Evidence-Based Policy Recommendations for Public Health Emergency Operation Centers in Regional Settings: A Case Study in Indonesia.

Background: Public health emergencies require integration between multiple stakeholders in different sectors to monitor the situation and carry out an appropriate response. As a country with a large land area consisting of thousands of islands, Indonesia's centralized Public Health Emergency Operation Center (PHEOC) system is currently unable to effectively contain diseases. A PHEOC system reform is required to accommodate Indonesia's circumstances, particularly at the regional level. We have outlined potential models at the sub-national level for PHEOC based on existing evidence. Policy Options and Recommendations: Based on existing evidence of PHEOC models internationally, we have formulated three policy models for regional-level PHEOC. These models (the ad hoc agency model, the independent agency model, and the Province Health Office (PHO)-based model) entail different chains of command, and each has its own benefits. Conclusion: We recommend that the Ministry of Health in Indonesia adopt the third PHEOC policy model, in which the chain of command lies under the PHO. This is the most practical approach, as the PHO has the authority to mobilize units and access resources in response to imminent public health emergencies. Further training and capacity-building are required to support the PHO as the commander of the regional PHEOC.

Assessment of effects of moon phases on hospital outpatient visits: An observational national study.

Objectives: A vast amount of literature has been conducted for investigating the association of different lunar phases with human health; and it has mixed reviews for association and non-association of diseases with lunar phases. This study investigates the existence of any impact of moon phases on humans by exploring the difference in the rate of outpatient visits and type of diseases that prevail in either non-moon or moon phases. Methods: We retrieved dates of non-moon and moon phases for eight years (1st January 2001-31st December 2008) from the timeanddate.com website for Taiwan. The study cohort consisted of 1 million people from Taiwan's National Health Insurance Research Database (NHIRD) followed over eight years (1st January 2001-31st December 2008). We used the two-tailed, paired-t-test to compare the significance of difference among outpatient visits for 1229 moon phase days and 1074 non-moon phase days by using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes from NHIRD records. Results: We found 58 diseases that showed statistical differences in number of outpatient visits in the non-moon and moon phases. Conclusions: The results of our study identified diseases that have significant variations during different lunar phases (non-moon and moon phases) for outpatient visits in the hospital. In order to fully understand the reality of the pervasive myth of lunar effects on human health, behaviors and diseases, more in-depth research investigations are required for providing comprehensive evidence covering all the factors, such as biological, psychological and environmental aspects.

SlimMe, a Chatbot With Artificial Empathy for Personal Weight Management: System Design and Finding.

As the obesity rate continues to increase persistently, there is an urgent need to develop an effective weight loss management strategy. Nowadays, the development of artificial intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offers professional dietitians an opportunity to provide extensive monitoring support to their clients through a chatbot with artificial empathy. This study aimed to design a chatbot with artificial empathic motivational support for weight loss called "SlimMe" and investigate how people react to a diet bot. The SlimMe infrastructure was built using Dialogflow as the natural language processing (NLP) platform and LINE mobile messenger as the messaging platform. We proposed a text-based emotion analysis to simulate artificial empathy responses to recognize the user's emotion. A preliminary evaluation was performed to investigate the early-stage user experience after a 7-day simulation trial. The result revealed that having an artificially empathic diet bot for weight loss management is a fun and exciting experience. The use of emoticons, stickers, and GIF images makes the chatbot response more interactive. Moreover, the motivational support and persuasive messaging features enable the bot to express more empathic and engaging responses to the user. In total, there were 1,007 bot responses from 892 user input messages. Of these, 67.38% (601/1,007) of the chatbot-generated responses were accurate to a relevant user request, 21.19% (189/1,007) inaccurate responses to a relevant request, and 10.31% (92/1,007) accurate responses to an irrelevant request. Only 1.12% (10/1,007) of the chatbot does not answer. We present the design of an artificially empathic diet bot as a friendly assistant to help users estimate their calorie intake and calories burned in a more interactive and engaging way. To our knowledge, this is the first chatbot designed with artificial empathy features, and it looks very promising in promoting long-term weight management. More user interactions and further data training and validation enhancement will improve the bot's in-built knowledge base and emotional intelligence base.

Community Mobility and COVID-19 Dynamics in Jakarta, Indonesia.

