ObjectiveEmergency 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.MethodsWe 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.ResultsA 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).ConclusionThe 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.

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.

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-GSK3βS9 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.

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.

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.

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The trend towards personalized healthcare has led to an increase in applying deep learning techniques to improve healthcare service quality and sustainability. With the increasing number of patients with multiple comorbidities, they need comprehensive care services, where comprehensive care is a synonym for complete patient care to respond to a patient’s physical, emotional, social, economic, and spiritual needs, and, as such, an efficient prediction system for comprehensive care suggestions could help physicians and healthcare providers in making clinical judgement. The experiment dataset contained a total of 2.9 million electrical medical records (EMRs) from 250 thousand hospitalized patients collected retrospectively from a first-tier medical center in Taiwan, where the EMRs were de-identified and anonymized and where 949 cases had received comprehensive care. Recurrent neural networks (RNNs) are designed for analyzing time-series data but are still lacking in studying predicting personalized healthcare. Furthermore, in most cases, the collected evaluation data are imbalanced with a small portion of positive cases. This study examined the impact of imbalanced data in model training and suggested an effective approach to handle such a situation. To address the above-mentioned research issue, this study analyzed the care need in the different patient groupings, proposed a personalized care suggestion system by applying RNN models, and developed an efficient model training scheme for building AI-assisted prediction models. This study observed several findings: (1) the data resampling schemes could mitigate the impact of imbalanced data on model training, and the under-sampling scheme achieved the best performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602, while the model trained with the original data had a very low PPV of 6.42% and a low F1 score of 0.1116; (2) patient clustering with multi-classier could predict comprehensive care needs efficiently with an ACC of 99.87%, a PPV of 77.90%, an NPV of 99.90%, a recall of 92.19%, and an F1 score of 0.8404; (3) the proposed long short-term memory (LSTM) prediction model achieved the best overall performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602.

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.