Middletonfallon4617

Schizophrenia as well as depressive and bipolar disorders were noted at the top of outpatient mental disorders. Antipsychotics are the most prescribed drugs, and a significant annual decrease in outpatient care wait time was noted (p less then 0.001). Conclusions Business analytics allowed CPU to monitor mental healthcare outpatient activity and to adopt its business processes according to outcomes. However, challenges mainly in the organizational dimension of the decision-making process and the definition of strategic key metrics, data structuration, and the quality of data entry had to be considered for the optimal use of business analytics.Objectives To examine the direct effects of risk factors associated with the 5-year costs of care in persons with alcohol use disorder (AUD) and to examine whether remission decreases the costs of care. Methods Based on Electronic Health Record data collected in the North Karelia region in Finland from 2012 to 2016, we built a non-causal augmented naïve Bayesian (ANB) network model to examine the directional relationship between 16 risk factors and the costs of care for a random cohort of 363 AUD patients. Jouffe's proprietary likelihood matching algorithm and van der Weele's disjunctive confounder criteria (DCC) were used to calculate the direct effects of the variables, and sensitivity analysis with tornado diagrams and analysis maximizing/minimizing the total cost of care were conducted. Results The highest direct effect on the total cost of care was observed for a number of chronic conditions, indicating on average more than a €26,000 increase in the 5-year mean cost for individuals with multiple ICD-10 diagnoses compared to individuals with less than two chronic conditions. Remission had a decreasing effect on the total cost accumulation during the 5-year follow-up period; the percentage of the lowest cost quartile (42.9% vs. 23.9%) increased among remitters, and that of the highest cost quartile (10.71% vs. 26.27%) decreased compared with current drinkers. Conclusions The ANB model with application of DCC identified that remission has a favorable causal effect on the total cost accumulation. A high number of chronic conditions was the main contributor to excess cost of care, indicating that comorbidity is an essential mediator of cost accumulation in AUD patients.Objectives Skills to employ nursing informatics to promote the health of individuals is of such importance that it is considered a core competence. Although investments are made to increase the use of e-health, there is no full understanding of the usability of e-health for healthcare. This paper presents a current picture of how e-health and m-health are defined and used as well as the effects their usage may have on the intended target group. Methods Peer-reviewed open-access papers and grey literature that define e-health and m-health from PubMed, SpringerLink, and Google.com were randomized. A mixed method design with an inductive approach was employed. Open-source software were used for analysis. Results The overview includes 30 definitions of e-health and m-health, respectively. The definitions were thematised into 14 narrative themes. The results of the study, and primarily a three-level model, provide an understanding of how different types of e-health and m-health can be put into practice, and the effects or consequences of using them, which may be either positive or negative. Conclusions Mobility and flexibility is important for both m-health and e-health. Five keywords that characterize the definitions of e-health and m-health are "health", "mobile", "use", "information", and "technology". E-health or m-health cannot replace human actors because e-health and m-health consist of social and material interactions. Using e-health and m-health is, thus, about developing healthcare without compromising native relics.Objectives Longitudinal data are prevalent in clinical research; due to their correlated nature, special analysis must be used for this type of data. Creatinine is an important marker in predicting end-stage renal disease, and it is recorded longitudinally. This study compared the prediction performance of linear regression (LR), linear mixed-effects model (LMM), least-squares support vector regression (LS-SVR), and mixed-effects least-squares support vector regression (MLS-SVR) methods to predict serum creatinine as a longitudinal outcome. Methods We used a longitudinal dataset of hemodialysis patients in Hamadan city between 2013 and 2016. To evaluate the performance of the methods in serum creatinine prediction, the data was divided into two sets of training and testing samples. Then LR, LMM, LS-SVR, and MLS-SVR were fitted. The prediction performance was assessed and compared in terms of mean squared error (MSE), mean absolute error (MAE), mean absolute prediction error (MAPE), and determination coefficient (R 2). Variable importance was calculated using the best model to select the most important predictors. Results The MLS-SVR outperformed the other methods in terms of the least prediction error; MSE = 1.280, MAE = 0.833, and MAPE = 0.129 for the training set and MSE = 3.275, MAE = 1.319, and MAPE = 0.159 for the testing set. Also, the MLS-SVR had the highest R 2, 0.805 and 0.654 for both the training and testing samples, respectively. Blood urea nitrogen was the most important factor in the prediction of creatinine. Conclusions The MLS-SVR achieved the best serum creatinine prediction performance in comparison to LR, LMM, and LS-SVR.Objectives Electronic Health Records (EHRs)-based surveillance systems are being actively developed for detecting adverse drug reactions (ADRs), but this is being hindered by the difficulty of extracting data from unstructured records. This study performed the analysis of ADRs from nursing