Popkov Andrey A, Barrett Tyson S, Hohl Jason, Shergill Amber, Deakin Susan L, Perry Melissa
Premier, Inc, 13034 Ballantyne Corporate Place, Charlotte, NC, 28277, USA.
Highmark Health, Pittsburgh, PA, USA.
J Behav Health Serv Res. 2025 Sep 17. doi: 10.1007/s11414-025-09972-0.
Depression, a prevalent health condition, substantially impacts both socioeconomic outcomes and individual wellbeing. Despite the availability of diagnostic tools, existing approaches for identifying depression severity often rely on single-indicator approaches, limiting accuracy. This retrospective study evaluates a multi-parameter analytics-enabled Identification and Stratification (IDS) framework designed to improve depression identification and severity stratification by leveraging health insurance claims and electronic health record data. For the evaluation, Highmark Health dataset was used, consisting of records for members aged 18 + with at least one healthcare encounter. The IDS framework identified 720,882 members with depression (16.6% of the population). The framework identified 258,206 more members (5.9% of the population) compared to using diagnoses alone. The stratification rules revealed variability in prevalence, with 5.0% mild, 8.5% moderate, 2.2% severe, with the remaining 0.9% in unknown, remission, or minimal. The IDS rules escalated 46% of mild and 19% of moderate cases to higher severity compared to single indicator assessments. Expenses for severe depression were, on average, 2.5 times higher than for minimal. The IDS framework demonstrated utility in identifying members with depression by linking fragmented data sources. Aligning multiple indicators provided a more comprehensive identification and a more nuanced severity evaluation compared to individual data elements. This enables targeting of cost-effective digital self-care tools to milder cases while reserving higher cost interventions for the most severely ill, potentially reducing costs while maintaining health outcomes. Implementation of this integrative platform can help focus efforts on those with the highest need and bridge the gap in treating depression.
抑郁症是一种普遍存在的健康状况，对社会经济成果和个人幸福感都有重大影响。尽管有诊断工具，但现有的识别抑郁症严重程度的方法往往依赖单一指标方法，限制了准确性。这项回顾性研究评估了一个启用多参数分析的识别与分层（IDS）框架，该框架旨在通过利用健康保险理赔和电子健康记录数据来改善抑郁症的识别和严重程度分层。为了进行评估，使用了Highmark Health数据集，该数据集由18岁及以上且至少有一次医疗就诊记录的成员记录组成。IDS框架识别出720,882名患有抑郁症的成员（占总人口的16.6%）。与仅使用诊断相比，该框架多识别出258,206名成员（占总人口的5.9%）。分层规则显示患病率存在差异，轻度为5.0%，中度为8.5%，重度为2.2%，其余0.9%为未知、缓解或轻微状态。与单一指标评估相比，IDS规则将46%的轻度病例和19%的中度病例提升到更高严重程度。重度抑郁症的费用平均比轻微抑郁症高出2.5倍。IDS框架通过链接分散的数据源，在识别抑郁症患者方面显示出实用性。与单个数据元素相比，整合多个指标提供了更全面的识别和更细致入微的严重程度评估。这使得能够将具有成本效益的数字自我护理工具针对较轻病例，同时为最严重的患者保留更高成本的干预措施，有可能在维持健康结果的同时降低成本。实施这个综合平台有助于将努力集中在最有需求的人群上，并弥合抑郁症治疗的差距。
