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1. Should you give the data out?
No, I cannot give the individual-level data because it contains private information. Sharing it would break privacy rules and ethical principles.2. How do you avoid violating the General Principles of Informatics Ethics?
I must keep all personal information confidential, protect patients’ privacy, ensure the database is secure, and use the data responsibly and honestly.3. If you want to provide the data, what and how will you do it?
I can only give anonymized or grouped information. I would remove names, contact numbers, and exact addresses, provide summaries by village, sub-district, or age group, and make a data-sharing agreement to ensure the data is used only for the approved study. -
1. What should you do?
I should keep the patient’s information confidential and not share it with anyone. My duty is to protect medical records.2. Can you tell your friend?
No. It is not allowed to share someone’s health information without their permission, even if it is my friend.3. Can you interfere with family issues?
No. I should not get involved in the patient’s personal or family matters.4. But should your friend know because she might be at risk?
I understand the risk, but I cannot tell her directly. If there is a real danger, I can report the case through official channels like a supervisor or ethics committee.5. How will you follow the principles of autonomy, doing good, and doing no harm?
Autonomy: Respect the patient’s right to decide who knows about their illness.
Doing good: Help indirectly by following procedures that protect health and safety.
Doing no harm: Avoid breaking trust or causing emotional or social damage.6. Isn’t it your obligation and the patient’s right to hold the information?
Yes, it is my obligation to keep the information safe, and it is the patient’s right to control who knows about their health condition. -
I would like to share about the change of the same previous project during the implementation of the data validation system, in line with the ADKAR model.
Awareness: Staff were informed about the importance of improving data quality and the need for an automated validation tool.
Desire: Users were motivated to use the system since it supported their work and reduced the burden of manual data checking.
Knowledge: Training sessions were conducted on how to run validation scripts and interpret validation reports.
Ability: After training, staff were able to apply the system independently and correct data based on the validation results.
Reinforcement: Regular monitoring, recognition of improved data quality, and ongoing system support reinforced continuous use.
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A successful system in my organization is the rule-based data validation system for patient data in the Health Management Information System (HMIS). The system was developed using an open-source Python application and is used to check the quality of data after data entry. It automatically identifies missing, inconsistent, or out-of-range values in submitted datasets, allowing the data team to review and correct them before final reporting.
Main Factors of Success
Data: The system significantly improved the accuracy and completeness of HMIS data through systematic post-entry validation.
Cost: Since it was developed using open-source tools, it minimized software and licensing costs.
Operation: It was designed to work after data entry, so it didn’t interrupt normal workflows but still ensured data quality before reporting.
Design: The rule-based and modular design made it easy to update or add new validation rules as reporting standards evolved.
People: Health information staff accepted the system quickly because it helped them identify data issues more easily and improve reporting performance. -
Example of Decision Support System (DSS) in our organization:
During my tenure with TDH, I learned about the Integrated e-Diagnosis Approach (IeDA), a digital health solution supporting primary health care workers in West Africa and Asia. Developed by TDH and the Burkina Faso Ministry of Health, IeDA digitalizes the WHO’s Integrated Management of Childhood Illness (IMCI) protocol. It guides healthcare workers to provide accurate diagnoses and effective treatments for newborns, children, and their mothers. The system also collects and analyses data to improve care quality and inform policy-making.Is it working well?
From knowledge-sharing sessions and reports, IeDA has proven effective in improving clinical decision-making, standardizing treatment protocols, and supporting healthcare workers, especially in rural areas or with limited experience.Factors that might influence DSS implementation:
Several factors could influence the successful implementation of a decision support system like IeDA for field-level health workers in Myanmar. One potential idea is to adapt the system to include the Burma Border Guideline, which would guide EHO health workers in following standardized protocols for common illnesses in newborns, children, and pregnant women in ethnic areas.
