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Question 1: How will you apply the results of this research article into different health care departments/settings or chronically ill patients, if this would be beneficial in your future practices e.g., Mental Health, Diabetes, Oncology or others?
= Polycystic ovary syndrome (PCOS) is a chronic condition for which integrating electronic patient-reported outcomes (ePROs) could enhance patient observation and treatment plans. Common symptoms of PCOS include irregular menstrual cycles, mood changes, and excess androgen levels (e.g., acne and excessive facial/body hair). While most smartphones already have cycle-tracking applications to record menstrual cycles and daily logs of general feelings and symptoms, integrating ePROs can provide deeper insights into the disease. Beyond tracking the menstrual cycle, ePROs can monitor symptoms such as irregular menstrual cycles, weight gain, mood swings, and acne, as well as lifestyle factors that may affect hormone levels, such as diet and sleep. This additional data can improve interventions, whether they involve medication or lifestyle adjustments.Question 2: What additional features or improvements would you suggest for ePRO systems to make them more effective and user-friendly for both patients and healthcare providers?
= I agree with Panyada and Soe that making the ePRO system user-friendly and simple will enhance its effectiveness. In addition to this, I would suggest incorporating features such as automated alerts that notify healthcare providers automatically when a patient reports abnormal symptoms. This would facilitate timely interventions. Another important feature to consider is a read-out-loud function, which would be beneficial for individuals with visual impairments or older adults who may prefer auditory assistance with questionnaires and educational content. -
1. How can integrating environmental data improve the accuracy and timeliness of malaria outbreak predictions compared to relying solely on epidemiological data?
= Integrating environmental data from satellite weather forecasts with epidemiological data significantly enhances the accuracy and timeliness of malaria outbreak predictions. Satellite weather forecasts provide several climate variables, such as rainfall, humidity, temperature, wind patterns, and seasonal trends. These climate variables directly influence mosquito breeding and malaria transmission. For example, high rainfall and humidity increase the chances of mosquito breeding and survival, while wind patterns help predict the spread of mosquitoes and potential transmission areas. By incorporating this environmental data, we can gain a better understanding of the conditions that lead to outbreaks, allowing for more effective and timely public health interventions.
2. What are the key benefits and challenges of involving public health stakeholders continuously throughout the development and implementation of a system like EPIDEMIA, and how can their feedback shape the systemās effectiveness?
= – Benefits
Continuously involving public health stakeholders throughout the development and implementation of EPIDEMIA or similar systems enhances its long-term efficacy. Each stakeholder ensures the system meets the needs of the public health sector by providing accurate and reliable data. Their continuous feedback can identify areas that need enhancement, leading to overall improvements in the system’s effectiveness, whether in malaria forecasting or response.
– Challenges
However, stakeholders must invest more time and effort into the system’s development, which can be overwhelming in real-world scenarios. Additionally, disagreements between stakeholders on implementation plans can arise, making balanced communication a challenge for ongoing involvement and feedback. -
1. Why was the author interested in investigating the suicide problem in Thailand during the time?
= The author conducted the study to investigate the rising suicide rate in Thailand by examining economic, non-economic, and social factors. This interest was driven by the limited research available on this topic using macro-level data. Additionally, existing research primarily used time-series analysis, which might not fully capture the relationship between suicide rates and these factors. In contrast, this study used cross-sectional analysis to provide a better understanding of the cultural, social, and economic differences between regions and their impact on the suicide rate.2. Each of the students picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate?
= Age 60+
The study indicated that provinces with a higher percentage of elderly had higher suicide rates. In Thailand, many elderly people over 60 live in rural areas, while younger generations tend to live/move to urban and industrial areas. This often leaves the elderly without adequate family support. Additionally, as they age, they face more health problems, which can increase stress levels and contribute to suicidal behavior. There are also not enough financial and social support programs for the elderly in Thailand, making it difficult for them to care for themselves. Moreover, the regression results in Models 1 and 2 showed an increasing trend of suicide rates for individuals over 40, which correlates with the findings for those aged 60 and above.3. How statistical modeling can contribute to investigate the epidemiology and spatial aspects of Thai suicide problem?
= Statistical modeling, specifically multiple regression analysis, significantly contributes to investigating the pattern of suicide rates in Thailand by identifying and quantifying the relationships between various risk factors and suicide rates across different regions. This approach also aids in predicting trends in suicide rates, allowing for the identification of high-risk locations (provinces or regions). By pinpointing these factors and areas, policymakers, healthcare providers, and other involved organizations can develop targeted strategies to reduce the high suicide rate in those particular areas. -
Q1: In your experience, what are the biggest challenges to achieving sustainability in health information systems, and how can they be addressed?
