Data from the patient classification database, quality monitoring database, and medical cost database indicate excessive nursing workload and underpayment from the Taiwan NHI program. Current nursing workload was significantly associated with care quality. This study provides empirical data for administrators to consider when revising nursing staffing and NHI payment policies.

The National Early Warning Score (NEWS) is an early warning system that predicts clinical deterioration. The impact of the NEWS on the outcome of healthcare remains controversial. This study was conducted to evaluate the effectiveness of implementing an electronic version of the NEWS (E-NEWS), to reduce unexpected clinical deterioration. We developed the E-NEWS as a part of the Health Information System (HIS) and Nurse Information System (NIS). All adult patients admitted to general wards were enrolled into the current study. The “adverse event” (AE) group consisted of patients who received cardiopulmonary resuscitation (CPR), were transferred to an intensive care unit (ICU) due to unexpected deterioration, or died. Patients without AE were allocated to the control group. The development of the E-NEWS was separated into a baseline (October 2018 to February 2019), implementation (March to August 2019), and intensive period (September. to December 2019). A total of 39,161 patients with 73,674 hospitalization courses were collected. The percentage of overall AEs was 6.06%. Implementation of E-NEWS was associated with a significant decrease in the percentage of AEs from 6.06% to 5.51% (p = 0.001). CPRs at wards were significantly reduced (0.52% to 0.34%, p = 0.012). The number of patients transferred to the ICU also decreased significantly (3.63% to 3.49%, p = 0.035). Using multivariate analysis, the intensive period was associated with reducing AEs (p = 0.019). In conclusion, we constructed an E-NEWS system, updating the NEWS every hour automatically. Implementing the E-NEWS was associated with a reduction in AEs, especially CPRs at wards and transfers to ICU from ordinary wards.

Early prediction of clinical deterioration such as adverse events (AEs), improves patient safety. National Early Warning Score (NEWS) is widely used to predict AEs based on the aggregation of 6 physiological parameters. We took the same parameters as the features for AE prediction using deep learning algorithms (AEP-DLA) among hospitalized adult patients. The aim of this study is to get better performance than traditional naïve mathematical calculations by introducing novel vital sign data preprocessing schemes. We retrospectively collected the data from our electronic medical record data warehouse (2007 ~ 2017). AE rate of all 99,861 admissions was 6.2%. The dataset was divided into training and testing datasets from 2007-2015 and 2016-2017 respectively. In real-life clinical care, physiological parameters were not recorded every hour and missed frequently, for example, Glasgow Coma Scale (GCS). The expert domain suggested that missed GCS was rated as 15. We took two strategies (stack series records and align by hour) in the data preprocessing and tripling the values of negative samples for class balancing (CB). We used the last 28 hours' serial data to predict AEs 3 hours later with Random Forest, XGBoost, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). It is shown that CNN with CB and align by hour got the best results comparing to the other methods. The precision, recall and area under curve were 0.841, 0.928 and 0.995 respectively. The performance of the model is also better than those proposed in the published literatures.

The protocol for data retrieval, data cleaning, and data analysis for the paper: Investigating the accuracy of National Early Warning Score for predicting clinical deterioration in different disease categories and time frames

The outbreak of COVID-19 poses an immense global threat. Visitors to hospitalized patients during a pandemic might themselves be carriers, and so hospitals strictly control patients and inpatient companions. However, it is not easy for cancer patients to adjust the times of their medical treatment or to suspend treatment, and the impact of the pandemic on cancer inpatients and inpatient companions is relatively high. The objectives for this investigation are to study the correlations among emotional stress, pain, and the presence of inpatient companions in cancer patients during the COVID-19 pandemic. This study was a retrospective descriptive study. The participants were cancer inpatients and inpatient companions in a medical center in Taiwan. The data for this study were extracted from cross-platform structured and normalized electronic medical record databases. Microsoft Excel 2016 and SPSS version 22.0 were used for analysis of the data. In all, 75.15% of the cancer inpatients were accompanied by family, and the number of hospitalization days were 7.87 ± 10.77 days, decreasing year by year, with statistical significance of p < 0.001. The daily nursing hours were 12.94 ± 10.76, and the nursing hours decreased year by year, p < 0.001. There was no significant difference in gender among those who accompanied the patients, but there were statistical differences in the length of hospitalization, nursing hours, and pain scores between those with and without inpatient companions, with p < 0.001. The inpatient companions were mostly family members (78%). The findings of this study on cancer patient care and inpatient companions should serve as an important basis for the transformation and reform of the inpatient companion culture and for epidemic prevention care in hospitals.