Using machine learning to predict paediatric 30-day unplanned hospital readmissions: A case-control retrospective analysis of medical records, including written discharge documentation
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 stud...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
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CSIRO PUBLISHING
2021
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| Online Access: | http://purl.org/au-research/grants/arc/LP140100563 http://hdl.handle.net/20.500.11937/90897 |
| _version_ | 1848765454334558208 |
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| author | Zhou, Huaqiong Albrecht, Matthew Roberts, Pamela A. Porter, Paul Della, Philip |
| author_facet | Zhou, Huaqiong Albrecht, Matthew Roberts, Pamela A. Porter, Paul Della, Philip |
| author_sort | Zhou, Huaqiong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 (χ217 = 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. |
| first_indexed | 2025-11-14T11:35:30Z |
| format | Journal Article |
| id | curtin-20.500.11937-90897 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:35:30Z |
| publishDate | 2021 |
| publisher | CSIRO PUBLISHING |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-908972023-05-08T01:23:55Z Using machine learning to predict paediatric 30-day unplanned hospital readmissions: A case-control retrospective analysis of medical records, including written discharge documentation Zhou, Huaqiong Albrecht, Matthew Roberts, Pamela A. Porter, Paul Della, Philip Science & Technology Life Sciences & Biomedicine Health Care Sciences & Services Health Policy & Services administrative data clinical information discharge planning discharge summary follow-up plan machine learning medical records paediatric hospital readmissions paediatric unplanned readmissions retrospective analysis social history social predictors written discharge documentation Australia Case-Control Studies Child Documentation Humans Machine Learning Medical Records Patient Discharge Patient Readmission Retrospective Studies Risk Factors Western Australia Humans Patient Discharge Patient Readmission Medical Records Risk Factors Case-Control Studies Retrospective Studies Documentation Child Australia Western Australia Machine Learning 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 (χ217 = 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. 2021 Journal Article http://hdl.handle.net/20.500.11937/90897 10.1071/AH20062 English http://purl.org/au-research/grants/arc/LP140100563 http://creativecommons.org/licenses/by-nc-nd/4.0/ CSIRO PUBLISHING fulltext |
| spellingShingle | Science & Technology Life Sciences & Biomedicine Health Care Sciences & Services Health Policy & Services administrative data clinical information discharge planning discharge summary follow-up plan machine learning medical records paediatric hospital readmissions paediatric unplanned readmissions retrospective analysis social history social predictors written discharge documentation Australia Case-Control Studies Child Documentation Humans Machine Learning Medical Records Patient Discharge Patient Readmission Retrospective Studies Risk Factors Western Australia Humans Patient Discharge Patient Readmission Medical Records Risk Factors Case-Control Studies Retrospective Studies Documentation Child Australia Western Australia Machine Learning Zhou, Huaqiong Albrecht, Matthew Roberts, Pamela A. Porter, Paul Della, Philip Using machine learning to predict paediatric 30-day unplanned hospital readmissions: A case-control retrospective analysis of medical records, including written discharge documentation |
| title | Using machine learning to predict paediatric 30-day unplanned hospital readmissions: A case-control retrospective analysis of medical records, including written discharge documentation |
| title_full | Using machine learning to predict paediatric 30-day unplanned hospital readmissions: A case-control retrospective analysis of medical records, including written discharge documentation |
| title_fullStr | Using machine learning to predict paediatric 30-day unplanned hospital readmissions: A case-control retrospective analysis of medical records, including written discharge documentation |
| title_full_unstemmed | Using machine learning to predict paediatric 30-day unplanned hospital readmissions: A case-control retrospective analysis of medical records, including written discharge documentation |
| title_short | Using machine learning to predict paediatric 30-day unplanned hospital readmissions: A case-control retrospective analysis of medical records, including written discharge documentation |
| title_sort | using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation |
| topic | Science & Technology Life Sciences & Biomedicine Health Care Sciences & Services Health Policy & Services administrative data clinical information discharge planning discharge summary follow-up plan machine learning medical records paediatric hospital readmissions paediatric unplanned readmissions retrospective analysis social history social predictors written discharge documentation Australia Case-Control Studies Child Documentation Humans Machine Learning Medical Records Patient Discharge Patient Readmission Retrospective Studies Risk Factors Western Australia Humans Patient Discharge Patient Readmission Medical Records Risk Factors Case-Control Studies Retrospective Studies Documentation Child Australia Western Australia Machine Learning |
| url | http://purl.org/au-research/grants/arc/LP140100563 http://hdl.handle.net/20.500.11937/90897 |