= The biggest challenge in sustaining health information systems (HIS) in the long run is securing long-term funding and investment. This funding is essential for system maintenance and updates, as well as for resources like hiring and training technical teams to support the system. Having enough funding to support HIS is crucial for its longevity. To address this challenge, in addition to receiving support from the government and health sectors, donor activities and developing systems that require less re-training (which means less funding to support) can help maintain the systems more efficiently in the future.Q2: How has EHIS been designed to adapt to changing needs and technologies in your experience? If you havenāt encountered this, what features do you think are important for adaptability?
= During clinic hours at the OPD examination rooms, some hospital computers occasionally malfunctioned, preventing physicians or resident fellows from accessing the hospital health information system since other hospital computers were already occupied. However, a cloud system is being used exclusively by physicians and resident fellows, as it is restricted to their usernames and passwords. Nurses and other hospital staff cannot access this system. I have never used it personally since I do not have the right to access it, so I asked one of the fellows about its functionality out of curiosity. From what I remember, the cloud system essentially contains the same health information as the HIS on hospital computers and can be accessed on any device, such as a smartphone or iPad. This feature makes it easier for doctors to review their patients’ medical histories without needing to find a hospital computer. Therefore, this cloud system was created to address the limited access to hospital computers and improve overall efficiency. -
1. Please discuss how you think the perceived ease of use and usefulness may differ among the different demographics.
ā Age
The younger generation tends to access and utilize technology more easily than the older generation because they have more experience with technology from a young age, whether at school (e.g., computers and educational software for learning) or through the use of smartphones in daily life. These factors influence younger users to feel more comfortable accessing the Personal Health Record (PHR) System. In contrast, the older generation may feel more challenged when using the technology.
ā Gender
I do not think there is a significant difference between genders in the usage of a PHR system as long as the system’s design is user-friendly and well-designed. Both men and women should be able to keep track of their personal health records for whatever purposes they desire.
ā Education level
Individuals with higher education levels are likely to feel more comfortable and able to access advanced systems better than those with lower education levels. This is due to their familiarity with technology, which allows higher education individuals to navigate and utilize more complex features of the PHR system effectively.2. In your experience of using e-health applications or systems, what are some external factors or variables that should be considered to extend the proposed model for assessing the intention to use the system?
= In my opinion, interoperability and healthcare provider support are the factors that should be considered to improve the system’s use. Interoperability: Users are more likely to use a system that can seamlessly transfer and receive secure health data (e.g., EHR) with other healthcare systems and devices. Additionally, healthcare provider support: The support and recommendations from healthcare professionals can enhance the system’s reliability and boost users’ confidence in using the system. -
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1. What are possible reasons locations in epidemiological research have not been incorporated as much as other components in epidemiological research?
= In my opinion, limitations in data availability and quality, along with privacy concerns, are the most likely reasons for limiting the incorporation of locations in epidemiological research. Obtaining high-quality spatial data requires significant resources, including personnel, data collection devices, and funding, and it is a time-consuming process. Additionally, location data can reveal personal information about subjects. Therefore, ensuring informed consent for the use of detailed location data is a significant concern that limits the use of location information in spatial epidemiological research.
How can spatial epidemiology be considered as an interdisciplinary science?
= Because the purpose of using spatial epidemiology is to understand how health and disease spread in different areas (locations), the combination of multiple types of data, including epidemiological, clinical, geographic, and even environmental data, needed to be collected and analyzed to get the results. Therefore, spatial epidemiology is considered to be an interdisciplinary science.2. Explain why it is widely recognized that the place where an individual lives or works should be considered as a potential disease determinant and give some examples?
= The place where an individual lives or works usually influences their health conditions, acting as a potential disease determinant. As mentioned in the paper, an individual who lives in a poor environment (e.g., poor sanitation, pollutants, low socioeconomic status) tends to experience negative health outcomes. This conclusion can be illustrated by John Snow’s investigation of the cholera outbreak in London in 1854. The poor management of a water pump’s sanitation led to its contamination with cholera. People living near the contaminated pump used it and contracted the disease, which eventually turned into an outbreak. Also, the investigation showed that living closer to the contaminated water pump led to a higher number of infected people, highlighting the significant impact of living and working environments on health outcomes. -
1. From the results, what would you recommend to Tak Hospital to improve the syphilis surveillance system?
= In addition to the suggestions already mentioned, I would like to highlight the importance of conducting regular data audits. Surveillance systems often encounter missing or incomplete data issues, leading to low sensitivity and suboptimal evaluations. Implementing regular audits can address these issues by verifying data accuracy, identifying incomplete records, and uncovering root causes. Furthermore, audits can enhance the consistency of data collection by following established protocols.2. Do you have experience with disease surveillance systems? What are the strengths and weaknesses of that system?
= I had the experience of collecting data for a retrospective study of cirrhosis patients (1,000 participants) to investigate the causes of the disease, with data collected over a five-year duration. The strengths and weaknesses of this study are as follows:Strengths:
– Case Record Forms: These forms minimized the risk of incomplete data and ensured consistency in all data entries for later analysis.
