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Yes, I experienced a situation where Availability was affected in a health system strengthening project in Myanmar.
What happened?
As the internet connection in some townships of Myanmar was cut off due to conflicts/politics, our reporting system could not be accessed by teams from those townships.How did it affect the system or users?
Field staff could not submit monthly and quarterly reports on time.
Workload increased because staff had to collect data in other ways of communication like phone.
Decision-making was delayed.How to prevent it?
Use offline data collection tools (ODK, Kobo).
Go to where internet is available (to other nearby townships) and report.
Create a simple offline reporting plan. -
To have effective communication in my team, I focus on three things:
Be open: I encourage everyone to share their ideas and feelings freely. I listen carefully and make sure all voices are heard.
Be honest: I speak clearly and truthfully, even when the topic is difficult (although there might be a limit of transparency level by rank and position of staff). I also expect others to do the same.
Be respectful: I respect different opinions and never interrupt when someone is talking. I use kind words and stay calm during discussions.
These simple habits help build trust and teamwork in my work. -
Throughout a decade of my career, I had overcome many professional challenges. While I worked for the Chin State Health Department in Myanmar to improve the health system, one of my jobs was to draft state health plan of Chin State including voices from the lowest level of the health system, the Rural Health Centers. During that time, it was nearly impossible and quite challenging to gather the health budget data as well as the health infrastructure plan for the whole State. For the budget data, I had to work with the State Finance Officer and for the infrastructure plan, with a state engineer in addition to my challenging tasks. I also had to work on public holidays, while solving some misunderstanding between Township Medical Officer which meant more workload.
Finally, due to the relentless effort of our team, we could publish the Chin State Health Report 2018 which is the only health plan drawn from a bottom-up approach different from other top-down approach plans.
Leadership means bringing people together for a common goal. My journey in public health had taught me the importance of visionary leadership and teamwork which is needed to improve the Myanmar health system. -
Dear Sirithep,
I agree with Salin that you might need further explanation on how active surveillance will be implemented. For the case definition, it is quite challenging to narrow down to RSV among other respiratory illnesses of COVID 19 and flu too that you might need to increase your data accuracy somehow to work efficiently. Thanks for interesting surveillance system. -
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In Singapore, Prime Minister Lee Hsien Loong demonstrated strong alignment with all six CERC principles. He communicated promptly and consistently (Be First), providing accurate, science-based information supported by health authorities (Be Right). His calm, transparent demeanor enhanced credibility and public confidence (Be Credible). Lee also expressed empathy toward citizens’ anxieties, acknowledged frontline workers’ efforts, and called for social unity (Express Empathy). His messages included clear behavioral guidance, such as hygiene practices and social distancing (Promote Action), and were delivered respectfully in multiple languages to reach all communities (Show Respect). Consequently, Singapore’s communication strategy fostered public trust and compliance.
In contrast, U.S. President Donald Trump’s public addresses showed partial adherence to the CERC principles. Although he communicated early in the pandemic (Be First), his messages were often inconsistent or factually disputed (Be Right), which weakened public trust (Be Credible). Empathy and respect were inconsistently conveyed, with politicized rhetoric overshadowing reassurance (Express Empathy, Show Respect). Furthermore, conflicting statements regarding preventive measures, such as mask use and testing, undermined public understanding and compliance (Promote Action). These gaps reflected limited integration of the CERC framework into national communication efforts.
Overall, Singapore’s leadership demonstrated a comprehensive application of CERC principles, resulting in clear, credible, and empathetic communication. The United States’ approach, by contrast, suffered from inconsistency and diminished credibility, which weakened public confidence and adherence to health measures. This comparison highlights the importance of consistent, transparent, and empathetic communication in managing public behavior and trust during health emergencies.
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Based on Al Knawy et al. (2022), the five themes for strong digital health systems are team, transparency and trust, technology, techquity, and transformation. In my country, Myanmar, some progress has been made, but several areas still need improvement:
Team: There is weak coordination among ministries, NGOs, and ethnic health organizations. Health workers also need more training in digital tools and data use.
Transparency and trust: Myanmar lacks clear data protection laws. People have low trust in how health data are collected and shared.
Technology: Many rural areas have poor internet and limited electronic systems, making it hard to collect and share health information quickly.
Techquity: There is a big digital gap between urban and rural areas. Some communities have little access to mobile or online health services.
Transformation: Digital health activities are small and scattered. There is no strong national digital health strategy linked to pandemic preparedness. -
Could you list the disease outbreaks that have been declared as the Public Health Emergency of International Concern (PHEIC)?
