Department of Biostatistics (AWM),University of Kentucky, Lexington, Kentucky
Departments of Statistics and Biostatistics (RC, RK) University of Kentucky, Lexington, Kentucky
Med Decis Making. 2012 Jan-Feb;32(1):93-104. doi: 10.1177/0272989X10394462. Epub 2011 Mar 10.
This study sought to identify factors that increase or decrease patient time with a physician, determine which combinations of factors are associated with the shortest and longest visits to physicians, quantify how much physicians contribute to variation in the time they spend with patients, and assess how well patient time with a physician can be predicted. Data were acquired from a modified replication of the 1997-1998 National Ambulatory Medical Care Survey, administered by the Kentucky Ambulatory Network to 56 primary care clinicians at 24 practice sites in 2001 and 2002. A regression tree and a linear mixed model (LMM) were used to discover multivariate associations between patient time with a physician and 22 potentially predictive factors. Patient time with a physician was related to the number of diagnoses, whether non-illness care was received, and whether the patient had been seen before by the physician or someone at the practice. Approximately 38% of the variation in patient time with a physician was accounted for by predictive factors in the tree; roughly 33% was explained by predictive factors in the LMM, with another 12% linked to physicians. Knowledge of patient characteristics and needs could be used to schedule office visits, potentially improving patient flow through a clinic and reducing waiting times.
本研究旨在确定增加或减少患者与医生相处时间的因素,确定与医生就诊时间最短和最长相关的因素组合,量化医生在与患者相处时间上的差异,并评估医生与患者相处时间的可预测性。数据来自于 1997-1998 年全国门诊医疗调查的修改复制,由肯塔基州门诊网络于 2001 年和 2002 年在 24 个实践地点由 56 名初级保健临床医生进行管理。回归树和线性混合模型(LMM)用于发现患者与医生相处时间与 22 个潜在预测因素之间的多变量关联。患者与医生相处的时间与诊断数量、是否接受非疾病护理以及患者是否曾由医生或该诊所的其他人就诊有关。预测因素在树中解释了患者与医生相处时间变化的约 38%;预测因素在 LMM 中解释了大约 33%,另有 12%与医生有关。了解患者的特征和需求可以用于安排门诊,有可能改善诊所的患者流量并减少等待时间。