In response to the COVID-19 pandemic, mobile-phone data on population movement became publicly available, including Google Community Mobility Reports (CMR). This study explored the utilization of mobility data to predict COVID-19 dynamics in Jakarta, Indonesia. We acquired aggregated and anonymized mobility data sets from 15 February to 31 December 2020. Three statistical models were explored: Poisson Regression Generalized Linear Model (GLM), Negative Binomial Regression GLM, and Multiple Linear Regression (MLR). Due to multicollinearity, three categories were reduced into one single index using Principal Component Analysis (PCA). Multiple Linear Regression with variable adjustments using PCA was the best-fit model, explaining 52% of COVID-19 cases in Jakarta (R-Square: 0.52; p < 0.05). This study found that different types of mobility were significant predictors for COVID-19 cases and have different levels of impact on COVID-19 dynamics in Jakarta, with the highest observed in "grocery and pharmacy" (4.12%). This study demonstrates the practicality of using CMR data to help policymakers in decision making and policy formulation, especially when there are limited data available, and can be used to improve health system readiness by anticipating case surge, such as in the places with a high potential for transmission risk and during seasonal events.

Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout.

BACKGROUND AND OBJECTIVE: Social media sentiment analysis based on Twitter data can facilitate real-time monitoring of COVID-19 vaccine-related concerns. Thus, the governments can adopt proactive measures to address misinformation and inappropriate behaviors surrounding the COVID-19 vaccine, threatening the success of the national vaccination campaign. This study aims to identify the correlation between COVID-19 vaccine sentiments expressed on Twitter and COVID-19 vaccination coverage, case increase, and case fatality rate in Indonesia. METHODS: We retrieved COVID-19 vaccine-related tweets collected from Indonesian Twitter users between October 15, 2020, to April 12, 2021, using Drone Emprit Academic (DEA) platform. We collected the daily trend of COVID-19 vaccine coverage and the rate of case increase and case fatality from the Ministry of Health (MoH) official website and the KawalCOVID19 database, respectively. We identified the public sentiments, emotions, word usage, and trend of all filtered tweets 90 days before and after the national vaccination rollout in Indonesia. RESULTS: Using a total of 555,892 COVID-19 vaccine-related tweets, we observed the negative sentiments outnumbered positive sentiments for 59 days (65.50%), with the predominant emotion of anticipation among 90 days of the beginning of the study period. However, after the vaccination rollout, the positive sentiments outnumbered negative sentiments for 56 days (62.20%) with the growth of trust emotion, which is consistent with the positive appeals of the recent news about COVID-19 vaccine safety and the government's proactive risk communication. In addition, there was a statistically significant trend of vaccination sentiment scores, which strongly correlated with the increase of vaccination coverage (r = 0.71, P<.0001 both first and second doses) and the decreasing of case increase rate (r = -0.70, P<.0001) and case fatality rate (r = -0.74, P<.0001). CONCLUSIONS: Our results highlight the utility of social media sentiment analysis as government communication strategies to build public trust, affecting individual willingness to get vaccinated. This finding will be useful for countries to identify and develop strategies for speed up the vaccination rate by monitoring the dynamic netizens' reactions and expression in social media, especially Twitter, using sentiment analysis.

Quality of National Disease Surveillance Reporting before and during COVID-19: A Mixed-Method Study in Indonesia.

BACKGROUND: Global COVID-19 outbreaks in early 2020 have burdened health workers, among them surveillance workers who have the responsibility to undertake routine disease surveillance activities. The aim of this study was to describe the quality of the implementation of Indonesia's Early Warning and Response Alert System (EWARS) for disease surveillance and to measure the burden of disease surveillance reporting quality before and during the COVID-19 epidemic in Indonesia. METHODS: A mixed-method approach was used. A total of 38 informants from regional health offices participated in Focus Group Discussion (FGD) and In-Depth Interview (IDI) for informants from Ministry of Health. The FGD and IDI were conducted using online video communication. Yearly completeness and timeliness of reporting of 34 provinces were collected from the application. Qualitative data were analyzed thematically, and quantitative data were analyzed descriptively. RESULTS: Major implementation gaps were found in poorly distributed human resources and regional infrastructure inequity. National reporting from 2017-2019 showed an increasing trend of completeness (55%, 64%, and 75%, respectively) and timeliness (55%, 64%, and 75%, respectively). However, the quality of the reporting dropped to 53% and 34% in 2020 concomitant with the SARS-CoV2 epidemic. CONCLUSIONS: Report completeness and timeliness are likely related to regional infrastructure inequity and the COVID-19 epidemic. It is recommended to increase report capacities with an automatic EWARS application linked systems in hospitals and laboratories.

Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients.

