Teeraboon Lertwanichwattana

[Teeraboon Lertwanichwattana]

1. Intervention Considered in the Model
The primary intervention considered in my model is the home isolation program (HI). This intervention is designed to reduce the spread of COVID-19 by isolating infected individuals with mild symptoms or non-urgent conditions at home rather than in hospitals. The model examines the impact of different initiation times of the home isolation program on the infection, hospitalization, and mortality rates in the Thai population.

The home isolation program is integrated into the model through additional compartments:

Exposed HI (EH): Individuals exposed to COVID-19 who are isolated at home.
Infected 1 HI (I1H): Non-urgent infections under home isolation.
Infected 2 HI (I2H): Urgent infections under home isolation.
Recovered HI (RH): Individuals who recover from COVID-19 while under home isolation.
Death HI (DH): Individuals who die under home isolation.

2. Characteristics of the Intervention
Coverage: This refers to the percentage of the population eligible for and adhering to the home isolation program. In my model, coverage will vary to reflect different levels of adoption of the home isolation program. For example, we can simulate scenarios with 50%, 75%, and 100% coverage of the population.

Efficacy: The efficacy of home isolation in reducing transmission is modeled by adjusting the transmission rate (beta). The assumption is that individuals in home isolation (EH, I1H, I2H) have a lower transmission rate than those not isolated. This reduction will be implemented by multiplying the base transmission rate by an efficacy factor, which represents the effectiveness of the isolation in preventing further transmission.

Timing: The initiation time of the home isolation program is a key intervention parameter. The model will simulate different scenarios where the home isolation program is initiated earlier or later in the epidemic. The assumption is that earlier intervention will reduce peak infection and hospitalization rates, while later intervention might lead to higher strain on healthcare resources.

3. Adding Interventions to the Model Structure
In the model, the home isolation program modifies the transitions from the exposed state to the infectious states. The rate at which exposed individuals enter home isolation (tau0) is a parameter that represents the program’s ability to capture exposed individuals. From there, the model tracks the movement of these individuals through the different home isolation compartments.

The home isolation compartments interact with the regular compartments (S, E, I1, I2, etc.), allowing us to compare the outcomes with and without the intervention. The difference in outcomes between these scenarios provides insight into the effectiveness of home isolation.

4. Relevant Citations/Sources
World Health Organization. Home care for patients with suspected novel coronavirus (COVID-19) infection presenting with mild symptoms, and management of their contacts: interim guidance, 04 February 2020. No. WHO/nCov/IPC/HomeCare/2020.2. World Health Organization, 2020.
Marome, Wijitbusaba, and Rajib Shaw. “COVID-19 response in Thailand and its implications on future preparedness.” International journal of environmental research and public health 18.3 (2021): 1089.
Triukose, Sipat, et al. “Effects of public health interventions on the epidemiological spread during the first wave of the COVID-19 outbreak in Thailand.” PLoS One 16.2 (2021): e0246274.

[Teeraboon Lertwanichwattana]

Model Structure for COVID-19 Home Isolation Program

The disease of interest is COVID-19, and I am using an SEIR-type compartmental model to evaluate the impact of a home isolation program. The model includes the following compartments:

Susceptible (S): Individuals who can be infected.
Exposed (E): Individuals who have been exposed but are not yet infectious.
Infected 1 (I1): Non-urgent infections that don’t require hospitalization.
Infected 2 (I2): Urgent infections that may lead to hospitalization or death.
Recovered (R): Individuals who have recovered.
Hospitalized (H): Individuals requiring hospital care.
Death (D): Individuals who have died from COVID-19.
Home Isolation (HI): Compartments for individuals under home isolation (EH, I1H, I2H, RH, HH, DH).

# Model parameters
parameters <- c(beta = 0.5, tau0 = 2/10, tau1 = 1/10, tau2 = 1/10, alpha1 = 1/10, alpha2 = 1/10, alpha3 = 1/10,
alpha4 = 1/10, alpha5 = 0, alpha6 = 0, mu1 = 1/10, gam1 = 1/10, tau1H = 1/10, tau2H = 1/10,
alpha1H = 1/10, alpha2H = 1/10, alpha3H = 1/10, alpha4H = 1/10, gam1H = 1/10, mu1H = 1/10)

# Set initial conditions
initP <- 5.5e6
initE <- 100 # Set an initial value for Exposed
initI1 <- 375
initI2 <- 375
initR <- 0
initH <- 0
initD <- 0
initEH <- 0
initI1H <- 0
initI2H <- 0
initRH <- 0
initHH <- 0
initDH <- 0

# Calculate susceptible population
initS <- initP – initE – initI1 – initI2 – initR – initH – initD – initEH – initI1H – initI2H – initRH – initHH – initDH
if (initS < 0) {
stop(“Initial susceptible population is negative. Check initial conditions.”)
}

# State variables
istate <- c(S = initS, E = initE, I1 = initI1, I2 = initI2, R = initR, H = initH, D = initD, EH = initEH,
I1H = initI1H, I2H = initI2H, RH = initRH, HH = initHH, DH = initDH)

# SEIRHD Model function
SEIRHD <-function(t, state, parameters) {
with(as.list(c(state, parameters)),{

P <- (S + E + I1 + I2 + R + H + D + EH + I1H + I2H + RH + HH + DH)
lam <- parameters[‘beta’] * (I1 + I2) / P

dS = – (lam * S)
dE = (lam * S) – (tau0 * E) – (tau1 * E) – (tau2 * E)
dI1 = (tau1 * E) – (alpha1 * I1) – (alpha2 * I1) – (alpha5 * I1)
dI2 = (tau2 * E) – (alpha3 * I2) – (alpha4 * I2) – (alpha6 * I2) – (gam1 * I2)
dR = (alpha1 * I1) + (alpha4 * I2)
dH = (alpha2 * I1) + (alpha3 * I2) – (mu1 * H)
dD = (mu1 * H) + (gam1 * I2)
dEH = (tau0 * E) – (tau1H * EH) – (tau2H * EH)
dI1H = (tau1H * EH) + (alpha5 * I1) – (alpha1H * I1H) – (alpha2H * I1H)
dI2H = (tau2H * EH) + (alpha6 * I2) – (alpha3H * I2H) – (alpha4H * I2H) – (gam1H * I2H)
dRH = (alpha1H * I1H) + (alpha4H * I2H)
dHH = (alpha3H * I2H) + (alpha2H * I1H) – (mu1H * HH)
dDH = (mu1H * HH) + (gam1H * I2H)

list(c(dS, dE, dI1, dI2, dR, dH, dD, dEH, dI1H, dI2H, dRH, dHH, dDH), P = P)
})
}

# Solve the model
out <- ode(y = istate, times = timesteps, func = SEIRHD, parms = parameters)

Variable
1. beta = Transmission rate = 0.5
2. tau0 = Transition rate from Exposed to HI = 2/10
3. alpha1 = Recovery rate (I1) = 1/10
4. alpha2 = Hospitalization rate (I1) = 1/10
5. gam1 = Death rate for severe cases = 1/10

Resources
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010525/

[Teeraboon Lertwanichwattana]

Topic: Mathematical Modeling of COVID-19 Home Isolation Policies in Thailand

Research Question: Is the current timing of COVID-19 home isolation policies in Thailand appropriate, or should they be implemented sooner or later?

