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I think the following themes are areas where Myanmar still needs to strengthen pandemic preparedness:
1. Team (Theme 1) – Collaboration among stakeholders needs to be enhanced in Myanmar.
2. Technology (Theme 3) – Public health systems need to strengthen to create and share health intelligence and to collect and use high-quality data.
3. Techquity (Theme 4) – Equitable access to digital health capability and access at the individual, local, national, regional, and global levels should be strengthened in Myanmar.
4. Transformation (Theme 5) – Digital health should transform individual and population health, and every stakeholder needs to accept change management.
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Could you list the disease outbreaks that have been declared as the Public Health Emergency of International Concern (PHEIC)?
The disease outbreaks that have been declared as the Public Health Emergency of International Concern are:
– 2009 H1N1 Pandemic,
– 2014 Polio,
– 2014 Ebola outbreak,
– 2015-2016 Zika Virus,
– 2018-2019 Kivu Ebola epidemic, and
– 2019 COVID-19 pandemicWhy do these outbreaks raise such concerns?
These outbreaks had a serious impact on public health, were unusual or unexpected, significant risk for international spread, and risk for trade or travel restrictions.
In your opinion, is there a disease or condition that may potentially lead to PHEIC in the future? Why?
A disease that may potentially lead to PHEIC in the future is Avian Influenza, which the WHO claimed that “There will be pandemics in the future, but when, where, and how they spread is difficult to predict. They can have significant health, economic, and social consequences. An influenza pandemic will occur when an influenza virus emerges with the ability to cause sustained human-to-human transmission, and the human population has little to no immunity against the virus. With the growth of global travel, a pandemic can spread rapidly.” That outbreak will meet at least 3 criteria for a potential PHEIC.
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1) Which single design limitation most threatens valid estimates of sensitivity and representativeness? How would you address it within six weeks?
In my opinion, the single design limitation most threatens valid estimates of sensitivity and representativeness would be personal bias of routine immunization (RI) providers who reported to the next level and not all facilities are conducting routine immunization, not all facilities are reporting, and not all cases are captured in the documentation. I would like to address it by mapping, enhancing standardization, providing training, implementing active reporting, monitoring, supervision and data quality checks, analyzing and feedback within 6 weeks.
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.
I would like to propose SMS reporting system from caregivers and CHWs to report AEFIs in real time. Automated dashboard developed and monitored and conducted follow up by RI officer or LGA officer. The expected trade-offs are false positives and data noise.
The indicators are as follows:
i) AEFI reporting rate per 10,000 doses
ii) Proportion of reports investigated within 48 hours
iii) Proportion of ineligible case3.) For a newly introduced vaccine, should the AEFI case definition be temporarily broadened to maximize early signal detection?
Yes, the AEFI case definition should be temporarily broadened to maximize early signal detection for a newly introduced vaccine. However, I would trigger to revert to the prior definition based on time, workload, and positive predictive value.
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Establish the existence of an outbreak: The surveillance system and dashboard can monitor the real-time disease trends, detect abnormal case increases and promote early detection.
Verify the diagnosis: Laboratory Management Information System enables rapid sharing of investigation results among public health professionals.
Prepare for field work: Mobile data collection and mapping tools enhance the early case detection and identify the hotspot.
Construct a working case definition: Electronic case reporting system can update case definitions and extract real-time case related information.
Find case systematically and record information: Mobile application allows real-time case data entry, cast listing and improve data quality.
Perform descriptive epidemiology: Statistical software and GIS tools can generate epidemic curves, maps and demographic summary.
Develop hypothesis: IT enables combination of multiple datasets and to identify possible exposure sources and transmission routes.
Analytical studies to test hypotheses: Statistical software is used to perform case-control or cohort analysis.
Special studies: Data management platforms support advanced modeling and analysis involving multiple teams.
Implementation of control measures and follow-up: Digital contract tracing tools and SMS alert systems are used for case monitoring and early warning.
Communication: Interactive dashboards, automated reporting systems and online platforms enhance results sharing with all relevant stakeholders.
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One digital technology from the article that I find particularly interesting is web-based epidemic intelligence tools and online syndromic surveillance driven by machine learning.
How it works: These tools collect data from many digital sources and conduct pre-processing and filtering. Machine learning is used to detect signals in this data by identifying symptom-related search queries, recognizing clusters of news and social media posts about respiratory symptoms, and forecasting trends. The outputs are shown on dashboards to help public health decisions.
