Thitikan Pohpoach

[Thitikan Pohpoach]

Regarding my previous model, COVID-19 modelling using SEIR compartment (Susceptible, Exposed, Infected and Recovered), it can incorporate various interventions such as isolation of infected individuals, quarantine of exposed contacts, and vaccination strategies. These interventions can significantly alter the transmission dynamics by reducing contact rates and increasing recovery rates.

Isolation of Infected Individuals: Isolating diagnosed cases can significantly reduce the transmission rate as these individuals would have limited contact with susceptible populations. The efficacy of isolation can be modelled by reducing β during periods when isolation measures are in effect.

Quarantine for Exposed Individuals: Quarantine measures for those who have been in contact with infected individuals can prevent them from transmitting the virus before they show symptoms. This can be represented by modifying σ to account for reduced exposure during quarantine.

Vaccination Strategies: Introducing a vaccination compartment could differentiate between vaccinated and unvaccinated susceptible individuals. The effectiveness of vaccines can be modelled by reducing both β and γ for vaccinated individuals, reflecting lower transmission rates and potentially faster recovery.

Additional compartments or modifications to transition rates could be introduced to integrate these interventions into the existing SEIR framework. For example,
– creating separate compartments for isolated and hospitalized individuals, allowing for more detailed tracking of disease progression and recovery
– implementing time-varying parameters that adjust β, σ, and γ

References
Peter OJ, Panigoro HS, Abidemi A, Ojo MM, Oguntolu FA. Mathematical Model of COVID-19 Pandemic with Double Dose Vaccination. Acta Biotheor. 2023 Mar 6;71(2):9. doi: 10.1007/s10441-023-09460-y. PMID: 36877326; PMCID: PMC9986676.

Anggriani N, Beay LK. Modeling of COVID-19 spread with self-isolation at home and hospitalized classes. Results Phys. 2022 May;36:105378. doi: 10.1016/j.rinp.2022.105378. Epub 2022 Mar 5. PMID: 35280116; PMCID: PMC8896885.

[Thitikan Pohpoach]

What model structure would the disease you are interested be and please start adjusting the R code to that structure.

COVID-19 modelling using SEIR compartment (Susceptible, Exposed, Infected and Recovered).

I have encountered numerous studies on mathematical models that analyze the spread and transmission of SARS-CoV-2. Mr. Teerabon highlighted one particularly intriguing study.

As someone just starting to learn R coding and mathematical modeling, I found the cited research, which examined two interacting populations—humans as hosts and pathogens—quite complex. This study formulated an SEIR-P model for transmission. To simplify my understanding, I focused on developing R code that addresses the four key factors: Susceptible, Exposed, Infected, and Recovered.

Please comment on the R code below.

library(deSolve)
SEIR.dyn <- function(t, var, par) {
S <- var[1]
E <- var[2]
I <- var[3]
R <- var[4]
N <- S + E + I + R
beta <- par[1]
sigma <- par[2]
gamma <- par[3]
dS <- -beta * S * I / N
dE <- beta * S * I / N – sigma * E
dI <- sigma * E – gamma * I
dR <- gamma * I
list(c(dS, dE, dI, dR))
}
beta <- 0.0115
sigma <- 0.09
gamma <- 0.05
SEIR.init <- c(S = 990, E = 0, I = 10, R = 0)
SEIR.par <- c(beta, sigma, gamma)
SEIR.t <- seq(0, 100, by = 1)
SEIR.sol <- lsoda(SEIR.init, SEIR.t, SEIR.dyn, SEIR.par)
TIME <- SEIR.sol[,1]
S <- SEIR.sol[,2]
E <- SEIR.sol[,3]
I <- SEIR.sol[,4]
R <- SEIR.sol[,5]
plot(TIME, S, type=”l”, col=”blue”, ylim=c(0, max(SEIR.sol)), ylab=”Population”, xlab=”Time (days)”, main=”COVID-19 SEIR Model Dynamics”)
lines(TIME, E, col=”orange”)
lines(TIME, I, col=”red”)
lines(TIME, R, col=”green”)
legend(“right”, legend=c(“Susceptible”, “Exposed”, “Infected”, “Recovered”), col=c(“blue”, “orange”, “red”, “green”), lty=1)
_________________________________________________________
The SEIR.dyn function defines the rates of change for each compartment:
dS: Change in susceptible individuals.
dE: Change in exposed individuals.
dI: Change in infected individuals.
dR: Change in recovered individuals.

Please state the key characteristics of the disease you are focusing on e.g. transmission, pathogenicity, symptoms, control measures etc. You can write it as text or start using the parameter table in the template provided. Anything you want to let me know about the disease you are going to work on would be useful.

Key characteristics:
– The rate at which susceptible individuals become infected upon contact with infected individuals >>> beta;
(In the paper, rate of transmission from S to E due to contact with I = 0.0115)
– The rate at which exposed individuals become infectious >>> sigma;
(In the paper, Progression rate from E to I = 0.09)
– The rate at which infected individuals recover >>> gamma;
(In the paper, rate of recovery of the symptomatic population = 0.05 days−1)

Reference: Mwalili S, Kimathi M, Ojiambo V, Gathungu D, Mbogo R. SEIR model for COVID-19 dynamics incorporating the environment and social distancing. BMC Res Notes. 2020 Jul 23;13(1):352. doi: 10.1186/s13104-020-05192-1. PMID: 32703315; PMCID: PMC7376536.

