Yu Chuan Li

Misleading Health-Related Information Promoted Through Video-Based Social Media: Anorexia on YouTube

Introduction The amount of information being uploaded onto social video platforms, such as YouTube, Vimeo, and Veoh, continues to spiral, making it increasingly difficult to discern reliable health information from misleading content. There are thousands of YouTube videos promoting misleading information about anorexia (eg, anorexia as a healthy lifestyle). Objective The aim of this study was to investigate anorexia-related misinformation disseminated through YouTube videos. Methods We retrieved YouTube videos related to anorexia using the keywords anorexia, anorexia nervosa, proana, and thinspo on October 10, 2011.Three doctors reviewed 140 videos with approximately 11 hours of video content, classifying them as informative, pro-anorexia, or others. By informative we mean content describing the health consequences of anorexia and advice on how to recover from it; by pro-anorexia we mean videos promoting anorexia as a fashion, a source of beauty, and that share tips and methods for becoming and remaining anorexic. The 40 most-viewed videos (20 informative and 20 pro-anorexia videos) were assessed to gauge viewer behavior. Results The interrater agreement of classification was moderate (Fleiss’ kappa=0.5), with 29.3% (n=41) being rated as pro-anorexia, 55.7% (n=78) as informative, and 15.0% (n=21) as others. Pro-anorexia videos were favored 3 times more than informative videos (odds ratio [OR] 3.3, 95% CI 3.3-3.4, P<.001). Conclusions Pro-anorexia information was identified in 29.3% of anorexia-related videos. Pro-anorexia videos are less common than informative videos; however, in proportional terms, pro-anorexia content is more highly favored and rated by its viewers. Efforts should focus on raising awareness, particularly among teenagers, about the trustworthiness of online information about beauty and healthy lifestyles. Health authorities producing videos to combat anorexia should consider involving celebrities and models to reach a wider audience. More research is needed to study the characteristics of pro-anorexia videos in order to develop algorithms that will automatically detect and filter those videos before they become popular.

Neural network modeling for surgical decisions on traumatic brain injury patients

Computerized medical decision support systems have been a major research topic in recent years. Intelligent computer programs were implemented to aid physicians and other medical professionals in making difficult medical decisions. This report compares three different mathematical models for building a traumatic brain injury (TBI) medical decision support system (MDSS). These models were developed based on a large TBI patient database. This MDSS accepts a set of patient data such as the types of skull fracture, Glasgow Coma Scale (GCS), episode of convulsion and return the chance that a neurosurgeon would recommend an open-skull surgery for this patient. The three mathematical models described in this report including a logistic regression model, a multi-layer perceptron (MLP) neural network and a radial-basis-function (RBF) neural network. From the 12,640 patients selected from the database. A randomly drawn 9480 cases were used as the training group to develop/train our models. The other 3160 cases were in the validation group which we used to evaluate the performance of these models. We used sensitivity, specificity, areas under receiver-operating characteristics (ROC) curve and calibration curves as the indicator of how accurate these models are in predicting a neurosurgeons decision on open-skull surgery. The results showed that, assuming equal importance of sensitivity and specificity, the logistic regression model had a (sensitivity, specificity) of (73%, 68%), compared to (80%, 80%) from the RBF model and (88%, 80%) from the MLP model. The resultant areas under ROC curve for logistic regression, RBF and MLP neural networks are 0.761, 0.880 and 0.897, respectively (P < 0.05). Among these models, the logistic regression has noticeably poorer calibration. This study demonstrated the feasibility of applying neural networks as the mechanism for TBI decision support systems based on clinical databases. The results also suggest that neural networks may be a better solution for complex, non-linear medical decision support systems than conventional statistical techniques such as logistic regression.

Novel solutions for an old disease: Diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks

BACKGROUND Diagnosing acute appendicitis clinically is still difficult. We developed random forests, support vector machines, and artificial neural network models to diagnose acute appendicitis. METHODS Between January 2006 and December 2008, patients who had a consultation session with surgeons for suspected acute appendicitis were enrolled. Seventy-five percent of the data set was used to construct models including random forest, support vector machines, artificial neural networks, and logistic regression. Twenty-five percent of the data set was withheld to evaluate model performance. The area under the receiver operating characteristic curve (AUC) was used to evaluate performance, which was compared with that of the Alvarado score. RESULTS Data from a total of 180 patients were collected, 135 used for training and 45 for testing. The mean age of patients was 39.4 years (range, 16-85). Final diagnosis revealed 115 patients with and 65 without appendicitis. The AUC of random forest, support vector machines, artificial neural networks, logistic regression, and Alvarado was 0.98, 0.96, 0.91, 0.87, and 0.77, respectively. The sensitivity, specificity, positive, and negative predictive values of random forest were 94%, 100%, 100%, and 87%, respectively. Random forest performed better than artificial neural networks, logistic regression, and Alvarado. CONCLUSION We demonstrated that random forest can predict acute appendicitis with good accuracy and, deployed appropriately, can be an effective tool in clinical decision making.

