Patel Sadiq Y, Barnett Michael L, Basu Sanjay
Clinical Product Development, Waymark, San Francisco, CA, USA.
School of Social Policy and Practice, University of Pennsylvania, Philadelphia, PA, USA.
NPJ Digit Med. 2025 Jul 2;8(1):393. doi: 10.1038/s41746-025-01797-7.
Low-income populations have disproportionately low completion of recommended healthcare services, from missed vaccinations to cancer screenings. While machine learning models help identify high-risk patients for targeted treatment, they have rarely been evaluated for quality measure gap completion-or among low-income populations underrepresented in typical datasets. Analyzing 14.2 million Medicaid recipients-including those excluded from electronic health records and without prior utilization-we developed models to predict gaps in nine nationally adopted quality measures, including preventive care and chronic disease management. Using clinical data to prioritize outreach, the clinical-only model improved accuracy by 32.5 percentage points (pp) over non-predictive methods such as alphabetical calling or birthday reminders (AUROC: 0.88, F1-score: 0.69). Incorporating social determinants of health data further improved performance by 2.0pp in accuracy (to 84.5%) and increased F1-score by 5.0pp (to 0.74), with no change in AUROC (area under the receiver operating characteristic curve). Compared to the clinical-only model, the SDoH model also reduced pre-existing Black-White disparities in prediction accuracy. Model performance was especially sensitive to SDoH factors like healthcare workforce and facility availability.
低收入人群在完成推荐的医疗服务方面比例极低，从错过疫苗接种到癌症筛查皆是如此。虽然机器学习模型有助于识别需要针对性治疗的高风险患者，但很少针对质量指标差距的填补情况进行评估，也未在典型数据集中代表性不足的低收入人群中进行评估。通过分析1420万医疗补助接受者（包括那些被排除在电子健康记录之外且此前未接受过治疗的人群），我们开发了模型来预测九项全国采用的质量指标中的差距，包括预防保健和慢性病管理。利用临床数据来确定外展工作的优先级，仅基于临床数据的模型比按字母顺序打电话或生日提醒等非预测性方法的准确率提高了32.5个百分点（AUROC：0.88，F1分数：0.69）。纳入健康的社会决定因素数据后，准确率进一步提高了2.0个百分点（达到84.5%），F1分数提高了5.0个百分点（达到0.74），而AUROC（受试者工作特征曲线下面积）没有变化。与仅基于临床数据的模型相比，包含健康的社会决定因素的模型在预测准确率方面也减少了现有的黑人和白人之间的差距。模型性能对医疗劳动力和设施可用性等健康的社会决定因素尤为敏感。