[Thitikan Pohpoach]

Research question: The impact of vaccination campaigns on the transmission of variants like Omicron JN.1 in the U.S.

Despite advancements in vaccination and treatment, COVID-19 remains a significant concern, particularly in Thailand, where new infections are reported weekly. The dominant COVID-19 variants in the U.S., JN.1 and HV.1, are responsible for over half of the cases and have demonstrated increased transmissibility due to genetic changes from the original Omicron strain. Experts suggest that these variants are likely present in Thailand as well.

Mathematical modelling serves as a critical tool for understanding the dynamics of infectious diseases, particularly in evaluating the impact of vaccination campaigns on the transmission of variants like Omicron JN.1 in the U.S.

I think the models could help in understanding how vaccination alters the dynamics of disease spread by:

– Incorporating Vaccine Efficacy: Models can quantify the effectiveness of vaccines against infection, symptomatic disease, and severe outcomes.
– Simulating Vaccination Rates: By varying the vaccination uptake rates among the population, models can predict how changes in public responsiveness to vaccination campaigns affect transmission dynamics.
– Assessing Immunity Loss: Models can account for the waning immunity over time, which is essential for understanding the long-term impacts of vaccination campaigns.

Moreover, mathematical modeling provides valuable insights for policymakers by:

– Forecasting Outcomes: By simulating different intervention strategies, such as booster campaigns or changes in public health guidelines, models can help forecast potential spikes in cases or hospitalizations, allowing for timely responses.

– Evaluating Intervention Efficacy: Models can assess the effectiveness of vaccination campaigns in reducing transmission rates compared to non-vaccination scenarios. This evaluation is vital for understanding the role of vaccines in controlling outbreaks and preventing healthcare system overload.

– Guiding Resource Allocation: Understanding how vaccination affects transmission dynamics can inform resource allocation for healthcare services, ensuring that areas with higher transmission rates receive adequate support

[Thitikan Pohpoach]

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?

ePRO systems facilitate real-time communication between patients and healthcare providers. By using mobile applications or web-based platforms, patients can easily report their symptoms and experiences from the comfort of their homes. Different healthcare departments can tailor ePRO tools to meet the unique needs of their patient populations. For example:
Mental Health: ePRO can be used to monitor symptoms of anxiety and depression, allowing for adjustments in therapy or medication based on real-time feedback.
Diabetes: Patients can report their blood sugar levels and dietary habits, enabling healthcare providers to offer personalized advice and interventions.
Oncology: Cancer patients can report symptoms related to treatment, such as pain or fatigue, which can help oncologists manage side effects more effectively and improve the overall patient experience

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?

– Addressing data security and technical skills concerns is a major opportunity for ePRO systems. Enhancing data security and privacy protections is critical for building trust and increasing adoption among patients and providers. This could involve implementing robust encryption, secure data storage, and clear data usage policies

– Tailoring ePRO systems to provide personalized care plans, educational resources, and feedback based on each patient’s unique needs and preferences can significantly improve the user experience. Allowing patients to customize their ePRO interface and receive personalized insights from their data can enhance engagement and outcomes

[Thitikan Pohpoach]

1. How can integrating environmental data improve the accuracy and timeliness of malaria outbreak predictions compared to relying solely on epidemiological data?

Environmental factors such as temperature and rainfall are critical in determining mosquito breeding patterns and malaria transmission dynamics. For instance, temperature increases can accelerate the life cycle of malaria parasites and mosquitoes, while rainfall influences the availability of breeding sites. By incorporating these climatic variables, models can provide early warnings of potential outbreaks before an increase in malaria cases is observed. Moreover, environmental data can help identify areas at higher risk for malaria transmission by analyzing factors such as land use, water bodies, and seasonal variations. This comprehensive approach allows for better-targeted interventions and resource allocation in public health strategies

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 system’s effectiveness?

Key Benefits
Continuous stakeholder engagement allows the system to be tailored to the specific needs of the community it serves. Stakeholders can provide insights into local health issues, enabling the system to prioritize relevant health concerns and interventions. Moreover, involving stakeholders fosters trust and collaboration among various public health entities. This can lead to more effective partnerships and resource sharing, which are essential for comprehensive public health strategies

Challenges
Continuous engagement requires significant time and resources, which may be limited in many public health settings. Stakeholders may struggle to participate fully due to competing priorities or lack of funding. Furthermore, stakeholders often have differing priorities and perspectives, leading to conflicts or challenges in reaching consensus. Managing these differences requires skilled facilitation and negotiation

[Thitikan Pohpoach]

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 experience in government agency stakeholders (directors, officers, policy-makers, customers, etc.) are hesitant to fully transition from paper-based systems to Electronic Health Information Systems (EHIS) because of worries regarding possible risks. This reluctance can lead to parallel usage of both systems which complicates integration and undermines the potential benefits of EHIS.

To address this issue, engaging all relevant stakeholders in the planning and implementation of EHIS can build trust and reduce hesitance towards transitioning completely from paper systems. This engagement should include regular communication and feedback loops to address concerns and adapt the system to user needs. Moreover, investing in organizational capacity is crucial to train staff and create a supportive environment for ongoing learning and adaptation of the systems.

Question 2: How has 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?

