Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

Background: The prevalence of peritoneal dialysis (PD) in Thailand is increasing rapidly in part because of Thailand’s Peritoneal Dialysis First policy. PD is a home-based renal replacement therapy in which patients with chronic kidney disease perform up to 4 exchanges of dialysate fluid per day in the peritoneal cavity. Overhydration is one of the most common complications in patients on PD and is associated with increased morbidity and mortality. To monitor hydration status, patients collect hydration metrics, including body weight, blood pressure, urine output, and ultrafiltration volume, from each dialysis cycle and enter this information into a PD logbook. This information is reviewed bimonthly at PD clinic appointments. The chronic kidney disease-PD (CKD-PD) app with near-field communication (NFC) and optical character recognition (OCR) was developed to automate hydration metric collection. The information was displayed in the app for self-monitoring and uploaded to a database for real-time monitoring by the PD clinic staff. Early detection and treatment of overhydration could potentially reduce the morbidity and mortality related to overhydration. Objective: This study aims to identify usability issues and technology adoption barriers for the CKD-PD app with NFC and OCR and a monitoring system and to use this information to make rapid cycle improvements. Methods: A multidisciplinary team of nephrologists, PD clinic nurses, computer programmers, and engineers trained and observed 2 groups of 5 participants in the use of the CKD-PD app with NFC and OCR and a monitoring system. The participants were observed using technology in their homes in 3 phases. The data collected included the Unified Theory of Acceptance and Use of Technology questionnaire, think-aloud observation, user ratings, completion of hydration metrics, and upload of hydration metrics to the central database. These results were used by the team between phases to improve the functionality and usefulness of the app. Results: The CKD-PD app with NFC and OCR and a monitoring system underwent 3 rapid improvement cycles. Issues were identified regarding the usability of the NFC and OCR data collection, app stability, user interface, hydration metric calculation, and display. NFC and OCR improved hydration metric capture; however, issues remained with their usability. App stability and user interface issues were corrected, and hydration metrics were successfully uploaded by the end of phase 3. Participants’ scores on technology adoption decreased but were still high, and there was enthusiasm for the self-monitoring and clinical communication features. Conclusions: Our rapid cycle process improvement methodology identified and resolved key barriers and usability issues for the CKD-PD app with NFC and OCR and a monitoring system. We believe that this methodology can be accomplished with limited training in data collection, statistical analysis, and funding.

To determine the prevalence of substandard drugs in urban (Ulaanbaatar) and rural (selected provinces) areas of Mongolia, samples of 9 common, therapeutically important drugs were collected from randomly selected drug outlets in Ulaanbaatar and 4 rural provinces by “mystery shoppers”. Samples were analyzed by visual inspection, registration status, and biochemical analysis. Samples failing to meet all Pharmacopeia quality tests were considered substandard. In the rural provinces, 69 out of 388 samples were substandard, giving an estimated prevalence of substandard drugs of 17.8% (95% CI: 14.1-22.0). There were 85 unregistered samples, giving a prevalence estimate of unregistered drugs of 21.9%. (95% CI: 17.9-26.3). In the urban Ulaanbaatar districts, 112 out of 848 samples were substandard, giving an estimated prevalence of substandard drugs of 13.2% (95% CI: 11.0-15.7). There were 150 unregistered samples, giving a prevalence estimate of unregistered drugs of 17.7% (95% CI: 15.2-20.4). In the rural provinces, 35 out of 85 (41.2%) unregistered samples were substandard; whereas 34 out of 303 (11.2%) registered samples were substandard. (p < 0.0001) In the urban districts, 18 out of 150 (12.0%) unregistered samples were substandard, whereas 94 out of 698 registered were substandard. (13.5%) (p = 0.6). The prevalence of substandard and unregistered drugs is higher in rural provinces. There is a significant association between substandard and unregistered drugs in the provinces but not in the urban districts. The underlying causes for substandard drugs need to be further investigated in order to help formulate strategies to improve pharmacovigilance and the drug supply quality in Mongolia.

A Health Hackathon provides an opportunity for healthcare professionals to collaborate with IT developers and designers to solve health issues using technology and thus serves as a potential venue for interprofessional education. The present paper reports the views and experiences of participants on how the KKU mHealth Hackathon 2017 served as a venue for interprofessional education. A phenomenological approach was used involving semi-structured in-depth interviews of three faculty members and three students who participated in the hackathon. Participants expressed their learning experiences during the event, as well as factors that promoted or hindered learning. Our findings suggest that a health hackathon can serve as a suitable venue for interprofessional education as interviewees reported how they had learnt to successfully collaborate in interprofessional teams, move beyond their prior views and appreciate complementary work from other professions, focus on solving problems practically, and create a collegial, collaborative atmosphere. There were also some potential downsides of the hackathon that could be solved with an improved design in future occasions. A Health Hackathon can be an important opportunity for interprofessional education. Further studies should focus on methods to reproduce these positive learning experiences, mitigate the negative aspects, and investigate their long-term effects.

Background: Labeling color fundus photos (CFP) is an important step in the development of artificial intelligence screening algorithms for the detection of diabetic retinopathy (DR). Most studies use the International Classification of Diabetic Retinopathy (ICDR) to assign labels to CFP, plus the presence or absence of macular edema (ME). Images can be grouped as referrable or nonreferrable according to these classifications. There is little guidance in the literature about how to collect and use metadata as a part of the CFP labeling process. Objective: This study aimed to improve the quality of the Multimodal Database of Retinal Images in Africa (MoDRIA) by determining whether the availability of metadata during the image labeling process influences the accuracy, sensitivity, and specificity of image labels. MoDRIA was developed as one of the inaugural research projects of the Mbarara University Data Science Research Hub, part of the Data Science for Health Discovery and Innovation in Africa (DS-I Africa) initiative. Methods: This is a crossover assessment with 2 groups and 2 phases. Each group had 10 randomly assigned labelers who provided an ICDR score and the presence or absence of ME for each of the 50 CFP in a test image with and without metadata including blood pressure, visual acuity, glucose, and medical history. Specificity and sensitivity of referable retinopathy were based on ICDR scores, and ME was calculated using a 2-sided t test. Comparison of sensitivity and specificity for ICDR scores and ME with and without metadata for each participant was calculated using the Wilcoxon signed rank test. Statistical significance was set at P<.05. Results: The sensitivity for identifying referrable DR with metadata was 92.8% (95% CI 87.6-98.0) compared with 93.3% (95% CI 87.6-98.9) without metadata, and the specificity was 84.9% (95% CI 75.1-94.6) with metadata compared with 88.2% (95% CI 79.5-96.8) without metadata. The sensitivity for identifying the presence of ME was 64.3% (95% CI 57.6-71.0) with metadata, compared with 63.1% (95% CI 53.4-73.0) without metadata, and the specificity was 86.5% (95% CI 81.4-91.5) with metadata compared with 87.7% (95% CI 83.9-91.5) without metadata. The sensitivity and specificity of the ICDR score and the presence or absence of ME were calculated for each labeler with and without metadata. No findings were statistically significant. Conclusions: The sensitivity and specificity scores for the detection of referrable DR were slightly better without metadata, but the difference was not statistically significant. We cannot make definitive conclusions about the impact of metadata on the sensitivity and specificity of image labels in our study. Given the importance of metadata in clinical situations, we believe that metadata may benefit labeling quality. A more rigorous study to determine the sensitivity and specificity of CFP labels with and without metadata is recommended.