– Larger Sample Size: The large sample size increased the potential to detect significant variables and draw more reliable conclusions.
Weaknesses:
– Laboratory and Procedure Orders: There were instances when doctors needed to request blood samples and ultrasound results during clinic hours. Sometimes, doctors forget to order these, leading to the exclusion of participants from the study due to incomplete data.
– Single Center: The study was conducted at a single hospital. Although the sample size was 1,000, the results may not represent the general population from other areas, where different environmental factors could influence the incidence of the disease. -
1. What additional factors should be considered to identify barriers and unmet needs in health information seeking among youth for HIV/STI and RH than in the paper?
= Among youth, education and influence from family and friends can be significant barriers to seeking and understanding health information. Higher education typically correlates with the ability to comprehend the importance of health behaviors and the medical terminology used in health information online. In contrast, a lack of understanding of health terms can lead to misconceptions about HIV/STIs and RH. Additionally, family and friends are the primary sources of influence on youths’ decisions. If they hold negative attitudes towards these diseases, youths may feel scared or ashamed to seek health information or care.2. Which types of vulnerable people in your community are missing or left behind in receiving necessary health information, and why? How can we best reach these individuals and measure the real impact of health information on their health-seeking behaviors to ensure its effectiveness?
= In Thailand, a vulnerable group left behind in receiving necessary health information includes people, especially youth, living in remote areas and/or with low socioeconomic status. These individuals may have limited access to healthcare facilities, health information, and proper education. To reach these groups, we can deploy community health workers to run targeted programs that disseminate health information, conduct training programs or seminars for teachers in these areas to equip them to teach their students effectively, and utilize social media to spread information online. -
1. How can the decision tree model be integrated into clinical practice to assist surgeons in preoperative planning and decision-making?
= Using the decision tree model to support surgeons in preoperative planning and decision-making enhances their ability to mitigate and prevent the risk of massive bleeding before surgery. The model acts as a tool for assessing patient risk, helping the surgical team prepare effectively and alerting them to any cautious conditions. Additionally, integrating the model into the electronic medical record (EMR) or ideally the electronic health record (EHR) allows the surgical team to access, evaluate, and make decisions before scheduling surgery for the patient. This ensures that the team and the patient are informed about any necessary additional procedures or treatments before proceeding with surgery.2. What are the potential benefits and limitations of using this model in a real-world clinical setting?
= The potential benefits and limitations of using the decision tree model in real-world clinical setting are as follows:
Benefits: Early risk assessment enables surgical teams to evaluate and identify high-risk patients promptly. This capability improves preparation and potentially enhances surgical outcomes which will mitigate risks and improve overall patient safety during surgical procedures.Limitations: A substantial volume of high-quality data is needed to train the model effectively before implementing it in real-world settings. Inadequate or inaccurate data may compromise the modelās predictions, leading to unreliable predictions or incorrect clinical decisions.
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1. Is there a similar electronic-based clinical decision support system that has been developed or is planned to be developed for NCD patients in remote areas of your country? Please share your experience about the tool’s advantages and disadvantages.
= To be honest, I am unfamiliar with any electronic-based clinical decision support system for NCD patients used in hospitals or healthcare facilities in remote areas in Thailand. However, last year, there was a Thai-developed medical AI project called PreceptorAI. This medical AI was developed by a team of Thai professionals combining expertise in medicine, AI, and software engineering, and it is still in its early stages. PreceptorAI aims “to help medical professionals diagnose and recommend treatments more accurately, comprehensively, and quickly.” The system is trained on medical guidelines from Thailand and abroad, leveraging the potential of medical professionals using PreceptorAI entirely. PreceptorAI is a chatbot similar to ChatGPT and can be accessed via the Line application, making it easy for Thai healthcare providers to use. From my experience, I tried it by pretending to have symptoms and asking for medical guidelines and diagnosis. The system provided many possible diagnoses and suggested what investigations should be done next and why for each diagnosis. Since I am not a medical student or doctor, I cannot verify its accuracy. Overall, I believe PreceptorAI will be helpful for medical professionals in the future because its medical guidelines will provide more ideas for diagnosis and investigations. This can help professionals decide which investigations are best for their patients.
Source: https://www.preceptorai.tech/#explore2. Do you think that nurse or community health worker-facilitated NCD management tool is the best option to compensate for the shortage of healthcare workers in managing NCDs in remote areas? Are there any alternative options to address insufficient healthcare workers and promote the quality of care to NCD patients?