Mobile information and communication in the hospital outpatient service.

OBJECTIVES Most healthcare providers provide mobile service for their medical staff; however, few healthcare providers provide mobile service as part of their outpatient service. The mobile outpatient service system (MOSS) focuses on illness treatment, illness prevention and patient relation management for outpatient service users. Initiated in a local hospital in Taiwan, the MOSS pilot project was developed to improve outpatient service quality and pursue higher patient safety. METHOD This study focuses on the development of the MOSS. The workflow, architecture and target users of the MOSS are delineated. In addition, there were two surveys conducted as part of this study. After a focus group of medical staff identified areas in which outpatient services might be improved by the MOSS, the first survey was administered to outpatients to confirm the focus groups intuitions. The second administration of the survey explored outpatient satisfaction after they used the MOSS service. RESULTS With regard to outpatient attitudes, about 93% of participants agreed that the mobile outpatient service improved outpatient service quality. In the area of outpatient satisfaction, about 89% of participants indicated they were satisfied with the mobile outpatient service. DISCUSSION/CONCLUSION Supported by our study finding, we propose that more diverse mobile outpatient services can be provided in the future.

Empowering village doctors and enhancing rural healthcare using cloud computing in a rural area of mainland China

BACKGROUND Chinas healthcare system often struggles to meet the needs of its 900 million people living in rural areas due to major challenges in preventive medicine and management of chronic diseases. Here we address some of these challenges by equipping village doctors (ViDs) with Health Information Technology and developing an electronic health record (EHR) system which collects individual patient information electronically to aid with implementation of chronic disease management programs. METHODS An EHR system based on a cloud-computing architecture was developed and deployed in Xilingol county of Inner Mongolia using various computing resources (hardware and software) to deliver services over the health network using Internet when available. The system supports the work at all levels of the healthcare system, including the work of ViDs in rural areas. An analysis done on 291,087 EHRs created from November 2008 to June 2011 evaluated the impact the EHR system has on preventive medicine and chronic disease management programs in rural China. RESULTS From 2008 to 2011 health records were created for 291,087 (26.25%) from 1,108,951 total Xilingol residents with 10,240 cases of hypertension and 1152 cases of diabetes diagnosed and registered. Furthermore, 2945 hypertensive and 305 diabetic patients enrolled in follow-up. Implementing the EHR system revealed a high rate of cholecystectomies leading to investigations and findings of drinking water contaminated with metals. Measures were taken to inform the population and clean drinking water was supplied. CONCLUSIONS The cloud-based EHR approach improved the care provision for ViDs in rural China and increased the efficiency of the healthcare system to monitor the health status of the population and to manage preventive care efforts. It also helped discover contaminated water in one of the project areas revealing further benefits if the system is expanded and improved.

Benzodiazepine Use and Risk of Dementia in the Elderly Population: A Systematic Review and Meta-Analysis

Background: Benzodiazepines are a widely used medication in developed countries, particularly among elderly patients. However, benzodiazepines are known to affect memory and cognition and might thus enhance the risk of dementia. The objective of this review is to synthesize evidence from observational studies that evaluated the association between benzodiazepines use and dementia risk. Summary: We performed a systematic review and meta-analysis of controlled observational studies to evaluate the risk of benzodiazepines use on dementia outcome. All control observational studies that compared dementia outcome in patients with benzodiazepine use with a control group were included. We calculated pooled ORs using a random-effects model. Ten studies (of 3,696 studies identified) were included in the systematic review, of which 8 studies were included in random-effects meta-analysis and sensitivity analyses. Odds of dementia were 78% higher in those who used benzodiazepines compared with those who did not use benzodiazepines (OR 1.78; 95% CI 1.33-2.38). In subgroup analysis, the higher association was still found in the studies from Asia (OR 2.40; 95% CI 1.66-3.47) whereas a moderate association was observed in the studies from North America and Europe (OR 1.49; 95% CI 1.34-1.65 and OR 1.43; 95% CI 1.16-1.75). Also, diabetics, hypertension, cardiac disease, and statin drugs were associated with increased risk of dementia but negative association was observed in the case of body mass index. There was significant statistical and clinical heterogeneity among studies for the main analysis and most of the sensitivity analyses. There was significant statistical and clinical heterogeneity among the studies for the main analysis and most of the sensitivity analyses. Key Messages: Our results suggest that benzodiazepine use is significantly associated with dementia risk. However, observational studies cannot clarify whether the observed epidemiologic association is a causal effect or the result of some unmeasured confounding variable. Therefore, more research is needed.

