University of Southampton, Southampton, UK.
Health Care Manag Sci. 2018 Jun;21(2):259-268. doi: 10.1007/s10729-017-9399-1. Epub 2017 Apr 11.
As an aid to predicting future hospital admissions, we compare use of the Multinomial Logit and the Utility Maximising Nested Logit models to describe how patients choose their hospitals. The models are fitted to real data from Derbyshire, United Kingdom, which lists the postcodes of more than 200,000 admissions to six different local hospitals. Both elective and emergency admissions are analysed for this mixed urban/rural area. For characteristics that may affect a patient's choice of hospital, we consider the distance of the patient from the hospital, the number of beds at the hospital and the number of car parking spaces available at the hospital, as well as several statistics publicly available on National Health Service (NHS) websites: an average waiting time, the patient survey score for ward cleanliness, the patient safety score and the inpatient survey score for overall care. The Multinomial Logit model is successfully fitted to the data. Results obtained with the Utility Maximising Nested Logit model show that nesting according to city or town may be invalid for these data; in other words, the choice of hospital does not appear to be preceded by choice of city. In all of the analysis carried out, distance appears to be one of the main influences on a patient's choice of hospital rather than statistics available on the Internet.
为了帮助预测未来的住院情况,我们将比较多项逻辑回归模型和效用最大化嵌套逻辑回归模型,以描述患者如何选择医院。该模型是根据英国德比郡的实际数据拟合的,其中列出了 6 家不同当地医院超过 20 万次的入院记录。对这个混合城市/农村地区的选择性和紧急性入院情况进行了分析。对于可能影响患者选择医院的特征,我们考虑了患者与医院的距离、医院的床位数量以及医院可用的停车位数量,以及 NHS 网站上公布的一些统计数据:平均等待时间、病房清洁度的患者调查评分、患者安全评分和整体护理的住院患者调查评分。多项逻辑回归模型成功地对数据进行了拟合。效用最大化嵌套逻辑回归模型的结果表明,根据城市或城镇进行嵌套对于这些数据可能无效;换句话说,医院的选择似乎不是城市选择的前提。在所有进行的分析中,距离似乎是影响患者选择医院的主要因素之一,而不是互联网上可用的统计数据。