I experienced using the EHIS in a private hospital. Key features that facilitate adaptation are usability and customization.

User-friendly interfaces were intuitive and minimize disruption to clinical workflows. Personalization options to tailor the system to the needs of different users, specialties, and care settings made things more convenient. Moreover, they offered ongoing monitoring and optimization to identify pain points and opportunities for improvement.

[Thitikan Pohpoach]

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

– Age:
Younger people will likely find Personal Health Record (PHR) systems more user-friendly because they are generally more accustomed to technology. Their adaptability to new tools often leads them to view these systems as beneficial for actively managing their health. In contrast, older adults may face challenges with technology, resulting in a perception that these systems are less easy to use and less valuable.

– Gender
Research may indicate that gender can influence technology acceptance, with women having higher concerns regarding privacy and security. For me personally, I do not find the different levels of engagement of PHR systems based on gender. More reassurance about privacy features can enhance the engagement of all users.

– Education level
People with higher education levels are more likely to understand the benefits of managing their health information digitally and may feel more empowered to use such systems 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?

– Regulatory Environment: Policies and regulations regarding e-health can either facilitate or hinder adoption. Supportive legislation can promote the use of e-health technologies, while restrictive regulations may pose barriers

– Technology Literacy: Users need a certain level of digital literacy to effectively utilize e-health applications

– Cultural Context: Cultural attitudes towards technology and healthcare can influence acceptance. Understanding local cultural norms and values is essential for tailoring e-health solutions that resonate with users

[Thitikan Pohpoach]

Hello everyone,

Please accept my apology for the delayed response.

https://lookerstudio.google.com/reporting/10aae725-042f-4494-8502-03f4f5803b53

Here is my COVID-19 dashboard illustrating the numbers of confirmed, recovered, and death cases in each country between January 2020 to April 2022. I’ve tried to keep it clean and simple. Please feel free to comment.

[Thitikan Pohpoach]

Please find the link to my Looker Studio report

https://lookerstudio.google.com/reporting/e0604091-bec6-444a-83dd-f4ebea5394a9

and please accept my apology for the delayed submission

Thank you for your time

[Thitikan Pohpoach]

Please find the link to my Looker Studio report

and please accept my apology for the delay submission

Thank you for your time

[Thitikan Pohpoach]

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

I may suggest two recommendations:
Enhance Reporting Sensitivity: The current sensitivity of reporting syphilis cases is approximately 67%. Efforts should be made to increase this rate by implementing training programs for healthcare providers on the importance of accurate and timely reporting of syphilis cases, particularly focusing on identifying cases among migrant populations who are currently underreported.

Improve Diagnosis and Coding: Incomplete diagnosis and coding were identified as significant issues. The hospital should adopt standardized protocols for diagnosing and coding syphilis cases to ensure comprehensive data collection. This could involve regular workshops and refresher courses for healthcare staff on the latest diagnostic criteria and coding practices.

2. Do you have experience with disease surveillance systems? What are the strengths and weaknesses of that system?

Thailand’s COVID-19 surveillance system has demonstrated notable strengths and weaknesses throughout its implementation and evolution during the pandemic.

Strengths:
– The introduction of an enhanced information system significantly improved the surveillance capabilities. Key features included an auto-verification system, laboratory reporting, and a confirmed case notification system. These enhancements led to increased completeness and timeliness of data reporting.
– The surveillance system proved to be flexible, allowing for adjustments based on the evolving nature of the pandemic.

Weaknesses:
– Challenges related to data quality persist, including inconsistencies in reporting and the accuracy of data collected.

[Thitikan Pohpoach]

Discussion
1. How can implementing artificial intelligence technologies in epidemic surveillance systems be enhanced to better detect and respond to disease outbreaks?

AI can leverage vast amounts of data from diverse sources, including social media, health records, and environmental data. By integrating these data streams, machine learning algorithms can identify patterns and predict outbreaks more accurately. For instance, real-time data analysis can help assess daily exposure behaviors and categorize risk groups, enabling targeted interventions.

2. What potential benefits do you see in utilizing AI for public preparedness, and what challenges might arise in implementing these technologies effectively?

Benefits: AI’s data processing capabilities enable emergency management organizations to evaluate numerous scenarios quickly, facilitating optimal resource allocation during crises.

Limitations
1. Technical Expertise Requirements: Successful AI integration necessitates a certain level of technical expertise, which may be lacking in smaller organizations or rural communities. This can create barriers to effective implementation

2. Bias in AI Systems: AI systems can unintentionally reproduce biases that exist in the data used to train them. This can result in unfair distributions of resources and unequal treatment when these AI systems are deployed to assist people. Continuous monitoring and evaluation are necessary to mitigate these biases

[Thitikan Pohpoach]

Discussion:
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?

Socio-cultural norms and parental attitudes: In many settings, open discussion of sexuality and RH is taboo. Parental attitudes and socio-cultural norms that restrict youth access to information should be addressed through community engagement.

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?

Low-Income Individuals: Many low-income consumers struggle to understand their healthcare options and often feel disrespected by healthcare providers, which can lead to mistrust and disengagement from health services.