LabPush: A Pilot Study of Providing Remote Clinics with Laboratory Results via Short Message Service (SMS) in Swaziland, Africa

Background Turnaround time (TAT) is an important indicator of laboratory performance. It is often difficult to achieve fast TAT for blood tests conducted at clinics in developing countries. This is because clinics where the patient is treated are often far away from the laboratory, and transporting blood samples and test results between the two locations creates significant delay. Recent efforts have sought to mitigate this problem by using Short Message Service (SMS) to reduce TAT. Studies reporting the impact of this technique have not been published in scientific literature however. In this paper we present a study of LabPush, a system developed to test whether SMS delivery of HIV related laboratory results to clinics could shorten TAT time significantly. Method LapPush was implemented in six clinics of the Kingdom of Swaziland. SMS results were sent out from the laboratory as a supplement to normal transport of paper results. Each clinic was equipped with a mobile phone to receive SMS results. The laboratory that processes the blood tests was equipped with a system for digital input of results, and transmission of results via SMS to the clinics. Results Laboratory results were received for 1041 different clinical cases. The total number of SMS records received (1032) was higher than that of paper records (965), indicating a higher loss rate for paper records. A statistical comparison of TAT for SMS and paper reports indicates a statistically significant improvement for SMS. Results were more positive for more rural clinics, and an urban clinic with high workload. Conclusion SMS can be used to reduce TAT for blood tests taken at clinics in developing countries. Benefits are likely to be greater at clinics that are further away from laboratories, due to the difficulties this imposes on transport of paper records.

Predicting Hospital-Acquired Infections by Scoring System with Simple Parameters

Background Hospital-acquired infections (HAI) are associated with increased attributable morbidity, mortality, prolonged hospitalization, and economic costs. A simple, reliable prediction model for HAI has great clinical relevance. The objective of this study is to develop a scoring system to predict HAI that was derived from Logistic Regression (LR) and validated by Artificial Neural Networks (ANN) simultaneously. Methodology/Principal Findings A total of 476 patients from all the 806 HAI inpatients were included for the study between 2004 and 2005. A sample of 1,376 non-HAI inpatients was randomly drawn from all the admitted patients in the same period of time as the control group. External validation of 2,500 patients was abstracted from another academic teaching center. Sixteen variables were extracted from the Electronic Health Records (EHR) and fed into ANN and LR models. With stepwise selection, the following seven variables were identified by LR models as statistically significant: Foley catheterization, central venous catheterization, arterial line, nasogastric tube, hemodialysis, stress ulcer prophylaxes and systemic glucocorticosteroids. Both ANN and LR models displayed excellent discrimination (area under the receiver operating characteristic curve [AUC]: 0.964 versus 0.969, p = 0.507) to identify infection in internal validation. During external validation, high AUC was obtained from both models (AUC: 0.850 versus 0.870, p = 0.447). The scoring system also performed extremely well in the internal (AUC: 0.965) and external (AUC: 0.871) validations. Conclusions We developed a scoring system to predict HAI with simple parameters validated with ANN and LR models. Armed with this scoring system, infectious disease specialists can more efficiently identify patients at high risk for HAI during hospitalization. Further, using parameters either by observation of medical devices used or data obtained from EHR also provided good prediction outcome that can be utilized in different clinical settings.

Telemedicine utilization to support the management of the burns treatment involving patient pathways in both developed and developing countries: a case study.

This case study reports on the utilization of telemedicine to support the management of the burns treatment in the islands of Sao Tome and Principe by Taipei Medical University-affiliated hospital in Taiwan. The authors share experiences about usage of telemedicine to support treatment of the burn victims in a low-income country that receive reconstructive surgery in a developed country. Throughout the entire care process, telemedicine has been used not only to provide an expert advice from distance but also to help establish and maintain the doctor-patient relationship, to keep patients in contact with their families, and to help educate and consult the medical personal physically present in Sao Tome and Principe. This case study presents the details of how this process has been conducted to date, on what were learned from this process, and on issues that should be considered to improve this process in the future. The authors plan to create instructional videos and post them on YouTube to aid clinical workers providing similar treatment during the acute care and rehabilitation process and also to support eLearning in many situations where it otherwise is not possible to use videoconferencing to establish real-time contact between doctors at the local site and remote specialists.

Developing guideline-based decision support systems using protégé and jess

The Institute of Medicine has identified both computerized physician order entry and electronic prescription as keys to reducing medication errors and improving safety. Many computerized clinical decision support systems can enhance practitioner performance. However, the development of such systems involves a long cycle time that makes it difficult to apply them on a wider scale. This paper presents a suite of guideline modeling and execution tools, built on Protégé, Jess and Java technologies, which are easy to use, and also capable of automatically synthesizing clinical decision support systems for clinical practice guidelines of moderate complexity.