Hsuan Chia Yang

LabPush: a pilot study of providing remote clinics with laboratory results via short message service (SMS) in Swaziland, Africa - a qualitative study.

BACKGROUND Developing countries are confronting a steady growth in the prevalence of the infectious diseases. Mobile technologies are widely available and can play an important role in health care at the regional, community, and individual levels. Although labs usually able to accomplish the requested blood test and produce the results within two days after receiving the samples, but the time for the results to be delivered back to clinics is quite variable depending on how often the motorbike transport makes trips between the clinic and the lab. OBJECTIVE In this study, we seek to assess factors facilitating as well as factors hindering the adoption of mobile devices in the Swazi healthcare through evaluating the end-users of the LabPush system. METHODS A qualitative study with semi-structured and in-depth one on one interviews were conducted over two month period July-August 2012. Purposive sampling was used; participants were those operating and using the LabPush system at the remote clinics, at the national laboratory and the supervisors of users at Swaziland. Interview questions were focused on perceived of ease of use and usefulness of the system. All interviews were recorded and then transcribed. RESULTS This study had aimed its primary focus on reducing TAT, prompt patient care, reducing bouncing of patients and defaulting of patients which were challenges that the clinicians have always had. Therefore, the results revealed several barriers and facilitators to the adoption of mobile device by healthcare providers in the Swaziland. The themes Shortens TAT, Technical support, Patient-centered care, Mindset, Improved communication, Missing Reports, Workload, Workflow, Security of smart phone, Human error and Ownership are sorted by facilitators to barriers. CONCLUSION Thus the end-users perspective, prompt patient care, reduced bouncing of patients, technical support, better communication, willing participant and social influence were facilitators of the adoption m-health in the Swazi healthcare.

A personalized medication management platform (PMMP) to improve medication adherence

OBJECTIVES Medication non-adherence caused by forgetting and delays has serious health implications and causes substantial expenses to patients, healthcare providers, and insurance companies. We assessed the effectiveness of a personalized medication management platform (PMMP) for improving medication adherence, self-management medication, and reducing long-term medication costs. METHODS We developed a mobile PMMP to reduce delayed and missed medications. A randomized control trial was conducted of three medical centers in Taiwan. A total 1198 participants who aged over 20 years, received outpatient prescription drugs for a maximum period of 14 days. 763 patients were randomly assigned to intervention group as receiving daily SMS reminders for their medications and 434 patients in control group did not. The primary outcome was change in delaying and forgetting medication between before and after intervention (after 7 days). RESULTS Medication delays were reduced from 85% to 18% (67% improvement) after SMSs for the intervention group and from 80% to 43% (37% improvement) for the control group. Patients forgot medications were significantly reduced from 46% to 5% (41% improvement) for the experimental group after SMSs and from 44% to 17% (27% improvement) for the control group. The SMSs were considered helpful by 83% of patients and 74% of them thought SMSs help in controlling diseases. 92% of patients would recommend this system to their family and friends. CONCLUSIONS A timely and personalized medication reminder through SMS can improve medication adherence in a nationalized healthcare system with overall savings in medication costs and significant improvements in health and disease management. TRIAL REGISTRATION ClinicalTrials.gov: NCT02197689.

The effect of particulate matter size on cardiovascular health in Taipei Basin, Taiwan

BACKGROUND Although the overall effect of particulate matter (PM) on cardiovascular disease (CVD) has been previously documented, the effect of different PM sizes (PM10, PM2.5-10 and PM2.5) has not been well studied. This study estimates the effect of different PM sizes on the incidence of CVD in Taipei, Taiwan. METHODS We collected outpatients with CVD from 2006 to 2010 and data on the concentrations of air pollutants such as PM10, PM2.5-10, PM2.5, sulfur dioxide, carbon monoxide, nitrogen dioxide, and ozone. A Distributed Lag Non-linear Model (DLNM) was used to explore the effect of different PM sizes on CVD risk. RESULTS In high air pollution events, PM2.5 was significantly associated with elevated risk (4.9%) [95% confidence interval (CI): 1.010-1.089] for CVD with increasing interquartile range (IQR) in single air pollutant model. PM2.5-10 and PM10 did not show a significant positive association with CVD in this study. After adjusting for other air pollutants such as SO2, CO, NO2, and O3, the estimated effect of PM2.5 only decreased 0.2%. Moreover, patients under 40 years old did not show a significant association between PM2.5 and CVD. CONCLUSION This study demonstrates that only PM2.5 is significantly positively correlated with the number of daily outpatient visits for CVD during high air pollution events.