Individuals with Mental Health Issues: Those with mental health challenges often face difficulties in engaging with health services, which can be exacerbated by a lack of accessible information tailored to their needs

To effectively reach these individuals,several strategies can be implemented. I think tailored communication can help. Develop health information materials that are culturally and linguistically appropriate. For example, providing lay language resources and using simple, clear language can help bridge the gap for those with low literacy levels

[Thitikan Pohpoach]

Discussion questions:
1. How can the decision tree model be integrated into clinical practice to assist surgeons in preoperative planning and decision-making?

Decision tree models can be used to develop evidence-based clinical practice guidelines for preoperative decision-making. The models can synthesize the best available evidence and expert opinion to provide surgeons with a structured framework for choosing the optimal preoperative strategy for a given clinical scenario.

2. What are the potential benefits and limitations of using this model in a real-world clinical setting?

Benefits: Decision trees provide a systematic approach to clinical decision making by considering multiple variables and their relationships. They help identify the most favorable treatment options based on patient data and outcomes

Limitations: I think decision trees can overfit the training data, leading to poor generalization to new patients. They are also sensitive to small variations in the training data, which can result in substantially different tree structures

[Thitikan Pohpoach]

1. In Thailand, several electronic-based CDDS have been developed or are in the planning stages to assist NCD patients, particularly in remote areas. Notably, the Pakkred Hospital in Nonthaburi Province has implemented innovative solutions during the COVID-19 pandemic to maintain care for NCD patients. They utilized technology for communication and care, including the LINE application for online consultations and a digital application called “PK COVID” for teleconsultation.

The integration of communication tools among healthcare providers facilitates better coordination of care, enabling healthcare workers to reach out to patients effectively and manage their conditions proactively. However, the use of digital platforms for health consultations raises concerns regarding the privacy and security of patient data, which must be carefully managed to protect sensitive information.

2. Community health workers can be an effective option to help address the shortage of healthcare workers in managing NCD. However, for CHWs to be effective, they need adequate training, support, supervision, supplies, and remuneration. Integrating CHWs into the primary care system and providing them with digital tools can further enhance their capacity and impact.

[Thitikan Pohpoach]

Thank you very much for all your comments!

[Thitikan Pohpoach]

1. Considering the model’s performance, what additional data or features do you think could further improve the accuracy of predicting PIH?
– Integrating the medication use (e.g. antihypertensive drugs, vasopressors) and intraoperative fluid administration/blood loss data into the machine learning model may enhance its ability to more accurately predict the risk of PIH.

2. What future research directions would you suggest to address the limitations of this study and enhance the predictive model’s applicability across various surgical procedures?

– Exploring the generalizability of the model by testing it on diverse patient populations, such as those with different surgical risk profiles or comorbidities
– Investigating the feasibility of incorporating the predictive model into real-time clinical decision support systems to enable proactive management of PIH during anesthesia induction

[Thitikan Pohpoach]

1. what stakeholders should be involved in applying machine learning in symptom prediction? what are their roles and responsibilities?

1.1 Researchers:
They should be able to identify the key predictors of cancer symptoms using machine-learning approaches and conduct rigorous validation of the developed machine-learning models before considering them for clinical practice.

1.2 Patients with cancer:
They can provide data and participate in studies that use machine learning to predict their symptoms. Moreover, they can give feedback on the usefulness and acceptability of the symptom prediction models developed using machine learning.

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?

I think ‘Machine learning’ is becoming a buzzword (at least in Thailand) and a black box at the same time. Clinicians and patients should understand how the models arrive at their predictions and be able to interpret and use them in clinical practice cautiously.

[Thitikan Pohpoach]

The COVID-19 dashboard provided by the UK Health Security Agency (UKHSA) offers a comprehensive overview of the pandemic situation in the UK.

https://ukhsa-dashboard.data.gov.uk/topics/covid-19

Here are some observations on what I like and areas for potential improvement:

Strengths:
– The dashboard presents a wide range of relevant metrics, including case rates, hospital admissions, and vaccine uptake, providing a holistic view of the COVID-19 situation.
– The data is updated regularly, with the latest figures available as of June 25, 2024, ensuring the information is current and relevant.
– The ability to filter results by location allows users to focus on specific regions or nations within the UK, enabling more targeted analysis.
– The inclusion of related links to NHS advice, surveillance reports, and data sources enhances the dashboard’s utility by providing additional context and resources.

Areas for Improvement:
– The dashboard could benefit from improved visual clarity and consistency. While the line charts effectively convey trends over time, the use of different scales and units across metrics can make comparisons challenging.
– The lack of annotations or explanations for the data points may hinder interpretation, especially for users unfamiliar with the metrics or their significance.
– The dashboard does not appear to offer any interactive features, such as the ability to customize date ranges or compare multiple metrics side-by-side, which could enhance user engagement and analysis

[Thitikan Pohpoach]

To collect continuous data (e.g. weight, height, SBP, DBP), I think the correct number of boxes for the answer should be provided. Any required decimal points, commas, or other punctuation should be preprinted.

To collect categorical data, I think numerical codes should be assigned for all possible categories.

[Thitikan Pohpoach]

In the US, one key benefit of using data standards like CDISC in clinical research is that it enables faster regulatory review and approval of new drugs and treatments. The US FDA requires clinical trial data to be submitted using CDISC standards, which provide a common format that regulators can easily process and analyze. This streamlines the review process compared to non-standardized data submissions.

[Thitikan Pohpoach]

I actually do not have much experience conducting studies but I have seen other research projects that have implemented data management processes similar to the ones you mentioned.