Starting renal replacement therapy (RRT) for patients with chronic kidney disease (CKD) at an optimal time, either with hemodialysis or kidney transplantation, is crucial for patient's well-being and for successful management of the condition. In this paper, we explore the possibilities of creating forecasting models to predict the onset of RRT 3, 6, and 12 months from the time of the patient's first diagnosis with CKD, using only the comorbidities data from National Health Insurance from Taiwan. The goal of this study was to see whether a limited amount of data (including comorbidities but not considering laboratory values which are expensive to obtain in low- and medium-income countries) can provide a good basis for such predictive models. On the other hand, in developed countries, such models could allow policy-makers better planning and allocation of resources for treatment. Using data from 8,492 patients, we obtained the area under the receiver operating characteristic curve (AUC) of 0.773 for predicting RRT within 12 months from the time of CKD diagnosis. The results also show that there is no additional advantage in focusing only on patients with diabetes in terms of prediction performance. Although these results are not as such suitable for adoption into clinical practice, the study provides a strong basis and a variety of approaches for future studies of forecasting models in healthcare.

Prediction of Clinical Events in Hemodialysis Patients Using an Artificial Neural Network.

Advanced chronic kidney disease (CKD) requires routine renal replacement therapy (RRT) that involves hemodialysis (HD) which may cause increased risk of muscle spasms, cardiovascular events, and death. We used Artificial Neural Network (ANN) method to predict clinical events during the HD sessions. The vital signs, captured using a non-contact bed-sensor, and demographic information from the electronic medical records for 109 patients enrolled in the study was used. Weka Workbench software was used to train and validate the ANN model. The prediction model was built using a Multilayer perceptron (MLP) algorithm as part of the ANN with 10-fold cross-validation. The model showed mean precision and recall of 93.45% and AUC of 96.7%. Age was the most important variable for static feature and heart rate for dynamic feature. This model can be used to predict the risk of clinical events among HD patients and can support decision-making for healthcare professionals.

Graft Rejection Prediction Following Kidney Transplantation Using Machine Learning Techniques: A Systematic Review and Meta-Analysis.

Kidney transplantation is recommended for patients with End-Stage Renal Disease (ESRD). However, complications, such as graft rejection are hard to predict due to donor and recipient variability. This study discusses the role of machine learning (ML) in predicting graft rejection following kidney transplantation, by reviewing the available related literature. PubMed, DBLP, and Scopus databases were searched to identify studies that utilized ML methods, in predicting outcome following kidney transplants. Fourteen studies were included. This study reviewed the deployment of ML in 109,317 kidney transplant patients from 14 studies. We extracted five different ML algorithms from reviewed studies. Decision Tree (DT) algorithms revealed slightly higher performance with overall mean Area Under the Curve (AUC) for DT (79.5% ± 0.06) was higher than Artificial Neural Network (ANN) (78.2% ± 0.08). For predicting graft rejection, ANN and DT were at the top among ML models that had higher accuracy and AUC.

Virtual reality among the elderly: a usefulness and acceptance study from Taiwan.

BACKGROUND: Virtual reality (VR) has several applications in the medical domain and also generates a secure environment to carry out activities. Evaluation of the effectiveness of VR among older populations revealed positive effects of VR as a tool to reduce risks of falls and also improve the social and emotional well-being of older adults. The decline in physical and mental health, the loss of functional capabilities, and a weakening of social ties represent obstacles towards active aging among older adults and indicate a need for support. Existing research focused on the effects of VR among older populations, and its uses and benefits. Our study investigated the acceptance and use of VR by the elderly. METHODS: This pilot study was conducted on 30 older adults who voluntarily participated during March to May 2018. Nine VR applications that promote physical activities, motivate users, and provide entertainment were chosen for this study. Participants were asked to use any one of the applications of their choice for 15 min twice a week for 6 weeks. At the end of 6 weeks, participants were asked to fill out a questionnaire based on the Technology Acceptance Model and a literature review, to evaluate their acceptance of VR technology. Cronbach's alpha reliability analysis was used to test the internal consistency of the questionnaire items. Pearson's product moment correlation was used to examine the validity of the questionnaire. A linear regression and mediation analysis were utilized to identify relationships among the variables of the questionnaire. RESULTS: In total, six male and 24 female participants aged 60~95 years volunteered to participate in the study. Perceived usefulness, perceived ease of use, social norms, and perceived enjoyment were seen to have had significant effects on the intention to use VR. Participants agreed to a large extent regarding the perceived usefulness, perceived enjoyment, and their experience of using VR. Thus, VR was seen to have high acceptance among this elderly population. CONCLUSIONS: Older people have positive perceptions towards accepting and using VR to support active aging. They perceived VR to be useful, easy to use, and an enjoyable experience, implying positive attitudes toward adopting this new technology.