The Seven PHEIC according to IHR are as belows;
1. In 2009, H1N1 Influenza (Swine Flu) Declared May 2009 and ended Aug 2010. It originated in Mexico/USA region.
2. In 2014, Poliomyelitis (Polio Resurgence) Declared May 2014 and ended May 2023. It originated in Pakistan, Afghanistan, Nigeria.
3. In 2014, Ebola Virus Disease (West Africa) Declared Aug 2014 and ended Mar 2016. It originated in Guinea, Liberia, and Sierra Leone.
4. In 2016, Zika Virus, Declared Feb 2016 and ended Nov 2016. It originated in Brazil.
5. In 2018–2020, Ebola Virus Disease Declared Jul 2019 and ended Jun 2020. It originated in Democratic Republic of the Congo.
6. In 2020, COVID-19 (SARS-CoV-2), Declared Jan 2020 and ended May 2023. It originated in China.
7. In 2022, Monkeypox (now “Mpox”), Declared Jul 2022 and ended May 2023. It originated in Multiple countries.Why do these outbreaks raise such concerns?
There outbreaks raise such concerns because of
-Rapid international spread (e.g., H1N1, COVID-19, Mpox).
-Severe health impact and high fatality rates (e.g., Ebola).
-Weak health systems unable to contain transmission (e.g., West Africa Ebola outbreak).
-Potential for mutation or vaccine escape (e.g., Polio resurgence, Influenza).
-Social and economic disruption, including travel and trade restrictions.
-Uncertainty about the disease’s nature, transmission, or control (e.g., Zika’s link to microcephaly).In your opinion, is there a disease or condition that may potentially lead to PHEIC in the future? Why?
In my opinion, the following conditions are strong candidates that could trigger a PHEIC in the future:
a. Avian Influenza (H5N1, H5N6, H7N9 variants)
Sporadic human infections with very high case fatality (>50% in some strains).
Increasing reports of infection in mammals (e.g., sea lions, cats, cattle).
If sustained human-to-human transmission occurs, it could spark a global pandemic.
b. Antimicrobial Resistance (AMR)
While not an “outbreak” in the traditional sense, the rise of drug-resistant infections (e.g., carbapenem-resistant Klebsiella, resistant gonorrhea, XDR-TB) could cause uncontrollable international health crises.
It fits the criteria of a PHEIC due to its global spread, lack of treatment options, and impact on healthcare systems. -
1. Which single design limitation most threatens valid estimates of sensitivity and representativeness? How would you address it within six weeks?
The biggest problem is that the AEFI system depends mostly on passive facility reporting, which misses many cases in the community and private clinics. This affects sensitivity and representativeness.
How to address it in six weeks:
Train community health workers (CHWs) and private clinics to report AEFIs using WhatsApp, SMS, or a simple online form. Collect reports from at least two sources (e.g., clinics and CHWs) and compare them using a simple capture–recapture method to estimate missing cases. Combine all data in one Excel or Google Sheet and analyze weekly.2. Using the CDC surveillance attributes, propose one low-cost intervention to increase sensitivity. State the expected trade-offs, and list 2–3 indicators to detect impact from the intervention.
Intervention: Let CHWs send reports of any health problems after vaccination through SMS or WhatsApp.
Trade-offs: We will get more reports, but many might not be true AEFIs (more work to verify).
Indicators to measure impact:
Number of AEFIs reported per 10,000 doses (should increase).
Median time from event to report (should decrease).
Percentage of reports verified within 48 hours (shows improved response).3. For a newly introduced vaccine, should the AEFI case definition be temporarily broadened to maximize early signal detection?
– If yes, what trigger would you use to revert to the prior definition?
– If no, why should this change not be implemented?
Yes, the AEFI case definition can be temporarily broadened when a new vaccine is introduced. This helps catch more possible reactions early.
When to return to the old definition: After 8–12 weeks, or when confirmed serious AEFIs stay stable for a month, and the verification workload becomes manageable.
If not broadened: The system may miss early safety signals. However, if the team cannot handle many reports, it’s better to strengthen capacity first before broadening. -
Verification and Preparation
Establish the existence of an outbreak: IT systems such as electronic health records (EHRs), and real-time dashboards help detect unusual rises in case numbers, triggering early warnings for potential outbreaks.
Verify the diagnosis: Digital Laboratory Systems, and teleconsultation facilitate rapid confirmation of the pathogen.
Prepare for field work: Mobile data collection apps, and Geographic Information Systems (GIS) assist in mapping affected areas, organizing field teams, and planning logistics efficiently.Describe the Outbreak
Construct a working case definition: Shared online documents and digital collaboration platforms (e.g., Google Workspace, Microsoft Teams) ensure clear understanding and updates of case definitions across all team members.