Scope: This research will focus on the Thai context, specifically investigating the effectiveness of home isolation policies in mitigating the spread of COVID-19.

Modeling Approach: I propose using a compartmental model, such as the SEIR model, incorporating age-specific parameters to better understand the disease dynamics within different age groups. The model will be calibrated using real-world data from Thailand.

Key Research Questions:
1. Impact of Home Isolation: How effective are current home isolation policies in reducing COVID-19 transmission rates?
2. Optimal Timing: What is the optimal timing for implementing or adjusting home isolation policies to minimize disease burden?
3. Age-Specific Effects: How do home isolation policies affect different age groups, and are there specific strategies for optimizing their impact on vulnerable populations?

[Teeraboon Lertwanichwattana]

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, Diabete, Oncology or others?

The potential applications of ePROs within healthcare settings are vast and promising. In the realm of anesthesia, I envision a transformative impact.

Preoperatively, ePROs can serve as invaluable tools for gathering comprehensive patient histories. By capturing detailed information about medical conditions, allergies, medications, and past surgical experiences, anesthesiologists can construct a more accurate and personalized risk assessment. Moreover, ePROs can facilitate efficient risk stratification by allowing patients to self-report symptoms and health status, enabling providers to identify those requiring closer attention or specific preoperative interventions.

Postoperatively, the utility of ePROs becomes even more apparent. Real-time pain assessments through ePROs empower clinicians to promptly adjust analgesic regimens, optimizing pain management and patient comfort. Furthermore, continuous monitoring of postoperative recovery parameters, such as nausea, vomiting, and mobility, can facilitate early detection of complications and expedite interventions.

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?

To maximize the effectiveness and user-friendliness of ePRO systems, several enhancements are essential. A user-centric design is paramount, incorporating intuitive interfaces and clear instructions to accommodate patients of varying technological proficiencies. Multilingual capabilities should be prioritized to ensure accessibility for diverse populations. Additionally, integrating accessibility features such as voice recognition and adjustable text size is crucial to accommodate patients with disabilities.

Seamless integration with electronic health records (EHRs) is another critical aspect. By streamlining data transfer between ePROs and EHRs, healthcare providers can access a comprehensive patient record, reducing the potential for errors and improving efficiency. Moreover, enhancing interoperability between different ePRO systems and healthcare platforms is essential for facilitating data sharing and collaboration among care providers.

Finally, fostering patient engagement through educational resources and technical support is crucial for the successful adoption of ePROs. By providing patients with clear explanations of the system’s purpose and benefits, as well as offering assistance when needed, healthcare providers can encourage consistent and accurate data reporting.

[Teeraboon Lertwanichwattana]

How can integrating environmental data improve the accuracy and timeliness of malaria outbreak predictions compared to relying solely on epidemiological data?
I think adding environmental data can really help us predict malaria outbreaks better. Things like temperature, rain, and humidity are super important because they affect how malaria spreads. By using these along with past cases, we can get a much better idea of when and where an outbreak might happen. Plus, making sure all the data fits together correctly, like matching up time and place, is key to making accurate predictions.

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 system’s effectiveness?
It’s really important to involve the people who actually use the system, like public health workers, from the very beginning. They know what they need, so we can make sure the system is easy to use and helpful. Also, they know their local area really well, so their input can help us make the system work better in different places. Lastly, we need to keep asking for their feedback to make sure the system is still working well and to find ways to improve it.

[Teeraboon Lertwanichwattana]

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

From my perspective, the biggest challenges to achieving sustainability in health information systems (HIS) include:
1. Technical Resource Constraints:
– Challenge: Limited availability of technical resources significantly impacts system usage, maintenance, upgrades, and repairs, particularly in resource-limited countries.
– Solution: To address this, scalable and adaptable technologies should be employed that can evolve with changing needs. Additionally, implementing user-friendly interfaces can encourage adoption and reduce resistance among users.
2. Stakeholder Engagement:
– Challenge: A lack of full stakeholder engagement can severely hinder the sustainability of HIS.
– Solution: It’s essential to involve all relevant stakeholders—such as government officials, healthcare providers, and community representatives—from the beginning of the project and maintain regular communication to ensure their ongoing commitment and support.

Question 2: How have EHIS been designed to adapt to changing needs and technologies in your experience? If you haven’t encountered this, what features do you think are important for adaptability?

While I may not have extensive experience with the latest technologies in this field, I believe that the most important features for ensuring the adaptability of EHIS include:

1. Flexibility:
Systems should be capable of adapting to changes in healthcare policies, disease patterns, and technological advancements. This means they need to be designed with flexibility in mind, allowing for easy updates and modifications as needed.
2. Advanced Data Analytics:
Incorporating advanced data analytics into EHIS is crucial. These tools can provide actionable insights for decision-makers, helping them respond effectively to emerging health trends and challenges.
3. Interoperability:
Ensuring that EHIS can communicate with other health systems and databases is vital for comprehensive data integration. Standardized data formats and protocols facilitate seamless data exchange, enhancing the system’s overall utility.

[Teeraboon Lertwanichwattana]

Perceived Ease of Use and Usefulness Across Demographics
1. Age
I feel like younger people are definitely more comfortable with technology. They usually pick up new things really fast. So, I think they’d probably find PHR systems pretty easy to use and helpful. But older folks might struggle a bit. They’ve grown up in a different era, you know? So, they might find these systems confusing and worry about their personal information getting out there.

2. Gender
Honestly, I don’t think being a guy or a girl makes much of a difference when it comes to using PHR systems. It’s more about how the system is designed. If it’s easy to understand and use, anyone can figure it out.

3. Education Level
People who’ve been to college or have higher degrees usually know more about computers and stuff. Because of that, I think they’d probably have an easier time with PHR systems. But people who didn’t go to college might find them tricky.

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?

I think there are a couple of other things we need to consider here. First off, people need to understand what they’re reading on the PHR system. If they don’t know medical terms, it’s going to be tough to use it properly. Secondly, having a smartphone or computer and good internet is a must. If you don’t have those things, it’s going to be really hard to use a PHR system.

