Enhancing readmission prediction models by integrating insights from home healthcare notes: Retrospective cohort study.

BACKGROUND: Hospital readmission is an important indicator of inpatient care quality and a significant driver of increasing medical costs. Therefore, it is important to explore the effects of postdischarge information, particularly from home healthcare notes, on enhancing readmission prediction models. Despite the use of Natural Language Processing (NLP) and machine learning in prediction model development, current studies often overlook insights from home healthcare notes. OBJECTIVE: This study aimed to develop prediction models for 30-day readmissions using home healthcare notes and structured data. In addition, it explored the development of 14- and 180-day prediction models using variables in the 30-day model. DESIGN: A retrospective observational cohort study. SETTING(S): This study was conducted at Ajou University School of Medicine in South Korea. PARTICIPANTS: Data from electronic health records, encompassing demographic characteristics of 1819 participants, along with information on conditions, drug, and home healthcare, were utilized. METHODS: Two distinct models were developed for each prediction window (30-, 14-, 180-day): the traditional model, which utilized structured variables alone, and the common data model (CDM)-NLP model, which incorporated structured and topic variables extracted from home healthcare notes. BERTopic facilitated topic generation and risk probability, representing the likelihood of documents being assigned to specific topics. Feature selection involved experimenting with various algorithms. The best-performing algorithm, determined using the area under the receiver operating characteristic curve (AUROC), was used for model development. Model performance was assessed using various learning metrics including AUROC. RESULTS: Among 1819 patients, 251 (13.80 %) experienced 30-day readmission. The least absolute shrinkage and selection operator was used for feature extraction and model development. The 15 structured features were used in the traditional model. Moreover, five additional topic variables from the home healthcare notes were applied in the CDM-NLP model. The AUROC of the traditional model was 0.739 (95 % CI: 0.672-0.807). The AUROC of the CDM-NLP model was high at 0.824 (95 % CI: 0.768-0.880), which indicated an outstanding performance. The topics in the CDM-NLP model included emotional distress, daily living functions, nutrition, postoperative status, and cardiorespiratory issues. In extended prediction model development for 14- and 180-day readmissions, the CDM-NLP consistently outperformed the traditional model. CONCLUSIONS: This study developed effective prediction models using both structured and unstructured data, thereby emphasizing the significance of postdischarge information from home healthcare notes in readmission prediction.

"Internet Plus" continuous nursing for patients with advanced bladder cancer: A retrospective observational study.

The "Internet Plus" system has made continuous nursing intervention much more feasible to implement, especially in terms of malignant tumors. We aimed to evaluate continuous nursing based on "Internet Plus" for patients diagnosed with bladder cancer with hematuria being treated by drug-eluting bead embolization. This retrospective study included 43 patients, diagnosed with bladder cancer with hemorrhages, who had undergone transcatheter bladder arterial chemoembolization by drug-eluting bead embolization at our hospital between January 2017 and January 2023. They were divided into a control (21 patients) and an observation group (22 patients). The Medical Coping Style Scale, disease knowledge (including regular follow-up and interventional treatment), patient satisfaction, and caregiver burden in both groups were compared on the day of discharge and at the 1-month follow-up for each patient. The confrontation score of the observation group was higher than that of the control group, whereas the resignation and avoidance scores were lower. The disease knowledge was higher in the observation group, and the caregiver burden scores of the observation group were significantly lower. The patient satisfaction scores of the control group (84.7 ±â€…2.6) were lower than those of the observation group (90.5 ±â€…5.4). Continuous nursing based on "Internet Plus" could improve the quality of life of patients and their satisfaction regarding the meeting of their and their families' psychological and nursing needs.

Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing.

Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challenging. In this study, we aimed to develop an explainable multimodal prediction model for TRD using structured electronic medical record data, brain morphometry, and natural language processing. In total, 247 patients with a new depressive episode were included. TRD-predictive models were developed based on the combination of following parameters: selected tabular dataset features, independent components-map weightings from brain T1-weighted magnetic resonance imaging (MRI), and topic probabilities from clinical notes. All models applied the extreme gradient boosting (XGBoost) algorithm via five-fold cross-validation. The model using all data sources showed the highest area under the receiver operating characteristic of 0.794, followed by models that used combined brain MRI and structured data, brain MRI and clinical notes, clinical notes and structured data, brain MRI only, structured data only, and clinical notes only (0.770, 0.762, 0.728, 0.703, 0.684, and 0.569, respectively). Classifications of TRD were driven by several predictors, such as previous exposure to antidepressants and antihypertensive medications, sensorimotor network, default mode network, and somatic symptoms. Our findings suggest that a combination of clinical data with neuroimaging and natural language processing variables improves the prediction of TRD.

Prediction Models for Readmission Using Home Healthcare Notes and OMOP-CDM.

This study developed readmission prediction models using Home Healthcare (HHC) documents via natural language processing (NLP). An electronic health record of Ajou University Hospital was used to develop prediction models (A reference model using only structured data, and an NLP-enriched model with structured and unstructured data). Among 573 patients, 63 were readmitted to the hospital. Five topics were extracted from HHC documents and improved the model performance (AUROC 0.740).