= Using a nurseāor community health worker-facilitated NCD management tool is a practical approach to addressing the shortage of healthcare workers. However, other strategies should be considered to ensure effective NCD management in remote areas. For example, Telemedicine, where specialists provide expertise in specific NCD areas, can significantly enhance the quality of care for patients. -
1. Besides medical algorithmic audits, are there some additional ways to improve the safety of medical AI systems?
= Besides what classmates mentioned, collecting large amounts of high-quality, real-world data can sometimes be challenging for training AI systems. Therefore,Ā another way to improve the safety of AI systems would be to generate synthetic data that can be used to provideĀ more varied and plentiful datasets. This method enhances the safety of medical AI systems by expanding data availability, decreasing human biases, and safeguarding patient privacy (because it does not include actual medical information). There are, however, limitations as well. As synthetic data is not derived from real-world sources, it may not encompass all potential outcomes, particularly uncommon or unexpected scenarios. It also lacks transparency, making it challenging to comprehend the process when the AI decides the results. Therefore, researchers have to balance synthetic and real-world data, and synthetic data should supplement real-world data rather than fully replace it to maintain the quality and reliability of the results.2. Please name some key/ideal characteristics that medical AI systems can build trust and confidence in the medical community (for example, in terms of safety, quality, efficacyā¦)
= Model Transparency: The model should explain decisions clearly and allow us to check and understand the decision-making process.
Ethical Considerations: It’s essential to get informed consent from patients and protect their data privacy. -
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1. Considering the modelās performance, what additional data or features do you think could further improve the accuracy of predicting PIH?
= 1.1 Integrating additional hemodynamic parameters like heart rate variability (HRV), pulse pressure variation (PPV), and laboratory data such as electrolyte levels and hemoglobin provides deeper insights into cardiovascular stability and physiological changes.
1.2 Continuous monitoring of real-time mechanical ventilation data, including tidal volume and oxygen saturation, offers valuable insights into respiratory function and its impact on hemodynamic and cardiovascular responses.
1.3 Considering external environmental factors, such as changes in patient position, ambient temperature variations, and other surgical influences that may affect incidence outcomes.2. What future research directions would you suggest to address the limitations of this study and enhance the predictive modelās applicability across various surgical procedures?
= Expanding the sample size is one of the key directions that research should take. Collecting larger and more diverse cohorts leads to robust validation of predictive factors across different clinical scenarios, patient demographics, and surgical contexts. Additionally, it enhances the reliability of the predictive model and can result in widely applicable predictive models in clinical practice. -
1.What are the stakeholders that should be involved in applying Machine Learning in symptom prediction? What are their roles and responsibilities?
= 1.1 Oncologists and Other Healthcare Professionals (Medical Expertise): Provide medical knowledge to guide model development and ensure the predictions align with clinical reality. Their role also includes ensuring ethical considerations in symptom management are addressed for patient safety.
1.2 Data Scientists (Machine Learning Expertise): Develop the model and ensure the machine learning algorithms are accurate, reliable, and technically robust.
1.3 Patients: Provide accurate information about their symptoms and experiences to improve the model’s accuracy. Once the model is launched, they can offer feedback on the effectiveness of the predictions.2. What potential ethical considerations or challenges should researchers and clinicians keep in mind when developing and deploying machine learning models to predict cancer symptoms?
= 2.1 Ethical considerations: Researchers and clinicians should prioritize data privacy and ensure informed consent from patients. This will provide transparency in how patient data is used during the model’s development and application. Protecting patient privacy throughout the data collection, storage, and analysis phases is essential to maintaining trust and ethical integrity.
2.2 Challenges: A significant challenge is managing biased data. While more input data generally improves the reliability and accuracy of machine learning models, biased input data can lead to skewed predictions. In the worst-case scenario, it could lead to inaccurate predictions that may harm patients. -
Hello, everyone. Here is the link to my final project assignment: https://lookerstudio.google.com/reporting/de3bf2a7-4202-4e8a-8bd1-7edd5d85b0c7/page/2DN4D
I aim to ensure my dashboard is as user-friendly as possible. It consists of seven pages. The first page is the main dashboard, displaying data for six countries (Brazil, Egypt, Germany, Japan, Mexico, and Thailand) from January 2020 to April 2022. This page includes a map, a summary of confirmed, recovered, and death cases, a line graph comparing three years of incidences, and dropdown lists for selecting continents, countries, or specific time intervals. Additionally, the country flag icons on the first page are links to the remaining pages, each dedicated to one country. These pages feature a pivot table and a detailed line graph showcasing the country’s data on a smaller scale.
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Hello, everyone. The images below are my data visualization for this week’s project, and here is the link to the project’s looker studio: https://lookerstudio.google.com/s/mi0JWOB9-nc
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Everyone introduces such a great data visualization dashboard for COVID-19 disease. I often used those dashboards to update the situation during the pandemic. For this discussion, I would like to introduce the COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) [a research collective housed within the Department of Civil and Systems Engineering (CaSE) at Johns Hopkins University (JHU), USA], and this is the link to the dashboard: https://coronavirus.jhu.edu/map.html
Even though this dashboard no longer updates the COVID-19 data worldwide since March 10, 2023, the data between January 22, 2020, and March 10, 2023, can still be accessed for cases, deaths, vaccines, testing, and demographics. The dashboard has a user-friendly design with the world map at the center and trend charts and details of the number of infections, deaths, and vaccinations around the world map center. The map represents the number of confirmed cases by red dots, and the size of the bigger dot means that the area has a high number of confirmed cases of COVID-19. Moreover, there are dropdown lists that you can use to customize how you view the map, such as selecting the region (North America, Europe, Eurasia, or the world) and choosing the base map (imagery, topographic, streets, navigation, etc.). In addition, when you zoom in on a red dot in a specific area, you can click it, and it will show you the name of the area and the number of cases and deaths. There is also a dropdown list for how you would like to see the data, such as totals, 28 days, incidence, case fatality ratio, and global/US vaccination.