1. Reliable devices and mobile networks, with offline functionality.
2. Training for field health workers on using the DSS.
3. Leadership support and clear policies for consistent use.
4. Alignment with local protocols, languages, and practices.
5. User trust, supported by demonstrated improvements in patient care. -
If hospitals do not use the ICD standard, health records will not be uniform. This causes confusion in diagnosis, problems in data sharing, weak public health reporting, difficulties with insurance claims, and trouble in international cooperation. In short, without ICD, the health system becomes less organized, less safe, and harder to manage.
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Yes, I agree with the finding of Muhiyaddin et al. (2022) that EMR/EHR is one of the top causes of physician burnout. Many health officers say EMR takes too much time and creates stress. The common problems are heavy documentation, poor system design, and too many alerts. Doctors often need to work overtime, sometimes even at home, just to finish their records. This makes them feel tired, lose balance between work and personal life, and sometimes even lose interest in their career.
From my experience working with clinics in the ethnic health organizations’ areas in Myanmar, I have also heard similar complaints. Many health workers there said they prefer simple paper records, because EMR systems are slow, require internet, and are not aligned with local context and workflow. They felt that instead of helping them save time, EMR made them spend more hours on data entry and less time with patients. This reduced their motivation and sometimes caused frustration in the team.
To reduce this problem,
1. Improve EMR design – make it simple, fast, and align with workflows.
2. Give proper training – so health workers know how to use it easily.
3. Reduce data amount – keep only the important ones.
4. Provide support staff – to help with data entry, so doctors can focus on patients.
5. Encourage balance – limit after-hours work with EMR and allow offline options in low-resource areas. -
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I think Electronic health records (EHRs) combined with wearable device data from thousands of patients. for example, Apple Watch is a great example of a device that generates Big Data in healthcare.
1. Volume:
It collects a large amount of data continuously such as heart rate, steps, sleep patterns, and ECG readings.2. Velocity:
Data is recorded in real-time and can be synced instantly to the user’s phone or cloud.3. Variety:
Different types of data: numeric (heart rate), categorical (activity type), time-series (sleep patterns), and notifications from health apps.4. Veracity:
Some measurements may be inaccurate due to movement, sensor errors, or incorrect usage, so data quality varies.5. Value:
When analyzed, it helps track fitness, detect irregular heart rhythms, monitor chronic conditions, and provide insights for personal health. -
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1. Have you ever observed a health informatics project in your (other) organization? Please provide a brief introduction.
I have observed a health informatics project in my current organization that focuses on disease surveillance through an Early Warning, Alert, and Response System (EWARS). The project is adapted from the WHO EWARS framework and tailored to the context of ethnic areas. It integrates two main approaches including Indicator-Based Surveillance (IBS) and Event-Based Surveillance (EBS).
In IBS, selected health facilities report weekly case data for the selected major diseases using the Kobo Collect mobile application, which works both online and offline. Once data are submitted to the central server, the surveillance focal point reviews the cases and verifies them with facility focal persons. Verified data are then processed in the surveillance dashboard, where automated analyses generate disease trends, alerts and key performance indicators.
For health events not captured under IBS, EBS is applied. Outbreak-related news is automatically extracted into the central server daily using Google Apps Script from multiple reliable media channels, including their telegram, RSS feeds, and websites. The surveillance focal monitors these inputs and verifies relevant information with local focal persons if needed. In addition, we also use the Program for Monitoring Emerging Diseases (ProMED) and HealthMap websites to watch the verified infectious disease outbreak news globally.2. How can this health informatics project help to improve the current practices?
This project helps to improve the current practices from reactive to more proactive surveillance, enabling quicker response to outbreaks.
Digital tools reduce delays and human errors in using paper-based reporting.
Automated data workflow ensures real-time visualization of trends to enable early warning and faster decision-making.
Early detection of outbreaks through EBS allows the surveillance team to capture health events outside of IBS.
Integration with global platforms (ProMED and HealthMap) expands situational awareness beyond the local level.
Evidence-based response planning is supported through surveillance dashboard that tracks disease trends and potential outbreaks.3. Are there any challenges or difficulties in implementing the project?
