Differences in Characteristics and Length of Stay of Elderly Emergency Patients before and after the Outbreak of COVID-19.

(1) Background: COVID-19 has spread worldwide and affected Taiwan's medical system and people's lives. This study aimed to explore the impact of medical utilization on the characteristics and length of stay (LOS) of elderly emergency department (ED) patients before and after COVID-19; (2) Methods: We gathered ED visits from January to September 2019 (pre-pandemic group) and from January to September 2020 (pandemic group). The data analysis methods included descriptive statistics, the Pearson's chi-square test, the independent sample t-test, and binary logistic regression; (3) Results: In 2020, during COVID-19, a significant decrease in ED monthly visits occurred from January; the maximum decrease was 32% in March. The average LOS during COVID-19 was shortened, with a significant reduction in diagnoses compared with the pre-pandemic period; (4) Conclusions: The threat of COVID-19 has changed the elderly's behavior in ED visits and shortened the LOS of ED. The study's results emphasize the importance of analyzing the medical utilization of elderly ED patients and understanding the medical quality of healthcare institutions. With Taiwan's rapidly aging society, the demand for healthcare increases from time to time. The overcrowding of medical attention is often a problem. The results recommend that the overcrowding problem has the opportunity to be solved.

Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care.

Holistic health care (HHC) is a synonym for complete patient care, and as such an efficient clinical decision support system (CDSS) for HHC is critical to support the judgement of physician's decision in response of patient's physical, emotional, social, economic, and spiritual needs. The field of artificial intelligence (AI) has evolved considerably in the past decades and many AI applications have been deployed in various contexts. Therefore, this study aims to propose an AI-assisted CDSS model that predicts patients in need of HHC and applies an improved recurrent neural network (RNN) model, long short-term memory (LSTM) for the prediction. The data sources include in-patient's comorbidity status and daily vital sign attributes such as blood pressure, heart rate, oxygen prescription, etc. A two-year dataset consisting of 121 thousand anonymized patient cases with 890 thousand physiological medical records was obtained from a medical center in Taiwan for system evaluation. Comparing with the rule-based expert system, the proposed AI-assisted CDSS improves sensitivity from 26.44% to 80.84% and specificity from 99.23% to 99.95%. The experimental results demonstrate that an AI-assisted CDSS could efficiently predict HHC patients.

Exploring emergency physicians' knowledge, attitudes, and behaviour towards Choosing Wisely in Taiwan.

BACKGROUND: In 2012, the American Board of Internal Medicine Foundation launched the Choosing Wisely campaign to reduce unnecessary care. However, it is unclear how much emergency physicians in Taiwan understand about Choosing Wisely. The purpose of this study was to explore the knowledge, attitude, and behaviour of emergency physicians in Taiwan regarding Choosing Wisely and its related factors; the intention was to identify the baseline knowledge on the basis of which to promote Choosing Wisely in Taiwan. METHODS: This was a cross-sectional study including emergency physicians in Taiwan as research subjects who answered online questionnaires. A 42-item questionnaire was designed according to the Knowledge, Attitude, and Behaviour model (KAB). The questionnaire linkages were delivered to emergency physicians through social media (eg., Line, Facebook) and received assistance from different hospital directors. A total of 162 valid questionnaires were collected. Data analyses include t-test, analysis of variance, chi-square test, Pearson's correlation, and multivariate linear regression model. RESULTS: The study determined that although only 38.9% of emergency physicians had heard of Choosing Wisely, the mean correct rate of knowledge score among emergency physicians was 70.1%. Attitude and the behaviour related to Choosing Wisely were positively associated, which means that the more positive the attitude towards Choosing Wisely is, the more positive the behaviour towards Choosing Wisely is. In multiple linear regression analyses, having served as a supervisor, belonging to divisions of health insurance service, and having heard of Choosing Wisely (P < 0.05) positively affect the knowledge of Choosing Wisely, but age presented a negative association. CONCLUSION: This study found that physicians' knowledge does not influence their attitudes and behaviours, which may be related to barriers of practicing Choosing Wisely activities. To effectively promote Choosing Wisely campaign, it is recommended to focus on the significant factors associated with emergency physicians' perceptions regarding knowledge, attitude, and behavior of Choosing Wisely. Based on these factors, appropriate practice guidelines for Choosing Wisely can be formulated and promoted.