However, this COVID-19 dashboard has some limitations. The first one is that the color scheme is not suitable for colorblind users because the dashboard uses red for the number of total cases and green for the number of vaccinations. In addition, the scroll-down list of cases and deaths in each country on the left side of the dashboard should have small country flags next to the country’s name for quicker identification of specific countries.
In conclusion, although the dashboard offered a user-friendly interface and detailed data visualization, improvements for colorblind users and quicker country identification could have made it even better. Despite these limitations, it remains a valuable resource for reviewing COVID-19 data.
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Good work Suppasitš, here is my suggestions for improving your case record form.
ć Screening
1. This is the part for checking the eligible criteria. So, I think it would be nice to add another question at the end asking whether the subject will be included, temporarily excluded, or excluded, along with the reasons for exclusion.
2. Age: Asking for the subjectās age in exact months they have at the time might be redundant for the subject or the interviewer since they have to calculate for a few seconds. I know it’s just a simple calculation, but I think we can skip the calculation part by letting Excel or the system do the work for you by asking for the month and year of the subjectās birthday instead.
3. Increasing the length of some blank space, especially for the symptoms of influenza illness. Providing more space will ensure that the subject and the interviewer write more details.
4. Asking for the date of completing the CRF and the position of the recorder after the recorderās name would be nice for auditing and tracking the person who filled in the CRF.ć Enrollment
1. Adding another question or a blank space to specify the reasons in case the subject did not measure the vital signs. This one will definitely be queried from the data manager back to the staff at the study site since it is required data. So, you will know how to answer this question. Also, it would be nice to record the time of taking vital signs as well.
2. The study is an observer-blind study, so the question asking the type of vaccines should be excluded since the research team does not know which vaccine they injected into the subject. Instead, asking to write down the code on the vial designed to check what type of vaccine the subject received would be better.
3. Adding the question asking for the location site of injection (left or right arm) in case there are AEs or SAEs.
4. Asking the question for the date and time of collecting blood sample. -
Besides the date format, as Khun Teerawat mentioned, an example of how to fill out the case record form on the date of the visit and the date of the informed consent form signed should be provided so that any confusion that might occur during data collection and data entry can be avoided.
The corrected version should appear on the CRF as follows,
Date of Visit: ________________________ (DD/MMM/YYYY, e.g., 01/JAN/2024)
Date of the informed consent form signed: ____________________________ (DD/MMM/YYYY, e.g., 01/JAN/2024)Also, I agree with Dr. Teeraboon, who mentioned above, that we should not gather the date of birth data if it is unnecessary. The Siriraj Medical Research Center (SIMR), where I worked, has a protocol that if the date of birth needs to be collected, the researcher can collect only the month and the year, not the date, to avoid indirectly identifiable information.
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A significant advantage of implementing data standards in clinical research is interoperability. Given that high-impact clinical studies often involve multiple centers, having data standards ensures clear communication and seamless data exchange between these centers. This not only reduces the risk of misunderstandings and confusion during data collection but also facilitates the integration of data from various centers which enhancing the reliability and robustness of research outcomes.
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I have experience collecting data for a project known as the GIVES-21 project, which stands for Global Inflammatory Bowel Disease (IBD) Visualization of Epidemiology Studies in the 21st Century. This study aims to understand the factors contributing to the rapid increase in IBD cases across 29 regions worldwide. It is a multicenter study hosted by the Chinese University of Hong Kong (CUHK). For data collection, storage, and management, we utilize REDCap (Research Electronic Data Capture) which is a web-based application created by Vanderbilt University, USA, designed to help researchers build and manage online surveys and databases. The REDCap implemented those features of the data managament process, as explain as follows:
– Audit Trial/Time Stamp: CUHK requires the name and email of the study coordinator responsible for data collection and entry into the electronic Case Report Form (eCRF). Access to the system is granted exclusively to authorized personnel, ensuring accountability for data entry and edits.
– User Authentication and Access Control Level: In addition to a username and password, a two-factor authentication process is implemented. After logging in, a 6-digit verification code is sent to the user’s email, which must be entered within 2 minutes to access the system. If not, the verification code will expire.
