亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation

文档 医学 逻辑回归 病历 审计 回顾性队列研究 统计的 急诊医学 医疗急救 统计 外科 计算机科学 会计 内科学 业务 数学 程序设计语言
作者
Huaqiong Zhou,Matthew A. Albrecht,Pamela D. Roberts,Paul M. Porter,Philip R. Della
出处
期刊:Australian Health Review [CSIRO Publishing]
卷期号:45 (3): 328-337 被引量:4
标识
DOI:10.1071/ah20062
摘要

Objectives To assess whether adding clinical information and written discharge documentation variables improves prediction of paediatric 30-day same-hospital unplanned readmission compared with predictions based on administrative information alone. Methods A retrospective matched case-control study audited the medical records of patients discharged from a tertiary paediatric hospital in Western Australia (WA) between January 2010 and December 2014. A random selection of 470 patients with unplanned readmissions (out of 3330) were matched to 470 patients without readmissions based on age, sex, and principal diagnosis at the index admission. Prediction utility of three groups of variables (administrative, administrative and clinical, and administrative, clinical and written discharge documentation) were assessed using standard logistic regression and machine learning. Results Inclusion of written discharge documentation variables significantly improved prediction of readmission compared with models that used only administrative and/or clinical variables in standard logistic regression analysis (χ2 17 = 29.4, P = 0.03). Highest prediction accuracy was obtained using a gradient boosted tree model (C-statistic = 0.654), followed closely by random forest and elastic net modelling approaches. Variables highlighted as important for prediction included patients’ social history (legal custody or patient was under the care of the Department for Child Protection), languages spoken other than English, completeness of nursing admission and discharge planning documentation, and timing of issuing discharge summary. Conclusions The variables of significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary add value to prediction models. What is known about the topic? Despite written discharge documentation playing a critical role in the continuity of care for paediatric patients, limited research has examined its association with, and ability to predict, unplanned hospital readmissions. Machine learning approaches have been applied to various health conditions and demonstrated improved predictive accuracy. However, few published studies have used machine learning to predict paediatric readmissions. What does this paper add? This paper presents the findings of the first known study in Australia to assess and report that written discharge documentation and clinical information improves unplanned rehospitalisation prediction accuracy in a paediatric cohort compared with administrative data alone. It is also the first known published study to use machine learning for the prediction of paediatric same-hospital unplanned readmission in Australia. The results show improved predictive performance of the machine learning approach compared with standard logistic regression. What are the implications for practitioners? The identified social and written discharge documentation predictors could be translated into clinical practice through improved discharge planning and processes, to prevent paediatric 30-day all-cause same-hospital unplanned readmission. The predictors identified in this study include significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
7秒前
千早爱音应助科研通管家采纳,获得20
7秒前
11秒前
ding应助典雅的曼文采纳,获得10
15秒前
add5a2发布了新的文献求助10
16秒前
26秒前
手撕蛋完成签到 ,获得积分10
1分钟前
1分钟前
香蕉觅云应助Biutii采纳,获得10
1分钟前
笨蛋美女完成签到 ,获得积分10
1分钟前
1分钟前
浮游应助李剑鸿采纳,获得100
1分钟前
1分钟前
2分钟前
BNN1203381110完成签到 ,获得积分10
2分钟前
2分钟前
王葆蕾完成签到 ,获得积分10
2分钟前
顺心的满天完成签到,获得积分10
2分钟前
2分钟前
Leone发布了新的文献求助10
2分钟前
Leone完成签到,获得积分10
2分钟前
时尚雁玉完成签到,获得积分10
2分钟前
顺利奇异果完成签到,获得积分20
2分钟前
ruixuezhou完成签到,获得积分10
3分钟前
add5a2完成签到 ,获得积分10
3分钟前
3分钟前
执意完成签到 ,获得积分10
3分钟前
3分钟前
3分钟前
123发布了新的文献求助10
4分钟前
科研通AI6应助科研通管家采纳,获得150
4分钟前
4分钟前
充电宝应助123采纳,获得10
4分钟前
碝磩完成签到 ,获得积分10
4分钟前
浮游应助甜美的起眸采纳,获得30
4分钟前
4分钟前
时尚雁玉发布了新的文献求助10
4分钟前
4分钟前
Eileen完成签到 ,获得积分10
4分钟前
zs完成签到 ,获得积分10
5分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Kolmogorov, A. N. Qualitative study of mathematical models of populations. Problems of Cybernetics, 1972, 25, 100-106 800
FUNDAMENTAL STUDY OF ADAPTIVE CONTROL SYSTEMS 500
微纳米加工技术及其应用 500
Nanoelectronics and Information Technology: Advanced Electronic Materials and Novel Devices 500
Performance optimization of advanced vapor compression systems working with low-GWP refrigerants using numerical and experimental methods 500
Constitutional and Administrative Law 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5302544
求助须知:如何正确求助?哪些是违规求助? 4449661
关于积分的说明 13848586
捐赠科研通 4335935
什么是DOI,文献DOI怎么找? 2380642
邀请新用户注册赠送积分活动 1375637
关于科研通互助平台的介绍 1341930