Pilot Report for Intracranial Hemorrhage Detection with Deep Learning Implanted Head Computed Tomography Images at Emergency Department.

Hemorrhagic stroke is a serious clinical condition that requires timely diagnosis. An artificial intelligence algorithm system called DeepCT can identify hemorrhagic lesions rapidly from non-contrast head computed tomography (NCCT) images and has received regulatory clearance. A non-controlled retrospective pilot clinical trial was conducted. Patients who received NCCT at the emergency department (ED) of Kaohsiung Veteran General Hospital were collected. From 2020 January-1st to April-30th, the physicians read NCCT images without DeepCT. From 2020May-1st to August-31st, the physicians were assisted by DeepCT. The length of ED stays (LOS) for the patients was collected. 2,999 patients were included (188 and 2811 with and without ICH). For patients with a final diagnosis of ICH, implementing DeepCT significantly shortened their LOS (560.67 ± 604.93 min with DeepCT vs. 780.83 ± 710.27 min without DeepCT; p = 0.0232). For patients with a non-ICH diagnosis, the LOS did not significantly differ (705.90 ± 760.86 min with DeepCT vs. 679.45 ± 681.97 min without DeepCT; p = 0.3362). For patients with ICH, those assisted with DeepCT had a significantly shorter LOS than those without DeepCT. For patients with a non-ICH diagnosis, implementing DeepCT did not affect the LOS, because emergency physicians need same efforts to identify the underlying problem(s) with or without DeepCT. In summary, implementing DeepCT system in the ED will save costs, decrease LOS, and accelerate patient flow; most importantly, it will improve the quality of care and increase the confidence and shorten the response time of the physicians and radiologists.

Impact of New Bed Assignment Information System on Emergency Department Length of Stay: An Effect Evaluation for Lean Intervention by Using Interrupted Time Series and Propensity Score Matching Analysis.

A long waiting period for available beds in emergency departments (EDs) is the major obstacle to a smooth process flow in ED services. We developed a new bed assignment information system that incorporates current strategies and resources to ease the bottleneck in the service flow. The study's purpose was to evaluate the effect of the lean intervention plan. We included 54,541 ED patient visits in the preintervention phase and 52,874 ED patient visits in the postintervention phase. Segmented regression analysis (SRA) was used to estimate the level and trend in the preintervention and postintervention phases and changes in the level and trend after the intervention. After the intervention, the weekly length of stay (LOS) for patient visits, admitted patient visits, and nonadmitted patient visits decreased significantly by 0.75, 2.82, and 0.17 h, respectively. The trendline direction for overall patient visits and nonadmitted patient visits significantly changed after the intervention. However, no significant change was noted for admitted patient visits, although the postintervention trend visually differed from the preintervention trend. The concept of lean intervention can be applied to solve various problems encountered in the medical field, and the most common approach, SRA, can be used to evaluate the effect of intervention plans.

Effects of an Intranet-Based Call-for-Help System on Teamwork, Work Efficiency, Job Satisfaction, and Job Stress in Nurses.

OBJECTIVES: In the medical environment, teamwork among medical care personnel is closely related to patient safety and care quality. This research has developed an Intranet-based call-for-help system to establish an emergency support system. Few studies have explored the effects of the timely call-for-help system on teamwork. This study explored the effects of the timely call-for-help system intervention. METHODS: This study was designed by 2 groups of pretest and posttest. This research was conducted at a medical center in Taiwan. The subjects of the study were nurses in inpatient wards. The newly established call-for-help system was used in the 4 experimental wards, enabling nurses to immediately seek help from each other when faced with challenges and difficulties. This study was blind with the outcome evaluator and data analyst blinded to the group of participants. RESULTS: A total of 165 nurses were included in the study, 84 in the intervention group and 81 in the control group. After the intervention of the call-for-help system, the experimental group had significantly better teamwork, work efficiency, job satisfaction, and lower job stress than the control group ( P < 0.01). The generalized estimating equation showed that the progress scores of the experimental group on the 4 scales were significantly higher than those of the control group ( P < 0.01). CONCLUSIONS: The implementation of timely call-for-help system can enable nursing staff to get immediate support, strengthen teamwork, create a positive nursing practice environment, and improve patient safety and quality of care. These improvements in teamwork and support are very important and worthy of promotion.