Artificial-Intelligence-Based Prediction of Clinical Events among Hemodialysis Patients Using Non-Contact Sensor Data.

Non-contact sensors are gaining popularity in clinical settings to monitor the vital parameters of patients. In this study, we used a non-contact sensor device to monitor vital parameters like the heart rate, respiration rate, and heart rate variability of hemodialysis (HD) patients for a period of 23 weeks during their HD sessions. During these 23 weeks, a total number of 3237 HD sessions were observed. Out of 109 patients enrolled in the study, 78 patients reported clinical events such as muscle spasms, inpatient stays, emergency visits or even death during the study period. We analyzed the sensor data of these two groups of patients, namely an event and no-event group. We found a statistically significant difference in the heart rates, respiration rates, and some heart rate variability parameters among the two groups of patients when their means were compared using an independent sample t-test. We further developed a supervised machine-learning-based prediction model to predict event or no-event based on the sensor data and demographic information. A mean area under curve (ROC AUC) of 90.16% with 96.21% mean precision, and 88.47% mean recall was achieved. Our findings point towards the novel use of non-contact sensors in clinical settings to monitor the vital parameters of patients and the further development of early warning solutions using artificial intelligence (AI) for the prediction of clinical events. These models could assist healthcare professionals in taking decisions and designing better care plans for patients by early detecting changes to vital parameters.

Artificial intelligence in neurosciences: A clinician's perspective.

Even after making allowance for an unprecedented hype, it is an undeniable fact that, in the coming decade, deployment of Artificial Intelligence (AI) will cause a paradigm shift in the delivery of healthcare. This paper will review the practical utility of AI in neurosciences from a clinician's perspective. Steering clear of the complex, technical, computational jargon, the authors will critically review the exponential development in this area from a clinical standpoint. The reader will be exposed to the fundamentals of AI in healthcare and its applications in different areas of neurosciences. Powerful AI techniques can unlock clinically relevant information, hidden in massive amounts of data. Translating technical computational success to meaningful clinical impact is, however, a challenge. AI requires a thorough and systematic evaluation, prior to integration in the clinical care. Like other disruptive technologies in the past, its potential for causing a great impact should not be underestimated. A scenario in which medical information, gathered at the point of care, is analyzed using sophisticated machine algorithms to provide real-time actionable analytics seems to be within touching distance.

Mobile and wearable technologies in healthcare for the ageing population.

The 16th World Congress on Medical and Health Informatics (MedInfo 2017) was held August 21-25, 2017, in Hangzhou, China. It provided a valuable platform for sharing the latest medical and health informatics research and related applications to the scientists, medical practitioners, entrepreneurs, and educators as well as students. During this event, on August 23, 2017, an important related topic was presented in a panel discussion entitled "Wearable technologies: Advancing the healthcare in ageing population" by panelists Shabbir Syed-Abdul, Panagiotis Bamidis, Chun-Por Wong, and Xinxin Zhu. Recent advances in health technologies, focusing on the aging population, their benefits and challenges were discussed, and these topics are summarized in this paper. The need for technology to improve of the life of older population, influential and beneficial technologies, for delivering these technologies to patients are described in this paper.

Roles of Sedentary Behaviors and Unhealthy Foods in Increasing the Obesity Risk in Adult Men and Women: A Cross-Sectional National Study.

Sedentary behaviors and dietary intake are independently associated with obesity risk. In the literature, only a few studies have investigated gender differences for such associations. The present study aims to assess the association of sedentary behaviors and unhealthy foods intake with obesity in men and women in a comparative manner. The analysis presented in this study was based on the data from a population-based, cross-sectional, nationally representative survey (Indonesian Basic Health Research 2013/RISKESDAS 2013). In total, 222,650 men and 248,590 women aged 19­55 years were enrolled. A validated questionnaire, physical activity card, and food card were used for the assessments. The results showed that the prevalence of obesity (body mass index of ≥27.5 kg/m²) was higher in women (18.71%) than in men (8.67%). The mean body mass index in women tended to be higher than in men. After adjusting for age and education, the gender effect on obesity persisted in women and was more significant than in men. There was also a positive and significant effect on obesity of sedentary behaviors and unhealthy foods intake. Moreover, fatty and fried foods displayed a positive multiplicative interaction, increasing obesity risk in women more than in men and indicating a possible dietary risk in in women in relation to obesity. The study suggests that the implementation of educational programs on nutrition and physical activity is particularly important for promoting a healthy body weight among Indonesian women.