[Teeraboon Lertwanichwattana]

1. From the results, What would you recommend to Tak Hospital to improve the syphilis surveillance system?
In my opinion, I recommend several enhancements to the system. First, physicians and in-charge nurses, particularly in Antenatal Care (ANC) and STI/HIV clinics, should improve documentation practices for syphilis diagnosis. This includes clearly documenting syphilis in both the principal diagnosis and as a comorbidity to ensure comprehensive capture within the surveillance system. Second, coders need to diligently search for mentions of syphilis in both the principal diagnosis and comorbidity sections of patient records to accurately record all diagnosed cases in the hospital’s data system. Third, officers at the health screening unit should conduct regular checks to verify if individuals diagnosed with syphilis are following up at the STI/HIV clinic. This ensures continuity of care and precise tracking of patient outcomes. Lastly, establishing protocols for migrant workers lacking identification data is essential to enhance system completeness, ensuring comprehensive inclusion in surveillance efforts regardless of documentation status. These steps collectively aim to strengthen the accuracy and effectiveness of syphilis surveillance and management within healthcare settings.

2. Do you have experience with disease surveillance systems? What are the strengths and weaknesses of that system?
Yes, I have experience with disease surveillance systems, particularly in monitoring the COVID-19 system among medical students. The strength of this system relied on its military command structure, which enabled daily implementation and facilitated prompt reporting by students. This structure ensured that students could report symptoms directly, enhancing the system’s responsiveness. However, a notable weakness was its reliance on self-reporting, which could vary in accuracy depending on individual experiences and perceptions. Despite these challenges, the system’s organizational framework and daily implementation were crucial in effectively managing and monitoring COVID-19 among medical students.

[Teeraboon Lertwanichwattana]

1. How can implementing artificial intelligence technologies in epidemic surveillance systems be enhanced to better detect and respond to disease outbreaks?
In my opinion, AI can aid in detection through several methods. Firstly, by integrating multidimensional open-source data, utilizing NLP to analyze diverse sources like social media and news reports, early warning signals of disease outbreaks can be identified. This approach requires the capability to process and interpret large volumes of data to recognize potential health threats. Additionally, AI systems can employ pattern recognition and localization to identify local and regional patterns in detected signals. This involves analyzing the geographical and temporal distribution of disease mentions and reports to pinpoint outbreak locations and predict potential spread. Moreover, AI can be used to model and simulate outbreak behaviors, predicting the rate of spread, potential impact, and effectiveness of intervention strategies. Such simulations can provide valuable insights for public health responses and policymaking.

2. What potential benefits do you see in utilizing AI for public preparedness, and what challenges might arise in implementing these technologies effectively?
AI provides substantial benefits in epidemic surveillance and response. Firstly, it can generate early warnings of epidemics by analyzing open-source data, enabling swift responses even in low-resource settings or areas with data censorship. This capability has the potential to eradicate newly emergent epidemics before they escalate. Secondly, AI enhances health security by accurately identifying specific diseases and clinical syndromes, thereby aiding in pandemic prevention. Integration with late-stage interventions like diagnostics, drugs, and vaccines further strengthens these efforts. Lastly, AI supports weak health systems by detecting epidemic signals earlier than traditional methods, facilitating prompt investigation and response strategies. This proactive approach significantly improves epidemic control and enhances overall public health preparedness against emerging health threats.

[Teeraboon Lertwanichwattana]

1. How can the decision tree model be integrated into clinical practice to assist surgeons in preoperative planning and decision-making?
In my opinion, incorporating this model into clinical workflows requires integration with electronic health records and training for surgical teams to interpret and act on the model’s predictions effectively. This integration can enhance various methods such as personalized risk assessment, where surgeons input preoperative patient characteristics into the decision tree model to obtain a personalized risk assessment for massive intraoperative blood loss (IBL), enabling tailored surgical strategies for each patient. Additionally, the model can assist in surgical procedure selection by identifying the surgical procedure as a critical predictor of massive IBL, guiding surgeons to choose the most appropriate surgical approach and potentially opting for alternative techniques if the risk is deemed too high. Furthermore, for patients identified at high risk, surgeons can consider preoperative interventions to modify conditions that increase IBL risk, such as managing diabetes mellitus effectively in patients undergoing pancreaticoduodenectomy (PD).

2. What are the potential benefits and limitations of using this model in a real-world clinical setting?
The decision tree model offers promising benefits for clinical settings, including personalized patient care through tailored risk assessments for massive intraoperative blood loss (IBL), efficient resource management by predicting IBL risks, and aiding surgeons in making informed decisions based on patient-specific conditions. However, its effectiveness depends on addressing several limitations: reliance on high-quality and diverse data for accurate predictions, potential lower accuracy compared to more complex machine learning methods, and challenges in integrating into existing clinical practices due to technological, privacy, and training barriers. Despite these challenges, optimizing the model’s integration and addressing data quality issues could significantly enhance its utility in improving surgical outcomes and patient care.

[Teeraboon Lertwanichwattana]

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?
In my opinion, to identify barriers and unmet needs in health information seeking among youth for HIV/STI and RH, additional factors should be considered to deepen our understanding. Socioeconomic status is crucial, as financial constraints limit access to healthcare and information. Cultural and social norms, including stigmas and traditional beliefs, deter individuals from seeking necessary information and care. The rise of digital platforms necessitates assessing digital literacy and access among YKAP, as limited access or skills can hinder information seeking. Privacy and confidentiality concerns are paramount; fears about privacy can prevent youth from seeking health information and services. Finally, the attitudes and competencies of healthcare providers are critical, as unfriendly behavior and reluctance to offer services to YKAP can be significant barriers. Addressing these factors by enhancing provider attitudes and competencies could improve access to information and 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 my opinion, identifying and reaching vulnerable populations that are missing or left behind in receiving necessary health information requires a nuanced understanding of the barriers they face. These groups often include:
Marginalized Ethnic Minorities: Cultural and language barriers can prevent these communities from accessing health information. Tailored outreach programs that respect cultural nuances are essential.
Individuals with Disabilities: Physical and informational accessibility issues can hinder their ability to seek health information. Creating accessible health materials and services is crucial.
Elderly Population: Digital literacy gaps and mobility issues can limit their access to health information. Community-based interventions and support networks can bridge this gap.
Homeless Individuals: Lack of access to technology and healthcare facilities makes it difficult for them to obtain health information. Mobile health units and partnerships with shelters can improve reach.

[Teeraboon Lertwanichwattana]

In Thailand, several electronic-based clinical decision support systems have been developed to monitor patient outcomes, including those for NCDs. One notable tool is a self-monitoring application that allows patients to track their health parameters, leading to more proactive management of their condition. These tools have several advantages, such as enabling continuous health monitoring and providing real-time data to patients and healthcare providers, empowering patients by giving them control over their health data, and enhancing accessibility, especially in remote areas with limited access to healthcare facilities. However, there are also disadvantages, including challenges related to technology access and literacy, especially among older populations or those in very remote areas. Additionally, ensuring the privacy and security of patient data can be a concern, and over-reliance on electronic tools might reduce necessary face-to-face interactions with healthcare providers.