Developing an Integrated Electronic Medication Reconciliation Platform and Evaluating its Effects on Preventing Potential Duplicated Medications and Reducing 30-Day Medication-Related Hospital Revisits for Inpatients.

The aims were to develop an integrated electronic medication reconciliation (ieMR) platform, evaluate its effects on preventing potential duplicated medications, analyze the distribution of the potential duplicated medications by the Anatomical Therapeutic and Chemical (ATC) code for all inpatients, and determine the rate of 30-day medication-related hospital revisits for a geriatric unit. The study was conducted in a tertiary medical center in Taiwan and involved a retrospective quasi pre-intervention (July 1-November 30, 2015) and post-intervention (October 1-December 31, 2016) study design. A multidisciplinary team developed the ieMR platform covering the process from admission to discharge. The ieMR platform included six modules of an enhanced computer physician order entry system (eCPOE), Pharmaceutical-care, Holistic Care, Bedside Display, Personalized Best Possible Medication Discharge Plan, and Pharmaceutical Care Registration System. The ieMR platform prevented the number of potential duplicated medications from pre (25,196 medications, 2.3%) to post (23,413 medications, 3.8%) phases (OR 1.71, 95% CI, 1.68-1.74; p < .001). The most common potential duplicated medications classified by the ATC codes were cardiovascular system (28.4%), alimentary tract and metabolism (26.4%), and nervous system (14.9%), and by chemical substances were sennoside (12.5%), amlodipine (7.5%), and alprazolam (7.4%). The rate of medication-related 30-day hospital revisits for the geriatric unit was significantly decreased in post-intervention compared with that in pre-intervention (OR = 0.12; 95% CI, 0.03-0.53; p < .01). This study indicated that the ieMR platform significantly prevented the number of potential duplicated medications for inpatients and reduced the rate of 30-day medication-related hospital revisits for the patients on the geriatric unit.

Vitamin D Attenuates Loss of Endothelial Biomarker Expression in Cardio-Endothelial Cells.

Vitamin D is associated with cardiovascular health through activating the vitamin D receptor that targets genes related to cardiovascular disease (CVD). The human cardiac microvascular endothelial cells (HCMECs) were used to develop mechanically and TGF-ß1-induced fibrosis models, and the rat was used as the isoproterenol (ISO)-induced fibrosis model. The rats were injected with ISO for the first five days, followed by vitamin D injection for the consecutive three weeks before being sacrificed on the fourth week. Results showed that mechanical stretching reduced endothelial cell marker CD31 and VE-cadherin protein expressions, as well as increased α-smooth muscle actin (α-SMA) and fibronectin (FN). The transforming growth factor-ß1 (TGF-ß1) reduced CD31, and increased α-SMA and FN protein expression levels. Vitamin D presence led to higher protein expression of CD31, and lower protein expressions of α-SMA and FN compared to the control in the TGF-ß1-induced fibrosis model. Additionally, protein expression of VE-cadherin was increased and fibroblast-specific protein-1 (FSP1) was decreased after vitamin D treatment in the ISO-induced fibrosis rat. In conclusion, vitamin D slightly inhibited fibrosis development in cell and animal models. Based on this study, the beneficial effect of vitamin D may be insignificant; however, further investigation of vitamin D's effect in the long-term is required in the future.

Perceptions of shared decision making among health care professionals.