Information noise in EBS: Automated extraction rarely gets relevant and usable information, while outbreak sensitivity in local media remains low.
Connectivity gaps: Limited or unstable internet access in remote areas can delay data submission.
Technical capacity: some health workers need digital skills to use data collection tools effectively.
Verification bottlenecks: Surveillance focal point may be overwhelmed by large volumes of data, especially an increase of surveillance sites and during outbreaks.
Data Utilization: encouraging leadership to use data in the dashboard for decision-making is still a gradual process. -
Thanks for sharing, Yin Moe Khaing. As additional preventive measures, I would like to include the following:
Training – to conduct trainings and awareness campaigns for the employees and simulate the phishing exercises periodically to keep them aware of digital security.Regular Security Audits and Penetration Testing – to conduct regular security audits and penetration tests to find weaknesses in the systems, such as a bug bounty program.
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Thanks a lot for sharing.
As additional prevention measures for the large organization, I would like to share about the bug bounty programs. Big companies such as Google and Facebook invite the ethical hackers to find security problems in their systems. If the hackers find a bug, they can report it. Then, they can earn money or rewards. It helps companies fix security issues before real hackers exploit them. It’s like hiring friendly thieves to test your house locks before a real one tries to break in.
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Thanks for sharing this case. I understand that this case was caused due to the lack of two-factor authentication.
Just for sharing. Nowadays, technology is developing too fast, and attackers are getting smarter. Even if we set up 2FA for our accounts, our accounts are still at risk. Recently, I read the post in which hackers use session tokens to bypass 2FA. For example, when we log in to our account on a browser like Chrome, the browser saves it as a session token. The hackers try to get this token using malware or a fake extension on the browser. Once they get it, they can access the accounts without a password or 2FA.
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Since no system is 100% secure, attackers have many methods to exploit vulnerabilities.
Default Passwords – Attackers use the default passwords of the devices or platforms to get unauthorized access. For example, in Myanmar, some fiber internet service providers set up WiFi routers for homes, offices, and companies but leave the default admin passwords unchanged. Most users are not aware that a WiFi router has two passwords: the admin password and the Wi-Fi password. An attacker on the same network can use the default admin password to access the admin page of the other routers, change settings, monitor traffic, or even connect to other devices. Sometimes, staff from the internet provider may even sell this password information to scammers or hackers.
Social Engineering – Attackers trick people into giving passwords or money. For example, making a fake phone call that looks like a bank asking you to “verify your account” or “upgrade your account level” to get money from the account. It is very common in Myanmar and Southeast Asia.
AI-Powered Attacks and Deepfake – Using AI to create fake videos or voices of someone you know to scam you. For example, a video that looks like someone you trust asking for money transfers. It is now starting to be used by the scammers.
Insider Attacks – People who work in an organization, like employees or contractors, can steal or misuse data. For example, a staff copying customer personal information including phone number to sell it to scammers.
Hacking Tools – Attackers use ready-made tools to break into systems. For example, using software like Kali Linux to test websites for weak passwords and to penetrate databases by using SQL injection methods.
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Why would you choose cloud server, rather than physical server?
I would choose a cloud server instead of a physical server for the patient appointment system because it is cheaper, easier to manage, and more reliable. A physical server would require a large upfront investment in hardware, maintenance, security and backup systems. On the other hand, cloud servers can run a pay-as-you-go model which means we only pay for the resources we use. Since our hospital has only a IT officer, the cloud servers would be easier to manage because the provider takes care of updates, backups, and security. Moreover, they can easily scale up if the app performance and storage are needed to increase in the future. In addition, cloud providers such as Google Cloud and AWS follow international standards for data security in handing sensitive patient information.What kind of cloud computing service model would be most appropriate (SaaS, PaaS, IaaS)? Why?