How important it is: A Digital signal can provide an early warning and alert of rising transmission than a formal report. Once the infrastructure is set up, these tools can continuously monitor a large volume of data across the globe with low incremental cost compared to traditional surveillance systems that have delays. These tools enable public health authorities to make decisions for effective interventions.
But some limitations remain. -
1. How can surveillance help to detect and control the disease?
For dengue, surveillance helps to detect and control the disease in terms of early outbreak detection by monitoring the disease patterns, highlighting outbreak areas, and enhancing resource allocation. This surveillance enables responding to the outbreak early and controlling the disease with the efficient use of resources.2. Should we conduct active or passive surveillance or both for the disease, why?
Active surveillance will enable early case detection and control, but may have limitations in resources to be sustainable in large areas over long periods. So, passive surveillance will fill the sustainability gap, but in some cases, like mild cases, it can be missed. In dengue, a mixture of both surveillance methods should be conducted.3. Which method should be best to identify cases, why?
a. Cases in medical facilities VS community
Medical facility-based surveillance can capture moderate and severe cases but can miss mild cases. If the objective is to estimate total burden, community-based methods are more effective. However, we should opt for a combined approach, considering resource limitations.b. Sentinel VS population-based surveillance
Sentinel surveillance should be used, taking into account cost efficiency and higher data quality.c. Case-based VS aggregated surveillance
For dengue, I would favor case-based surveillance for detailed information collection per case, like demographic, location, time, etc. This data enables epidemiological analysis.d. Syndromic VS laboratory-confirmed surveillance
We should be used to syndromic surveillance for early detection in resource-limited areas. Laboratory-confirmed surveillance may take more time and be more costly.4. What dissemination tools will you choose to disseminate monkeypox surveillance information? Why do you choose this/these tools?
For the dissemination of monkeypox surveillance information, I would choose the following tools:
Dashboard: A national online surveillance information dashboard with interactive features will be applied by highlighting case numbers, geographical distribution, and trends. This enhances the real-time surveillance system and tracking of monkeypox.
Alert system: If an outbreak is detected, send an alert to relevant stakeholders. This will enable early warning and response.
Social media: I will use social media to inform the public about monkeypox risk, symptoms, preventive behaviors, and where to seek care. This will promote awareness in communities.
Report: For deeper analysis, I will develop a technical report and article to advocate for policymakers.
Report submission for international surveillance system: I will send the report via platforms such as WHO and ProMED, ensuring global visibility and allowing comparison with the data from other countries -
Should you give the data out?
No, I cannot share individual information without ethical approval, legal permission, and data sharing agreements to maintain privacy and confidentiality. If the purpose is good, I still must protect personal data.How do you not violate any of the General Principles of Informatics Ethics?
I will follow all general principles of information ethics, the principle of information privacy and disposition, the principle of security, principle of legitimate infringement.If you want to provide the data to them, what and how will you do it?
I will review the ethical and legal requirements. Then, I will de-identify and anonymize the data and sign a data sharing agreement. In addition, I will provide minimal information and monitor the usage of data during the research. -
What should you do?
I will keep the patient’s medical information secure and private.As a health information professional – can you tell your friend?
No, I cannot tell my friend. Revealing a patient’s health information to anyone constitutes a serious breach of confidentiality, a violation of professional ethics, and a breach of data protection laws.Can you interfere with other people or family issue?
No, I have no right to interfere with other people or family issues. My duty is to handle health data professionally. Personal intervention will exceed my professional role and could cause emotional, social, and legal harm.But, should your friend not know about this because she might be at risk?
My friend should know, but I am not the person who has the right or authority to disclose.How will you follow the fundamental principles about right to self-determination, doing good, and doing no harm to others?
Autonomy: The patient has the right to decide who knows about his medical condition.
Doing good: The best I can do is to report the concern through proper medical and public health channels, not personally.
Non-Maleficence: This information could cause serious harm, like social stigma and marriage breakdown.Isn’t it your obligation and the right of the subject to hold the information?
Yes, as a health information professional, it is my obligation to hold and protect the information. -
I have experience implementing Electronic Medical Record (EMR) systems to replace paper-based patient records. This system implementation can be considered a successful initiative that significantly improved data and information management and workflow efficiency. In my opinion, the organization appropriately managed the change in terms of ADKAR:
Awareness: All healthcare professionals are aware of why they have to adapt to this system. The general manager, director, and managing director of the clinic communicated and conducted orientation for all related personnel.
Desire: After receiving the orientation from the Management Team and repeated coaching, all healthcare professionals know the expectations and the value of change from paper-based to EMR. So, they have a desire to change.