Gout drugs use and risk of cancer: A case-control study

OBJECTIVE Firm conclusion about whether short and long-term gout medications use has an impact on cancer risk remain inconclusive. The aim of this study was to investigate the association between gout drugs use and risk of cancer. METHODS We conducted a retrospective longitudinal population-based case-control study in Taiwan. Cases were identified all patients who were aged 20years or above, and had a first time diagnosis of cancers for the period between 2001 and 2011. Adjusted odds ratios (AORs) and 95% confidence intervals (CIs) were calculated by using conditional logistic regression. RESULTS We examined 601,733 cases and 2,406,932 matched controls. The adjusted odd ratio for any gout drugs use and overall cancer risk was 1.007 (95% CI: 0.994-1.020). There was a significant risk of leukemia (AOR: 1.34, 95% CI: 1.20-1.50), endometrial cancer (AOR: 1.33, 95% CI: 1.12-1.57), non-Hodgkins (AOR: 1.24, 95% CI: 1.13-1.35), female breast cancer (AOR: 1.21, 95% CI: 1.13-1.29), cervical cancer (AOR: 1.21, 95% CI: 1.07-1.37). However, no association was observed in male group (AOR: 0.97, 95% CI: 0.95-0.98) but female showed a significantly increased risk of cancer at any site (AOR: 1.107, 95% CI: 1.08-1.13). CONCLUSION In summary, our results suggest that gout drugs increase risk of the most common cancers, particularly in leukemia, non-Hodgkins, endometrial, breast and cervical cancer.

Exploring the Association between Statin Use and the Risk of Parkinson’s Disease: A Meta-Analysis of Observational Studies

Background: Parkinson’s disease (PD) is a progressive disorder of the central nervous system. The prevalence of PD varies considerably by age group; it has a higher prevalence in patients aged 60 years and more. Several studies have shown that statin, a cholesterol-lowering medication, reduces the risk of developing PD, but evidence for this is so far inconclusive. The objective of this study is to evaluate the association between statin use and the risk of developing PD. Methods: PubMed, EMBASE, and the bibliographies of articles were searched for studies published between January 1, 1990, and January 1, 2017, which reported on the association between statin use and PD. Articles were included if they (1) were published in English, (2) reported patients treated with statin, and the outcome of interest was PD, (3) provided OR/HR with 95% CI or sufficient information to calculate the 95% CI. All abstracts, full-text articles, and sources were reviewed, with duplicate data excluded. Summary relative risk (RRs) with 95% CI was pooled using a random-effects model. Subgroup and sensitivity analyses were also conducted. Results: We selected 16 out of 529 unique abstracts for full-text review using our selection criteria, and 13 out of these 16 studies, comprising 4,877,059 persons, met all of our inclusion criteria. The overall pooled RR of PD was 0.70 (95% CI 0.58–0.84) with significant heterogeneity between estimates (I2 = 93.41%, p = 0.000) for the random-effects model. In subgroup analysis, the greater decreased risk was found in studies from Asia (RR 0.62 95% CI 0.51–0.76), whereas a moderate reduction was observed in studies from North America (RR 0.69 95% CI 0.47–1.00), but less reduction was observed in studies from Europe (RR 0.86 95% CI 0.80–0.92). Also, long-term statin use, simvastatin, and atorvastatin showed a higher rate of reduction with significance heterogeneity. Conclusion: Our results showed that statin use is significantly associated with a lower risk of developing PD. Physicians should consider statin drug therapy, monitor its outcomes, and empower their patients to improve their knowledge, therapeutic outcomes, and quality of life. However, preventive measures and their associated mechanisms must be further assessed and explored.

Benzodiazepines use and breast cancer risk: A population-based study and gene expression profiling evidence

The aim of this study was to investigate whether long-term use of Benzodiazepines (BZDs) is associated with breast cancer risk through the combination of population-based observational and gene expression profiling evidence. We conducted a population-based case-control study by using 1998 to 2009year Taiwan National Health Insurance Research Database and investigated the association between BZDs use and breast cancer risk. We selected subjects age of >20years old and six eligible controls matched for age, sex and the index date (i.e., free of any cancer at the case diagnosis date) by using propensity scores. A bioinformatics analysis approach was also performed for the identification of oncogenesis effects of BZDs on breast cancer. We used breast cancer gene expression data from the Cancer Genome Atlas and perturbagen signatures of BZDs from the Library of Integrated Cellular Signatures database in order to identify the oncogenesis effects of BZDs on breast cancer. We found evidence of increased breast cancer risk for diazepam (OR, 1.16; 95%CI, 0.95-1.42; connectivity score [CS], 0.3016), zolpidem (OR, 1.11; 95%CI, 0.95-1.30; CS, 0.2738), but not for lorazepam (OR, 1.04; 95%CI, 0.89-1.23; CS, -0.2952) consistently in both methods. The finding for alparazolam was contradictory from the two methods. Diazepam and zolpidem trends showed association, although not statistically significant, with breast cancer risk in both epidemiological and bioinformatics analyses outcomes. The methodological value of our study is in introducing the way of combining epidemiological and bioinformatics approaches in order to answer a common scientific question. Combining the two approaches would be a substantial step towards uncovering, validation and further application of previously unknown scientific knowledge to the emerging field of precision medicine informatics.

