Predicting length of stay from an electronic patient record system: a primary total knee replacement example.
Predicting length of stay from an electronic patient record system: a primary total knee replacement example.
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DOI：
10.1186/1472-6947-14-26
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发表时间：
2014-04-04
影响因子：
3.5
通讯作者：
Potts HW
中科院分区：
文献类型：
--
作者：
Carter EM;Potts HW
以初次全膝关节置换术为例，研究是否可以从电子病历系统中识别出显著影响住院时间的因素。研究是否可以根据这些因素建立一个模型来预测住院时间，以帮助资源规划和患者对住院时间的期望。从英国一家医院的电子病历系统中提取2007年1月至2011年12月（n = 2，130）初次全膝关节手术出院的数据，并使用Mann-Whitney和Kruskal-Wallis检验（离散数据）和斯皮尔曼相关系数（连续数据）分析其对住院时间的影响。使用Poisson回归和负二项建模技术对预测初次全膝关节置换术住院时间的模型进行了测试。发现对住院时间有显著影响的因素是年龄、性别、顾问、出院目的地、贫困和种族。对这些变量应用负二项模型是成功的。该模型预测了住院4-6天的患者（约50%的住院患者）的住院时间，在2天内的准确率为75%（模型数据）。总体而言，该模型预测5年内停留的总天数仅比实际多88天，上升了6.9%（测试数据）。通过对电子病历系统中的变量进行分析，可以找到有关住院时间的有价值信息。模型可以成功地创建，以帮助改善资源规划，并从其中可以产生一个简单的决策支持系统，以帮助病人对他们的住院时间的期望。
To investigate whether factors can be identified that significantly affect hospital length of stay from those available in an electronic patient record system, using primary total knee replacements as an example. To investigate whether a model can be produced to predict the length of stay based on these factors to help resource planning and patient expectations on their length of stay. Data were extracted from the electronic patient record system for discharges from primary total knee operations from January 2007 to December 2011 (n = 2,130) at one UK hospital and analysed for their effect on length of stay using Mann-Whitney and Kruskal-Wallis tests for discrete data and Spearman’s correlation coefficient for continuous data. Models for predicting length of stay for primary total knee replacements were tested using the Poisson regression and the negative binomial modelling techniques. Factors found to have a significant effect on length of stay were age, gender, consultant, discharge destination, deprivation and ethnicity. Applying a negative binomial model to these variables was successful. The model predicted the length of stay of those patients who stayed 4–6 days (~50% of admissions) with 75% accuracy within 2 days (model data). Overall, the model predicted the total days stayed over 5 years to be only 88 days more than actual, a 6.9% uplift (test data). Valuable information can be found about length of stay from the analysis of variables easily extracted from an electronic patient record system. Models can be successfully created to help improve resource planning and from which a simple decision support system can be produced to help patient expectation on their length of stay.
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影响因子：
5.7
作者：
Salinas, FV;Liu, SS;Mulroy, MF
通讯作者：
Mulroy, MF
DOI：
10.1308/003588413x13511609958299
发表时间：
2013-05-01
影响因子：
1.4
作者：
Sharma, A.;Muir, R.;Wilson-MacDonald, J.
通讯作者：
Wilson-MacDonald, J.
影响因子：
3
作者：
Zheng, FY;Cammisa, FP;Khan, SN
通讯作者：
Khan, SN
影响因子：
2.7
作者：
Raut, Shruti;Mertes, Stephan Christian;Khanduja, Vikas
通讯作者：
Khanduja, Vikas
影响因子：
2.3
作者：
Huang, S. -W.;Chen, P. -H.;Chou, Y. -H.
通讯作者：
Chou, Y. -H.