The Dartmouth Institute for Health Policy & Clinical Practice and Biomedical Data Science, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
Department of Computer Science, Dartmouth College, Hanover, New Hampshire, USA.
BMJ Open. 2022 May 19;12(5):e056328. doi: 10.1136/bmjopen-2021-056328.
Measures of variation in end-of-life (EOL) care intensity across hospitals are typically summarised using unidimensional measures. These measures do not capture the full dimensionality of complex clinical care trajectories over time that are needed to inform quality improvement efforts. The objective is to develop a novel visual map of EOL care trajectories that illustrates multidimensional utilisation over time.
United States' National Cancer Institute or National Comprehensive Cancer Network (NCI/NCCN)-designated hospitals.
We identified Medicare claims for fee-for-service beneficiaries with poor prognosis cancers who died between April and December 2016 and received the preponderance of treatment in the last 6 months of life at an NCI/NCCN-designated hospital.
For each beneficiary, we transformed each Medicare claim into two elements to generate a two-dimensional individual-level heatmap. On the y-axis, each claim was classified into a categorical description of the service delivered by a healthcare resource. On the x-axis, the date for each claim was converted into the day number prior to death it occurred on. We then summed up individual-level heatmaps of patients attributed to each hospital to generate two-dimensional hospital-level heatmaps. We used four case studies to illustrate the feasibility of interpreting these heatmaps and to shed light on how they might be used to guide value-based, quality improvement initiatives.
We identified nine distinct EOL care delivery patterns from hospital-level heatmaps based on signal intensity and patterns for inpatient, outpatient and home-based hospice services. We illustrate that in most cases, heatmaps illustrating patterns of multidimensional healthcare utilisation over time provide more information about care trajectories and highlight more heterogeneity than current unidimensional measures.
This study illustrates the feasibility of representing multidimensional EOL utilisation over time as a heatmap. These heatmaps may provide potentially actionable insights into hospital-level care delivery patterns, and the approach may generalise to other serious illness populations.
医院间终末期（EOL）护理强度的变化通常使用单一维度的指标来总结。这些指标无法捕捉到随着时间的推移，复杂临床护理轨迹的全部维度，而这些维度对于改进质量是必要的。本研究的目的是开发一种新的 EOL 护理轨迹的可视化图谱，以说明随时间推移的多维利用情况。
美国国家癌症研究所或国家综合癌症网络（NCI/NCCN）指定的医院。
我们确定了在 2016 年 4 月至 12 月期间患有预后不良的癌症且在 NCI/NCCN 指定医院接受了生命最后 6 个月的大部分治疗的医疗保险服务付费受益人的医疗记录。
对于每个受益人，我们将每个医疗保险索赔转换为两个元素，以生成二维个体水平热图。在 y 轴上，每个索赔被分类为医疗资源提供的服务的类别描述。在 x 轴上，将每个索赔的日期转换为它发生的死亡日期之前的天数。然后，我们对每个医院分配的患者的个体水平热图进行求和，以生成二维医院水平热图。我们使用四个案例研究来说明解释这些热图的可行性，并阐明它们如何用于指导基于价值的质量改进计划。
我们根据住院、门诊和家庭 Hospice 服务的信号强度和模式，从医院水平热图中确定了九种不同的 EOL 护理提供模式。我们表明，在大多数情况下，随着时间的推移，多维医疗利用模式的热图提供了更多关于护理轨迹的信息，并突出了更多的异质性，而不是当前的单一维度指标。
本研究说明了用热图来表示随时间推移的多维 EOL 利用的可行性。这些热图可能为医院层面的护理提供模式提供潜在的可操作见解，并且该方法可能适用于其他严重疾病人群。