Find cases systematically and record information: Integration of digital data collection tools, mobile survey systems (e.g., ODK, KoBoToolbox), and EMRs enables efficient case finding, and data entry.
Perform descriptive epidemiology: Data analysis and visualization tools (e.g., R, Epi Info, Power BI) generate epidemic curves, distribution maps, and summary of statistics, helping investigators describe the outbreak in terms of time, place, and person.Hypothesis and Testing
Develop hypotheses: Data mining, statistical analysis, and machine learning tools (e.g., R, Python) help identify patterns of exposure and potential sources, leading to the formulation of preliminary hypotheses.
Conduct analytical studies to test hypotheses: Analytical software such as SPSS, Stata, and R allows investigators to compare data between affected and unaffected groups, testing associations statistically to validate hypotheses.
Conduct special studies (e.g., environmental or laboratory): Environmental monitoring systems, and bioinformatics tools link environmental and genetic data to understand the source, mode, and spread of the pathogen.Response and Action
Implement control measures and follow-up: IT facilitates communication of interventions through mobile alerts, supports contact tracing through digital tracking systems, and monitors the effectiveness of control measures using real-time dashboards.
Communicate findings and prepare reports: Digital reporting platforms, online dashboards, and visualization tools enable timely dissemination of findings to policymakers, health professionals, and the public, ensuring transparency and coordinated response. -
One digital technology I like most apart from contact tracing app from Budd et al. (2020) is the chatbot used in the COVID-19 public health response. Chatbots are computer programs that can talk with people through websites, apps, or messaging platforms. They use artificial intelligence to answer questions, give advice, and guide people on what to do if they have symptoms.
During the COVID-19 pandemic, chatbots were used by the World Health Organization (WHO) and many national health agencies to provide accurate and timely information to the public. They helped people check their symptoms, learn about testing and vaccination, and avoid misinformation.
This technology is very important because it allows health authorities to reach millions of people quickly, even when healthcare workers are busy or when movement restrictions are in place. Chatbots are available 24 hours a day and can respond in different languages, making them a useful and low-cost tool for improving public communication and surveillance during health emergencies -
For dengue…
1. How can surveillance help to detect and control the disease?
It can detect outbreaks early and triggers rapid response (vector control, awareness campaigns) and monitor trends, seasonality, and geographic spread of dengue. Surveillance can help describe burden and risk groups (age, sex, area). It can evaluate the effectiveness of mosquito-control measures and also provide data for policy, planning, and advocacy.2. Should we conduct active or passive surveillance or both for the disease, why?
Both are needed. While passive surveillance by routine reporting from health facilities continues, active surveillance by public health teams for active cases in schools or communities during dengue outbreaks ensures timely detection and response.3. Which method should be best to identify cases, why?
a. Cases in medical facilities VS community (Cases in community because it can capture mild or unreported cases.)
b. Sentinel VS population-based surveillance (Sentinel because it can provide detailed, high-quality data)
c. Case-based VS aggregated surveillance (Case-based to analyze person, place, time and source)
d. Syndromic VS laboratory-confirmed surveillance (Syndromic because it allows early detection)4. What dissemination tools will you choose to disseminate monkeypox surveillance information? Why do you choose this/these tools?
Ministry of Health Dashboard including a hotline number: the best official transparant way to reach communities and raise public awareness of disease including case count, trends and epi data together with social media platform like facebook and tiktok.
Routine periodic tools: are useful for official updates and global reporting with interoperability between clinical and public health data system.
These tools are chosen because monkeypox needs fast and accurate information sharing. Using online and real-time platforms ensures that health professionals and the public receive timely alerts, helping prevent the spread of the disease and improving international coordination. -
In the given scenario, a Western research team is asking data in detail at individual level for each disease case.
1. Should you give the data out?
No, I should not give out detailed personal data directly. It would break data privacy and confidentiality rules.2. How do you not violate any of the General Principles of Informatics Ethics?
I must protect people’s privacy, get proper permission, and share data only in an ethical and legal way. I should make sure the data cannot identify any individual person.3. If you want to provide the data to them, what and how will you do it?
I will first ask approval from the Ministry of Health or relevant authority. Then, I will share only anonymized or aggregated data (without names, addresses, phone numbers, or exact locations). I will also sign a data-sharing agreement that limits how the researchers can use the data. -
According to the given scenario, my close friend’s husband has HIV and she did not know about that.
1. What should I do?
I should keep the patient’s information private and not tell anyone. My duty is to protect confidentiality.2. As a health information professional – can you tell your friend?