These processes are crucial for ensuring the quality, integrity, and security of research data. Maintaining an audit trail and time-stamping data is essential for tracking changes, ensuring data provenance, and enabling the reproducibility of the research. This typically involves logging all actions performed on the data, including who made the changes, when they were made, and what the changes were. Moreover, having a robust data backup and recovery plan is crucial to protect against data loss or corruption. This may involve regular backups to local or cloud-based storage, as well as the ability to restore data from these backups in the event of a system failure or other data-related incident.

For the software for data management, I have seen the researchers use specialized data management platforms (e.g., REDCap, OpenClinica, Qualtrics) which offer tailored features and workflows for their specific needs. Furthermore, cloud-based storage and collaboration tools were widely utilized and can facilitate secure data sharing, version control, and remote access.

[Thitikan Pohpoach]

Your CRF is impressive and well-organized! Thank you for Panyada’s feedback on the CRF. I also agree with your points. Here are some suggestions:

1. CRF Version and Date
Including the CRF version and date is indeed a valuable addition to ensure proper tracking and version control. By clearly indicating the version and date on the CRF, you can help maintain consistency across sites and ensure that all involved parties are working with the most up-to-date version. This practice is essential for maintaining data integrity and streamlining the data collection process.

2. Instructions Section
I am very impressed with the instruction section (that I did not have in my assignment haha). The inclusion of an instructions section in a CRF is a beneficial feature that can provide clarity and guidance to users completing the form. This section can help ensure that all users understand how to accurately fill out the CRF, reducing errors and improving data quality. Including detailed instructions can also help standardize data collection practices across different sites or users.

3. Use of ‘You’ vs. ‘Subject’
I am not quite sure that we should use ‘you’ or ‘subject’ in a CRF intended for investigators. It is important to consider the perspective and clarity of the instructions. Using ‘you’ can create a more direct and user-friendly tone, which may enhance comprehension and engagement with the form. However, it is essential to ensure that the language used is appropriate for the context and audience of the CRF. Ultimately, the choice between ‘you’ and ‘subject’ should be based on what best suits the specific requirements and user experience of the CRF.

[Thitikan Pohpoach]

Based on my involvement in the project initiation phase, I was responsible for preparing the site initiation and was involved in protocol discussion and data design. To improve my performance, I would like to focus on enhancing my Data Management Plan (DMP) workflow. A well-crafted DMP serves as a roadmap for the entire study, ensuring that data is collected, stored, and managed effectively throughout the project lifecycle. By strengthening my DMP workflow, I can establish a solid foundation for the study, minimizing potential issues and ensuring smooth data management.

[Thitikan Pohpoach]

Experience in Data Collection for Dialysis Cost-Effectiveness Research

1. Purpose of Data Collection: The data collection for the research project on dialysis cost-effectiveness aimed to assess the economic impact of dialysis treatment, focusing on factors such as treatment costs, outcomes, and resource utilization. The primary goal was to determine the cost-effectiveness of dialysis as a treatment option.

2. Primary or Secondary Data Collection: The data collection process involves primary data collection. This means that the data was gathered firsthand from the electronic medical records (EMR) of dialysis patients, specifically for this research project.

3. Methods Used for Data Collection: As a fifth-year pharmacy student involved in the research project, I utilized structured data collection forms to extract relevant information from the EMR. These forms may have included variables such as patient demographics, dialysis treatment details, laboratory results, medication use, and healthcare utilization patterns.

4. Problems Encountered: During the data collection process, challenges such as incomplete or missing data, discrepancies in record-keeping, and issues with data quality were found. It’s common to encounter difficulties in extracting specific data points from EMRs, especially when dealing with large datasets or complex medical histories.

[Thitikan Pohpoach]

I’ve attached the infographics in the drive below.

https://drive.google.com/drive/folders/1-6pTIvBhsMwUh-5p5XhalXrO-qr7i7qF?usp=sharing

[Thitikan Pohpoach]

I’ve attached the infographics in the drive below.

https://drive.google.com/drive/folders/1-6pTIvBhsMwUh-5p5XhalXrO-qr7i7qF?usp=sharing

[Thitikan Pohpoach]

I’ve attached the infographics in the drive below.

https://drive.google.com/drive/folders/1-6pTIvBhsMwUh-5p5XhalXrO-qr7i7qF?usp=sharing

[Thitikan Pohpoach]

I’ve attached the infographics in the drive below.

https://drive.google.com/drive/folders/1-6pTIvBhsMwUh-5p5XhalXrO-qr7i7qF?usp=sharing

[Thitikan Pohpoach]

I’ve attached the infographics in the drive below.

https://drive.google.com/drive/folders/1-6pTIvBhsMwUh-5p5XhalXrO-qr7i7qF?usp=sharing

[Thitikan Pohpoach]

Sex: female
Education: Pharm D
Occupation: Pharmacist
Workplace: Thai FDA
Hometown: Chiang Mai

[Thitikan Pohpoach]

To figure out why respondents are not using bednets, I might consider the following approaches:

1. Explore knowledge and perceptions about malaria and bednet use: Understanding individuals’ knowledge and perceptions about malaria and bednet use can help identify misconceptions or gaps in knowledge that might be attributed to non-use. Cultural beliefs and practices, as well as community norms, can influence bednet use.