Using a nurse or community health worker facilitated NCD management tool is a viable option to address the shortage of healthcare workers in remote areas. These tools can help bridge the gap by enabling CHWs to provide better care and support. The advantages include increased coverage, as CHWs can reach more patients in remote areas, providing essential health services and monitoring, cost-effectiveness compared to deploying more highly specialized healthcare workers, and better community engagement due to CHWs’ understanding of local communities. However, there are disadvantages such as significant investment required for training CHWs and nurses to use these tools effectively and the persistent resource limitations in remote areas despite the presence of such tools. Alternative options to address the shortage of healthcare workers and promote quality care for NCD patients include expanding telemedicine services to connect remote patients with specialist care without the need for physical travel, deploying mobile health clinics to bring healthcare services directly to remote areas on a regular basis, and empowering patients through community health education programs to promote self-care and better health management. Combining these approaches can help address the shortage of healthcare workers and improve the quality of care for NCD patients in remote areas.

[Teeraboon Lertwanichwattana]

While I definitely see the value of medical AI audits, I think it’s important to remember that doctors are the ultimate experts. AI should be a tool to supercharge their skills, not replace them. Doctors should always have the final say and understand the limitations of the AI system to make the best choices for their patients.

When it comes to building trust in the medical community, AI needs to be rock-solid safe. Rigorous testing is a must to minimize errors. It also needs to be consistently accurate, like a reliable partner doctors can depend on. Some study suggest that factors like explainability, transparency, interpretability, usability, and education are crucial for healthcare professionals to trust medical AI. These factors allow doctors to understand the AI’s reasoning, use it effectively, and ultimately make the best decisions for their patients. Consulting medical professionals throughout the development process of AI systems for clinical decision-making and diagnostic support is also vital to ensure these systems are designed with trust in mind. By focusing on these aspects, we can create medical AI that truly complements human expertise and improves patient care.

[Teeraboon Lertwanichwattana]

This is my COVID-19 dashboard. It consists of a single page where you can filter various components, such as continents, countries, and time periods. The data will update to display the selected information, and you can also download and access the report link.

https://lookerstudio.google.com/reporting/485f8279-0dde-47b5-8dce-ff6f105db32f

[Teeraboon Lertwanichwattana]

1.What are the stakeholders that should be involved in applying Machine Learning in symptom prediction? What are their roles and responsibilities?
When applying ML in symptom prediction, various stakeholders play crucial roles. One of these additional stakeholders is data manager. They are essential for ensuring data governance, storage, and security. They uphold compliance with data privacy regulations, protect confidentiality, and manage data integrity throughout the model lifecycle. Additionally, they play a key role in implementing effective anonymization and encryption practices to safeguard patient information.

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?
Regarding ethical considerations in developing and deploying ML models for cancer symptom prediction, researchers and clinicians must address several challenges. Firstly, they must prioritize data privacy and confidentiality, adhering to strict regulations, and obtaining informed consent from patients. Secondly, they need to mitigate biases that can influence predictions, such as those related to race or socioeconomic status. Finally, ensuring transparency and explainability in model decisions is crucial for building trust among stakeholders and enabling informed decision-making in clinical settings.

[Teeraboon Lertwanichwattana]

Running sum and comparison

Running Delta

Drill Down and Date


Pivot Table

Score Card

Time Series

Bar Chart

Or in this link: https://lookerstudio.google.com/reporting/b672146d-55b9-4cf1-9ae0-0a2bcbe32f74

[Teeraboon Lertwanichwattana]

I always rely on the visualization from Our World in Data’s COVID-19 Dashboard Our World in
https://ourworldindata.org/explorers/coronavirus-data-explorer?zoomToSelection=true&time=earliest..2021-12-31&facet=none&country=THA~USA~GBR&pickerSort=asc&pickerMetric=location&Metric=Confirmed+deaths&Interval=7-day+rolling+average&Relative+to+Population=true&Color+by+test+positivity=false

There are several reasons why I find this dashboard particularly useful:
1. Trusted Data Source: It leverages data from the WHO COVID-19 Dashboard, ensuring accuracy and reliability.
2. Comparative Analysis: It allows me to compare data from multiple countries, like Thailand, USA, and UK, which is fantastic for understanding global trends.
3. Multimodal Presentation: The ability to switch between maps and charts provides a well-rounded perspective on the data. Plus, the option to export the data for further analysis is a big plus.
4. Data Depth: The dashboard offers a variety of pre-summarized metrics like cumulative incidence and 7-day rolling average, making it easy to grasp trends without getting overwhelmed by raw numbers.
5. Topic Flexibility: It goes beyond just cases and deaths, allowing you to explore data on ICU admissions and other relevant topics.
6. Interactive Timeline: The timelapse feature is fantastic for visualizing changes over time and understanding the progression of the pandemic.

While the Our World in Data dashboard offers the great features, there are a few things to consider that might not be ideal for everyone:
1. Customization Complexity: While it offers a variety of options, creating very specific visualizations might require some exploration and understanding of the different settings.
2. Limited Drill-Down: Although it allows for country comparisons, it might not provide the most granular details within each country. For highly localized data needs, another resource might be necessary.

Overall, in my opinion, this dashboard provides a comprehensive and user-friendly platform for tracking the COVID-19 situation across the globe.

[Teeraboon Lertwanichwattana]

Thank you, both for the advice. I will take your feedback into account and make improvements based on it.
Thank you very much.

[Teeraboon Lertwanichwattana]

Please note that the following comments are simply my suggestions for improvement of the CRF and are not intended as judgments. I hope this might be helpful for your CRF.

Specific Comments
1. Instructions: The CRF lacks clear instructions. When administering the CRF, we need to specify whether participants will complete it independently or with an interviewer.
a. If participants complete it independently, the CRF should explain the objective and how to fill it out.
b. It should also clarify how participants should mark their answers (e.g., check a box) and how to correct any mistakes.

2. Paper-Based CRF: Since this is a paper-based CRF, we should consider potential consequences of losing a page. We might want to:
a. Implement strategies to prevent page loss.
b. Include a Case ID on each page to allow for reassembly if pages become separated.

3. Case ID and Screen ID: Using numbered boxes for entering Case ID and Screen ID would be helpful as it clarifies the expected number of digits for participants. This can minimize errors in data entry.
a. Additionally, some participants might use hyphens (e.g., 123-45) when entering IDs. Numbered boxes would discourage this practice and ensure consistency in data format.
b. It’s also important to address how leading zeros should be handled (e.g., 012345 vs. 12345). Numbered boxes would encourage participants to include leading zeros where necessary.