No, I cannot tell my friend. It would break patient confidentiality and professional ethics.3. Can you interfere with other people or family issue?
No, I cannot interfere in their personal or family matters. My role is only to manage patient information safely.4. But, should your friend not know about this because she might be at risk?
Even though she might be at risk, I still cannot tell her directly. I can encourage the patient to tell his wife or inform the doctor or counselor to handle it properly.5. How will you follow the fundamental principles about right to self-determination, doing good and doing no harm to others?
I respect the patient’s right to privacy (self-determination), I do good by protecting his trust, and I do no harm by avoiding gossip or breach of confidentiality.6. Isn’t it your obligation and the right of the subject to hold the information?
Yes. It is my obligation to keep the information confidential, and it is the patient’s right to decide who can know about his health condition. -
Using the same HMIS (DHIS-2) of Myanmar Public Health;
1. Awareness: Many staff are used to paper-based reporting and lack understanding of DHIS2 benefits. Communication meetings, orientation, and demonstrations were needed to explain why the change wass ncessary emphasizing improved data-driven decision-making.2. Desire: Resistance arose from fear of extra workload or job insecurity. To build motivation, recognize and reward early adopters as “DHIS2 Champions.”
3. Knowledge: Limited digital literacy and inconsistent training hindered adoption. Conducted many practical, step-by-step training in local languages, supported by user manuals and job aids, which was known as “ToT” “Training of Trainers” to cascade the training locally.
4. Ability: Challenges included poor internet, lack of devices, and double work during transition. Providing necessary equipment, on-site coaching to strengthen the application locally.
5. Reinforcement: To sustain change, integrated DHIS2 reports into routine supervision and performance reviews. Providing annual refresher training, ensuring leadership used DHIS2 data in decision-making, and allocating a stable budget for system maintenance from central government.
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Please give an example of a system in your organization (either successful or fail). What are main factors that make the system successful or failure (Data, Cost, Operation, Design, and People)?
I would like to analyse the HMIS (DHIS2) of government for partially successful factors.
Data : even though the coverage was not all, HMIS could cover most of the implementing partners and areas in the country.
Cost : Open Access Software, cost effective
Operation and Design : The system could be customized for user friendly features and easy to use with little to no technical knowledge
People : The training to input HMIS is not long even though the register in fields and reports from health facilities might need longer training on data definitiona and report system. -
One example of the decision support system for almost all organization I worked for, is checklist marking scale of recruitment. When we recruit a candidate, we transparently use predefined checklist of position we want with marking scale and during interview, we add or substract marks according to candidate’s answers.
The factors influencing the DSS might include the mental conditions of candidate, and interviewers as well as language barrier and understanding the questions or preparedness of the candidate.
However, the hiring manager had the final decision to make when the result of the individual interviews was not significant. -
If the hospitals in a country do not use the ICD Standard, we need to ask if they are using other Standard first.
If no standard is used, each hospital will write diseases differently making it hard to collect, compile, and compare in the national health information system or internationally (like in WHO system) which will cause obstacles in insurance claims, research, policy planning leading to health system failures and patients’ deaths. -
According to the article by Muhiyaddin R, et al., the six main problems causing Physicians’ Burnout are EHRs’ documentation and related tasks such as note taking and electronic communication in EHRs, EHRs’ poor design, workload, work overtime, inbox alerts, and alert fatigue.
Yes, I’ve heard similar complaints. Many health officers say that using EMR takes more time than expected, especially when the system is slow or complicated. Some prefer paper records because they feel faster during busy clinic hours. In Myanmar, where internet and electricity can be unstable, EMR use can add several extra stress instead of helping, it create extra workload addition to papar records.
Suggestions might include using offline EHR with simplified user-friendly design (maybe using a lot of drop-down lists), providing training prior to physicians, and hiring data-entry assistants for physicians. -
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Can you give an example of data that you think it could be considered as “Big Data”? What are the characteristics of the data that fit into 5Vs, or 7Vs, or 10Vs of Big data characteristics?
I had worked in Health System Strengthening project of Chin State in Myanamar. Considering the data I collected that time as “Big Data”, the characeristics of 5Vs are
1. Volume: State-wide health service providers, and facilities data including millions of individual and health organization records, laboratory results, vaccination data, population data.
2. Velocity: The speed is crucial to respond immediate public health threats (such as new cases of notifiable diseases, contact tracing)
3. Variety: Diverse data sources containing hospitals, HR, Equipments, commodities, and multiple registers and reports.
4. Veracity: Ensuring data quality, accuracy, and reliability is still challenging.
5. Value: When analyzed, support policymakers in strengtheing health service providing system. -
1.Have you ever observed a health informatics project in your (other) organization? Please provide a brief introduction.
