Center for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA.
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA.
BMC Endocr Disord. 2020 Aug 17;20(1):125. doi: 10.1186/s12902-020-00609-1.
Diabetes is a leading cause of Medicare spending; predicting which individuals are likely to be costly is essential for targeting interventions. Current approaches generally focus on composite measures, short time-horizons, or patients who are already high utilizers, whose costs may be harder to modify. Thus, we used data-driven methods to classify unique clusters in Medicare claims who were initially low utilizers by their diabetes spending patterns in subsequent years and used machine learning to predict these patterns.
We identified beneficiaries with type 2 diabetes whose spending was in the bottom 90% of diabetes care spending in a one-year baseline period in Medicare fee-for-service data. We used group-based trajectory modeling to classify unique clusters of patients by diabetes-related spending patterns over a two-year follow-up. Prediction models were estimated with generalized boosted regression, a machine learning method, using sets of all baseline predictors, diabetes predictors, and predictors that are potentially-modifiable through interventions. Each model was evaluated through C-statistics and 5-fold cross-validation.
Among 33,789 beneficiaries (baseline median diabetes spending: $4153), we identified 5 distinct spending patterns that could largely be predicted; of these, 68.1% of patients had consistent spending, 25.3% had spending that rose quickly, and 6.6% of patients had spending that rose progressively. The ability to predict these groups was moderate (validated C-statistics: 0.63 to 0.87). The most influential factors for those with progressively rising spending were age, generosity of coverage, prior spending, and medication adherence.
Patients with type 2 diabetes who were initially low spenders exhibit distinct subsequent long-term patterns of diabetes spending; membership in these patterns can be largely predicted with data-driven methods. These findings as well as applications of the overall approach could potentially inform the design and timing of diabetes or cost-containment interventions, such as medication adherence or interventions that enhance access to care, among patients with type 2 diabetes.
糖尿病是医疗保险支出的主要原因；预测哪些人可能费用较高对于针对干预措施至关重要。目前的方法通常侧重于综合措施、短期预测或已经是高利用者的患者，而这些患者的成本可能更难改变。因此，我们使用数据驱动的方法根据糖尿病患者在后续几年的支出模式，将医疗保险费用支付服务数据中糖尿病支出处于前 90%的患者最初归为低利用者，并使用机器学习来预测这些模式。
我们确定了在医疗保险费用支付服务数据中，在一年的基线期内，糖尿病支出处于糖尿病护理支出的 90%以下的 2 型糖尿病受益人的特征。我们使用基于群组的轨迹建模，根据两年随访期间的糖尿病相关支出模式对患者进行分类。使用广义增强回归（一种机器学习方法），根据所有基线预测因子、糖尿病预测因子和可能通过干预改变的预测因子，对预测模型进行估计。通过 C 统计量和 5 折交叉验证来评估每个模型。
在 33789 名受益人中（基线中位数糖尿病支出：$4153），我们确定了 5 种不同的支出模式，这些模式可以进行预测；其中，68.1%的患者支出模式一致，25.3%的患者支出模式快速上升，6.6%的患者支出模式逐渐上升。预测这些组别的能力为中等（验证 C 统计量：0.63 至 0.87）。对于支出逐渐上升的患者，最重要的影响因素是年龄、保险范围的慷慨程度、先前的支出和药物依从性。
最初为低支出的 2 型糖尿病患者表现出明显的长期糖尿病支出模式；这些模式的成员身份可以通过数据驱动的方法进行预测。这些发现以及整体方法的应用有可能为 2 型糖尿病患者的糖尿病或成本控制干预措施的设计和时间安排提供信息，例如药物依从性或提高获得医疗保健机会的干预措施。