notes for drug safety surveillance using the temporal difference method in reinforcement learning (TD learning). Methods Nursing notes of 8,316 patients (4,158 ADR and 4,158 non-ADR cases) admitted to Ajou University Hospital were used for the ADR classification task. A TD(λ) model was used to estimate state values for indicating the ADR risk. For the TD learning, each nursing phrase was encoded into one of seven states, and the state values estimated during training were employed for the subsequent testing phase. We applied logistic regression to the state values from the TD(λ) model for the classification task. Results The overall accuracy of TD-based logistic regression of 0.63 was comparable to that of two machine-learning methods (0.64 for a naïve Bayes classifier and 0.63 for a support vector machine), while it outperformed two deep learning-based methods (0.58 for a text convolutional neural network and 0.61 for a long short-term memory neural network). Most importantly, it was found that the TD-based method can estimate state values according to the context of nursing phrases. Conclusions TD learning is a promising approach because it can exploit contextual, time-dependent aspects of the available data and provide an analysis of the severity of ADRs in a fully incremental manner.Objectives To identify the effects of a mobile-app-based self-management program for elderly hemodialysis patients on their sick-role behavior, basic psychological needs, and self-efficacy. Methods A nonequivalent control group with a non-synchronized design was utilized, and 60 participants (30 in each of the experimental and control groups) were recruited from Chungnam National University Hospital from March to August 2018. The program consisted of continuous training on how to use the mobile-app, self-checking via the app, message transfer through Electronic Medical Records, and feedback. The control group received the usual care. Data were analyzed using the χ2-test, the t-test, the repeated-measures ANOVA, and the McNemar test. A formalized messaging program was developed, and the app was developed with consideration of the specific physical and cognitive limitations of the elderly. Results Comparisons were conducted between the experimental (n = 28) and control (n = 28) groups. Statistically significant increases in sick-role behavior, basic psychological needs, and self-efficacy were found in the experimental group (p less then 0.001). Physiological parameters were maintained within the normal ranges in the experimental group, and the number of non-adherent patients decreased, although the change was not statistically significant. Conclusions The mobile-app-based self-management program developed in this study increased the sick-role behavior, basic psychological needs, and self-efficacy of elderly hemodialysis patients, while physiological parameters were maintained within the normal range. Future studies are needed to develop management systems for high-risk hemodialysis patients and family-sharing apps to manage non-adherent patients.Objectives Recently, wearable device technology has gained more popularity in supporting a healthy lifestyle. Hence, researchers have begun to put significant efforts into studying the direct and indirect benefits of wearable devices for health and wellbeing. This paper summarizes recent studies on the use of consumer wearable devices to improve physical activity, mental health, and health consciousness. Methods A thorough literature search was performed from several reputable databases, such as PubMed, Scopus, ScienceDirect, arXiv, and bioRxiv mainly using "wearable device research" as a keyword, no earlier than 2018. As a result, 25 of the most recent and relevant papers included in this review cover several topics, such as previous literature reviews (9 papers), wearable device accuracy (3 papers), self-reported data collection tools (3 papers), and wearable device intervention (10 papers). Results All the chosen studies are discussed based on the wearable device used, complementary data, study design, and data processing method. All these previous studies indicate that wearable devices are used either to validate their benefits for general wellbeing or for more serious medical contexts, such as cardiovascular disorders and post-stroke treatment. Triton X-114 nmr Conclusions Despite their huge potential for adoption in clinical settings, wearable device accuracy and validity remain the key challenge to be met. Some lessons learned and future projections, such as combining traditional study design with statistical and machine learning methods, are highlighted in this paper to provide a useful overview for other researchers carrying out similar research.Constraint theory (Hammersley, 2014) offers a novel way of understanding addiction as a lack of cognitive, behavioural, and social constraints on substance use. Here, cannabis constraints were studied in a large online opportunity sample N = 302; 205 men, 97 women. Age ranged from 14 to 60 years (mean = 25, SD = 8.0). Most participants were from UK or North America. Participants completed a questionnaire assessing 15 cannabis constraints and standard self-report frequency measures of drug use. Factor analysis of the constraint questionnaire found 15 factors, similar to those proposed theoretically. These factors could discriminate well between past and current users and heavy and light users. The best discriminator was concerns about the possibility of becoming addicted; the less concerned the heavier was use, although those who actually felt addicted were more concerned than others. Past users also constrained due to using legal highs instead, concerns about illegality, and using only when others used. Light users constrained due to availability and cost issues, as well as unpleasant effects.