4. Age: Similar to IDs, using a numbered box for age would minimize recording errors.

5. Gender: While gender is traditionally categorized as male or female, it’s important to acknowledge the growing recognition of gender identities beyond this binary. However, if the CRF requires a two-category response, consider using a more inclusive term like “Sex assigned at birth” in the label.

6. Checkbox Values: The CRF should assign specific values to each checkbox option. For example, “Male” could be assigned a value of “1” and “Female” a value of “2”. This clarifies data entry for coders.

7. Date Format: To prevent errors, the CRF should specify the required date format. For example, the format could be YYYY-MM-DD (year-month-day). Additionally, consider specifying whether participants should enter dates according to the Anno Domini (AD) or Buddhist Era (BE) calendar system.

8. Decimal Notation: The CRF should clarify whether participants should include decimals when recording weight and height (e.g., 175.5 cm vs. 175 cm).

9. Laboratory Results: The CRF should specify the units of measurement required for laboratory results (e.g., mg/dL for blood sugar). This ensures consistency across all study sites.

I hope these suggestions are helpful. Thank you.

[Teeraboon Lertwanichwattana]

I believe we shouldn’t gather the date of birth because it’s considered indirectly identifiable information. Additionally, this CRF collects both the date of birth and age, which is redundant and unnecessary.

[Teeraboon Lertwanichwattana]

Implementing data standards in clinical research empowers clinicians by facilitating the generation and investigation of novel research hypotheses that extend beyond the original study’s scope. This seamless integration of data across diverse research sites paves the way for the development of new hypotheses and the creation of applications (e.g., apps) readily integrated into existing Electronic Health Record (EHR) systems. Furthermore, data standards act as a catalyst for generating novel research ideas for secondary data analysis. By ensuring consistent data collection and formatting across studies, researchers can readily access and integrate data from various sources, enabling the identification of new patterns and relationships that lead to the development of innovative hypotheses and research questions not originally explored in the primary studies.

Ref: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7665565/

[Teeraboon Lertwanichwattana]

In my study investigating participation and experience within the primary healthcare system, utilizing case record forms (CRFs), I implemented several data management processes within REDCap to ensure data quality and integrity:

Audit Trail/Time Stamp: REDCap automatically tracked user actions and data modifications, including timestamps, providing a comprehensive audit trail for identifying changes and their originators. This was particularly crucial as health village volunteers were responsible for CRF data collection. To ensure data completeness and trace potential issues, a participant coding system was implemented alongside a data collector coding system. This allowed us to identify missing codes and trace them back to the responsible data collector.

User Authentication and Access Control Level: Access to the data was restricted based on user roles and permissions within REDCap. This ensured that only authorized individuals could access and modify sensitive information. REDCap’s user management features allowed for granular control over edit and view permissions for the CRFs.

Edit Check and Logical Check: REDCap’s built-in data validation features helped prevent errors during data entry, such as enforcing numerical range limitations. Additionally, custom logic checks were implemented within the platform to identify inconsistencies and potential outliers, such as automatically skipping to the next question based on specific answer choices.

Data Backup and Recovery Plan: Data backups were conducted regularly on a secure PCM database. While the specific backup procedures within PCM might require further verification, data was also downloaded locally to our computers, ensuring comprehensive information protection.

Furthermore, the study employed a cross-sectional design, with data collection occurring in April and July.

[Teeraboon Lertwanichwattana]

Based on my experience in data collection and management, I have participated in several steps of the workflow, including:
Data Design: I assisted in categorizing the information to be collected during the project on non-communicable diseases among paramilitary personnel.
Data Collection: I actively participated in the data collection process, which involved using paper-based forms and setting up a database using Microsoft Access.
Database Management: I gained experience in managing the collected data within the Access database.

However, there are certain aspects of data management that I haven’t had the opportunity to engage in:
Data Coding: My experience has primarily focused on utilizing standardized coding systems like ICD-10. While valuable, I haven’t had the chance to work with alternative coding systems like MedDRA. Exploring this area would be beneficial, as it could potentially reveal more interesting research topics related to the initial research question by allowing for a more granular analysis of specific disease categories.
Serious Adverse Event Reconciliation: I haven’t had the opportunity to participate in the process of reconciling serious adverse events, which is a crucial aspect of ensuring data accuracy and patient safety in clinical research, because most of my projects were observational studies.

[Teeraboon Lertwanichwattana]

As a fourth-year medical student, I participated in a research project investigating the incidence of Opisthorchis viverrini infection through primary data collection. My team and I conducted interviews using mobile devices and Google Forms to gather data in a rural area of Chachengsao province.

We faced several obstacles. Verifying case records was time-consuming, and ensuring consistency in questioning required training to standardize our approach. Furthermore, working in a rural setting presented a language barrier. We actively learned new words to ensure clear communication with participants.

Despite the challenges, our initiative and adaptability ensured successful data collection. Through collaboration and training, we addressed standardization concerns. Moreover, our willingness to learn the local dialect facilitated effective communication with participants.

This experience not only solidified my interest in research but also highlighted the importance of resourcefulness and cultural sensitivity in data collection. The data we gathered contributed to a valuable project to understand the prevalence of Opisthorchis viverrini infection in this rural community.

[Teeraboon Lertwanichwattana]

This is my summary of what i have learned this week; https://www.canva.com/design/DAGC62MiXbQ/3QjvaO9B7i3KY917ZbcSLA/view?utm_content=DAGC62MiXbQ&utm_campaign=designshare&utm_medium=link&utm_source=editor

[Teeraboon Lertwanichwattana]

This is what I have learned this week.
https://www.canva.com/design/DAGBjy69lDM/YbU9MfOeq_93rU4yE9AWHA/view?utm_content=DAGBjy69lDM&utm_campaign=designshare&utm_medium=link&utm_source=editor

[Teeraboon Lertwanichwattana]

This is an infographic summarizing what I’ve learned this week.
https://www.canva.com/design/DAGA_PdraXY/6AI_IP3Zxl8JfSDccbWLdg/view?utm_content=DAGA_PdraXY&utm_campaign=share_your_design&utm_medium=link&utm_source=shareyourdesignpanel

[Teeraboon Lertwanichwattana]

This is a summary of what I learned this week.
https://www.canva.com/design/DAGAV_unfII/6dF_uduafVYZ7Io8dq13hw/view?utm_content=DAGAV_unfII&utm_campaign=designshare&utm_medium=link&utm_source=editor

[Teeraboon Lertwanichwattana]

I would like to highlight a point on page 344 regarding the superiority of confidence intervals over tests and P values. Confidence intervals enable us to redirect our focus away from the null hypothesis towards the entire spectrum of effect sizes consistent with the data. This shift in focus is advocated by numerous authors and an increasing number of journals.

