Department of Health Services, Policy and Practice, School of Public Health, Brown University, Providence, RI, USA.
Department of Health Services, Policy and Practice, School of Public Health, Brown University, Providence, RI, USA; Center for Gerontology and Healthcare Research, School of Public Health, Brown University, Providence, RI, USA; Center for Long-Term Care Quality and Innovation, School of Public Health, Brown University, Providence, RI, USA.
J Am Med Dir Assoc. 2024 Dec;25(12):105289. doi: 10.1016/j.jamda.2024.105289. Epub 2024 Sep 27.
Agitated behaviors (behaviors) are common in nursing home (NH) residents with Alzheimer's disease and related dementias (ADRD). Pragmatic trials of behavior management interventions rely on routinely collected Minimum Data Set (MDS) data to evaluate study outcomes, despite known underreporting. We describe a method to augment MDS-based behavioral measures with structured and unstructured data from NH electronic medical records (EMR).
Repeated cross-sectional analyses of EMR data from a single multistate NH corporation.
Long-stay residents (at least 90 days in NH) with ADRD from January 2020 through August 2022.
Quarterly and annual assessments of NH residents with ADRD during the study period were identified. For MDS, any behavior was defined as a score of 1 or higher on the Agitated and Reactive Behavior Scale. For structured EMR data, any behavior was defined as increased resident agitation, verbal aggression, or physical aggression on the Interventions to Reduce Acute Care Transfers, Change in Condition form (INTERACT). For unstructured EMR data, any behavior was defined using keyword searches of free-text orders.
A total of 77,936 MDS assessments for 19,705 long-stay residents with ADRD in 322 NHs were identified; 14.8% (SD 35.6) of residents had behaviors per month using MDS alone, 16.2% (SD 36.9) using MDS and INTERACT, and 18.6% (SD 38.9) using MDS, INTERACT, and orders. Supplementing MDS with EMR data increased behavior identification by 3.8 percentage points (a 25.7% relative increase). Less than 0.5% had behaviors noted in all 3 sources consistently across study months.
Using EMR data increased detectable behaviors vs the MDS alone. The 3 sources captured different types of behaviors and using them together may be a more comprehensive identification strategy. These results are important for better targeting of interventions and allocation of resources to improve the quality of life for NH residents with ADRD-related behaviors.
激越行为（behavior）在患有阿尔茨海默病和相关痴呆症（ADRD）的养老院（NH）居民中很常见。行为管理干预措施的实用临床试验依赖于常规收集的最小数据集合（MDS）数据来评估研究结果，尽管已知存在漏报。我们描述了一种利用 NH 电子病历（EMR）中的结构化和非结构化数据来扩充基于 MDS 的行为测量的方法。
对单个多州 NH 公司的 EMR 数据进行重复的横截面分析。
2020 年 1 月至 2022 年 8 月期间患有 ADRD 的长期居住者（在 NH 至少 90 天）。
在研究期间，每季度和每年评估患有 ADRD 的 NH 居民。对于 MDS，任何行为均定义为在激越和反应性行为量表上得分为 1 或更高。对于结构化 EMR 数据，任何行为均定义为干预减少急性护理转移、状况变化表（INTERACT）上居民激越、言语攻击或身体攻击的增加。对于非结构化 EMR 数据，任何行为均使用对自由文本订单的关键字搜索来定义。
确定了 322 家 NH 中有 19705 名患有 ADRD 的长期居住者的 77936 次 MDS 评估；每月单独使用 MDS 记录行为的居民占 14.8%（SD 35.6），使用 MDS 和 INTERACT 的居民占 16.2%（SD 36.9），使用 MDS、INTERACT 和订单的居民占 18.6%（SD 38.9）。用 EMR 数据补充 MDS 可将行为识别率提高 3.8 个百分点（相对增加 25.7%）。在整个研究月份中，不到 0.5%的人始终在所有 3 个来源中记录有行为。
与 MDS 单独使用相比，使用 EMR 数据增加了可检测的行为。这 3 个来源捕获了不同类型的行为，同时使用它们可能是一种更全面的识别策略。这些结果对于更好地针对干预措施并分配资源以改善患有 ADRD 相关行为的 NH 居民的生活质量非常重要。
