Panyada Cholsakhon

1. Since the objectives of ePROs are to effectively monitor the outcomes of treatment reported by patients, a study was conducted where rheumatologists completed a survey to investigate the acceptance, usage, and barriers to using ePROs. The results showed that not many rheumatologists currently use ePROs, and less than half are planning to use…[Read more]

1. Integrating environmental data with epidemiological data can significantly enhances the accuracy and timeliness of malaria outbreak predictions. Since malaria is a vertor-borne disease, environmental factors such as temperature, rainfall, and humidity play critical roles in the lifecycle of malaria vectors which is mosquitoes and the…[Read more]

1. In your experience, what are the biggest challenges to achieving sustainability in health information systems, and how can they be addressed?

Based on my experience, financial factors are the biggest influence on adopting an EHIS in hospitals. The initial investment is substantial, covering not just the system cost but also infrastructure…[Read more]

1. Please discuss how you think the perceived ease of use and usefulness may differ among the different demographics.

– Age: Age definitely affects how easily people use technology. From what I’ve seen in my workplace, older people often find new tech more complicated and are more hesitant to use it. When new technologies are introduced at w…[Read more]

1. From the results, What would you recommend to Tak Hospital to improve the syphilis surveillance system?

Since there is a higher number of Syphilis cases among non-Thai patients, with many cases going unreported due to gaps in the screening unit. To enhance the surveillance system I would like to suggest that before migrant workers begin…[Read more]

1. Implementing AI technology in an epidemic surveillance system can generate many insightful information in response to disease outbreaks. AI can support the epidemic surveillance system by leveraging diverse data sources, including social media platforms like Twitter and Facebook. These platforms can help trigger alerts for further investigation…[Read more]

Thanks for sharing Myatt. This is very interesting topic.

1.In my opinion, environmental factors such as stigma and discrimination can significantly impact young people’s willingness to seek health care. Societal attitudes towards HIV/STIs, sexual minorities, and sex work can create a climate of fear and shame, deterring young people from…[Read more]

1. I have very little experience using or observing electronic-based clinical decision support systems for NSD patients in rural area. However, in Thailand, there are many mobile applications that support the self-management of NCD patients. For example, the DMThai Diary application is a collaboration between the Queen Sirikit National Institute…[Read more]

Thanks for sharing. I just learned about the information systems like JCHIS that facilitate primary healthcare from your discussion. I hope in the future they can strengthen their interoperability features. This would greatly benefit nurses in primary units as well as patients. And of course, VHVs (Village Health Volunteers) are like heroes in…[Read more]

Dear Thitikan, your topic is really informatives, thank you for sharing ka.

1. When considering patient safety in using medical AI systems, in my opinion, medical doctors who use it are one of the most important factors. Their knowledge and experience are crucial in understanding AI recommendations and making decisions that fit each patient’s…[Read more]

1. To enhance the accuracy of predicting post-induction hypotension (PIH), we can consider additional features like fluid balance status and electrolyte levels. These can provide a more comprehensive baseline for predictions. Additionally, continuous real-time hemodynamic monitoring, including heart rate variability, cardiac output, and pulse…[Read more]

1. The stakeholders that should involves in applying machine learning in symptoms prediction can be many professionals such as physician nurses pharmacists radiologist depends on which objective that machine learning will be doing for. In the study, for example, when focusing on the cancer symptom predictions in nursing field, by utilizing model…[Read more]

Dear Ajarns and Friends,

Good afternoon! I would like to share my COVID-19 Dashboard with you all. The dashboard provides an overview of cases: confirmed, recovered, and deaths on the home page where the scorecard in three different colours show the total number in each category. At this main page, you can easily select a specific time frame to…[Read more]

Hi Siriluk,

First of all, I love the theme color of the layout you picked. It’s super neat and soft. I find your dashboard is easy to understand with all the key metrics provided. The dashboard shows all important information at a glance, including the scorecard showing the total number of cases. You have also provided the shortened, compact…[Read more]

Dear Ajarns and friends..This is my screen shot of report from Looker Studio. You all did amazing works. Thanks for sharing!!!

https://snipboard.io/7E8YrW.jpg
https://snipboard.io/cU6zua.jpg
https://snipboard.io/ADEsYm.jpg
https://snipboard.io/EBxzpZ.jpg
https://snipboard.io/hNb83m.jpg

I would like to share my selected COVID-19 dashboard from the University of Virginia https://nssac.bii.virginia.edu/covid-19/dashboard/. The last update of it was in August 2023. The dashboard theme and layout, in my opinion, is well organized and contains many interesting features such as the interactive chart, meaningful colours, animation of…[Read more]

First of all, in my opinion, you made a really well-organized CRF, and it is quite easy to follow with all necessary parts already there. I might not have many comments for you because it is already a great work. Please find some of my suggestions for the improvement below,

Your CRF has all important IDs, including Study ID, Subject ID, and Site…[Read more]

Using data standards for clinical research can help make sure data is collected and reported consistently and accurately. They give clear instructions on how data should be presented, which reduces mistakes and uncertainties. This makes the data more reliable and trustworthy.

For me, I have never conducted or seen clinical trial research collect data electronically in eCRF and using EDC software like my friends mentioned. It’s quite interesting to learn how benefit of having such technology to help manage the data. From my experience in clinics and my thesis, I simply collected data via paper-based questionnaires and…[Read more]