– Edit Check and Logical Check: Let me give you an example from a part of the questions in the eCRF. In this section, participants are asked about their dietary habits. Specifically, they’re prompted to indicate the quantity (numerator) per frequency (denominator) of a certain type of food they consume. The quantity might be measured in terms of a bowl, cup, milliliter, or pill, depending on the food item. Meanwhile, the frequency could be expressed per day, week, month, or year. Once I input the numbers into the numerator and denominator boxes, the system automatically calculates the ratio. However, if I type characters that shouldn’t be there (e.g., /, ąø), or if the resulting ratio falls outside the predefined range, the system highlights the box in red to indicate an error.
– Data Backup and Recovery Plan: REDCap does not offer automatic backup and recovery features, but the host institution can export data in various formats for backup. Throughout the one-year data collection period, CUHK implements routine data validation and backup after six and twelve months. Any queries regarding unclear responses are addressed, and after corrections, the filled eCRF is logged to prevent further edits.
Source:
https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-023-01944-2
https://projectredcap.org/wp-content/resources/REDCapTechnicalOverview.pdf -
According to the three main data management processes (project initiation, during the study conduct, and during the study end), I was involved in project initiation and conducting the most during the study. I was involved in some steps during the end of the study. I had duties to design data workflow, CRF, data entry/validation, write progress reports to EC, and document archives based on the discussion with my professor at the beginning of the project. Data manipulation and analysis are the only steps I was not involved in. To talk about which step I should have done better, I would say the data acquisition method because I used paper-based CRF due to its convenience for interviewing the participants, then I transferred the answers to Excel; however, it affected the document archive because we have to keep the documents for several years and we do not have much space to store the paper-based CRF. We have new research every year, so adopting Electronic Data Capture (EDC) for data collection would have been more efficient, offering better data storage and retrieval capabilities.
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I was a research assistant at the Faculty of Medicine Siriraj Hospital, a top-ranking institution for medical education and research in Thailand. So, I have several research projects to manage, and data collection plays a big role in my responsibilities.
1. Purpose of data collection:
= The research projects I was involved in about Inflammatory Bowel Disease (IBD), which is infrequently found in Thailand and other Asian countries but is common in Western countries. However, the number of IBD cases in Asia has been increasing significantly. So, the main purpose of the data collection in the research is to investigate the potential causes and treatment of IBD among the Thai population.2. Was it primary or secondary data collection?
= Both types of data collection were performed. The data of projects are mainly collected by recording the disease score when the patients visit each week, interviewing the patients with the quality-of-life questionnaire, and interviewing their diet questionnaire by nutritionist, etc. In addition, some research projects needed some history health records as well, so reviewing the patient’s history health record in the HIS is a part of the data collection.3. Methods used for data collection
= Paper-based data collection is the method I used most because data collection, like quality-of-life questionnaires, usually happens while patients wait in front of the examination room, and some patients could answer the questions on their own since the questions are based on their daily life behavior. Also, they prefer paper over Google Forms. After that, I had to recheck and fill out that data in Excel, which was duplicate work. In contrast, few projects have collaborated with the Chinese University of Hong Kong, and they created the case record form via REDCap, a secure web application for online surveys and databases. I like this system because it decreases the cost of paper used in the project. However, the Siriraj Hospital Research Center only provides REDCap through an intranet system, which makes it hard to use REDCap in other hospital research because it requires the use of the hospital computer while collecting data, which is impossible for me during OPD time since the doctors occupied all computers.4. Were there any problems that occurred regarding data collection?
= I will divide the problems I have encountered into primary and secondary data collections.
– Primary data collection: low response rate (Some patients do not want to participate in the research study), data recording missing or error.
– Secondary data collection: missing or ambiguous data when reviewing the history of medical records. -
This is my summary of the topic of personal health information. Please see the attached picture or in the following link –> https://snipboard.io/LwrhEe.jpg
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I want to share my summary for this week. Here is the link: https://snipboard.io/BIYvbD.jpg
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This is my infographic summary for this week’s topic (Code of Ethics for Health Informatics): https://snipboard.io/laNCnP.jpg
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Hi, everyone. This is a summary of what I learned about AI and ethics in health in the first week.
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My job as a research assistant required me to deal with the health data and interact with all patients in the clinic for research projects. Also, some projects relate to the effect of COVID-19 on patients as well. So, the ethical principles and good practices that I used to do during the pandemic are as follows:
1. Ensure that all participants have informed consent and understand the research process, risks, and benefits. Also, I respect the right of participants to withdraw from the project at any time.
2. Follow the research protocols, limit data assessment to only authorized persons, and double-check data for errors to maintain data integrity and security.
