Barrett Meredith A, Allen Angier, Vuong Vy T, Zhu Daniel, Rainey Allison J, McConnell Will M, Kho Abel N, Salisbury Annette, French Dustin D
Resmed Science Center, San Diego, CA, USA.
Resmed Science Center, San Diego, CA, USA.
J Am Med Dir Assoc. 2025 Jul;26(7):105680. doi: 10.1016/j.jamda.2025.105680. Epub 2025 May 28.
This study evaluated the impact of an electronic health record (EHR) system enhanced with artificial intelligence and machine learning (EHR+AI) on quality measures in nursing homes in the United States.
A difference-in-differences (DiD) design was used to estimate the effect of the EHR+AI intervention on quality measures among nursing homes with and without the AI intervention. The intervention included a feature that analyzed 150 daily clinical data elements per patient, alerting staff to changes in conditions, acuity, fall risk, and medication monitoring.
The analysis included 218 nursing homes, with 94 using EHR+AI and 124 using EHR only. Baseline differences in organizational characteristics, acuity index, neighborhood affluence, and racial or ethnic composition were evaluated.
Eighteen quality measures from the Centers for Medicare and Medicaid Services (CMS) were analyzed over 6 quarters before and 5 quarters after EHR+AI implementation. A DiD approach with linear mixed effects models was used, adjusting for significantly different baseline characteristics.
Statistically greater improvements were observed in 16 of 18 quality measures (89%) in EHR+AI sites, with 11 measures (61%) also meeting the parallel trends assumption. Notably, EHR+AI sites demonstrated larger improvements in functional status, including greater reductions in major falls (-9%, 95% CI -17, -1; P = .034) and residents needing help with daily activities (-22%, 95% CI -29, -15; P < .001), and a 5% larger increase in residents who made improvements in function (95% CI 2, 7; P = .001). Higher decline in depressive symptoms and the use of antipsychotic, antianxiety, or hypnotic medications were also noted. These results were observed among sites with higher patient acuity and neighborhood diversity.
These findings suggest that an EHR enhanced with AI can improve the quality and efficiency of care in nursing homes through real-time monitoring and response of resident assessment protocol triggers for clinical modification, but further research is needed.
本研究评估了增强人工智能和机器学习的电子健康记录(EHR+AI)系统对美国养老院质量指标的影响。
采用差异-in-差异(DiD)设计来估计EHR+AI干预对有和没有AI干预的养老院质量指标的影响。该干预包括一项功能,可分析每位患者每天150个临床数据元素,提醒工作人员注意病情、 acuity、跌倒风险和药物监测的变化。
分析包括218家养老院,其中94家使用EHR+AI,124家仅使用EHR。评估了组织特征、 acuity指数、邻里富裕程度和种族或族裔构成的基线差异。
在实施EHR+AI之前的6个季度和之后的5个季度,分析了来自医疗保险和医疗补助服务中心(CMS)的18项质量指标。使用带有线性混合效应模型的DiD方法,对显著不同的基线特征进行了调整。
在EHR+AI站点的18项质量指标中的16项(89%)观察到统计学上更大的改善,其中11项指标(61%)也符合平行趋势假设。值得注意的是,EHR+AI站点在功能状态方面表现出更大的改善,包括重大跌倒的更大减少(-9%,95%CI -17,-1;P = 0.034)和需要日常活动帮助的居民减少(-22%,95%CI -29,-15;P < 0.001),以及功能改善的居民增加5%(95%CI 2,7;P = 0.001)。还注意到抑郁症状以及抗精神病药、抗焦虑药或催眠药的使用下降幅度更大。这些结果在患者 acuity较高和邻里多样性较高的站点中观察到。
这些发现表明,增强AI的EHR可以通过对居民评估协议触发因素进行实时监测和响应以进行临床修改,从而提高养老院护理的质量和效率,但仍需要进一步研究。