I find this point particularly helpful because, during statistical analysis, we often encounter situations where the p-value is significant but the confidence interval is not significant or encompasses 1. In such cases, it raises the question of what we should believe. This insight provides a valuable answer for me.

[Teeraboon Lertwanichwattana]

Hello everyone,

I’m Teeraboon, but you can call me Boon. I work as a teaching assistant at the Department of Military and Community Medicine, Phramongkutklao College of Medicine, Thailand. My main role involves guiding medical students in their research projects, especially in epidemiology and statistics.
I also work as a researcher, where I’m involved in various stages of research projects, from data collection to analysis and publication. I’m comfortable using statistical software like SPSS, STATA, R, and Python.
I’ve been learning about data analysis since my medical school days, and now I’m fortunate to continue teaching it as a TA. I’ve worked on several epidemiological projects that have helped me apply statistical concepts in practical settings.
Overall, while I’m still building my confidence, I’ve gained valuable experience in statistics through my work and studies, and I’m eager to continue learning and growing in this field.

[Teeraboon Lertwanichwattana]

Sex: Male
Education: MD and MSc BHI student
Occupation: Teaching assistant
Workplace: Department of Military and Community Medicine, Phramongkutklao College of Medicine
Future Plans: To become an Anesthesiologist and Informatician

[Teeraboon Lertwanichwattana]

In my assessment, it is essential to understand why respondents are not utilizing bed nets for malaria prevention in order to design effective interventions. To achieve this, I propose the following comprehensive methods:

1. Qualitative Study:
– Focus Group Discussions: Participants will be divided into two groups: those who have not used bed nets and those who have. Through open conversations, we aim to extract insights into the motivations behind their respective actions. For instance, in the group of non-users, we would delve into the reasons contributing to their decision not to use bed nets. On the other hand, in the group of users, we would explore the factors influencing their choice to adopt this preventive measure.
– In-depth Interviews: Focusing on individuals consistently using bed nets, these interviews will delve deeper into the motivations and inspirations behind their consistent usage.
– Key Informant Interviews: Engaging leaders who advocate for bed net usage and experts providing relevant information will provide additional perspectives. For example, speaking with a community leader who encourages bed net usage can offer insights into influential factors within the community. Similarly, an expert’s input may shed light on the information that shapes individuals’ perceptions.
– All participants will be queried about their perceptions regarding the usefulness and ease of use of bed nets in preventing malaria and whether they find them easy to incorporate into their daily routines.

2. Observation:
– Observations will be conducted to explore various aspects of the lifestyle of the group that does not use bed nets. For example, this might involve understanding their nightly routines, the specific measures they take to prevent mosquito bites, and any barriers they face in adopting bed nets into their practices.

[Teeraboon Lertwanichwattana]

In the context of the TAM, the model suggests that perceived usefulness is a crucial factor influencing users’ acceptance and adoption of technology. If you are replacing an old technology with a new one that is more useful and facilitates easier completion of tasks, the TAM would generally suggest that the new technology should be perceived as at least as useful as the old technology, if not more useful.

The TAM posits that users are more likely to accept and adopt a new technology if they perceive it as providing value, benefits, and usefulness in their tasks or activities. Therefore, when introducing a new technology, it’s essential to ensure that users perceive it as delivering at least the same level of usefulness, if not greater, compared to the old technology.

In summary, the TAM implies that the relative usefulness of the new technology, in comparison to the old one, is critical for successful adoption. Therefore, efforts should be made to communicate and demonstrate the enhanced or at least equivalent value that the new technology brings, using persuasive messages that highlight its user-friendly and efficient features.

[Teeraboon Lertwanichwattana]

I believe several external factors can influence how an individual views the ease of using or the usefulness of a new technology. These factors include:

1. Computer Self-Efficacy and Anxiety: An individual’s confidence in using computers (self-efficacy) and their anxiety levels related to technology can significantly shape their perceptions. For instance, someone who is confident in their computer skills may find a new software program more user-friendly.

2. Peer Influence and Perceptions: The opinions and experiences of friends, family, and colleagues play a crucial role in shaping an individual’s perceptions. For example, if a colleague has a positive experience with a new app, it might influence others to view it more favorably.

3. Affordability: The overall cost of acquiring and maintaining the technology can greatly impact how individuals perceive its usefulness. A cost-effective solution may be perceived as more valuable, encouraging adoption.

4. Age and Gender: Demographic factors, like age and gender, can play a role in how individuals approach and adopt new technologies. Younger individuals, for instance, may be more open to embracing cutting-edge technologies compared to older individuals.

[Teeraboon Lertwanichwattana]

1. Efficacy
– Definition: Efficacy refers to the ability of a specific intervention or treatment to produce a desired result under ideal or controlled conditions. It is often assessed in clinical trials or laboratory settings, where researchers can carefully control and monitor variables to measure the intervention’s impact.
– Measurement: Efficacy is typically measured through randomized controlled trials (RCTs) or experimental studies. These studies aim to isolate the effect of the intervention by controlling for confounding variables and ensuring a high level of internal validity.
Calculation Formula: The efficacy of an agent being tested, such as a vaccine, can be expressed in terms of the rates of developing the disease in the vaccine and placebo groups. The formula for calculating efficacy is: [(Rate in those who received the placebo) – (Rate in those who received the vaccine)] / (Rate in those who received the placebo)

2. Effectiveness
– Definition: Effectiveness refers to the ability of an intervention to produce a desired effect in real-world or practical settings, considering a broader and more diverse population than in controlled conditions. It assesses how well a treatment works when applied in everyday, routine circumstances.
– Measurement: Effectiveness is often studied through observational studies or implementation research. These study designs aim to capture the intervention’s impact in more realistic conditions, considering factors like patient adherence, healthcare provider variability, and external influences.

3. Efficiency
– Definition: Efficiency relates to the extent to which resources, such as discomfort, pain, absenteeism, disability, and social stigma, are used to achieve the desired outcome. It involves maximizing the benefit while minimizing waste or unnecessary costs.
– Measurement: Efficiency can be measured through cost-effectiveness analyses, where the costs associated with an intervention are weighed against its outcomes. This involves evaluating the economic value of the intervention and determining whether it provides good value for the resources invested.

[Teeraboon Lertwanichwattana]

In my current role in controlling COVID-19 policies, ethical principles and good practices are fundamental, encompassing various protocols. Firstly, a crucial protocol involves ensuring the accuracy and truthfulness of information, particularly in the context of COVID-19 where misinformation can lead to stigma against infected individuals, potentially causing harm and fostering a negative environment. This aligns with the ethical principle of non-maleficence, emphasizing the commitment to avoid causing harm to others.

