VA Salt Lake City Health Care System, Salt Lake City, Utah.
Department of Pharmacotherapy, University of Utah College of Pharmacy, Salt Lake City, Utah.
Int J Eat Disord. 2015 Dec;48(8):1082-91. doi: 10.1002/eat.22427. Epub 2015 May 8.
The objective of this study was to compare the one-year healthcare costs and utilization of patients with binge-eating disorder (BED) to patients with eating disorder not otherwise specified without BED (EDNOS-only) and to matched patients without an eating disorder (NED).
A natural language processing (NLP) algorithm identified adults with BED from clinical notes in the Department of Veterans Affairs (VA) electronic health record database from 2000 to 2011. Patients with EDNOS-only were identified using ICD-9 code (307.50) and those with NLP-identified BED were excluded. First diagnosis date defined the index date for both groups. Patients with NED were randomly matched 4:1, as available, to patients with BED on age, sex, BMI, depression diagnosis, and index month. Patients with cost data (2005-2011) were included. Total healthcare, inpatient, outpatient, and pharmacy costs were examined. Generalized linear models were used to compare total one-year healthcare costs while adjusting for baseline patient characteristics.
There were 257 BED, 743 EDNOS-only, and 823 matched NED patients identified. The mean (SD) total unadjusted one-year costs, in 2011 US dollars, were $33,716 ($38,928) for BED, $37,052 ($40,719) for EDNOS-only, and $19,548 ($35,780) for NED patients. When adjusting for patient characteristics, BED patients had one-year total healthcare costs $5,589 higher than EDNOS-only (p = 0.06) and $18,152 higher than matched NED patients (p < 0.001).
This study is the first to use NLP to identify BED patients and quantify their healthcare costs and utilization. Patients with BED had similar one-year total healthcare costs to EDNOS-only patients, but significantly higher costs than patients with NED.
本研究旨在比较暴食障碍(BED)患者与非暴食障碍特定饮食失调(EDNOS-only)患者以及无饮食障碍(NED)患者的一年医疗保健成本和利用情况。
通过自然语言处理(NLP)算法从 2000 年至 2011 年退伍军人事务部(VA)电子健康记录数据库的临床记录中识别出 BED 成年患者。使用 ICD-9 代码(307.50)识别出 EDNOS-only 患者,并排除了 NLP 识别的 BED 患者。首次诊断日期定义了两组的索引日期。按年龄、性别、BMI、抑郁诊断和索引月份,以 4:1 的比例随机匹配 NED 患者。包括有成本数据(2005-2011 年)的患者。检查了总医疗保健、住院、门诊和药房费用。使用广义线性模型比较了总一年医疗保健成本,同时调整了基线患者特征。
共确定了 257 名 BED、743 名 EDNOS-only 和 823 名匹配的 NED 患者。未经调整的 2011 年美元一年总费用平均值(标准差)分别为 BED 患者 33716(38928)美元、EDNOS-only 患者 37052(40719)美元和 NED 患者 19548(35780)美元。调整患者特征后,BED 患者一年总医疗保健费用比 EDNOS-only 患者高 5589 美元(p=0.06),比匹配的 NED 患者高 18152 美元(p<0.001)。
这是第一项使用 NLP 识别 BED 患者并量化其医疗保健成本和利用情况的研究。BED 患者的一年总医疗保健费用与 EDNOS-only 患者相似,但明显高于 NED 患者。