Leveraging Electronic Health Records and Machine Learning to Tailor Nursing Care for Patients at High Risk for Readmissions

Brom, Heather; Carthon, J. Margo Brooks; Ikeaba, Uchechukwu; Chittams, Jesse

doi:10.1097/ncq.0000000000000412

Cited by 27 publications

(29 citation statements)

References 30 publications

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“…The details of the types of model validation used in each study could be found in Additional supporting file 2 : Supporting Information Table S2. Twenty-one studies (49%) randomly partitioned data into training/testing parts or training/validation/testing parts [ 51 , 53 , 55 , 56 , 58 , 60 , 65 – 67 , 70 , 73 , 74 , 76 , 78 , 79 , 82 – 87 ], and most of these studies utilized some form of cross-validation in the training sets for model construction. Thirteen studies (30%) validated using various types of resampling procedures, such as k-fold cross-validation [ 49 , 54 , 57 , 61 , 68 , 69 , 77 , 81 ] (19), stratified k-fold cross-validation [ 61 , 80 ], repeated k-fold cross-validation [ 48 ], and repeated random test-train splits [ 50 ].…”

Section: Resultsmentioning

confidence: 99%

Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Huang

Talwar

Chatterjee

et al. 2021

BMC Med Res Methodol

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Background Advances in machine learning (ML) provide great opportunities in the prediction of hospital readmission. This review synthesizes the literature on ML methods and their performance for predicting hospital readmission in the US. Methods This review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) Statement. The extraction of items was also guided by the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Electronic databases PUBMED, MEDLINE, and EMBASE were systematically searched from January 1, 2015, through December 10, 2019. The articles were imported into COVIDENCE online software for title/abstract screening and full-text eligibility. Observational studies using ML techniques for hospital readmissions among US patients were eligible for inclusion. Articles without a full text available in the English language were excluded. A qualitative synthesis included study characteristics, ML algorithms utilized, and model validation, and quantitative analysis assessed model performance. Model performances in terms of Area Under the Curve (AUC) were analyzed using R software. Quality in Prognosis Studies (QUIPS) tool was used to assess the quality of the reviewed studies. Results Of 522 citations reviewed, 43 studies met the inclusion criteria. A majority of the studies used electronic health records (24, 56%), followed by population-based data sources (15, 35%) and administrative claims data (4, 9%). The most common algorithms were tree-based methods (23, 53%), neural network (NN) (14, 33%), regularized logistic regression (12, 28%), and support vector machine (SVM) (10, 23%). Most of these studies (37, 85%) were of high quality. A majority of these studies (28, 65%) reported ML algorithms with an AUC above 0.70. There was a range of variability within AUC reported by these studies with a median of 0.68 (IQR: 0.64–0.76; range: 0.50–0.90). Conclusions The ML algorithms involving tree-based methods, NN, regularized logistic regression, and SVM are commonly used to predict hospital readmission in the US. Further research is needed to compare the performance of ML algorithms for hospital readmission prediction.

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Section: Resultsmentioning

confidence: 99%

Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Huang

Talwar

Chatterjee

et al. 2021

BMC Med Res Methodol

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“…The literature has demonstrated that these methods are useful when managing a large amount of data, as shown in research aimed at identifying critical elements for predicting risk status in nursing documentation [ 32 ]. Another recent study in the nursing field considered MLTs and administrative data to identify patients at high risk for hospital readmission [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Profiling Patients by Intensity of Nursing Care: An Operative Approach Using Machine Learning

Ocagli

Lanera

Lorenzoni

et al. 2020

JPM

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Physical function is a patient-oriented indicator and can be considered a proxy for the assignment of healthcare personnel. The study aims to create an algorithm that classifies patients into homogeneous groups according to physical function. A two-step machine-learning algorithm was applied to administrative data recorded between 2015 and 2018 at the University Hospital of Padova. A clustering-large-applications (CLARA) algorithm was used to partition patients into homogeneous groups. Then, machine learning technique (MLT) classifiers were used to categorize the doubtful records. Based on the results of the CLARA algorithm, records were divided into three groups according to the Barthel index: <45, >65, ≥45 and ≤65. The support vector machine was the MLT showing the best performance among doubtful records, reaching an accuracy of 66%. The two-step algorithm, since it splits patients into low and high resource consumption, could be a useful tool for organizing healthcare personnel allocation according to the patients’ assistance needs.

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“…All the analyses were conducted on the de‐identified patient dataset, and patients who were admitted to other hospitals not defined above were not captured. The preprocessed features ( n = 481), including labs, demographics, the number of outpatient visits prior to the current visit, and the number of Emergency Department (ED) visits prior to the current visit, 15,16,38 were extracted from the EHR data using both data‐driven methods and clinical knowledge (Table II). The dataset also included 21 variables extracted according to the LACE 13 and HOSPITAL 14 indices: the length of stay, the number of ED visits in the past six months, the number of (unplanned) hospital admissions in the past year, whether any procedure was performed during the hospitalisation, and 17 ICD‐9/ICD‐10 code groups to calculate the Charlson comorbidity index score in the LACE index.…”

Section: Methodsmentioning

confidence: 99%

Machine‐learning algorithms for predicting hospital re‐admissions in sickle cell disease

Patel

Gan

et al. 2020

Br J Haematol

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Reducing preventable hospital re-admissions in Sickle Cell Disease (SCD) could potentially improve outcomes and decrease healthcare costs. In a retrospective study of electronic health records, we hypothesized Machine-Learning (ML) algorithms may outperform standard re-admission scoring systems (LACE and HOSPITAL indices). Participants (n = 446) included patients with SCD with at least one unplanned inpatient encounter between January 1, 2013, and November 1, 2018. Patients were randomly partitioned into training and testing groups. Unplanned hospital admissions (n = 3299) were stratified to training and testing samples. Potential predictors (n = 486), measured from the last unplanned inpatient discharge to the current unplanned inpatient visit, were obtained via both data-driven methods and clinical knowledge. Three standard ML algorithms, Logistic Regression (LR), Support-Vector Machine (SVM), and Random Forest (RF) were applied. Prediction performance was assessed using the C-statistic, sensitivity, and specificity. In addition, we reported the most important predictors in our best models. In this dataset, ML algorithms outperformed LACE [C-statistic 0Á6, 95% Confidence Interval (CI) 0Á57-0Á64] and HOSPITAL (C-statistic 0Á69, 95% CI 0Á66-0Á72), with the RF (C-statistic 0Á77, 95% CI 0Á73-0Á79) and LR (C-statistic 0Á77, 95% CI 0Á73-0Á8) performing the best. ML algorithms can be powerful tools in predicting re-admission in high-risk patient groups.

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Leveraging Electronic Health Records and Machine Learning to Tailor Nursing Care for Patients at High Risk for Readmissions

Cited by 27 publications

References 30 publications

Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Profiling Patients by Intensity of Nursing Care: An Operative Approach Using Machine Learning

Machine‐learning algorithms for predicting hospital re‐admissions in sickle cell disease

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