While I worked with National AIDS Program of Myanmar, I had a chance to participate in collecting HIV Sentinel Surveillance (HSS) Survey. As it was a paper-based surveillance, it was costly in terms of paper, labor and space for storing and so on compared to modern electronice surveillance system mention in papers.How can this health informatics project help to improve the current practices?
The HSS identify the HIV positivity among key population which in terms helps setting HIV prevention and control targets of the National AIDS Program.Are there any challenges or difficulties in implementing the project?
In additiona to the difficulties of paper-based surveillance mentioned above, there were misunderstanding of data collection process by the clinical specialists, privacy breach, and loss of quality data in the long data collection process. -
Hi Ma Wah Wah,
This is a serious case, and you explained it well. The Yale New Haven Health System breach shows how network hacking can put millions of patients’ personal data at risk and damage an organization’s trust and reputation.
To prevent future breaches, YNHHS and other health systems should use stronger network protections (like firewalls, encryption, and multi-factor authentication), keep all systems updated, and train staff to spot phishing attacks. Regular security audits and a clear incident response plan are also key to reducing harm when threats occur. -
Dear Phyoe,
Good summary of the Blue Shield case. It shows how even unintentional mistakes, like misconfiguration, can expose millions of people’s sensitive health information. This kind of incident hurts both patients’ trust and the company’s reputation. To prevent this in the future, Blue Shield and other organizations should do regular audits, encrypt all sensitive data, and carefully review third-party tools before using them. Last but not least, staff training and strict HIPAA compliance on data encryption are also very important to reduce human errors and keep patient information safe. -
Hi Ko Aung,
Thanks for sharing an interesting case study. Your report clearly shows that the CHS breach was not just a technical failure but also a lesson in organizational preparedness. With proper patch management, real-time monitoring, and stronger security culture, such a breach could have been avoided. This case remains a reminder that cybersecurity is as much about governance and quick action as it is about technology. -
Possible Ways of Cyber Attack
An attacker might try to get physical access to a device, server, or a password book, because many users leave computers unlocked, save passwords in their browsers, or write passwords on paper.
He/she might also try reused, simple, default, or demographic-based passwords (like names, birthdays, or ID numbers) since these are easy to guess and can be used in many accounts.
He/she could use social engineering which is tricking an employee or someone close to the victim, or secretely watch someone’s screen to steal passwords.
Phishing emails or fake websites are common ways to steal login details, and ready-made tools like keyloggers, malware, or ransomware can be used without building anything new. More advanced attacks need technical skill, known as targeted attacks, where an attacker or a group of attackers designs a special attack for one person or organization; these are often complex and hard to detect. -
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Hello Nang Phyoe Thiri,
Thanks for your interest in data interoperability. As fellow Myanmar, we might need initial survey and analysis on what data standard and system are using by our service providers in Myanmar. According to my knowledge, the government hospital part might not be a problem while the involvement of different data system by private hospitals might need detail solution.
In short, we will need data scientists for this data interoperability analysis I think. -
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Dear Ma Wah,
Thanks for your comprehensive and excellent response.
During my work in MSF, we, MD, had to fill up the FUCHIA forms which take around 3 to 5 minutes by hands on paper excluding the patient consultation and examination time (which altogether last around 15 to 30 mins per patients). However, the MD did not fill the EHR to system and there were data assistants who filled up the FUCHIA Form into the EHR system. The MD used the offline-paper-based FUCHIA files only. I could read other MD handwritings as well as mine to go through the medical records of patients without the stree caused by the EHR in the paper.
As in Myanmar, offline EHR with help of data assistant might be solution for physicians’ burnout related to EHR. -
Dear Sayar Aung,
I can read your msg on LINE but my LINE app had some issues to reply anyone. You can reach me @ +959448001552 on viber, whatsapp or zamliankham@gmail.com for the group assignment.
I also agree with you on EMR being the first world’s problem. In developed countries, I heard that so many clinical and laboratory tests are carried out by the physicians to get accurate and comprehensive diagnosis without missing any diseases by laboratory tests or to avoid lawsuit. Probably, the aftermath of these tests in EMR, migth burn out the physicians again which will never happen in resource-limited countries like ours. -
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Thanks for sharing your thoughts. In my opionions, an easiest breach would happen without the need to send phishing emails if the employee lost the notebook where his/her passwords were noted down (the physical access). I had seen some elderlys who wrote down their pin numbers nearest to their gadgets or credit cards.
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