In the short term, I would choose SaaS such as Google Appsheet or Microsoft Power Apps based on the eco system of our hospital’s emails. These no-code platforms can be considered as SaaS, but it has also the characteristics of PaaS. They can be used to create, customize and deploy apps without worrying about the backend like other PaaS tools. It would allow us to quicky build a patient appoint system without needing knowledge of coding because we have only one IT officer in our hospital.
In long-term, most appropriate option would be PaaS such as Google App Engine and AWS. It would be better for us to develop a custom web application that fits our hospital’s specific needs and integrates with other platforms and advanced features in the future.
In conclusion, I prefer starting with a SaaS like Google Appsheet for a quick, low-cost deployment. Then, move to a PaaS like Google App Engine for a scalable, secure and customized long-term system. -
In outbreak management, the roles of health informatics and information technology are vital to detect outbreaks early and respond timely. In my recent experience on a suspected whooping cough outbreak in a hard-to-reach village with a lack of internet connection. The first case was a school-aged child identified at the village tract health center. His mother informed the health worker that there were also many cases like her child in her village. Therefore, the health worker notified the surveillance focal and submitted the case report via Kobo Collect to the server. After receiving the alert, the surveillance focal reviewed the patient data from the server and verified with the health worker. In that process, the data focal person played a vital role as the health informatician to be the smooth flow of information to detect the outbreak early and ease the review of the data for surveillance focal by using information technology.
After case verification, the rapid response team (RRT) conducted the initial meeting to plan the investigation and response. The team decided to send a mobile clinic team to that village for the active case finding and treatment. During the mobile clinic session, health worker used their mobile phone in offline mode to collect the whooping cough cases (~70) including children and adults across 4 nearby villages. Then, the team returned to the internet access area for the submission. In the case, we can see the role of health informatics’ skills on mHealth app set up to be ready for the offline data collection even in the situation of no internet connectivity.
Then, the team carried out contact tracing and provided health education to the community in these villages. After exploring existing immunization data from the immunization database, it was found that those villages were outside of immunization coverage. So, the team coordinated and collaborated with the local authorities and an implementing partner for the outbreak response immunization (ORI). With the support of some organizations, ORI activities were conducted in that area. Eventually, the outbreak was controlled. The whole process was documented in the database. After that outbreak, we analyzed the existing immunization data to identify the immunization coverage and to advocate the donor organizations to fill immunization gaps and to consider those areas for priority in the upcoming projects.
In conclusion, even in a resource-limited setting, health informatics can strengthen and play a vital role in the outbreak detection, response, and control process by using IT, communication, and collaboration across different units.
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Based on my educational background and work experience in health, I have knowledge of public health basic concepts. But to excel as a public health informatics, I would need to learn biostatistics, epidemiology and research more details for conducting research, presenting, and advocating the results to the stakeholder and policy makers based on the evidence-based findings.
Even though I have been using many data tools such as Python and automation tools (e.g., n8n) in my work, I would need to gain communication skills to be able to present the process and findings effectively and clearly to the stakeholders and decision-makers for the changes.
In my work, I have developed some application and automation workflows such as disease surveillance reporting in local context. For the further improvement, I want to learn health information systems like EHRs and interoperability standards such as HL7 and FHIR to be seamless data integration in working with other platforms or systems. Additionally, I would need to explore on data privacy regulations (e.g., HIPAA) in handling sensitive health information. Lastly, as a public health informatics role, project management and teamwork skills will be required to be able to work across different units including clinical, public health, and IT domains.
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If I were a director of a hospital in Myanmar, I would carefully plan a gradual move to cloud computing that fits our local context. In Myanmar, many hospitals still use paper records and basic computer systems, so cloud computing would be a significant improvement.
I would start with a simple step – using Google App Engine to host an appointment booking app with non-sensitive data. Since these don’t involve patient data and we can learn the technology safely. Moreover, this PaaS solution would reduce a lot of workload on server maintenance and security. This app would let the patients book appointments through the website and generate a QR code for check-in at the hospital. It can reduce a lot of waiting time at the hospital and increase patient satisfaction with service delivery.
