目的是评估定期收集的患者报告的健康相关社会需求 (HRSN) 指标对于预测利用率和健康结果的价值。作者确定了梅奥诊所患有癌症、糖尿病或心力衰竭的患者。 HRSN 测量值作为 2019 年 6 月至 12 月患者报告筛查的一部分收集，并于 2020 年确定结果（住院、30 天再入院和死亡）。对于每种结果和疾病组合，使用 4 种模型：梯度增强机（GBM）、随机森林（RF）、广义线性模型（GLM）和弹性网络（EN）。其他预测因素包括临床因素、人口统计数据和基于地区的 HRSN 措施——地区剥夺指数 (ADI) 和农村地区。使用或不使用常规收集的 HRSN 测量值作为曲线下面积 (AUC) 的变化来评估模型的预测性能。还评估了变量的重要性。 AUC 的差异是混合的。 3 种癌症死亡模型（GBM：0.0421、RF：0.0496、EN：0.0428）、3 种住院模型（GBM：0.0372、RF：0.0640、EN：0.0441）和 1 种死亡模型（RF：0.0754）有显着改善）用于糖尿病，1 个再入院模型（GBM：0.1817），以及 3 个心力衰竭死亡模型（GBM：0.0333，RF：0.0519，GLM：0.0489）。年龄、ADI 和 Charlson 合并症指数在变量重要性方面排名前 3 位，并且始终比常规收集的 HRSN 指标更重要。添加常规收集的 HRSN 测量结果对模型的预测性能产生了不同程度的改善。这些发现表明，现有因素和 ADI 对于这些情况下的预测更为重要。需要做更多的工作来确定能够持续提高模型性能的预测变量。

The objective was to assess the value of routinely collected patient-reported health-related social needs (HRSNs) measures for predicting utilization and health outcomes. The authors identified Mayo Clinic patients with cancer, diabetes, or heart failure. The HRSN measures were collected as part of patient-reported screenings from June to December 2019 and outcomes (hospitalization, 30-day readmission, and death) were ascertained in 2020. For each outcome and disease combination, 4 models were used: gradient boosting machine (GBM), random forest (RF), generalized linear model (GLM), and elastic net (EN). Other predictors included clinical factors, demographics, and area-based HRSN measures-area deprivation index (ADI) and rurality. Predictive performance for models was evaluated with and without the routinely collected HRSN measures as change in area under the curve (AUC). Variable importance was also assessed. The differences in AUC were mixed. Significant improvements existed in 3 models of death for cancer (GBM: 0.0421, RF: 0.0496, EN: 0.0428), 3 models of hospitalization (GBM: 0.0372, RF: 0.0640, EN: 0.0441), and 1 of death (RF: 0.0754) for diabetes, and 1 model of readmissions (GBM: 0.1817), and 3 models of death (GBM: 0.0333, RF: 0.0519, GLM: 0.0489) for heart failure. Age, ADI, and the Charlson comorbidity index were the top 3 in variable importance and were consistently more important than routinely collected HRSN measures. The addition of routinely collected HRSN measures resulted in mixed improvement in the predictive performance of the models. These findings suggest that existing factors and the ADI are more important for prediction in these contexts. More work is needed to identify predictors that consistently improve model performance.