2. Analyze survey data: If survey data is available, we can use statistical methods to identify factors associated with bednet use or non-use. Moreover, we can investigate socio-demographic factors (i.e., age, gender, education level, and income) whuich can influence bednet use.

3. Assess access and availability of bednets: Limited access to bednets or difficulties in obtaining them can be barriers to use.

4. Conduct qualitative research: This could involve focus group discussions or in-depth interviews with respondents to explore their perceptions, attitudes, and experiences related to bednet use. This approach can provide rich insights into the reasons behind non-use and can help identify potential barriers and facilitators to bednet use

[Thitikan Pohpoach]

The Technology Acceptance Model (TAM) suggests that the relative usefulness of a new technology is closely related to the perceived usefulness, which is defined as the degree to which an individual believes that using a particular technology would enhance their job performance or make a task easier.

Therefore, if a new technology is easier to use than an old one, the TAM would predict that users would perceive it as more useful and be more likely to adopt and use it. This is because the ease of use of a technology is directly related to the perceived usefulness, as a technology that is easy to use is more likely to be perceived as enhancing job performance or making a task easier.

Moreover, the TAM also takes into account the perceived ease of use, which is defined as the degree to which an individual believes that using a particular technology would be free of effort. If a new technology is easier to use than an old one, it is likely that users would perceive it as having a higher degree of perceived ease of use. This, in turn, would lead to an increase in the perceived usefulness, as a technology that is easy to use is more likely to be perceived as enhancing job performance or making a task easier.

[Thitikan Pohpoach]

In my understanding, external variables that might influence an individual’s perceived ease of use or perceived usefulness of a new technology include:

– Perceived enjoyment
This refers to a person’s enjoyment or pleasure in using a technology. Higher perceived enjoyment is associated with a more positive perception of ease of use and usefulness of new technologies.

– Job Relevance
This refers to a person’s belief about whether a technology is relevant to their job or task. Higher job relevance is associated with a more positive perception of the usefulness of new technologies

– User Attitudes
This refers to a person’s attitudes towards using a technology. Higher user attitudes are associated with a more positive perception of ease of use and usefulness of new technologies.

[Thitikan Pohpoach]

In my understanding, efficacy refers to the performance of an intervention under ideal and controlled circumstances, effectiveness refers to its performance under ‘real-world’ conditions, and efficiency is about doing things economically, whether in terms of time, energy, or money.

Efficacy and effectiveness exist on a continuum, with efficacy trials determining whether an intervention produces the expected result under ideal circumstances and effectiveness trials measuring the degree of beneficial effect under ‘real world’ clinical settings. Efficiency is about doing things in the most economical way. In clinical trials, two drugs could be equally effective, but if one of them is much more costly than the other one, that drug won’t be considered as efficient

[Thitikan Pohpoach]

Some potential confounders to consider in this scenario could be socioeconomic status, occupation, living arrangements, and mobility patterns. These factors may impact both the likelihood of being a young adult and the frequency of social interactions. I would like to mention socioeconomic status. Young adults may have different social behaviors based on their income level or educational background. The socioeconomic background of teenagers significantly impacts their health, well-being, academic achievement, and overall development.

[Thitikan Pohpoach]

1. Maternal Mortality Rate
The maternal mortality rate is defined as the number of maternal deaths within 42 days of pregnancy termination due to complications of pregnancy, childbirth, and the puerperium in a specified geographic area divided by the total resident live births for the same area, usually expressed per 100,000 live births for a specified time period. It is a crucial indicator of a geographic area’s overall health status or quality of life.

The calculation of the maternal mortality rate involves dividing the number of resident maternal deaths by the number of resident live births, then multiplying by 100,000. For example, if there were 84 maternal deaths in a year among state residents with 130,000 live births, the calculation would be 84/130,000 x 100,000 = 64.6 maternal deaths per 100,000 live births.

The maternal mortality rate is considered essential for assessing the health status and quality of life in a specific area. It helps in understanding the risks associated with pregnancy and childbirth, highlighting areas that may need improvement in healthcare services and maternal care. The rate is used to monitor trends over time, identify disparities across regions, and guide public health interventions to reduce maternal mortality and improve maternal health outcomes.

2. Neonatal mortality rate
Neonatal mortality rate is a crucial indicator of healthcare quality and development, defined as the number of deaths among live-born infants within the first 28 completed days of life per 1,000 live births in a given year or period. It reflects the overall health of newborns and the effectiveness of healthcare systems in providing care for mothers and infants during pregnancy, childbirth, and the neonatal period.

Calculating the neonatal mortality rate involves dividing the number of neonatal deaths by the total number of live births in a given year or period, then multiplying the result by 1,000. This calculation provides a rate per 1,000 live births, enabling comparisons between different regions, countries, and periods.

The neonatal mortality rate is a useful indicator for several reasons. For example, the neonatal mortality rate can be used to track the progress of healthcare systems and countries in improving the health and well-being of newborns. Decreasing rates indicate improvements in maternal and child health, while increasing rates may signal the need for intervention and improvement.

[Thitikan Pohpoach]

I would like to choose point 6 (page 341); A null-hypothesis P value greater than 0.05 means
that no effect was observed, or that the absence of an
the effect was shown or demonstrated.

In my opinion, it is not true that a null hypothesis P value greater than 0.05 means that no effect was observed or that the absence of an effect was shown or demonstrated. A P value greater than 0.05 does not indicate the absence of an effect, on the other hand, it suggests that there is not enough evidence to reject the null hypothesis.

