脊柱手术特别容易受到医疗事故索赔的影响。美国医疗责任制度的批评者认为,它推高了成本,而支持者认为它阻止了疏忽。这里,作者研究了医疗事故索赔密度与结局之间的关系。
使用了以下方法:1)国家从业者数据库用于确定每100名医生的医疗事故索赔数量,国家,2005年至2010年;2)全国住院患者样本被查询为脊柱融合患者;3)区域资源文件被查询,以确定医生的密度,由国家。关于每100名医生的医疗事故索赔频率,将各州分为4个四分位数。为了评估医疗事故索赔和死亡之间的关联,放电处理,停留时间(LOS)和总成本,使用逆概率加权回归校正估计器。作者控制了患者和医院的特征。协变量被用来训练机器学习模型来预测死亡,出院处置不在家,LOS,和总成本。
总的来说,确定了脊髓融合后的549,775个放电,495,640提供了每100名医生的医疗事故索赔频率的州一级信息。其中,124,425(25.1%),132,613(26.8%),130,929(26.4%),和107,673(21.7%)来自最低,第二低,第二高,和最高四分位数状态,分别,每100名医生的医疗事故索赔。与索赔最少的州(最低四分位数)相比,在索赔最多的州(最高四分位数)的手术显示出非家庭出院的几率在统计学上显着较高(OR1.169,95%CI1.139-1.200),较长的LOS(平均差0.304,95%CI0.256-0.352),和更高的总费用(平均差异[对数量表]0.288,95%CI0.281-0.295)与死亡率没有显着关联。对于包括医疗事故索赔密度作为协变量的机器学习模型,死亡和出院处置曲线下的面积分别为0.94和0.87,LOS和总费用的R2值分别为0.55和0.60。
医疗事故发生频率较高的州的脊柱融合手术与非家庭出院的几率增加有关,较长的LOS,和更高的总收费。这表明,法医学气候可能会改变给定脊柱外科医生的实践模式,并可能对医疗责任改革具有重要意义。以医疗事故索赔密度为特征的机器学习模型在预测方面令人满意,可能对患者有帮助。外科医生,医院,和付款人。
Spine surgery is especially susceptible to malpractice claims. Critics of the US medical liability system argue that it drives up costs, whereas proponents argue it deters negligence. Here, the authors study the relationship between malpractice claim density and outcomes.
The following methods were used: 1) the National Practitioner Data Bank was used to determine the number of malpractice claims per 100 physicians, by state, between 2005 and 2010; 2) the Nationwide Inpatient Sample was queried for spinal fusion patients; and 3) the Area Resource File was queried to determine the density of physicians, by state. States were categorized into 4 quartiles regarding the frequency of malpractice claims per 100 physicians. To evaluate the association between malpractice claims and death, discharge disposition, length of stay (LOS), and total costs, an inverse-probability-weighted regression-adjustment estimator was used. The authors controlled for patient and hospital characteristics. Covariates were used to train machine learning models to predict death, discharge disposition not to home, LOS, and total costs.
Overall, 549,775 discharges following spinal fusions were identified, with 495,640 yielding state-level information about medical malpractice claim frequency per 100 physicians. Of these, 124,425 (25.1%), 132,613 (26.8%), 130,929 (26.4%), and 107,673 (21.7%) were from the lowest, second-lowest, second-highest, and highest quartile states, respectively, for malpractice claims per 100 physicians. Compared to the states with the fewest claims (lowest quartile), surgeries in states with the most claims (highest quartile) showed a statistically significantly higher odds of a nonhome discharge (OR 1.169, 95% CI 1.139-1.200), longer LOS (mean difference 0.304, 95% CI 0.256-0.352), and higher total charges (mean difference [log scale] 0.288, 95% CI 0.281-0.295) with no significant associations for mortality. For the machine learning models-which included medical malpractice claim density as a covariate-the areas under the curve for death and discharge disposition were 0.94 and 0.87, and the R2 values for LOS and total charge were 0.55 and 0.60, respectively.
Spinal fusion procedures from states with a higher frequency of malpractice claims were associated with an increased odds of nonhome discharge, longer LOS, and higher total charges. This suggests that medicolegal climate may potentially alter practice patterns for a given spine surgeon and may have important implications for medical liability reform. Machine learning models that included medical malpractice claim density as a feature were satisfactory in prediction and may be helpful for patients, surgeons, hospitals, and payers.