Secondly, as a physician, a vital protocol centers around the meticulous handling of health-related data, adhering rigorously to data protection regulations to uphold patient privacy and confidentiality. This commitment resonates with the broader ethical principle of respecting individuals’ rights to privacy.

The third important practice involves empowering patients with the right to make decisions regarding their healthcare. This is achieved through the provision of sufficient, independent, and transparent information, thereby upholding the ethical principle of autonomy.

Furthermore, the fourth protocol emphasizes compliance with and adaptability to public health guidelines. Given the evolving nature of the COVID-19 situation, it is imperative to stay informed and be ready to adjust policies accordingly. This adaptability is crucial in managing patients effectively, especially during the initial phases of the pandemic when understanding of the disease was limited. By aligning with this protocol, the ethical principle of beneficence is upheld, ensuring actions are in the best interest of patient well-being.

In summary, these protocols and practices not only contribute to effective COVID-19 control but also reflect a commitment to ethical principles, emphasizing the importance of truthfulness, privacy, autonomy, and adaptability in the pursuit of public health.

[Teeraboon Lertwanichwattana]

In my opinion, in Thailand, the Universal Health Coverage (UHC) scheme has played a crucial role in benefiting many patients, particularly those with chronic conditions such as diabetes. The provision of free medications and laboratory exams, especially for essential tests like HbA1c, which measures blood sugar control over time, has significantly contributed to the well-being of patients. The inclusion of expensive but critical tests like HbA1c under the UHC scheme is a noteworthy advantage.

From my perspective as a physician, the success of UHC in Thailand lies in its emphasis on discussing patients’ health conditions as the primary focus. By prioritizing health outcomes and incorporating statistical data on prevalent health issues across the country, the UHC system becomes a valuable tool in addressing and solving health challenges. Moreover, the comprehensive benefit packages offered by the UHC system extend beyond laboratory tests to cover various aspects such as contact tracing, quarantine measures, clinical services, vaccines, and vaccination costs.

One notable strength of Thailand’s UHC system is its commitment to providing universal and equitable access to healthcare. The system ensures that individuals, regardless of their financial capacity or social status, can access necessary medical care. Additionally, the UHC system in Thailand demonstrates inclusivity by extending its services to migrant workers, further emphasizing its commitment to widespread healthcare coverage.

However, like any system, there are weaknesses that need attention. One potential weakness is the strain on resources, especially in terms of funding. As more individuals benefit from the UHC scheme, there may be challenges in sustaining the financial aspects of the program. It is crucial for policymakers to continuously evaluate and adjust funding strategies to ensure the long-term viability of the UHC system.

Another potential area for improvement is in the efficiency of service delivery. Despite the comprehensive coverage, there may be instances where delays or inefficiencies occur in providing healthcare services. Addressing these operational challenges can enhance the overall effectiveness of the UHC scheme in Thailand.

In conclusion, while Thailand’s UHC system has made significant strides in providing accessible healthcare services and improving health outcomes, there is a need for ongoing evaluation and adaptation to address potential weaknesses and ensure the sustained success of the program.

[Teeraboon Lertwanichwattana]

From my perspective, Thailand has witnessed remarkable strides in the field of health informatics, as evidenced by the active involvement of numerous organizations in research projects. The country boasts a growing community of professionals dedicated to advancing health informatics, with a notable focus on fostering expertise through education and training programs. Thailand’s commitment to information technology is exemplified by initiatives such as data.gov, which provides a wealth of datasets, expanding research opportunities within the healthcare sector.

The workforce in health informatics appears promising, with efforts underway to ensure a sufficient number of qualified professionals to meet the increasing demands of the healthcare sector. The emphasis on education and continuous professional development underscores a commitment to enhancing skills and staying abreast of technological advancements.

Thailand’s dedication to technology infrastructure in healthcare institutions is essential for the effective implementation of health informatics solutions. Ongoing evaluations of interoperability and adherence to standards in health information systems reflect the country’s recognition of the importance of standardization for seamless communication and data exchange among diverse healthcare entities.

In addressing challenges, Thailand faces hurdles such as limited resources, both financial and human, which may impede the development and implementation of health informatics initiatives. Overcoming resistance to change within healthcare professionals or institutions remains a significant challenge, necessitating effective change management strategies. Ensuring data governance and quality is crucial, requiring the establishment of robust frameworks to address issues related to accuracy, completeness, and reliability. The complexity of integrating health informatics solutions with traditional healthcare systems underscores the importance of addressing compatibility and interoperability issues during the integration process.

Despite these challenges, Thailand’s commitment to advancing health informatics is evident, with ongoing efforts to overcome obstacles and foster continued growth in the field. The proactive approach to workforce development and technological integration reflects a positive trajectory, positioning Thailand favorably in the realm of health informatics.

[Teeraboon Lertwanichwattana]

In managing a dataset from my country, the decision on whether to share the data would be contingent on various factors, with a primary focus on maintaining the confidentiality and privacy of individuals, especially in the case of sensitive information such as healthcare data.

Firstly, the decision to share the data would hinge on the implementation of robust encryption measures. Encryption serves as a crucial safeguard, ensuring that the data remains secure and inaccessible to unauthorized parties. This is particularly pertinent in healthcare datasets where patient information is highly sensitive and must be protected from potential breaches.

Moreover, ethical considerations play a pivotal role in determining whether to share the data. Respecting the privacy rights of individuals is paramount, and any decision to share the dataset must align with ethical standards and legal regulations. Striking a balance between advancing research or public interests and safeguarding the rights of individuals is crucial in making an informed decision.

Additionally, the potential benefits of data sharing should be carefully weighed against the risks. If sharing the data can contribute to advancements in research, public health, or other significant societal benefits, it may be justified. However, this should be done in a manner that minimizes the risk of re-identification and misuse.

In conclusion, the decision to share a dataset from my country would be contingent on stringent measures to ensure data security, ethical considerations, and a thorough assessment of the potential benefits and risks. Balancing the pursuit of knowledge and societal progress with the imperative to protect individual privacy is crucial in making responsible and informed decisions regarding data sharing.

[Teeraboon Lertwanichwattana]

I propose a discussion on technological proficiency as a potential confounder of the association under consideration. Young adults may exhibit greater comfort and skill in using technology, leading to heightened participation in the contact tracing app and, consequently, the reporting of more contacts. I identify technological proficiency as a confounding factor because it may influence the ability to respond to the application. Additionally, as mentioned earlier, this proficiency is associated with age. Importantly, it is worth noting that technological proficiency does not serve as an intermediate step between age and the outcome.