The P value is a measure of the strength of the evidence against the null hypothesis, with a P value less than 0.05 typically considered statistically significant, indicating that the observed results are unlikely to have occurred by random chance. Therefore, a P value greater than 0.05 does not imply the absence of an effect but rather a lack of statistical significance in rejecting the null hypothesis.

[Thitikan Pohpoach]

Hello everyone,
My name is Thitikan, a pharmacist at the Thai FDA. Mostly, my work is related to regulatory science and I coordinate with researchers and officers helping them to develop products in line with regulations. I use basic statistics and data visualization to present and develop my routine work, mainly using Google Sheet. I have experience using SPSS in my health-technology assessment (HTA) research project during my Bachelor’s in pharmacy school. I realized that I do need to learn new skills and gain more experience in statistics and hope that this course will help.

[Thitikan Pohpoach]

As a regulator at the FDA, regulatory decisions were involved in resolving the health crisis at nearly every stage. Regulation affects the availability of tools and products to identify and fight the disease (tests, products, and devices). Regulation frames the ability of public utilities to maintain critical services, of food to be produced and delivered, of essential services to continue functioning – even in a lockdown situation, where much of both the private and the public sector no longer function normally.

Good regulatory practices must be complied with, even in a crisis. When time is of the essence, it becomes hard to anticipate, analyze, and thoroughly discuss the impacts of regulations designed to deal with urgent issues. Lifting non-critical administrative barriers can help expedite the delivery of critical products. “Emergency” regulations can be adopted through “fast-track” procedures. All the measures are decided based on the level of risk. However, once the crisis is over or its impacts dip below a critical threshold, all regulations adopted through a fast-track procedure should be subject to careful postimplementation reviews.

[Thitikan Pohpoach]

In Thailand, a universal healthcare coverage scheme can be referred to as the ‘30-baht scheme’ (1 USD = 35-40 baht). During the election campaign, the party indicated that the scheme would provide health care for everyone. After acquiring the majority vote, the government put forward the scheme quickly. The 30-Baht Scheme covers everyone who is not covered by other government-sponsored forms of insurance; the Civil Servant (and public enterprise workers’) Medical Benefit Schemes (CSMBS) and the Social Security Scheme (SSS). The 30-Baht Scheme was intended to remove financial burdens connected with health care, in that illness can be unanticipated and the cost concerned might be unpredictable.

Strength
most people feel more secure with this scheme in place, as they now have insurance against a drastic or catastrophic illness that they could suffer in the future.

Challenges
In my perspective, four main challenges are described as follows:
1. Unsustainability of revenue-raising method: the hospital operating cost from the government budget has been decreasing steadily.
2. High administrative cost and loss of negotiating power
3. Intractable and rapidly rising healthcare cost
4. Health-seeking behaviors
It can be observed that patients have flooded into their hospitals after the introduction of the 30-Baht Scheme. Some health-care providers observed that several people seek health services earlier than they had in the past. According to them, some people seek services for health concerns that could easily have been taken care of by themselves, especially during nighttime or on the weekend when most public hospitals would be understaffed with doctors.
Moreover, the inadequacies of hospitals and health personnel, especially doctors in small public hospitals, remain the problems.

[Thitikan Pohpoach]

The previous pandemic accelerated the digital transformation of many services in Thailand, including healthcare. From my perspective, we do not have enough skilled workforce in health informatics. Health informatics expertise is required in three key areas:

– advances in electronic medical records (EMR): Before the pandemic, EMRs were available mostly in private settings. I observed that the pandemic boosted EMR system, speeding up the Ministry of Public Health’s implementation of the availability of EMR in public settings and linking the data across the country. Interoperability has remained the big challenge. Interoperability has increased between providers, payers, and health tech developers, with new tools and interoperability platforms being integrated into EMR systems. 

– expansion of telehealth: When lockdowns and restricted capacity in medical offices occurred during the COVID-19 epidemic, telemedicine and telepharmacy emerged as critical healthcare tools. Patients no longer have to travel to the office, expose themselves to infectious diseases, and make appointments in the comfort (and convenience) of their own homes. Telehealth can lead the development of hybrid care models. However, a few challenges can be observed, like secure connections, accessibility, and technical malfunctions.

– cybersecurity: Hospitals and healthcare systems have found themselves targets in this rising cybercrime wave, with multiple hospitals often targeted at once in coordinated ransomware attacks. The knowledge of cybersecurity in healthcare is necessary, including the protection of electronic information and assets from unauthorized access, use, and disclosure.

From my experience, the main challenges are
– Lack of trained workforce: in most healthcare organizations, there are either IT technicians or healthcare professionals, not health informaticians.
– Lack of use of common interoperability standards
– Health IT policy was developed, but the healthcare workers did not know enough to implement it effectively.

[Thitikan Pohpoach]

Data sharing undoubtedly produces more effective results. If I were in charge of a data set, I would consider data sharing with a ‘Data sharing agreement’. Data sharing agreements outline the conditions under which data may be shared between parties, with full attention to confidentiality and legal compliance.