RATIONALE AND AIMS: Scholars have progressively promoted shared decision making (SDM) as an optimal model of treatment decision making in clinical practice. Nevertheless, it is unclear whether health care professionals (a) understand SDM well, (b) believe that SDM is helpful in their daily practice, and (c) are willing to practice SDM during their daily activities. These are crucial research topics; however, such research is still limited. The aim of this study was to apply the knowledge-attitude-behavior (KAB) model to probe health care professionals' perceptions of SDM. METHODS: A questionnaire was delivered to health care professionals working in various hospitals in southern Taiwan from 9 November 2018 to 8 January 2019. In addition to KAB constructs, this study explored the barriers to SDM practice and willingness to practice SDM among health care professionals. Predictive variables were subjected to multiple linear regression analysis to investigate health care professionals' views of SDM. RESULTS: Valid respondents numbered 400, including physicians, pharmacists, nurses, and other health care professionals. The characteristics of these health care professionals significantly affected the mean scores of the KAB model. A correlation analyses indicated that the KAB constructs were positively correlated with each other. The top three barriers reported were lack of time (57.50%), lack of knowledge (38.25%), and difficulty of developing patient decision aids (37.75%). Respondents who were willing to practice SDM opined that SDM can provide the best health care for patients (81.62%), can maintain and improve individual clinical expertise (77.38%), and can meet patient and social expectations (48.40%). CONCLUSIONS: Continuous emphasis on education regarding SDM and continuous promotion of a positive attitude of SDM acceptance can influence the behaviour of practicing SDM among health care professionals. Further study is required to assess the SDM practices of various health care professionals in different settings.

Emergency department overcrowding: Quality improvement in a Taiwan Medical Center.

BACKGROUND/PURPOSE: Overcrowding of hospital emergency departments (ED) is a worldwide health problem. The Taiwan Joint Commission on Hospital Accreditation has stressed the importance of finding solutions to overcrowding, including, reducing the number of patients with >48 h stay in the ED. Moreover, the Ministry of Health and Welfare aims at transferring non-critical patients to district or regional hospitals. We report the results of our Quality Improvement Project (QIP) on ED overcrowding, especially focusing on reducing length of stay (LOS) in ED. METHODS: For QIP, the following 3 action plans were initiated: 1) Changing the choice architecture of patients' willingness to transfer from opt-in to opt-out; 2) increasing the turnover rate of beds and daily monitoring of the number of free beds for boarding ED patients; 3) reevaluation of patients with a LOS of >32 h after the morning shift. RESULTS: Transfer rates increased minimally after implementation of this project, but the sample size was too small to achieve statistical significance. No significant increase was observed in the number of free medical beds, but discharge rates after 12 pm decreased significantly (p < 0.001). The proportion of over 48 h LOSs decreased from 4.9% to 3.7% before and after QIP implementation, respectively (p < 0.001). CONCLUSION: Patients with LOS of >32 h were reevaluated first. After QIP, the proportion of LOSs of >48 h dropped significantly. Changing the choice architecture may require further systemic effort and a longer observation duration. Higher-level administrators will need to formulate a more comprehensive bed management plan to speed up the turnover rate of free inpatient beds.

Mechanical Stretching Simulates Cardiac Physiology and Pathology through Mechanosensor Piezo1.

The dynamics of a living body enables organs to experience mechanical stimulation at cellular level. The human cardiomyocytes cell line provides a source for simulating heart dynamics; however, a limited understanding of the mechanical stimulation effect on them has restricted potential applications. Here, we investigated the effect of mechanical stimulation on the cardiac function-associated protein expressions in human cardiomyocytes. Human cardiomyocyte cell line AC16 was subjected to different stresses: 5% mild and 25% aggressive, at 1 Hz for 24 h. The stretched cardiomyocytes showed down-regulated Piezo1, phosphorylated-Ak transforming serine473 (P-AKTS473), and phosphorylated-glycogen synthase kinase-3 beta serine9 P-GSK3ßS9 compared to no stretch. In addition, the stretched cardiomyocytes showed increased low-density lipoprotein receptor-related protein 6 (LRP6), and phosphorylated-c-Jun N-terminal kinase threonine183/tyrosine185 (P-JNKT183/Y185). When Piezo inhibitor was added to the cells, the LRP6, and P-JNKT183/Y185 were further increased under 25%, but not 5%, suggesting that higher mechanical stress further activated the wingless integrated-(Wnt)-related signaling pathway when Piezo1 was inhibited. Supporting this idea, when Piezo1 was inhibited, the expression of phosphorylated-endothelial nitric oxide synthase serine1177 (P-eNOSS1177) and release of calcium ions were reduced under 25% compared to 5%. These studies demonstrate that cyclic mechanical stimulation affects cardiac function-associated protein expressions, and Piezo1 plays a role in the protein regulation.

Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan.

OBJECTIVE: Emergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits. METHODS: We retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses. RESULTS: A series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visitt=7111.161+(at+0.37462 at-1). CONCLUSION: The ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes.