Myat Htoo Linn

[Myat Htoo Linn]

Thank you so much friends for all of your valuable discussion.
I have noted all of your comments which can be considered in future research around health information dissemination.

[Myat Htoo Linn]

1) The findings of the article on the use of ePROs can be highly beneficial in various healthcare settings where ongoing symptom monitoring is crucial for effective treatment. It would be highly useful in mental health conditions like depression, anxiety, and bipolar disorder because these symptoms can fluctuate over time and are difficult to track through periodic in-person appointments. The patient under treatment can report mood, anxiety levels, and sleep patterns regular basis through ePRO. This continuous monitoring will be valuable as the ePRO system could alert the clinician to adjust medications or intervene earlier if a patient’s mood worsens. This system can also empower the patients to actively monitor their symptoms and engage them by understanding the connection between daily behaviors and their mental health, leading to better self-management and treatment adherence. The data collection can be also customized by tailoring the individual’s specific symptoms such as feelings of worthlessness, concentration issues, or irritability where the providers can gather more relevant and actionable information through the system.

2) I would suggest the integration of ePRO data with existing EHR systems would be crucial which can also ensure that healthcare providers can easily access and review ePRO data alongside other clinical information. This will be also better to add the ability to receive automatic notification when a patient’s score surpasses the critical threshold. The feature can help providers take immediate action in response to worsening symptoms as in mental health issues. If the system adds two-way communication features, it would allow patients to contact their healthcare provider if their condition worsens and can enhance patient-provider engagement and foster shared decision-making.

[Myat Htoo Linn]

1) By integrating environmental data in the malaria outbreak predictions, the first thing I am sure is it will improve the spatial and temporal precision. The reason is that environmental data often provides continuous, real-time monitoring of conditions across large areas. I believe this will allow for more geographically specific and timely predictions, as opposed to epidemiological data, which can be delayed due to reporting lags or underreporting. Another one is the provision of lead time for outbreak notification, incorporating the environmental data can make predictions before a noticeable spike in malaria cases as environmental changes often precede changes in epidemiological data. For instance, heavy rainfall might signal a future increase in mosquito breeding grounds. It can also improve the model accuracy by accounting for the dynamic nature of the vector habitats in the prediction models.

2) Based on our work in public health, I think it will be essential to consider and meet the local context and needs. Continuous working with the stakeholders will ensure that the system addresses the real-world challenges and priorities of public health needs in the area. Public health stakeholders can offer critical insights into local epidemiological patterns, resource limitations, and cultural considerations that may affect the adoption of predictive tools like EPIDEMIA. It can also enhance the adoption rate of the system as stakeholders who participate in the design process are more likely to trust and adopt the system. But, sometimes the availability and diverse needs of different stakeholders with various expectations can be challenges as well. Receiving ongoing feedback from stakeholders can help fine-tune the system in response to changing malaria patterns and new environmental data. It can also guide the system’s features, such as the frequency of updates, user interface design, and the type of alerts, and these all can shape the system’s effectiveness.

[Myat Htoo Linn]

1) There are lots of challenges such as funding and technology constraints, interoperability, regularity compliance, user adoption, workforce capacity, etc., especially in developing countries for the sustainability of health information systems. Based on my experience, the lack of interest of the decision-maker in the system development is the major limitation. In my country, different healthcare service providers within the health industry have varying perspectives on the goals of an HIS. If the main stakeholder doesn’t pay interest, especially in the absence of the policy setting to guard the system, they will not prioritize it, and the misalignment can lead to the discontinuation of the system. Improving the digital literacy between the different stakeholders (authorized persons, various healthcare providers, patients, etc.) will be important in that case for sustainable use of the HIS. The establishment of shared goals, engaging healthcare workers in system design, clear communication between the different stakeholders, and feedback process to foster a sense of ownership can contribute to long-term sustainability.

2) For the adaptability of the EHIS, interoperability with the other systems is crucial I think. Most of the EHIS needs to be considered in the design to work alongside other health systems, such as laboratory information systems or national health databases, and the development of standard data formats and communication protocols. I would consider it crucial to adapt EHIS by enabling seamless integration with other healthcare applications and data can be shared across different platforms. To improve adaptability, the application of cloud-based infrastructure to facilitate easier updates, backups, and data recovery and the modular system design to adapt to new healthcare priorities are also important.

[Myat Htoo Linn]

Thank you so much for the brilliant presentation of this information, Ko Pyae. I would like to share some of my opinions;
1) This is sure the different demographic information will have various influences on the perceived ease of use and usefulness of the e-health applications. For Age, no doubt that young age, who are often tech-savvy will be familiar with the technology and it is easier to use the system when older adults may face more challenges with the use of technology with some limitations in digital literacy generally. However, the older may find the usefulness of the PHRs if they recognize the better management for their health benefits. For Gender, some research said men may be likely to adopt new technologies based on their perceived usefulness, whereas women might be more influenced by ease of use and user-friendliness but I think it might also depend on the age, education, environment, and personal preferences. It seems that users with higher education levels tend to perceive technology as easier to use and more useful possibly due to more exposure to digital platforms and a better understanding of the advantages of the use, whereas users with lower education levels may find more difficulties in the ease of use of the applications.

2) I would consider Accessibility and Device Availability as the primary external factors that could have an impact on the intention to use the system. This is significant in our surroundings, with very low internet connectivity and compatible devices where the system could not even be considered. In one research for telehealth initiatives in rural India, frequent disconnections due to low bandwidth made it difficult for doctors to assess patients effectively where the perceived usefulness of the system was low among both healthcare providers and patients. Another important one is health literacy, I believe. This is crucial to understand why the users need to use the applications, and what can be benefits and disadvantages of using and not using it. Users with higher health literacy may improve the content’s readability and offer clear explanations which can enhance adoption rates in the long run. Users with low health literacy may find the applications difficult to navigate, even if the system itself is easy to use.

[Myat Htoo Linn]

Based on my previous discussion and study, the endemic model of malaria transmission considered SIR in the human population and SI in the mosquito population. The vector component of the model does not include an immune class as mosquitoes never recover from the infection. The immune class in the mosquito population is negligible and natural death occurs equally in all groups. In the model, human enter the infected class through immigration rate and infected rate. It leaves the infected class through the recovered rate.

The primary intervention considered is the prevention of infected human immigrants, which is modeled by including an immigration rate parameter for infected humans. This is modeled by incorporating an infected immigration rate in the equations for the infected human population.
The next-generation operator approach as described by Diekmann et al. (1990) is used to find the basic reproduction number R0 as the number of secondary infections that one infected individual would create for the infected period. The formulation of this next-generation matrix involves determining two classes, infected and non-infected, from the model. The stability analysis of the Disease Free Equilibrium point is obtained by equating the system of differential equations to zero which is the steady-state solution of the model in the absence of the malaria disease. If the reproduction number is less than one then the DFE is locally and globally asymptotically stable, only susceptible is present and the other populations reduce to zero, and the disease dies out. If the reproduction number is greater than one then DFE is unstable.

The intervention covers all the population that migrates particularly focusing on controlling the number of infected immigrants. The efficacy is demonstrated through the reproduction number, a lower infected immigration rate leads to a reduction in the disease. To reduce the basic reproduction number below one, it is necessary to focus on the reduction of the infected immigration rate of the human population. To decrease malaria infection, malaria testing is recommended before immigration from one place to the other place. From the numerical results in the model, it is noticed the prevention of infected immigrants has a strong impact on malaria disease control.

Resource: https://www.iosrjournals.org/iosr-jm/papers/Vol14-issue5/Version-1/C1405011021.pdf

[Myat Htoo Linn]

I am interested in the impact of population movement/migration/human mobility and malaria transmission. People move for a number of reasons, including environmental deterioration, economic necessity, conflicts, and natural disasters. Identifying and understanding the influence of these population movements can improve prevention measures and malaria control programs. Political unrest has led to significant cross-region population movements and increased risks and rates of malaria infection in Myanmar.

Key Characteristics of the Malaria
Malaria is a life-threatening disease spread to humans by some types of mosquitoes. It is mostly found in tropical countries. It is preventable and curable. The infection is caused by a parasite and does not spread from person to person. According to the latest World Malaria report, there were 249 million cases of malaria in 2022 compared to 244 million cases in 2021. The estimated number of malaria deaths stood at 608,000 in 2022 compared to 610,000 in 2021. (WHO, 2023)
Transmission: Malaria mostly spreads to people through the bites of some infected female Anopheles mosquitoes. Blood transfusion and contaminated needles may also transmit malaria. There are 5 Plasmodium parasite species that cause malaria in humans and 2 of these species – P. falciparum and P. vivax – pose the greatest threat. P. falciparum is the deadliest malaria parasite and the most prevalent on the African continent. P. vivax is the dominant malaria parasite in most countries outside of sub-Saharan Africa. The other malaria species which can infect humans are P. malariae, P. ovale and P. knowlesi.
Pathogenicity: The fever and chills of malaria are associated with the rupture of erythrocytic-stage schizonts. In severe falciparum malaria, parasitized red cells may obstruct capillaries and postcapillary venules, leading to local hypoxia and the release of toxic cellular products. Obstruction of the microcirculation in the brain (cerebral malaria) and in other vital organs is thought to be responsible for severe complications. The cytokines, reactive oxygen intermediates, and other cellular products released during the immune response are probably responsible for the fever, chills, sweats, weakness, and other systemic symptoms associated with malaria. (Medical Microbiology. 4th edition)
Symptoms: The most common early symptoms of malaria are fever, headache, and chills. Symptoms usually start within 10–15 days of getting bitten by an infected mosquito. Some types of malaria can cause severe illness and death. Infants, children under 5 years, pregnant women, travelers, and people with HIV or AIDS are at higher risk. (WHO, 2023)
Control Measures: Vector control is a vital component of malaria control and elimination strategies as it is highly effective in preventing infection and reducing disease transmission. The 2 core interventions are insecticide-treated nets (ITNs) and indoor residual spraying (IRS). (WHO, 2023)
Malaria may be prevented by chemoprophylaxis and personal protective measures against the mosquito vector and by community-wide measures to control the vector. Exposure to night-feeding Anopheles mosquitoes is reduced by using protective clothing, insect repellents, insecticides, insecticide-impregnated bed nets, etc. Mosquitoes may be reduced by destroying breeding places and by application of insecticides. Vaccines are being developed. (Medical Microbiology. 4th edition)
Since October 2021, WHO has recommended broad use of the RTS,S/AS01 malaria vaccine among children living in regions with moderate to high P. falciparum malaria transmission.
Over the last decade, partial artemisinin resistance has emerged as a threat to global malaria control efforts in the Greater Mekong subregion. Regular monitoring of antimalarial drug efficacy is needed to inform treatment policies in malaria-endemic countries, and to ensure early detection of, and response to, drug resistance.

Adjusting Rcode for the Model:
Based on these papers (Merveille Koissi Savi, 2022), (John M. Marshall, et.al. 2018), (Alemu Geleta Wedajo, et.al. 2018), this was considered the SIR model (consists of compartments for Susceptible (S), Infected (I), and Recovered (R) individuals in the human population, and Susceptible (S) and Infected (I) compartments for mosquitoes) for malaria transmission and migration of the people by adding migration terms in the equations for susceptible and infected populations. The model description and adjusted Rcode is as follows;

sir_model <- function(time, state, pars) {
with(as.list(c(state, pars)), {
dSh <- mu_h * N_h – beta_h * Sh * Iv / N_v – mu_h * Sh
dIh <- beta_h * Sh * Iv / N_v – (gamma_h + mu_h + alpha_h) * Ih – delta_h *
dRh <- gamma_h * Ih – mu_h * Rh

dSv <- mu_v * N_v – beta_v * Sv * Ih / N_h – mu_v * Sv
dIv <- beta_v * Sv * Ih / N_h – mu_v * Iv
# Return the rate of change
list(c(dSh, dIh, dRh, dSv, dIv))
})
}
Result <- ode(y = initial_state, times = times, func = sir_model, parms = pars)

There will be the initial state for both human and mosquito populations for the identified locations and time steps for the model. Location dynamics can be added to the model and need to adjust Rcode.

The parameters considered in the model will be as follows:
beta_h # Transmission rate from mosquito to human
gamma_h # Recovery rate in humans
mu_h # Natural death rate for humans
beta_v # Transmission rate from human to mosquito
mu_v # Natural death rate for mosquitoes
delta_h # Migration rate of infected humans
alpha_h # Disease-induced death rate for humans
N_h # Total human population in the specified location (may be more than one)
N_v # Total mosquito population

Resources:
https://www.who.int/news-room/fact-sheets/detail/malaria
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2640853/pdf/10756143.pdf
https://www.ncbi.nlm.nih.gov/books/NBK8584/
https://www.mdpi.com/2076-3271/11/1/3
https://www.nature.com/articles/s41598-018-26023-1
https://shorturl.at/a4Kvl

[Myat Htoo Linn]

Sorry for my late discussion Friend!
1. To improve the syphilis surveillance system at Tak Hospital, I would emphasize mainly the coordination between the different work units within the hospital. The officers from the health screening unit and different professionals from the STI/HIV clinic, blood bank, ANC clinic, and laboratory work closely to notify the syphilis case identified and integrate the case management. The missing diagnosis and case reporting would occur in the surveillance system without clear and accurate coding and coordination between the different departments. The regular auditing should be also enhanced by the hospital’s epidemiology unit. These audits should engage relevant departments to ensure that all syphilis cases are captured accurately. Such audits can help identify and close any gaps in the current surveillance system.

2. I have some experience in the use of the Early Warning, Alert, and Response System (EWARS) in Myanmar which is used to monitor and respond to infectious disease outbreaks in the community. This was mostly managed among the NGOs and local CSOs with the support from WHO which plays a key role in tracking diseases such as malaria, dengue fever, etc. One of the strengths is it can provide community-based reporting from community-based health volunteers and different CSOs which can be crucial in remote and conflict-affected areas where access to healthcare facilities is very limited. This can also support expanding the coverage of the areas for disease outbreak notification.
There are several challenges such as infrastructure limitations and varying levels of training among health workers which may lead to inconsistent data quality. I think it cannot also cover private hospitals in urban areas which can be a barrier for the areawise notification. With the political instability and conflicts, it is difficult to collect and report data consistently in EWARS, the volunteers and health service providers from these areas can only report weekly which may limit to quick identification of potential outbreaks and facilitate timely interventions.

[Myat Htoo Linn]

I am personally interested in exploring the suicide risks around us, especially among youth in my country. I am also quite aware of suicide as an act of behavior rather than a disease but by associating it with various underlying factors such as depression, physiological stress, political driving forces, social factors, etc., modeling the suicide risks would also be worth receiving valuable insights into our community.

Suicide and attempted suicide are recognized as pressing public health issues that affect individuals, families, and communities. This death rate was rising particularly among younger ages and it is the third most important contributor to life years under 65 lost. Different kinds of risk factors were associated with the suicide rate. The demographic variables of age and sex and the different types of mental illness with ecological variation also explain suicidal behavior (Ovenstone and Kreitman, 1974). Wing (1992) informed both mental health disorders and suicidal behavior are inflated with distinct social problems (e.g. homelessness and drug abuse). By examining it, the research findings can inform policymakers, healthcare providers, and mental health organizations, helping them tailor interventions to address the suicidal risks effectively and reduce the rates.

I think the statistical and mathematical modeling can help to answer the following research questions;
What are the temporal patterns and trends in suicide rates among youth in the country?
How the political situation and armed conflicts have an impact on the underlying factors of suicidal risks and what will be the trend over time?
Are there any specific demographic factors (age groups, gender, areas with conflicts/relatively stable, urban/rural, etc.) more susceptible to suicide?
What are the primary socio-economic factors (income, employment, education, cultural norms, etc.) associated with increased suicide rates in the country?
What role do mental health issues (depression, anxiety, trauma, etc.) and access to mental healthcare play in suicide rates?

[Myat Htoo Linn]

Yes, Toby!
I also got the same map of Local Moran’s I as yours which differs from the video.
Maybe this could be the cause of the raw data set for which cannot revise it?

[Myat Htoo Linn]

Thanks all bros!!
You are life saviours.
I am also resolved.

[Myat Htoo Linn]

1. I think the author is interested in investigating the suicide problem in Thailand due to reasons of unique socio-economic landscape with limited research at the micro level which differs from developed nations. A substantial portion of the Thailand population is engaged in agriculture, which is why agrarian employment is present in the research model to fill the research gap. There is also a high suicide rate annually, ranging from 3,600 to 4,000 between 2005 and 2014 and it has generally been on the rise since 2011. The author would interested in investigating this problem to provide insights into the impact of cultural, social, and economic differences among regions.

2. A potential risk factor mentioned in the paper is alcohol consumption. Both of the two models showed that suicide rates in Thailand were significantly driven by alcohol, expected increase rate of an average of 0.086 and 0.090, respectively. Alcohol consumption among the population can lead to an increase in the likelihood of suicide which impairs judgment, and loss of self-control, and sometimes makes individuals more prone to act on suicidal thoughts, especially for severely depressed people. A reflection of our surroundings is also apparent that alcohol addicts are more prone to attempt suicide and lose their lives based on many social, economic, and cultural influences. Moreover, there is a correlation between alcohol consumption among adults and underage with higher suicide rates can also suggest that alcohol use worsens mental health issues or life stressors, contributing to higher suicide rates.

3. The main contribution of statistical modeling in epidemiology would be the ability to identify the patterns and correlations between various risk factors and the outcome, suicide rates in this case. Using the multiple regression analysis with the two models, the study can explore the impact of economic and social factors that may have a relationship with the suicide rate in Thailand. The spatial analysis also contributed to spotting the disparities based on different geographical areas of provinces which also provided insights on understanding of suicide problems. These allowed for the identification of high-risk areas and can help policymakers to implement targeted interventions and resource allocations.

[Myat Htoo Linn]

1. Implementing AI technologies in epidemic surveillance systems can be transformative in low-resource settings. Using AI can overcome the limitations of weak health infrastructure by processing open-source data to detect early signs of disease outbreaks. The application of mobile technology for data collection and integration of AI tools that are tailored to local languages and contexts would be crucial to enhance AI system use if I consider based on my standpoint. It can also improve by integrating advanced natural language processing (NLP) for better data processing and interpretation. Building partnerships with international health organizations can provide the necessary support and expertise to implement and maintain the systems.

2. I specifically think utilizing AI may provide early detection by the ability to predict disease outbreaks, model the disease spread even in vast datasets, and enhance communication strategies by optimizing limited healthcare resources. It will also support critical insights into disease patterns and potential hotspots, enabling targeted interventions.
Many things to consider in implementing AI systems, infrastructure and technological constraints could be the main considerations, especially in low-resource settings. To ensure data reliability and accuracy, ethical concerns, overcoming resistance from healthcare professionals to adopt AI technologies, and receiving the community’s trust can be also challenges. Significant investment in the infrastructure, inclusive training, regular monitoring, and collaboration among professionals will be needed for the successful implementation of AI in public health preparedness.

[Myat Htoo Linn]

1. What are possible reasons locations in epidemiological research have not been incorporated as much as other components in epidemiological research? How can spatial epidemiology be considered as an interdisciplinary science?

As I can understand from reading the assigned papers, one reason is the limited incorporation of the places in epidemiological research rather than focusing on the person and time over place historically. This may also be one reason as GIS evolved only in the early 1960s with the primary intention of using forestry mapping as an operational. Historical integration and insufficient software for special analysis would be a challenge in epidemiological studies. This is also linked to the lack of appropriate databases regarding the use of special data for epidemiological research.
Another reason will be data availability and quality which hinder the integration of special analysis. In the Special Epidemiology paper, the accurate small-area studies needed high-quality data, the studies relying on routine data sources cannot carry out a detailed validation process and this will lead to the inaccuracy of the findings in the studies. For instance, there is no national cancer registry in many regions, which makes it difficult to study environmental health problems accurately. It can also reflect our real setting and practices in my country, we didn’t usually collect special data concerning public health and epidemiological studies which was not incorporated as the other components, so we couldn’t get insight into it. Other confidentiality issues and study design could be also hindrances to location incorporation in epidemiological research.

Spatial epidemiology will be an interdisciplinary science as it integrates the concepts of epidemiology, geography, statistics, environmental science, and public health. It enables studying spatial patterns of the diseases with statistical methods based on the potential geographical, environmental, and social determinants. It can also explore the individual-level risk factors that interact with the above factors which may offer insights into public health interventions.

2. Explain why it is widely recognized that the place where an individual lives or works should be considered as a potential disease determinant and give some examples?

The main consideration will be the interaction between genetic factors, lifestyle, cultural practices, and environment can be different between the places where the individual lives or works, and these divergent interactions can be the potential disease determinants in the areas. Geographic variations in these factors can significantly influence health outcomes as living in industrial areas with poor air quality can be harmful to an individual’s health and urban planners for healthier environments can impact lower rates of the related diseases. It is also granted in some geographic correlation studies for the health outcomes; the study of local lung cancer excess was associated with residence near or employment in the arsenic industry (Blot and Fraumeni 1975, 194) and a positive association of mortality with measures of particulate matter pollution was found across six cities in USA, adjusting for other potential confounding data measured at the individual level (Dockery et al. 1993). This was also recognized only a highly localized or individual-based study can investigate the issue, and we could work on it to understand more spatial epidemiological studies.

[Myat Htoo Linn]

1. As the colleagues discussed, the decision tree model can be integrated into the clinical decision support system which can greatly assist surgeons in preoperative planning and decision-making. It can analyze a patient’s preoperative characteristics and predict potential complications, such as massive intraoperative blood loss, as demonstrated in the presented study on pancreatic surgery. The decision tree model can also categorize patients based on risk levels, helping prioritize cases needing more resources or special interventions. These models simplify complex decision-making processes by providing clear, interpretable results, aiding in quicker and more informed clinical decisions.

2. Using the decision tree model in a clinical setting offers several benefits. I think it can be developed individualized surgical plan based on specific patient risk factors and this tailored intervention can lead to more effective and targeted care. This can also improve patient outcomes; the model can help reduce the incidence of adverse effects by predicting potential complications and adjusting patient care. Patient trust and engagement can be also enhanced by providing personalized care plans and specific needs are addressed.

I wonder if it can be applied to low-resource settings. Adequate training to interpret correctly and encouragement from the medical superintendents to use the model effectively will be needed. The limited and varying levels of technological proficiency among clinical practitioners can be also a challenge. The lack of high-quality and comprehensive data necessary to train accurate models can also lead to less reliable predictions in these settings as there is instability and small changes in the data can result in a completely different tree being generated. As an ethical consideration, there is also a risk that the model might reinforce preexisting biases in healthcare data, which would result in unequal treatment outcomes for different patient groups.

[Myat Htoo Linn]

1. With the limited digital infrastructure and resource constraints, I didn’t see the electronic-based clinical decision support system specifically using or plan to use for NCD patients in Myanmar. One of my friends said there is one application called the Community-Based Health Assistance app which is using in MAM (Medical Action Myanmar) at the community health workers (CHW) level in remote areas. The application consists of different basic healthcare management around 15 types and the use of the volunteers was controlled by team leaders who are medical professionals. Unfortunately, I didn’t get and find the detailed pros and cons of the tool.

2. Based on my experience mostly with the MNCH and communicable diseases at the community level, I believe that one option as the NCD management tool will support management for better patient care and monitoring but don’t think as the best option to compensate for the shortage of healthcare workers in underserved areas, especially in Myanmar. A lot of infrastructure in terms of technology, health literacy, access difficulties, time constraints, altitude/cultural challenges, and no law enforcement could become the limitations for the sustainable use of the support system at the community level with the current situation.

Currently, the health cluster and WHO in Myanmar are encouraging and applying iCCM (integrated Community Case Management) strategy to train and support CHW for childhood illnesses: malaria, pneumonia, and diarrhea. https://shorturl.at/TIizd
It could be beneficial to integrate NCD patient care into this strategy to support healthcare providers in low-resource settings. Regular mobile clinics using the iCCM approach, supplemented with NCD management tools, could also be one option and can enhance the quality of care. This is sure we should take the strength of community-based resources and technology in addressing the healthcare workforce shortage in remote areas.

[Myat Htoo Linn]

1. I favor the interdisciplinary collaboration among a diverse team of experts and continuous education of users is important to improve the safety of medical AI systems. All of the stakeholders like clinicians, data scientists, and patient representatives can work together as clinicians can understand the rationale behind AI decisions, data scientists can train with diverse data sets with more understanding of clinical settings to reduce the risk of biased outcomes, and the patient’s perspective can also consider which is crucial for the trust and verification. This collaborative approach ensures that different viewpoints are considered where ethical consideration is vital.

2. In my opinion, transparency is the main key characteristic that can build trust and confidence in medical AI systems, where AI decisions and processes are clear and understandable to clinicians. To ensure relevance and practicality, incorporating clinical expertise during the development and deployment stages will be also crucial. This also has to ensure high standards of data privacy and security to protect patient information and adhering to regulatory standards can also establish trust in the system use within the community.

[Myat Htoo Linn]

I truly appreciate your fantastic presentation, Teeraboon! We learned further understanding.

1. I would consider incorporating additional physiological data could be beneficial. Integrating real-time data might provide a more comprehensive picture of the patient’s status during anesthesia induction. Additionally, continuous blood pressure monitoring, collecting data on patients’ hydration status, and fluid management during the perioperative period may also enhance model performance. Using advanced feature selection methods to identify the most predictive variables from a broad dataset can further refine the accuracy of the model.

2. As others discussed, I would suggest that future research should focus on validating the predictive model across diverse surgical populations and settings to enhance its generalizability. This could involve a variety of surgical procedures and patient demographics to ensure the model’s robustness. The reliance on electronic health record data that can capture all relevant variables, and developing user-friendly interfaces for real-time clinical decision support tools and interpretability could facilitate the practical application of the models in different clinical environments.

[Myat Htoo Linn]

Thanks much for sharing the paper on the machine learning approach and the wonderful presentation, Toby!

1. I have no more additions for the stakeholders to the colleagues’ discussions previously. This should just be patient-centric perspectives in applying machine learning to symptom prediction. I would love to focus on regulatory bodies and ethical committees that play a critical role in overseeing the machine learning models in healthcare. In addition to ensuring the health and data protection regulations, I consider these stakeholders should also be guided on the best practices in conducting the model with thorough reviews, which will also uphold the integrity of the research and maintain public trust.

2. I would consider simply that the machine learning model should be interpretable so that clinicians and patients can understand easily and trust the predictions. As discussed in the seminar, it should include diverse and representative data in the training process. If the model was trained predominantly on data from common cancer types, it may not accurately predict symptoms for patients with rare cancers, leading to disparities in care and outcomes.

[Myat Htoo Linn]

Hello!
Let me share mine and you may check here!
https://lookerstudio.google.com/reporting/7b04cfb5-d14e-4be0-bbd5-82b8014d3bdc

I have used features that could simply summarize the COVID-19 information at a glance in the main dashboard.
The scorecard was used with chunking and color theme which may bring up your short-term/working memory. Other relevant structures and controls as possible as I have learned were added for familiarity which may enhance our long-term memory.
Please check the filter, get the link, download the report in the dashboard and your suggestions are welcomed.
Many thanks, friends!

https://snipboard.io/a3Ojz0.jpg

[Myat Htoo Linn]

Dear Ajarn/Friends! Let me share mine!

Please kindly check the link here!
1. https://snipboard.io/5hia2Z.jpg
2. https://snipboard.io/Zvcx9j.jpg
3. https://snipboard.io/vFDVfz.jpg
4. https://snipboard.io/589XQ6.jpg
5. https://snipboard.io/Sq9cin.jpg
6. https://snipboard.io/1DWN0n.jpg
7. https://snipboard.io/WkjVws.jpg

Direct link to Looker studio report!
https://lookerstudio.google.com/reporting/7b04cfb5-d14e-4be0-bbd5-82b8014d3bdc

[Myat Htoo Linn]

I would like to share the following COVID-19 dashboard that was created by a group of contributors led by one medical doctor during the COVID-19 crisis in Myanmar around 2020-2022. The data were extracted from the Ministry of Health and Sport (MoHS), Myanmar’s daily announcement, and worked with the Google Data Studio (current Looker Studio) at that time. The data is not updated in the meantime.
http://surl.li/ujshh

Let me discuss my preferences and dislikes, especially from the data visualization and presentation perspective. I like the following presentations.
1. It used different patterns and shapes such as bar charts, line graphs, pie charts, tables, maps, etc. which enhance our pre-attentive process.
2. It used the line graph comparison for most of the time series analysis and it is good to capture the trend over time. There are also less than 5 lines in the graph.
3. It provided some concepts of chunking to improve working memory such as separating the number digits with commas, setting the axis label as K and M for larger numbers, and separating sheets for different structures and meanings.

I feel as if there is a lot of detailed information that may be missed to capture the main point of the presented data even if the information were complete. I think the following might be needed to improve.
1. It would be better to consider the color-blind users by emphasizing the color intensity rather than the colorful bar and stacked charts.
2. There is some gap on the Axis scales (e.g. Total PUI in State/Region), very few numbers cannot be identified with the line graph and small scales would be more applicable. Avoiding complicated graphs and grid lines will be needed to keep it simple.
3. Putting different values in the same graph (e.g. test in different labs) makes us confused in the comparison and using a stacked bar chart will not align the scale between the segments.

[Myat Htoo Linn]

Hello! This is my summary for this week.

https://snipboard.io/cflU3P.jpg

[Myat Htoo Linn]

Let me share my infographic for this week!
https://snipboard.io/5Gmy6R.jpg

[Myat Htoo Linn]

Possible combination of non-identifiable data that probably identifies me;
Sex – Male
Hometown – Htilin, Myanmar
Total family members – 5
Occupation – MEAL (Monitoring, Evaluation, Accountability, and Learning) Manager
Education – Current BHI student, Medical Technology graduate

[Myat Htoo Linn]

Hello! Here’s mine!

[Myat Htoo Linn]

For this kind of study, I think mixed methods will be the better selection. To get to know the social and behavioral challenges of not using the bednets, a qualitative study should be also undertaken. In-depth interviews and focus group discussions can be utilized to better explore and understand the issues and underlying factors around using bednets for prevention of the malaria. Some other community engagement to understand their perspectives on bednets usage and observational studies within the community can also provide insights into social norms, cultural beliefs, and practical challenges that can be also applicable to the study.

One of the malaria studies along the Thai-Myanmar border also used the mixed method to assess the utilization of insecticide-treated net (ITN) or long-lasting insecticidal net (LLIN). Semi-structured interviews were conducted with selected participants, thematic analysis approach was used, which helped to understand the reasons for the use or non-use of bed nets among community members at risk of malaria along the Thai-Myanmar border.

Reference:
Pooseesod, K., Parker, D. M., Meemon, N., Lawpoolsri, S., Singhasivanon, P., Sattabongkot, J., … & Phuanukoonnon, S. (2021). Ownership and utilization of bed nets and reasons for use or non-use of bed nets among community members at risk of malaria along the Thai-Myanmar border. Malaria journal, 20, 1-12.

[Myat Htoo Linn]

Hello friends! This is my summary!

[Myat Htoo Linn]

I would like to present point 9 on page 341, “the P value is the chance of our data occurring if the test hypothesis is true”.

This is also a common misinterpretation about P values in hypothesis testing as the direct translation of 0.05 P value to 5% chance of observing the data if the null hypothesis is true. In my understanding, the P value doesn’t only consider the observed data but also includes observations of more extreme than what was observed.

The P value actually will refer to data frequency when all assumptions used to compute it are correct, and a low p-value (like 0.05) suggests the data is unlikely due to chance alone because it falls outside the range expected under the null hypothesis. The accuracy of the p-value will also depend on the validity of the assumptions used for the test like taking random sampling, no manipulating to get a desired P value, and no missing or biased data.

[Myat Htoo Linn]

The idea of relative usefulness in replacing an old technology is not specifically addressed by the TAM. However, as all of my friends discussed, TAM explains the perceived usefulness of a technology significantly influences its adoption. The new technology should enhance the performance to at least the same level as the old one. People will not embrace new technology if they believe it to be less efficient and functional, and does not provide any additional benefits, especially over an extended period.

According to TAM, perceived usefulness is also influenced by perceived ease of use in the Viswanath Venkatesh et al.: paper. This is because, other things being equal, the easier the system is to use the more useful it can be. In that case, replacing an old technology requires the new one to be at least as functionally useful, while offering a clear advantage of easier use of the technology.

[Myat Htoo Linn]

Task Complexity: I would start with this one as we mostly experienced it in the real setting. If people have experience with the complexity of completing the task, they would give up easily using the technology. These challenges were related to log-ins and UI design and older people with computer anxiety mostly encounter it.

Accessibility: This is also important to be easily accessible for the new technology and that we are also experiencing it. Recently I tried to use one application that could support my learning but this was not supported in my country and I couldn’t use it. The same factor like sometimes we couldn’t use the purchase application, that also relates to the economic influence. Other factors such as accessibility for people with disabilities, language support, and device compatibility can be also considered to impact the use of a technology.

Advertisement and Recommendation: I consider advertisements and recommendations would also influence the individual to use new technology. The way a technology is advertised can shape perceptions of its ease of use and usefulness, as well as create expectations about its capabilities. The word of mouth sharing is also important, we mostly use new technology especially when trusted sources recommend it and the opinions/recommendations of our friends can influence our perceptions of a technology’s usefulness and ease of use.

[Myat Htoo Linn]

Efficacy refers to how well the intervention works under ideal and controlled conditions. It focuses on whether the intervention can achieve the desired outcome under optimal circumstances. The purpose of assessing efficacy is to determine whether the intervention has the potential to work in real-world settings before evaluating its effectiveness in broader populations. It is mostly measured in Randomized Controlled Trials (RCTs).

Effectiveness refers to how well the intervention works in real-world conditions or everyday clinical practice settings. It also considers factors such as patient diversity, adherence to treatment protocols, and variability in healthcare delivery. Unlike efficacy trials, effectiveness studies are conducted in more diverse and representative populations. Observational studies can be used for effectiveness where researchers observe participants without directly manipulating variables.

Efficiency refers to how well the intervention achieves its results compared to the resources (such as time, money, manpower) used. An intervention can be considered efficient if it produces significant benefits relative to its costs, maximizing the value of resources invested. Assessing the efficiency of interventions is crucial for ensuring that healthcare resources are utilized effectively to improve population health outcomes. A cost-effectiveness study can tell the efficiency in comparing the resources and outcomes.

[Myat Htoo Linn]

Hello friends!
Glad to meet you all!

I am Myat Htoo Linn, but call me “Myat Htoo” is fine. Currently, I am employed in the Monitoring and Evaluation (M&E) domain at an NGO focusing on humanitarian and multisectoral development including health in Myanmar. I am mostly familiar with Excel and SPSS for statistical analysis.

I have to take care of the different projects’ assessments, baselines, and evaluations dealing with the external consultant, enumerators, and internal project management team where I am allowed to engage with some statistical analysis. In the previous year, I had the opportunity to conduct nutrition research within a program recently concluded in Myanmar. I believe statistics is a broad area that we can’t stop learning and I am passionate about continuing to learn in this course.

[Myat Htoo Linn]

I would consider the mobility pattern of the respondents can be also one of the confounders in this case. Mobility can differ between age groups based on travel and commuting habits and it will also not be the by-product of age. Mobility can also be associated with the contact pattern as young adults may travel for leisure, commute to work or school, or socialize with friends in different places, while older adults may be less mobile due to fewer daily commutes or less travel for social or recreational purposes.

[Myat Htoo Linn]

Case-Fatality Rate (CFR): This is the proportion of individuals diagnosed with a specific disease or condition that results in death within a specified period.
CFR Calculation: Number of deaths from a specific disease or condition/Number of diagnosed cases of the disease or condition x 100%
It helps in understanding the severity and lethality of a particular disease or condition. It can guide public health interventions and resource allocation.

Maternal Mortality Rate (MMR): This is the number of maternal deaths per 100,000 live births from any pregnancy-related cause during pregnancy, childbirth, or postpartum period.
MMR Calculation: Number of maternal deaths / Number of live births x 100,000
It reflects the quality of maternal healthcare services and societal factors affecting women’s health during pregnancy and childbirth. It guides efforts to improve maternal health outcomes and reduce maternal mortality.

Neonatal Mortality Rate (NMR): This is the number of deaths of infants within the first 28 days of life per 1,000 live births.
NMR Calculation: Number of neonatal deaths (within first 28 days of life) / Number of live births x 1,000
It specifically focuses on the mortality of newborns during the most vulnerable period of their lives. It is useful in identifying areas for improvement in neonatal care and interventions to reduce neonatal mortality.

[Myat Htoo Linn]

Let me share some of my experiences I have taken part in one of the COVID-19 Response projects that relate to the control of COVID-19.
To begin with, we started with developing the project design based on the assessment findings of the project area. We focused on implementing preventive measures, disseminating information, providing vaccination, and treatment at the public health centers, and adhering to the World Health Organization (WHO) control policies at the time. These plans were thoroughly documented and shared among project team members to ensure everyone had a clear understanding of our objectives.

Our team engaged with village leaders, community opinion leaders, and local communities to raise awareness about COVID-19 prevention and readiness. We communicated details about our project plans, preventive activities, provision of materials, quarantine protocols, vaccination referrals, and treatment options to mitigate any biases or uncertainties. This communication and community engagement fostered trust and transparency, enabling us to work together with community leaders to combat the disease effectively.

Furthermore, the project established criteria in collaboration with local health authorities and village leaders regarding access to quarantine centers, adherence to social distancing measures, vaccination, and accessing treatment centers. All the criteria were based on the current COVID-19 infection, prevention efforts, community needs, and service access situation within the community. Additionally, COVID-19 task forces were formed within villages to manage quarantine centers, provide essential support like food and water, share preventive measures/materials within the community, and respond swiftly to emergencies and referrals. These are all likely to be effective and beneficial in reducing the harm to the project participants in the community.

[Myat Htoo Linn]

In the previous 2015-2020 government term, A Roadmap Towards Universal Health Coverage (UHC) in Myanmar (2016-2030) was released in 2016. My country committed to the attainment of UHC by 2030 and the National Health Plan (NHP) (2017- 2021) was developed with the aim to strengthen the country’s health system and support the implementation of UHC. Discussing the UHC scheme is challenging due to the absence of a comprehensive assessment at present. Moreover, the scheme’s progress may have been halted following the coup and amid ongoing conflicts in the country.

I think one of the notable strengths of the UHC initiative was its inclusive approach, involving government, non-government stakeholders, ethnic health organizations (EHOs), civil society organizations (CSOs), and the private sector. This collaborative effort aimed to address health inequalities by prioritizing primary healthcare investment for the entire population by 2030.

However, significant challenges persisted. Government health expenditure remained low, representing only 16.7% of total health expenditure, indicating a need for increased investment in the health sector. Out-of-pocket payments were widespread, underscoring the limited financial protection for the population. To address these challenges, Myanmar’s public financial management system requires reform and strengthening to ensure efficient fund utilization. Establishing a National Health Insurance system could provide crucial financial protection against catastrophic health expenditures.

Another challenge is the health-related workforce, the health workforce allocation system was centralized and didn’t consider the specific contextual and health needs of different states and regions. Some health workforces were needed like HR for HIS and financial management staff in terms of quantity and capacity within the MoHS. The Inclusive Township Health Plan (iTHP) aimed to provide essential health services throughout the country but encountered operational challenges, such as health staff being overwhelmed with multiple responsibilities.

The previous UHC plan was initiated in the country and worked out somewhat. This was also planned in successive government terms (2021-2025) by 2030 plan but we don’t know how much it continue with the current conflicts in the country this time. Looking ahead, the successful implementation of UHC necessitates continued collaboration among public and non-public stakeholders, leveraging government resources and support from development partners. The legislative support and process will be the priority to achieve it.

Reference: Synthesis Report; Keeping Universal Health Coverage in Myanmar on Track; Hnin Hnin Tha Myint, Nay Nyi Nyi Lwin, Si Thura, Than Tun Sein; December 2020
https://www.cpintl.org/uploads/8/7/0/2/87020218/2020-12_cpi_synthesis_report_keeping_uhc_in_myanmar_on_track_web.pdf

[Myat Htoo Linn]

I support all of my friends’ discussions from Myanmar, these all reflect the current situation happening in the country regarding to health informatics workforce.

In my view, the integration of health science and IT, particularly in the area of health informatics, is in its nascent stages in the country. Only a few numbers of well-established private hospitals have begun implementing Electronic Medical Records (EMRs), and the majority of healthcare centers, both public and private, lack dedicated positions for health informatics. Previously, international NGOs have supported the Ministry of Health’s information systems, primarily in public health initiatives, and also some organizations started these efforts in their community healthcare projects separately.
As I can catch up around, these workforces came mainly from medical graduates with personal interests in IT or individuals who have pursued health informatics education abroad, but these are very few numbers.

I think the main challenge is most people including those from the healthcare industry aren’t aware of the importance of health informatics. Advocacy efforts aimed at policymakers are crucial for garnering support and recognition for these initiatives within the industry and community. Moreover, there are challenges in fostering collaboration among experts from various organizations and departments to facilitate patient-centered care in the country. Infrastructure needs, including high implementation costs, a shortage of trained personnel, lack of interoperability standards, limited internet accessibility, and inadequate data privacy and security policies, pose additional hurdles for this workforce.

[Myat Htoo Linn]

It will depend on the situation that is going to share the data. Like in the conversation, I agree that in an emergency or epidemic, we ought to share the data without a doubt and that each patient’s identity should be kept private. If sharing the data could have a detrimental effect on the patient, we cannot do so without that patient’s consent. I think data on a topic will offer fresh perspectives that can help prevent the detrimental effects on the population’s health.

I will be thinking about more how the data will be shared for one organization or nation. In that way, I like the fact that the “Governance system for data sharing” is important. We should set up the data sharing guidelines primarily as an organization or ministry, which should decide what categories and levels of data can be shared with which others. Obtaining the study participants’ consent and de-identifying the data will be essential for releasing the research findings. Additionally, I believe that disseminating aggregate data might be a means of offering condensed insights without compromising individual data.

Let me share a good example per my experiences, the information management section of the World Health Organization gathers data quarterly from development or humanitarian organizations that deliver any healthcare services per area cluster. This is a good example of aggregate data sharing. The organization provided aggregate data—which does not include personal information—under this 5W data, which is useful for understanding the healthcare system’s situation in the area.

[Myat Htoo Linn]

I think most of the healthcare settings (both private and public) in Myanmar adopt parallel settings using both paper-based and electronic-based registration. One of the feasibility assessments also said that the overall readiness for the adoption of EMR in tertiary hospitals, in Myanmar was 54.2% (Oo, H. M., Htun, Y. M., Win, T. T., Han, Z. M., Zaw, T., & Tun, K. M. (2021)).

In my experience of using both ways in health service data management, the first advantage of using EMR is the provision of efficient workflow between in/out departments and facilities without taking time for data retrieving. This can also reduce the workload and time of the staff by avoiding paper-based records and doing the data entry. In using EMR, data interoperability, and standardization can also be improved, which will have a lot of impact on the patient’s data accessibility and quality among the different facilities. Moreover, using EMR can reduce the time and distance of accessing data by the service providers, which will be important for rapid decisions and better health care.

In my opinion, rather than the bad of using EMR, difficulties in deploying EMR will be more outweighed. The first one is the needs of the infrastructures where financial, electricity, and technology will be included. Developing EMR is costly and difficult to adopt in no or poor internet coverage and shortage of power supply areas. Even if it was started for better care it may put more burden on the providers if we cannot fully adopt and sustain it. Another one is related to human resources, the staff’s willingness and ICT knowledge/training will be important for the EMR implementation, and the previous paper also supported it. The people’s concerns about using EMR when they are settled with the old practices of using paper-based, worry that they didn’t receive enough technical support, may take more time, and can have an impact on the service providers-patient relationship are also considerable in implementation of EMR.

[Myat Htoo Linn]

The paper also highlighted the challenges facing the implementation of big health data challenges in cardiovascular care and CDV research. In consideration of the challenges, my suggestions will be as follows:

Missing Data: This is sure missing data is unavoidable in big health data especially when the information in HER is collected non-systematically as the paper mentioned. My first point is whether can we reduce the amount of missing data. I think without the addition of the unnecessary data points, preparation of systematic tools and guidelines, setting up the validation process at each level, and provision of the proper training to all users would impact some extent to reduction of missing data in any areas. The other one is as suggested in the paper, alternative statistical methods also existed in studies with 10-60% missing data where the application of imputation techniques, choosing with the complete case analysis and testing on sensitivity analysis to assess primarily on the impact of missing data would be considerable.

Selection Bias: As a large data volume does not entail a representative sample, selecting different demographics of the subjects would be of great value in that case. Planning the study with awareness of potential sources of bias and ensuring the strategies to avoid it will be also needed.

Data Analysis and Training: In my opinion, the main issue is that healthcare professionals didn’t have much exposure to big data analytics except for those who are interested and learning about it. The development of continuous learning programs on statistical and methodological tools for healthcare professionals will be beneficial by working together among different domains of health researchers, IT, and data scientists. Application practice is of the greatest importance in addition to the training.

Interpretation and Translational Applicability of Results: Transparency will be important regarding the methodology and limitations to avoid manipulation by analysis and exaggerated claims in the study. External validation of the findings in independent datasets can be also considered. Interpreting and translating the results together with the participating stakeholders (researchers, clinicians, etc.) is also actionable in the study.