From a legal perspective, data sharing agreements must adhere to relevant laws and regulations governing privacy, security, and intellectual property rights. For instance, in Thailand, personal data can be shared only if certain conditions are met and comply with the Personal Data Protection Act 2019 (‘PDPA’). Moreover, the agreement should comply with industry regulations: specific regulations may apply to data sharing agreements, for example, healthcare records under HIPAA, in the US. Ensuring data security and privacy is crucial in the big data era. Businesses that gather and exchange data need to handle third-party access cautiously, establish strong security protocols, and understand consent.

[Thitikan Pohpoach]

As the world continues to integrate digital tools into daily operations, healthcare is no different. In Thailand, the pandemic has meant an acceleration in digital transformation by months or even years. The system has a significant impact on patient care procedures, patient data access, sharing, and storage, and overall patient care quality.

Advantages of EMR

EMRs offer expedited access to comprehensive patient data. With just a few clicks, medical professionals can access a patient’s whole medical history, including test results, diagnoses, treatments, and other relevant information. This makes it possible for anyone to access complete health information in real-time, which is very helpful for organizing patient care and allowing for more informed clinical decisions.

The sharing of information is also made easier by electronic records. Quick record sharing between patients and other medical providers facilitates consultations in emergencies. EHR systems often have built-in alerts and reminders to notify of potential patient health risks, helping healthcare organizations prevent errors and improve the quality of patient care.

Information loss or misplacing is a common problem with paper records that is much decreased with electronic records. Using EMR also addresses the issue of storage by eliminating the need for physical space, and significantly reduces the risk of unauthorized access, strengthening the level of security.

Disadvantages of EMR

The electronic system may have a learning curve for healthcare practitioners, and the procedure may initially slow down documentation. Furthermore, especially for smaller settings, the expense of establishing and maintaining an EHR system can be daunting. When working with electronic records, potential risks like data breaches and their repercussions are also taken into consideration.

Nevertheless, I believe that the many benefits that an EMR provides—like immediate access to patient data, better care coordination, and increased administrative effectiveness—outweigh these difficulties.

[Thitikan Pohpoach]

According to the publication by Silverio et al, the challenges of implementing big data in cardiovascular care were mentioned, including missing data, selection bias, and data analysis and training. I proposed my suggestions to cope with those challenges.

Missing data
As the publication points out, there are various statistical techniques for handling missing data. The goal is to avoid creating biased estimations resulting in inaccurate outcomes. From my understanding, two main methods for handling missing data are data removal and imputation. The imputation technique replaces missing data with reasonable predictions. When the percentage of missing data is low, it is most helpful. If the portion of missing data is too high, the results lack natural variation that could result in an effective model. The other option is to remove data. This technique is a good option when dealing with data that is missing at random.

Selection bias
Selection bias occurs when the sample data doesn’t accurately reflect the larger population causing inaccuracies in conclusions and decision-making. Scrutinizing the sample’s representativeness can uncover the selection bias by comparing the characteristics of the sample with those of the larger population it’s intended to represent. Another method is to examine the data collection process. To cope with this challenge, randomized selection methods, stratified sampling, or oversampling of underrepresented groups can be utilized.

Data analysis and training
Data analysts have made it possible for businesses to make informed decisions by turning raw data into practical insights. I proposed that the organization should have both data analysts and data scientists who work together in the data-driven decision-making process.

[Thitikan Pohpoach]

I completely agree with those four recommendations for combating corruption in health systems. To successfully combat corruption, public health experts should convene key stakeholders to identify the scale and nature of corruption, prioritize Action, take a holistic view, and conduct research. These methods should be customized to the unique circumstances of every health system. To guarantee the long-term efficacy of anti-corruption initiatives, ongoing assessment and modification are also required. Furthermore, I think that combating corruption requires improving accountability and openness in healthcare systems. This involves encouraging transparency in finance, promoting open access to information, and carrying out routine audits by providing the public with easy access to information.

To be honest, there is widespread corruption in the country and the majority of people in the public sector do not see corruption as a serious problem. The most effective way to stop or prevent corruption is whistleblowing. However, I believe that most people in the public sector may witness corruption but few people are willing to file a report. The main reason is that they did not know where to report and they felt there was no protection for whistleblowers.

[Thitikan Pohpoach]

Hello Teerapat,

Thank you for sharing your insights about the Covid-19 dashboard you created in Looker Studio. It’s great to see such a thoughtful approach to data visualization and decision-making. Your emphasis on interactive filters is spot on! They significantly enhance user experience by allowing individuals to tailor the data view to their specific needs. The choice of bright and clear colors is crucial in making complex information more digestible. Visual elements like line and bar charts can indeed convey trends and comparisons effectively. I love it!

[Thitikan Pohpoach]

Big thanks to all of you for sharing the insights! I also agree with Nicha’s thoughts on keeping the database secure and agree with Teeraboon’s comment. 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.

[Thitikan Pohpoach]

That’s interesting that you use SQL queries to extract information from the Health Data Center’s database. Your work to automate the categorization process through coding is also very impressive, as it not only saves time but also reduces the potential for human error. From your experience, what else could we do to improve the utilization of the national database?

[Thitikan Pohpoach]

I completely agree with Teerawat, especially in breaking down silos. Silos can often lead to organizational paralysis when one department waits for another to take action before it can move forward. Silos can also make it difficult for team members to share knowledge and learn from each other. By breaking down the silos, the teams can work towards a unified vision and goal that goes beyond individual goals.

[Author]

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