Knowledge: IT personnel, MEL staff, and EMR focal conducted training and mentoring sessions and provided necessary technical support to improve and have the additional skills they need to use EMR.
Ability: After providing the training, healthcare professionals used the EMR as a pilot. In this phase, necessary technical assistance and mentoring sessions are also continuously provided.
Reinforcement: After having the ability to use the EMR and knowing a clear shared vision, this system is sustainable to maintain.
In conclusion, the organization appropriately managed the change according to the ADKAR model.
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I have experience implementing Electronic Medical Record (EMR) systems to replace paper-based patient records.
The system is successful, but some modifications and challenges remain.Data: Patient data are more complete, validated, and easier to access; however, data migration for old patients and real-time data entry pose challenges due to limited internet connection, resistance to change, and IT issues.
Cost: I assume that this system implementation is efficient because we use the existing server and 1 core programmer for maintenance and innovations related to EMR.
Operation: Daily workflow improved and reduced the healthcare professionals’ burden to some extent, but error fixing in clinic hours and internet connection are challenges.
Design: The user interface (UI) is user-friendly, but double-clicking for a single task is an issue.
People: Some healthcare professionals are resistant to change, but after training and support, most adapt well.
In conclusion, the EMR system implementation can be considered a successful initiative that significantly improved data and information management, workflow efficiency. But we need to improve interoperability and health information exchange (HIE).
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In my experience, I have not worked with a Clinical Decision Support System (CDSS) except for paper-based clinical guidelines. But we are planning to integrate the CDSS for the existing EMR that I mentioned in the previous topic discussion. But I think several factors that might influence the CDSS implementation:
1. User acceptance: Healthcare staff must trust and know the value of the DSS
2. Workflow fit: If the DSS interrupts clinical workflow or slows them down, adoption will be poor
3. Organizational culture: DSS adoption might depend on the organizational culture
4. Cost and technical infrastructure: Reliable IT systems, interoperability, and funding are critical
5. Training and support: Proper training and support enhance healthcare staff’s understanding of how to use the DSS recommendations -
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My opinion about the findings
The study highlights six main causes of EHR-related physician burnout: EHRs’ documentation and related tasks, EHRs’ poor design, excessive workload, overtime, inbox alerts, and alert fatigue. These problems are well-known in practice, but they can vary depending on the features of the application. These burnouts have consequences such as low-quality care, physician dissatisfaction, turnover, mental health issues, increased substance abuse, behavioral problems, and patient dissatisfaction.
I have experience with some of these problems, such as EHRs’ documentation and related tasks, EHRs’ poor design, excessive workload, and overtime.
My experiences
EHRs’ documentation and related tasks – Healthcare professionals often spend more time typing than interacting with patients, which shifts focus away from patient care.
EHRs’ design – Healthcare professionals and the EHR testing team complained about double clips for simple tasks.
Excessive workload and overtime – Although we expect real-time data entry during consultations, application errors and poor internet connections affect the excessive workload and data re-entry in the free time of healthcare professionals.
Suggestions to reduce these problems
To optimize these problems, I think we can improve the following:
– EHR design can use a simpler user interface, fewer clicks for common tasks.
– Alert management can cut down on unnecessary notifications and prioritize important ones.
– Providing training to healthcare professionals to use EHR efficiently and provide responsive IT support to fix errors quickly.
– Policy and working culture should adapt by recognizing EHR-related stress and allowing more flexible schedules. -
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In my opinion, telecom data is one of the big data because it includes a massive dataset from millions of users, such as call records, billing history, location data, mobile data usage, etc. The characteristics are as follows:
Volume: A massive amount of data is generated daily by millions of users.
Velocity: The data is produced and processed in real time, such as call logs and mobile data usage.
Variety: The dataset includes multiple data types, such as structured (billing records) and unstructured (customer feedback).
Value: Information is important for improvement and detecting fraud.
Veracity: Accuracy and reliability of data are important for billing, location data, and network management. -
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Thank you for the interesting case study. My key learning point from this case study is that healthcare organizations must proactively secure their IT systems, monitor for unusual activity, and address system vulnerabilities to protect patient data. This case also highlights the importance of cybersecurity monitoring, robust access controls, and staff awareness to minimize operational, reputational, and financial risk from ransomware attacks.
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Thank you for the interesting case study. I found a lot of consequences. I learned that robust cybersecurity measures, continuous monitoring, and strong vendor security management are essential to protect sensitive health care data and minimize the impact of ransomware attacks through your preventive measures.