3. Stay informed about the latest COVID-19 research and guidelines to adapt to the research protocol if needed. For example, making amendments for phone calls instead of interviews at the hospital keeps the patients safe in their homes during the pandemic, especially during the curfew period with high infectious numbers. -
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Thailand has three main health schemes: Universal Health Coverage, Social Security, and Civil Servant Medical Benefit Scheme. Among these health schemes, Universal Health Coverage (UHC) plays a significant role in healthcare in Thailand because it is given to individuals automatically regardless of their status or occupation and covers a broad range of healthcare services for Thai people. During the past twenty years, the benefit of this type of health scheme has expanded gradually, enhancing the accessibility of healthcare for Thais. As our classmate mentioned above, improved progress access to healthcare, health outcomes, and health equity are the strengths that Thailand has achieved so far after starting the UHC. However, balancing financial sustainability with rising healthcare costs, increasing the aging population, the quality of care (long wait times), and equity gaps (stateless persons or migrants) are challenges of the UHC in Thailand right now and must be improved.
Therefore,
– Finding solutions for financial mechanisms like health insurance top-ups would be an option for dealing with rising healthcare costs.
– Investing in the healthcare workforce is a way to improve the quality of care.
– Providing a program for non-Thai nationals (stateless) would help give more accessibility to the healthcare system.
– Planning a long-term care service is an excellent way to cope with the aging population in the future. -
In my opinion, Thailand has been working hard to enhance the informatics abilities of the healthcare workforce over the past two decades. However, due to a limited number of health informaticists and insufficient knowledge among some existing healthcare professionals. Thus, it’s undeniable that health informaticists and healthcare workers are pivotal in driving this transformative change. The following examples illustrate improvements in the health informatics workforce situation in Thailand:
– Many hospitals have implemented electronic medical records (EMRs) as the primary tool for healthcare professionals, aiming to improve overall efficiency.
– Some hospitals have developed their own applications, allowing patients to manage visit appointments, request telemedicine services, and more. These activities require direct confirmation by healthcare workers.
– Certain hospitals have established data centers like the Siriraj Informatics and Data Innovation Center (SiData+: Siriraj Hospital). The center focuses on regulating data governance, consolidating data management, and delivering data innovation for medical services, research, and education.
– Based on my experience as a healthcare worker in a hospital, there have been numerous online training courses and sessions to educate staff on proper health data management and the implementation of new hospital systems.
– Many universities in Thailand offer academic degrees in Health Informatics, allowing interested healthcare workers or individuals from other fields to enhance their health informatics skills and apply them to their jobs.However, several challenges persist:
1. Lack of Skills: Healthcare technologies are evolving rapidly, necessitating constant skill updates for healthcare professionals.
2. Resources: Implementing health technology requires significant time and investment from institutions to hire qualified personnel for system maintenance and provide training programs for employees to acquire the necessary skills.
3. Data Security and Privacy: Implementing health technologies raises concerns about data security and privacy. It is crucial not only to use health technologies correctly but also for healthcare employees to be aware of the importance of securing patient data by staying up-to-date on the latest security measures and compliance standards. -
When overseeing a country’s data set, contemplating data sharing is crucial and requires collaboration from stakeholders from different backgrounds. Therefore, before launching such a system, a robust infrastructure is needed. Also, two main concerns guide this approach: safeguarding the security and privacy of individuals and ensuring easy accessibility. Implementing strict security measures, such as de-identification and strong encryption, is paramount to protect sensitive information and comply with governmental regulations. Simultaneously, balancing accessibility and control is essential, allowing authorized access while preventing misuse. Also, a user-friendly interface and open communication contribute to the responsible data-sharing ethos by encouraging collaboration while upholding individual privacy and data integrity.
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Electronic medical records (EMR) were the primary tool I used as a medical research assistant since the needed data was mostly provided in this system. The system significantly enhances the ability of healthcare workers to work better and faster; however, there are benefits and drawbacks to implementing EMR in a hospital.
= Benefits =
1. Patient care: Improved patient care and safety by making patient information and medical history more accessible.
2. Work efficiently: Boosted efficiency and production by minimizing paperwork and streamlining administrative tasks.
3. Research: Improved data management and analysis for research and quality assurance objectives.
4. Data sharing: Better communication and collaboration amongst healthcare providers.= Drawbacks =
1. Costly: Expensive implementation and maintenance costs.
2. Technical issue: Potential technical issues and system downtime can disrupt patient care.
3. Data privacy and security: Concerns about data privacy and security, especially in the event of a data breach. -
The paper discusses the challenges and opportunities of utilizing big health data to improve the quality of cardiovascular disease research. According to the paper, challenges such as missing data, selection bias, data analysis and training, interpretation and translational applicability of results, and privacy and ethical issues hinder high-quality research.
Therefore, the paper suggests coping with these challenges in the following ways:
1. Missing data:
– Establishing a standardized data format across the healthcare system to ensure complete data, fostering interoperability for data sharing between hospitals, and facilitating future surveillance. Additionally, having all healthcare facilities establish Electronic Health Records (EHR) agreements would ensure a common understanding among all stakeholders.
2. Selection bias:
– Implementing randomization, blinding, and clearly defining eligibility criteria as effective and budget-friendly methods to avoid selection bias.
