
医疗保险和医疗补助服务中心要求医院报告质量指标，这些指标用于对表现最差的医院进行经济处罚。手术部位感染（SSI）是针对与医疗保健相关感染的质量指标的关键组成部分。然而，这种医院概况的准确性受到小手术量的严重影响，这导致在估计标准化医院特定感染率时存在大量不确定性。目前，手术量低于预期的 SSI 的医院被排除在排名之外，但这种排除标准的效果尚不清楚。缺乏可以量化分类准确性并确定达到所需准确性水平所需的最小手术量的工具。我们研究了手术量对基于标准化感染比识别表现不佳医院的准确性的影响，并开发了基于模拟的算法来量化分类准确性。我们将我们的方法应用于 HCA Healthcare（2014-2016 年）的结肠手术患者 SSI 数据。我们估计，对于像结肠手术这样的手术，总体 SSI 率为 3%，为了对 HCA 结肠 SSI 数据集进行医院排名，手术量少于 200 例的医院有超过 10%的机会被错误地分配到表现最差的四分之一。需要最小手术量和预测事件标准来使医院评估可靠，这些标准因总体流行率和医院间变异性而异。