Social media as a primary source of medical knowledge acquisition and dissemination

The ubiquity and widespread adoption of social media sites such as Facebook and Twitter by everyone – medical professionals included – has changed the way that people share and exchange ideas and opinions. Social media platforms often disseminate medical quizzes to users for a variety of reasons that include generating traffic and revenue to wanting to educate the public. Medical quizzes are also a traditional means of administering continuing medical education to physicians. “Automatic extraction and identification of users’ responses in Facebook medical quizzes [1]” is a study that begins the task of mining this potentially rich data source in an automated way by crawling through the raw data and extracting general trends from it. The second editor’s choice article is “Analysis and visualization of intracardiac electrograms in diagnosis and research: Concept and application of KaPAVIE [2]”. This paper presents the author’s open source software that enables easier analysis and visualization of intracardiac electroanatomical mapping, which will enhance the diagnosis and treatment of cardiac arrhythmias. The software handles a variety of clinical scenarios and allows engineers and physicians to better communicate with each other. The third editor’s choice article is “Prosodic analysis of neutral, stress-modified and rhymed speech in patients with Parkinson’s disease [3]”. This study addresses the common speech problem of dysprosody in Parkinson’s patients. The authors present their work on a quantitative analysis of prosodic impairments by analyzing the speech of 98 Parkinson’s patients compared to 51 controls. They conclude that Parkinson’s patients indeed have reduced pitch variation, impaired speech intensity control, and speech rate abnormalities.

Applications of machine learning in fatty live disease prediction

: Fatty liver disease (FLD) is considered the most prevalent form of chronic liver disease worldwide. The prediction of fatty liver disease is an important factor for effective treatment and reduce serious health consequences. We, therefore construct a prediction model based on machine learning algorithms. A dataset was developed with ten attributes that included 994 liver patients in which 533 patients were females and others were male. Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Logistic Regression (RF) data mining technique with 10-fold cross-validation was used in the proposed model for the prediction of fatty liver disease. The performances were evaluated with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. In this proposed model, logistic regression technique provides a better result (Accuracy 76.30%, sensitivity 74.10%, and specificity 64.90%) among all other techniques. This study demonstrates that machine learning models particularly logistic regression model provides a higher accurate prediction for fatty liver diseases based on medical data from electronic medical. This model can be used as a valuable tool for clinical decision making.

E-health literacy and health information seeking behavior Among University Students in Bangladesh

Web 2.0 has become a leading health communication platform and will continue to attract young users; therefore, the objective of this study was to understand the impact of Web 2.0 on health information seeking behavior among university students in Bangladesh. A random sample of adults (n = 199, mean 23.75 years, SD 2.87) participated in a cross-sectional, a survey that included the eHealth literacy scale (eHEALS) assessed use of Web 2.0 for health information. Collected data were analyzed using a descriptive statistical method and t-tests. Finally logistic regression analyses were conducted to determine associations between sociodemographic, social determinants, and use of Web 2.0 for seeking and sharing health information. Almost 74% of older Web 2.0 users (147/199, 73.9%) reported using popular Web 2.0 websites, such as Facebook and Twitter, to find and share health information. Current study support that current Web-based health information seeking and sharing behaviors influence health-related decision making.

Pressing onward towards the goal: Engineering intelligent systems to improve clinical care

[ Smart decision support” focuses on features which make hem easy to use, to accommodate changes in the environent and the decision making approach of the user. This onth’s editor’s choice articles all make their own small ontributions towards our commonly shared vision of using omputers to improve the reliability and accuracy of clinical are. “Bayesian Network Modeling: a Case Study of an Epidemilogic System Analysis of Cardiovascular Risk [1]” is original esearch article on the use of Bayesian approaches towards ssessing cardiac risk. Most predictive models in clinical edicine to date have used logistic regression approaches, hich takes a frequentist view of data. By using a Bayesian pproach to cardiovascular risk factors, the authors wish to emonstrate the value of incorporating prior information and egrees of certainty when predicting future outcomes and isualize the interrelationships between features in the data. The second editor’s choice article lies in the area of autoated diagnoses and is titled, “Computer-aided diagnosis of soriasis skin images with HOS, texture and color features: A rst comparative study of its kind [2]”. This paper presents the rst comparative performance study of its kind using principal omponent analysis based on a computer-aided diagnosis sysem for psoriasis risk stratification and image classification. reliable, accurate computer-aided diagnosis system would ndoubtedly be useful clinically as it would increase access to are, make it safer over the long-term, and free physicians to o other tasks. The study shows encouraging results regarding he feasibility of using computers to accurately diagnosis a ommon skin condition. The third editor’s choice article is “A seamless ubiquitous mergency medical service for crisis situations [3]” This study ims to embed intelligence in our wireless networks so that hey are always online and operating smoothly. In emergency cenarios having a redundant and robust wireless signal is aramount since time is of the essence. When emergency ersonnel can depend on having clean, strong wireless conectivity, live EKG tracings, vital signs, and other medical data can be transmitted from the scene and en route to the hospital. The authors propose a mobile middleware using cognitive radio for improving the wireless communication. Cognitive radio understands the context in which its transceiver is operating and is able to choose the best wireless channel and bridge any gaps in connectivity using available 3G and wifi networks. The authors present their architecture for seamless communication over heterogeneous wireless networks and show that their middleware allows for seamless communication throughout their area of the city.