Health insurers often use algorithms to identify members who would benefit from care and condition management programs, which provide personalized, high-touch clinical support. Timely, accurate, and seamless integration between algorithmic identification and clinical intervention depends on effective collaboration between the system designers and nurse care managers. We focus on a high-risk pregnancy (HRP) program designed to reduce the likelihood of adverse prenatal, perinatal, and postnatal events and describe how we overcome three challenges of HRP programs as articulated by nurse care managers; (1) early detection of pregnancy, (2) accurate identification of impactable high-risk members, and (3) provision of explainable indicators to supplement predictions. We propose a novel algorithm for pregnancy identification that identifies pregnancies 57 days earlier than previous code-based models in a retrospective study. We then build a model to predict impactable pregnancy complications that achieves an AUROC of 0.760. Models for pregnancy identification and complications are then integrated into a proposed user interface. In a set of user studies, we collected quantitative and qualitative feedback from nurses on the utility of the predictions combined with clinical information driving the predictions on triaging members for the HRP program.

翻译：健康保险公司常使用算法识别可从健康管理计划中受益的成员，这类计划提供个性化、高接触的临床支持。算法识别与临床干预之间的及时、准确及无缝整合，取决于系统设计者与护理管理护士的有效协作。我们聚焦于一项旨在降低产前、围产期及产后不良事件概率的高危妊娠（HRP）计划，阐述了如何克服护理管理护士提出的三大挑战：（1）妊娠早期检测，（2）准确识别可干预的高危成员，（3）提供可解释的指标以补充预测。我们提出了一种新颖的妊娠识别算法，在回顾性研究中比既往基于代码的模型提前57天识别妊娠。随后构建了预测可干预妊娠并发症的模型，其AUROC达到0.760。妊娠识别与并发症预测模型被集成至拟定的用户界面中。在一系列用户研究中，我们收集了护士对预测结果及其驱动的临床信息在HRP计划成员分诊中实用性的定量与定性反馈。