3. Data analysis and training / Interpretation and translational applicability of results:
– Continuous training programs for healthcare workers in research should be instituted to enhance their data analysis and informatics skills. This strategy aims to increase awareness and understanding of utilizing health data effectively.
4. Privacy and ethical issues:
– Implementing advanced technology for encryption and differentiated privacy measures to ensure the privacy and security of the data. Additionally, the government should play a significant role in establishing robust ethical governance frameworks and oversight mechanisms for health data research. -
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Thanks for sharing your dashboard, Weerapat. The feature that allows comparison of cases for each country is excellent. It lets us see the differences in confirmed, recovered, and death cases among countries. This is a great function to include in a data visualization dashboard. However, I have a couple of suggestions: consider changing the map’s layer type style to a bubble style. Besides showing the location of each country on the map, it can quickly convey the magnitude of confirmed cases by the size of the bubble, with more giant bubbles indicating higher confirmed cases. Additionally, on the map, change the variable name from ‘daily confirmed cases’ to ‘confirmed cases’ since your number is the sum of cases over three years of the pandemic which will prevent the user’s confusion. Lastly, great work on your dashboard.š
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Well done, Aung Thura Htoo! Placing all the scorecard results at the top really catches the user’s attention right away. I also have some suggestions for improving the dropdown lists. It would be more user-friendly and preferable to list the months starting with January (in ascending order). Beginning with December at the top can make it more difficult for users to find and select the desired option.
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Thank you so much for the comments, Aung Thura Htoo. I did not realize the comma issue at first. I think I accidentally changed the display format of the scorecards to “none,” which is why the comma did not appear between the numbers. Thanks for pointing that out to me. Also, I did not combine the country flags and action buttons. Initially, I considered placing the country flag and the action button together, but I later discovered that the image could be used as an action button. When you click on the image, a property pop-up shows the image link URL setting, where you can set the direction of the link to another page. It was a neat trick I stumbled upon!
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Thank you for sharing the COVID-19 Tableau Dashboard. I regularly checked this dashboard during the peak pandemic because it provides detailed and reliable data from Thailandās Department of Disease Control (DDC). I agree that the language barrier is a significant issue. Having an English version would be helpful for non-Thai speakers living in Thailand or those interested in Thailand’s COVID-19 situation. Also, I agree that the line chart without labels is hard to understand. To be honest, I did not even notice the chart when I first saw the dashboard, so adding labels would make it much clearer.
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Thank you for sharing your insights, especially on database lock, security, and access control, areas I’m not very familiar with. I’d like to share my opinion on some aspects of your discussion.
I particularly like the idea to implement multi-factor authentication for database access control. As a study coordinator working with a database system where I update medical data of subjects, the requirement to change passwords every 90 days can be challenging, especially since I do not access the system frequently (mostly there are 4-6 months follow-up visit according to the protocol). It’s not just about creating new passwords but also ensuring they are different from previous ones. Sometimes, I find myself forgetting my current password. Thus, integrating multi-factor authentication could enhance database security.
However, there are certain limitations to consider. Many study site staff, including myself, often use hospital computers to access the system. Features like fingerprint and face recognition might not be feasible in such environments. Therefore, it would be beneficial if the database system offered a dropdown menu to select the preferred method for identity verification.
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Your project is really fascinating, and as someone from Chachoengsao province, I find it meaningful to know that there was such research being conducted in my hometown. Also, I agree that language can pose a significant barrier during data collection. Miscommunication with participants can lead to errors impacting the study’s results.
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I agree with you, Soe. The increased accessibility of patient data via EMRs raises concerns about unauthorized access, which demands strong cybersecurity safeguards to protect sensitive information and maintain confidence between healthcare personnel and patients. Furthermore, overreliance on EMRs creates operational risks, particularly during technical glitches, which may compromise the seamless continuity of patient care. To mitigateĀ these challenges, prioritizing user-friendly interfaces and providing ongoing training for healthcare professionals is critical for effective EMR utilization, ensuring that this technology will not affect data security or the operational integrity of healthcare delivery.
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I agree that missing data has a crucial impact on the integrity of studies and analyses. I’ve seen similar scenarios in which patients express concerns about data security during study interviews. The strategies you’ve suggested to increase patient participation are critical, highlighting the need for transparency and communication about data protection and the possible advantages of the study. Building trust with patients is essential for receiving complete and accurate information.
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Hello, Nakarin. I do agree with you. Sometimes, the problem is caused by people themselves, regardless of how efficient the resources are in the health system. If powerful individuals are trying to engage in corruption, the anti-corruption people might be reluctant to prevent that wrongdoing because of their lower positions. So, there is a need for legal protection and safeguards for public health professionals to ensure their safety while making things right.
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Hi, Aung Thura Htoo. I completely agree that it’s crucial to make sure healthcare professionals receive thorough training. They are the ones who will practically use the EMR system. Providing them with a proper understanding of the system through effective training will yield the best results. Thanks for sharing!
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