After successfully running this appointment system, we would slowly add other hospital functions to the cloud. I think this step-by-step approach builds trust among the staff and ensures we maintain patient privacy while transforming our services to the cloud.
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When I was young, I think it might be around 2017. I got a game file from cyber cafe and copied to my uncle’s computer. After a few days, all the files are locked, and I couldn’t open them in computer. I told my uncle about the issue and he told me it was due to the WannaCry ransomware. Then, we reinstalled the window and the important software like MS excel from the start. After that, I learned that it is very important to be careful with downloads from unknown sources, back up the important files and use antivirus software.
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I would focus on a hybrid app with QR code integration throughout the hospital workflows. For the patient side, this app would allow them to make appointments by using the mobile app, view their medical and payment history, and also use it for check-in at various departments by using the QR code. For doctors, they can easily access their patient information through this app to track the medical history and lab results with the integration of different units of the hospital. For other staff like pharmacy and reception, they can easily process their work seamlessly with hospital workflow due to the QR code integration. This approach can reduce a lot of waiting time for patients at the various processes of different units of the hospital, can improve patient satisfaction, and can minimize the human errors. Finally, it can lead to better medical health outcomes and a more efficient healthcare system.
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Hello everyone,
I am Myo Oo from Myanmar. You can call me Mio. I received my Bachelor in community health. I have been working in different roles in the humanitarian sector especially as data management roles. I expect to learn different health information systems and apply them to my work with partner organizations. Looking forward to learning from all of you.
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From my experience, I believe that the electronic health record (EHR) system is still a critical need in the health facilities, especially in public hospitals in Myanmar. Currently, most patient information is recorded on paper. It is difficult to analyze data for data-driven decision-making and policymaking.
I think all components of the health information system (hardware, software, data, processes, and people) are needed to improve. Investment in health funding from the government would be important to implement such a system effectively with the collaboration of organizations and experts.
In Myanmar, one of the main challenges is the lack of health informatics professionals with combined expertise in both health and IT domains. At present, no universities in the country are producing graduates with this skill set, limiting capacity to advocate for policy making and manage EHR implementation and digital transformation effectively.
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Thanks for sharing, Ahma. Open-source platforms like OpenMRS or OpenEMR are great for sustainability, though they can be a bit weak on performance, UI and UX. On the other hand, creating new custom platforms gives more flexibility, but they’re costly and create long-term dependency. I’m still figuring out the best balance between the two approaches.
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Agree with you. In the current context of Myanmar, many young people use social media such as Facebook and LinkedIn and upload their CVs to be seen by potential employers. However, they often include a lot of personal information, such as date of birth, phone number, national ID, and detailed address. I understand most of them urgently need jobs, but they are not aware of digital security. It’s very common on job-seeking pages of social media. Scammers on these social media platforms can use this information to scam or threaten them. In some cases, it even leads to human trafficking, such as fake job offers near the Thai-Myanmar border.
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Thanks for sharing, Ahma. PaaS would be the best fit for the hospital setting.
Just for sharing, you may already know about the platforms below.Nowadays, we can easily transform our ideas into real-world apps without requiring the coding knowledge with the help of AI-driven coding.
Among the free platforms, I like vercel (https://vercel.com/) for building apps with more advanced features and beatiful UI design. I am also using Streamlit (https://streamlit.io/) in my work to create data apps. for example, automation to check our databases based on the rule-based criteria and data transformation.
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Before 2021 in Myanmar, some NGOs tried to bring together health information systems from the central government and ethnic health organizations(EHO). They even held workshops to facilitate integration of health information. But after the coup, these efforts had gone.
Now, each organization is building its own system for HMIS. So, having a health information exchange system will be very important in the future of the federal healthcare system in Myanmar.
It could help to compile data from different systems and platforms across the country, helping with national-level decision-making, tracking health indicators, and advocating for health policies.
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