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

Background: Uric acid is associated with noncommunicable diseases such as cardiovascular diseases, chronic kidney disease, coronary artery disease, stroke, diabetes, metabolic syndrome, vascular dementia, and hypertension. Therefore, uric acid is considered to be a risk factor for the development of noncommunicable diseases. Most studies on uric acid have been performed in developed countries, and the application of machine-learning approaches in uric acid prediction in developing countries is rare. Different machine-learning algorithms will work differently on different types of data in various diseases; therefore, a different investigation is needed for different types of data to identify the most accurate algorithms. Specifically, no study has yet focused on the urban corporate population in Bangladesh, despite the high risk of developing noncommunicable diseases for this population. Objective: The aim of this study was to develop a model for predicting blood uric acid values based on basic health checkup test results, dietary information, and sociodemographic characteristics using machine-learning algorithms. The prediction of health checkup test measurements can be very helpful to reduce health management costs. Methods: Various machine-learning approaches were used in this study because clinical input data are not completely independent and exhibit complex interactions. Conventional statistical models have limitations to consider these complex interactions, whereas machine learning can consider all possible interactions among input data. We used boosted decision tree regression, decision forest regression, Bayesian linear regression, and linear regression to predict personalized blood uric acid based on basic health checkup test results, dietary information, and sociodemographic characteristics. We evaluated the performance of these five widely used machine-learning models using data collected from 271 employees in the Grameen Bank complex of Dhaka, Bangladesh. Results: The mean uric acid level was 6.63 mg/dL, indicating a borderline result for the majority of the sample (normal range <7.0 mg/dL). Therefore, these individuals should be monitoring their uric acid regularly. The boosted decision tree regression model showed the best performance among the models tested based on the root mean squared error of 0.03, which is also better than that of any previously reported model. Conclusions: A uric acid prediction model was developed based on personal characteristics, dietary information, and some basic health checkup measurements. This model will be useful for improving awareness among high-risk individuals and populations, which can help to save medical costs. A future study could include additional features (eg, work stress, daily physical activity, alcohol intake, eating red meat) in improving prediction.

Medical staff carry an inordinate risk of infection from patients, and many doctors, nurses, and other healthcare workers are affected by COVID-19 worldwide. The unreached communities with noncommunicable diseases (NCDs) such as chronic cardiovascular, respiratory, endocrine, digestive, or renal diseases became more vulnerable during this pandemic situation. In both cases, Remote Healthcare Systems (RHS) may help minimize the risk of SARS-CoV-2 transmission. This study used the WHO guidelines and Design Science Research (DSR) framework to redesign the Portable Health Clinic (PHC), an RHS, for the containment of the spread of COVID-19 as well as proposed corona logic (C-Logic) for the main symptoms of COVID-19. Using the distributed service platform of PHC, a trained healthcare worker with appropriate testing kits can screen high-risk individuals and can help optimize triage to medical services. PHC with its new triage algorithm (C-Logic) classifies the patients according to whether the patient needs to move to a clinic for a PCR test. Through modified PHC service, we can help people to boost their knowledge, attitude (feelings/beliefs), and self-efficacy to execute preventing measures. Our initial examination of the suitability of the PHC and its associated technologies as a key contributor to public health responses is designed to “flatten the curve”, particularly among unreached high-risk NCD populations in developing countries. Theoretically, this study contributes to design science research by introducing a modified healthcare providing model.

The Portable Health Clinic (PHC) system endeavors to take healthcare facilities along with remote doctors' consultancy to the doorsteps of the unreached people using an advanced telemedicine system. Thus, the necessity of having physical healthcare peripheries specially in the developing countries can be mitigated. The PHC system promotes preventive healthcare by encouraging regular health checkups so that diseases can be prevented as well as their severity can be mitigated, leading to a reduction on healthcare expenses. Thus, the number of patients along with excessive workload on existing healthcare human resources can be minimized. The current project in rural Bangladesh alone has served more than 41,000 people so far by the PHC system and a simple analysis of this data shows some significant findings on regional health status. A simple expansion of this program, covering a wider service area, can produce a big data to reflect the whole country's health profile.

The COVID-19 pandemic has limited routine community health services, including screening for non-communicable diseases (NCDs). An adaptive and innovative digital approach is needed in the health technology ecosystem. A portable health clinic (PHC) is a community-based mobile health service equipped with telemonitoring and teleconsultation using portable medical devices and an Android application. The aim of this study was to assess the challenges and potential improvement in PHC implementation in Indonesia. This study was conducted in February–April 2021 in three primary health centers, Mlati II in Sleman District, Samigaluh II in Kulon Progo, and Kalikotes in Klaten. In-depth interviews were conducted with 11 health workers and community health workers. At the baseline, 268 patients were examined, and 214 patients were successfully followed-up until the third month. A proportion of 32% of the patients required teleconsultations based on automatic triage. Implementation challenges included technical constraints such as complexity of applications; unstable networks; and non-technical constraints, such as the effectivity of training, the availability of doctors, and the workload at the primary health center. PHCs were perceived as an added value in addition to existing community-based health services. The successful implementation of PHCs should not only be considered with respect to technology but also in terms of human impact, organization, and legality.

Co-design and co-production with non-academic stakeholders has been recognized as a key approach in transdisciplinary sustainability research. The majority of transdisciplinary studies have been conducted in Europe and North America, with a marked lack of such research in the Asian context—particularly with regard to healthcare. Utilizing a case study involving mobile health check-ups performed using a portable health clinic system in Jaipur, India, from March 2016 to March 2018, this study identifies key factors in co-design and co-production that should be considered to ensure the project’s sustainability. Thoroughly reviewing all of the documents and materials related to the case study’s co-design and co-production, this study identifies the following key factors: (1) mutual stakeholder agreement on a long-term research plan, protocol, and budget; (2) harmonizing research objectives, frames, and the scale of stakeholder expectations; (3) stakeholders’ commitment and a sense of ownership derived from their needs and priorities; (4) stakeholder trust; (5) effective coordinators; (6) personality type and characteristics of stakeholder leaders; (7) capacity building and the empowerment of local research staff and participants; and (8) continuous efforts to involve stakeholders throughout the co-design and co-production processes. Facilitating effective co-design and co-production, these factors will help ensure the future sustainability of projects.