Shah Baiju R, Hux Janet E, Laupacis Andreas, Zinman Bernard, Cauch-Dudek Karen, Booth Gillian L
Institute for Clinical Evaluative Sciences, G106-2075 Bayview Avenue, Toronto, ON, Canada M4N 3M5.
Health Serv Res. 2007 Aug;42(4):1783-96. doi: 10.1111/j.1475-6773.2006.00681.x.
To validate algorithms using administrative data that characterize ambulatory physician care for patients with a chronic disease.
Seven-hundred and eighty-one people with diabetes were recruited mostly from community pharmacies to complete a written questionnaire about their physician utilization in 2002. These data were linked with administrative databases detailing health service utilization.
An administrative data algorithm was defined that identified whether or not patients received specialist care, and it was tested for agreement with self-report. Other algorithms, which assigned each patient to a primary care and specialist physician, were tested for concordance with self-reported regular providers of care.
The algorithm to identify whether participants received specialist care had 80.4 percent agreement with questionnaire responses (kappa=0.59). Compared with self-report, administrative data had a sensitivity of 68.9 percent and specificity 88.3 percent for identifying specialist care. The best administrative data algorithm to assign each participant's regular primary care and specialist providers was concordant with self-report in 82.6 and 78.2 percent of cases, respectively.
Administrative data algorithms can accurately match self-reported ambulatory physician utilization.
使用行政数据验证用于描述慢性病患者门诊医生护理情况的算法。
2002年,主要从社区药房招募了781名糖尿病患者,让他们填写一份关于其医生使用情况的书面问卷。这些数据与详细记录医疗服务使用情况的行政数据库相关联。
定义了一种行政数据算法,用于确定患者是否接受了专科护理,并对其与自我报告的一致性进行了测试。还测试了其他算法,这些算法将每位患者分配给一名初级保健医生和一名专科医生,并与自我报告的常规护理提供者进行一致性比较。
用于确定参与者是否接受专科护理的算法与问卷回复的一致性为80.4%(kappa=0.59)。与自我报告相比,行政数据识别专科护理的敏感性为68.9%,特异性为88.3%。将每位参与者的常规初级保健和专科护理提供者分配到最佳的行政数据算法,分别在82.6%和78.2%的病例中与自我报告一致。
行政数据算法能够准确匹配自我报告的门诊医生使用情况。