Privacy and Ethical Issue: It is ethically fundamental to respect the people’s privacy. Ensuring the patient’s informed consent for the use of data, using the data anonymously, controlling data access with only authorized persons in various ways, and establishing strong security protocols can maintain the patient’s privacy and an ethical way of researching big data.

[Myat Htoo Linn]

I agree with all of the four recommendations that public health professionals can do to fight corruption in health systems as Transparency International finds that the health sector is among the most corrupt in many countries.

Among them, I am mostly convinced with the first and third recommendations. The first one is to call together all the key stakeholders in the health system because the nature and scale of corruption will be also different between these stakeholders under the health system development, this is crucial to be a supportive and empowered environment to speak openly on the different corruptions and making the appropriate measures based on the sectors.
Also, the third recommendation filled up the gap in my considerations that it will be needed for a multi-disciplinary working and response to the corruption by taking a holistic view. This is why I support research on corruption should also be published/presented in the health literature and databases, not only in the political journals where the healthcare professional may fail to hear it. The final recommendation is also close to the third one to take part range of disciplines on corruption research. I consider the second one could be the different priorities among the countries on the corruption that is happening in their health system.

I would suggest integrating ethical learning in the academic programs for medical practitioners concerning health system corruption would be beneficial. I don’t want to leave behind the other population for ethical learning, especially businessmen because there will be both sides in some cases for the corruption. Another one is to place an accountability mechanism in the health system where everyone can be the whistleblower. Making sure of a power-balanced environment between the different stakeholders and a non-retaliation policy for the whistleblower would be crucial in that case. My last suggestion would be to ensure the rule of law in fighting corruption in the country, with the proper punishment for the actors of abusive or exploitative behavior, there will be more enforcement to fight the corruption in the healthcare system.

[Myat Htoo Linn]

Thanks, Ajarn! I am aware of suicide as an act of behavior rather than a disease and wanted to know how the modeling can help in predicting the suicidal risks and outcomes. Let me add Malaria to the discussion, already been completed for week 2 discussion on the model structure.

Malaria is one of the greatest historical killers of mankind, continues to claim around half a million lives annually, with almost all deaths occurring in children under the age of five living in tropical Africa. (Steffen E. Eikenberry, 2018) https://link.springer.com/article/10.1007/s00285-018-1229-7
Malaria remains a major public health issue in many parts of the world, especially in tropical regions. Understanding how human migration and climate change influence its spread is crucial for developing more effective prevention and treatment strategies.
According to the latest World Malaria report, there were 249 million cases of malaria in 2022 compared to 244 million cases in 2021. The estimated number of malaria deaths stood at 608,000 in 2022 compared to 610,000 in 2021. (WHO, 2023)

Research questions where modeling is likely to help answer;
1) How does population migration affect the geographic spread of malaria?
2) Can predict malaria outbreaks in new areas by integrating migration data with malaria incidence?
3) How do socio-economic factors (armed conflicts, natural disasters, economic necessity, etc.) interplay with migration in malaria transmission dynamics?
4) What are the optimal strategies for resource allocation and intervention based on these models?

[Myat Htoo Linn]

Hello Sirithep!
Thank you so much for sharing your final project.
This is much appreciated for your work, I especially liked the use of color and clarity in your dashboard presentation. The simplicity effectively captures attention and enhances our pre-attentive processing of the COVID-19 data.
I have two comments I would like to discuss with you. First, regarding the map: I’m not sure if you used a bubble map or a filled map, as I couldn’t clearly see the presented data (such as confirmed cases, recoveries, or deaths) ~~ I checked on the link and didn’t notice it (maybe the error from my side?). It would be helpful if the map could provide this information as well.
Another one is just a suggestion unrelated to data presentation and it’s not a big deal. On the main page, you used two line graphs. I consider it might be worth considering adding another feature or chart type, such as a bar chart or pivot table where relevant, which may make the dashboard more comprehensive.
Great job, thanks and keep it up, bro!

[Myat Htoo Linn]

Hello Nichcha! Thanks for sharing the JHU’s COVID-19 dashboard with us. I also support your preference for a user-friendly design and using the bubble chart to present higher-lower numbers with the bubble size. I also like it can navigate from sheet to sheet easily to see different information on Incidence rate, CFR, Vaccinations, etc. I consider it would be better if it could filter or drill down easily for the specific country information on the left side of the dashboard. It might also catch up the eye and help improve the working memory by chunking in the card visual as K, M, etc to visualize the total number of cases, deaths, vaccinations, etc.

[Myat Htoo Linn]

Hello bro! I like the empathy that you discussed in adding the control policy of the disease, sometimes we fail to act on it in our interactions and knowing the concerns of the individuals. Another one is that adaptative and collaborative management would be also workable in the COVID-19 pandemic in the country. One of our projects also practiced reducing the number of people in group sharing sessions and was led by the camp volunteer when the staff were not allowed to join the camp at that time. I would consider these all contributed to the maximum benefits for the participants and followed the principle of non-maleficence.

[Myat Htoo Linn]

Hello Ko Phyo! Thanks much for your discussion.
In Myanmar, we have one program called Health Information Technology (HIT) which is a bachelor program at the University of Medical Technology (MDY/YGN). As far as I am aware, this initiative was launched four or five years ago, working with MOHS, UMT and Melbourne University to support the academic curriculum at the time. I think this may become a valuable workforce for the health information system in the country primarily under MOH for both public and private sectors. This is not well-known, and not many people are aware of it. I’m not sure if there are any graduates of the program in the meantime, and who went on to contribute to the workforce following the coup with the CDM.

[Myat Htoo Linn]

Hello Nichcha! Thanks much for your discussion. I also have another view of data privacy and security based on our current settings and my experience. We have some physical threats to the project’s data including health services data due to the current conflicts in the project areas if we store the paper-based records at the office or elsewhere. There is also a high likelihood that we will be questioned or captured at the checkpoints by SAC when we are transferring paper records between the different locations and facilities. In that situation, we could say trying the electronic-based records and keeping them on one server is the better option and beneficial to keep for the data security which also has implications for the security of the organization.

[Myat Htoo Linn]

Hello Weerapat! I love the idea of investment in anti-corruption education among health professionals and the public. But, I didn’t see it much in my surroundings and believe that one-time sharing is not enough. I think this could be fundamental for all the people to get to know and talk about what is morally right and what will be the corruptive forms in the system which can also be the initial preventative measures for the society.

[Myat Htoo Linn]

Hello Bro! I share your discussion that a community-based healthcare system would be of crucial importance in this context, especially considering the vast disparity in healthcare access between urban and rural areas. In your project, I can also imagine there will be a lot of challenges in communication and coordination, getting permission, and following the different regulations of the two sides, MOH and EHOs. The system might also collapse during the ongoing conflicts. Thanks for your sharing!

[Author]

Posts