[Teeraboon Lertwanichwattana]

Pros of EMR at My Hospital

In my opinion, the evolution of Electronic Medical Records (EMR) represents a long-awaited improvement, with a notable impact on simplifying patient files and streamlining the retrieval of dedicated data items. This progress has not only resulted in positive financial returns but also addressed crucial concerns related to patient safety and facilitated more efficient data analysis. Given the sensitivity of the data originating from patients, including confidential information, we have proactively developed protocols to ensure secure access, extending this assurance even to healthcare professionals. Furthermore, the transition to EMR is poised to significantly support healthcare providers in their daily tasks, particularly in terms of seamless access to and review of patient data.

Cons of EMR at My Hospital

Despite the potential advantages, there are significant concerns linked to the initial adoption of EMR. Healthcare providers are cautious about the potential increase in workload and the extensive training required during the implementation phase. Worries also extend to issues of data accessibility, confidentiality, technical support availability, and the compatibility of our current infrastructure. Additionally, some users express hesitancy in embracing the proposed EMR system, citing its perceived complexity and the need for user-friendliness. However, it’s important to note that this concern is being actively addressed at our hospital. We have implemented a flexible approach, allowing older users to continue utilizing traditional record-keeping methods while actively training and persuading new users to adopt the EMR system. We emphasize the benefits, particularly highlighting the ease of data retrieval for research purposes, aiming to showcase the significant advantages of using EMR in enhancing efficiency and advancing healthcare practices.

[Teeraboon Lertwanichwattana]

The article, titled “Big Health Data and Cardiovascular Diseases: A Challenge for Research, an Opportunity for Clinical Care,” provides a critical assessment of the pivotal role played by big health data in both cardiovascular disease (CVD) research and clinical care. It sheds light on the substantial burden CVD places on healthcare systems and underscores how big data holds promise in enhancing patient outcomes and ensuring the sustainability of healthcare systems. The authors delve into the contemporary challenges faced by the medical field, such as escalating costs, intricate procedures, and data fragmentation, which impede both quality care and research progress.

The discussion emphasizes the disparities between traditional randomized controlled trials and real-world scenarios, as well as the variations in treatment approaches and prognoses. The paper advocates for a big data approach that harnesses electronic health records (EHR) from diverse sources, characterized by high volume, variety, and acquisition speed. This strategy aims to facilitate large-scale population studies at reduced costs and with minimized biases. The authors propose the development of algorithms for personalized CVD treatments by integrating vast datasets. Despite the theoretical potential, the translation of big data into tangible improvements in patient outcomes remains limited. The paper outlines the imperative need to overcome challenges such as data security risks and the intricate processes of data processing and clinical application.

To confront the challenges posed by big health data in cardiovascular research, the authors recommend a multifaceted approach. Firstly, in addressing the issue of missing data, researchers are advised to employ statistical techniques that consider the joint distribution of measurement and drop-out mechanisms, moving away from the limitations of complete-case or available-case analyses, often inapplicable in EHR contexts. Secondly, to cope with the rapid generation of data and the gap between data volume and analytical capacity, a focus on developing advanced data analytics tools and algorithms is suggested. Thirdly, ensuring data security and privacy can be approached through broad consent models and continuous exploration of new data security solutions to strike a balance between the benefits of data sharing and privacy concerns. Lastly, for effective clinical application, the integration and harmonization of data from diverse sources, including patient-reported information, medical imaging, biomarkers, and omics data, are deemed essential to providing a comprehensive view of patient health. By addressing these challenges, big health data can be harnessed more effectively, significantly improving the quality of cardiovascular research and clinical care.

[Teeraboon Lertwanichwattana]

Hi Soe,

Just wanted to echo my thanks! Your dashboard was incredibly helpful in understanding the COVID-19 situation. The clarity and responsiveness to the current data were fantastic.

I had one suggestion that might make the dashboard even more useful for public health policymakers. Including cumulative deaths could be a valuable addition. This would give them a clearer picture of the overall impact and help them make informed decisions about interventions during this crisis.

[Teeraboon Lertwanichwattana]

Thanks, Soe Htike! Your description is incredibly informative. There’s one additional feature I’d love to see: the ability to zoom in on specific countries. While the dashboard is fantastic, Thailand, for example, appears quite small on the global map, making it difficult to see details. Adding a zoom function for chosen countries would be a great improvement.

This addition would allow for a more focused analysis of data within each country.

[Teeraboon Lertwanichwattana]

In my opinion, while I personally prefer interfaces in Thai, the usefulness of this dashboard likely depends on what you’re using it for. It was definitely a helpful, resource during the peak of the pandemic, as it was often featured in the news. However, having an option to switch the language to English, instead of a permanent change, might be a good improvement.

[Teeraboon Lertwanichwattana]

I want to add a bit more to this. Additionally, let’s ensure that the date format is clear. We should standardize it to have the month written out clearly, such as ‘Jan’ instead of just ’01’, to avoid redundancy.

[Teeraboon Lertwanichwattana]

Thank you for sharing your experience with data collection!
My institute, Phramongkutalo College of Medicine, also utilizes REDCap for data collection and has found it to be a highly valuable tool, particularly for gathering survey data. We find this tool to be incredibly beneficial, particularly within the realm of survey data management.

One of the key advantages REDCap offers is the automated generation of data dictionaries. This feature significantly streamlines the data organization process compared to platforms like Google Forms, where manual creation of data dictionaries is often necessary. This automation not only saves valuable time but also reduces the risk of human error associated with manual data entry.

[Teeraboon Lertwanichwattana]

I completely agree with Nicha’s insights on database security. Working as a study coordinator in a hospital environment, I understand the challenges of frequent password changes, especially with infrequent system access. Implementing multi-factor authentication would significantly enhance security without relying solely on biometrics, which might not be feasible in all hospital settings due to the lack of fingerprint scanners or facial recognition technology. Offering a dropdown menu for preferred authentication methods, like ID cards or one-time codes, would provide greater flexibility and address these limitations.

[Teeraboon Lertwanichwattana]

Hello, K. Punyada. I wholeheartedly agree with your perspective on the vital role education plays in addressing corruption within institutes. Expanding on this, I stress the importance of effective teaching, often exemplified by being an example. A competent and exemplary leader can significantly influence the overall quality. It goes beyond words, actively contributing to fostering a more meaningful and enriching educational environment.

[Teeraboon Lertwanichwattana]

Thank you for sharing this important information.

It’s concerning to learn about the neglect of screening processes, particularly in the context of diabetes and its complications. I believe the ministry should be made aware of these issues, as proper screening is crucial in preventing further complications. It’s troubling to consider how the changing era of the ministry might be contributing to such problems. Your news is genuinely enlightening and emphasizes the importance of addressing these issues promptly.

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