The empirical literature on hospital report cards typically assumes that report card cannot interact with consumer’s private learning. This study examines the impact of the implementation of FL hospital quality repor...The empirical literature on hospital report cards typically assumes that report card cannot interact with consumer’s private learning. This study examines the impact of the implementation of FL hospital quality reporting system in late 2004 on hospital admission patterns using a pre-post difference-in-difference design. The estimation model allows for the possibility that report-card learning may interact with non-report-card learning. The study sample is comprised of all patients admitted to any FL hospital between 2000 and 2008 with a principal diagnosis of acute myocardial infarction (AMI). We find that hospital admission patterns for AMI patients did not respond to report card information. However, we find evidence consistent with the possibility that the implementation of a report card system may stimulate consumers (either patients or physicians) to seek higher quality hospitals through private information channels.展开更多
尽管基于城市轨道交通自动售检票(automatic fare collection,AFC)系统采集的智能卡数据(smart card data,SCD)能够精准记录人们的出行时间和地点,但无法直接反映出行目的或活动类型.本研究提出一种方法,将约束种子K-means算法的站点聚...尽管基于城市轨道交通自动售检票(automatic fare collection,AFC)系统采集的智能卡数据(smart card data,SCD)能够精准记录人们的出行时间和地点,但无法直接反映出行目的或活动类型.本研究提出一种方法,将约束种子K-means算法的站点聚类与隐含狄利克雷分布(latent Dirichlet allocation,LDA)模型的客流出行目的挖掘相结合,以揭示城市轨道交通客流出行数据中的潜在活动模式.首先,基于车站周边的人口特征、客流特征及兴趣点(points of interest,POI)分布,使用约束种子K-means算法将站点划分为8类:就业集聚型、居住集聚型、职住复合型、商业中心型、旅游景点型、综合枢纽型、对外枢纽型以及客流培育型.其次,基于出站时间、活动时长、起点车站类型以及终点车站类型构建了LDA模型.该模型成功识别出5类主要活动,分别为购物消费、工作、回家、休闲旅游及其他.此外,这些模式进一步细分为若干子主题,每个子主题在时间和空间特征上具有显著差异,为深入理解节假日城市轨道交通客流出行行为提供了理论支持.展开更多
文摘The empirical literature on hospital report cards typically assumes that report card cannot interact with consumer’s private learning. This study examines the impact of the implementation of FL hospital quality reporting system in late 2004 on hospital admission patterns using a pre-post difference-in-difference design. The estimation model allows for the possibility that report-card learning may interact with non-report-card learning. The study sample is comprised of all patients admitted to any FL hospital between 2000 and 2008 with a principal diagnosis of acute myocardial infarction (AMI). We find that hospital admission patterns for AMI patients did not respond to report card information. However, we find evidence consistent with the possibility that the implementation of a report card system may stimulate consumers (either patients or physicians) to seek higher quality hospitals through private information channels.
文摘尽管基于城市轨道交通自动售检票(automatic fare collection,AFC)系统采集的智能卡数据(smart card data,SCD)能够精准记录人们的出行时间和地点,但无法直接反映出行目的或活动类型.本研究提出一种方法,将约束种子K-means算法的站点聚类与隐含狄利克雷分布(latent Dirichlet allocation,LDA)模型的客流出行目的挖掘相结合,以揭示城市轨道交通客流出行数据中的潜在活动模式.首先,基于车站周边的人口特征、客流特征及兴趣点(points of interest,POI)分布,使用约束种子K-means算法将站点划分为8类:就业集聚型、居住集聚型、职住复合型、商业中心型、旅游景点型、综合枢纽型、对外枢纽型以及客流培育型.其次,基于出站时间、活动时长、起点车站类型以及终点车站类型构建了LDA模型.该模型成功识别出5类主要活动,分别为购物消费、工作、回家、休闲旅游及其他.此外,这些模式进一步细分为若干子主题,每个子主题在时间和空间特征上具有显著差异,为深入理解节假日城市轨道交通客流出行行为提供了理论支持.