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Introduction of the health informatics project
I have experience in developing and setting up electronic medical records (EMR) for three clinics in Yangon, Myanmar. We worked with the three developers: an architect, a back-end developer, and a front-end developer. This project aims to replace paper-based records with a digital platform that could store patient histories, laboratory investigation results, prescriptions, and referrals in a standardized format. This system enhances data compilation, analysis, and supports informed decision-making.
How does the project help to improve the current practices
Efficiency and cost savings: Doctors, Nurses, and receptionists spent less time searching for paper files, reducing delays. The data entry staff spent less time on data entry. Integrated visualizations into the EMR application improved data compilation, validation, analysis, visualization, and evidence-based decision-making.
Patient satisfaction: Patients benefited from shorter waiting times and fewer repeated investigations.
Better outcomes: Service providers could make more informed decisions and ensure continuity of care across different facilities.
Challenges or difficulties in implementation
Technical issues: Application errors and a poor internet connection were among the challenges. We have a standby developer to solve the application error promptly.
Resistance to change: Some healthcare providers were resistant to change and required extensive advocacy and training to use the system effectively. Frequent advocacy and training sessions were conducted.
Interdisciplinary gap (communication gap): Differences in knowledge, language, and perspective between healthcare professionals and developers. Weekly, bi-weekly check-in meetings between healthcare professionals and developers to bridge this gap.
Interoperability issues: Challenges in exchanging data with different systems.
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As an attacker, I would like to identify the weakest part in a security system to get confidential information.
Physical access – One possible way is through direct physical access to a device or server, or a password book. For example, some users save their login credentials on browsers like Chrome and leave their computers unlocked. Besides, most people write down all of their passwords in a book or in a notepad.
Password reuse – In addition, many people use the same password across platforms. If you know that password, you can access all his confidential information.
Simple or default password – Another approach is to guess and try a simple password and the default password. Sometimes, the admin shares the user account with the default password like “admin”, “user”, or “serial number”, and the user has not changed that password. This is the easiest way to get confidential information.
Demographic information-based password: Furthermore, passwords created from demographic information, such as name, birthday, or NRC, are easy to breach. Finding this vulnerability is another way to gain access.
Through social engagement, I could also manipulate or trick an employee or someone close to the victim who is trusted. This is another way to access it.
Phishing: Lastly, I can conduct phishing attacks via email or a link.
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I want to propose choosing the cloud server rather than the physical server because of the following reasons:
1. Access to resource: Easy and fast access to computing power and storage, without purchasing expensive hardware
2. Mobility: The doctors and the patients can be accessed from anywhere in the world
3. Scalability: The cloud services use a monthly subscription and pay-as-you-go model. So, we can scale up or down based on the case load, patient demands, without over-investment
4. Data security: The providers offer strong security, such as encryption, backups
5. Cost effectiveness: No need the large investments in servers, equipment, or additional IT staff. This is cost-effective rather than a physical server
6. Maintenance and support: The providers handle the system updates and technical support, which reduces the burden on IT staffMoreover, I think Platform as a Service (PaaS) is the most appropriate cloud computing service model in the given setting. If we choose the Software as a Service (SaaS), the software may not fit the requirements of the hospitals. If we choose the Infrastructure as a Service (IaaS), the hospital may need more IT staff, or the existing IT staff can have an increased workload. So, I would like to propose choosing the PaaS to customize the patient appointment needs with interoperability measures without worrying about the server setup and technical issues.
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The usage of advanced information technologies in outbreak management is on the rise in this era. Besides, knowledge and skills in health informatics are strengthened in outbreak detection, response, and control.
Starting from outbreak detection, health informatics allows real-time surveillance by collecting data from healthcare workers, hospitals, laboratories, etc. The recent case of the COVID-19 outbreak has given an opportunity to showcase that these technologies have the capacity to produce accurate, real-time, and reliable predictions on issues as serious as a pandemic outbreak. According to the article (1), BlueDot and Metabiota managed to correctly predict the spread route of the virus days before such events happened and were officially announced by the World Health Organization. This case highlights how the usage of information technology and health informatics enhances early detection of outbreaks and outbreak responses.
The health information system (HIS) can also strengthen the outbreak response. With the help of HIS, like contact tracing, caseload mapping, the government officials can identify the high-risk group for early vaccination, lockdown townships, and quarantine facilities, and promote evidence-based decision-making and resource allocation during the COVID-19 pandemic in Myanmar.
Data visualizations, dashboard helps the authorities to track progress and adjust the strategies and policies. For example, Johns Hopkins COVID-19 Dashboard gave global, real-time updates that guided policy and public awareness to policymakers.
In conclusion, health informatics transforms raw health data into actionable knowledge, making early outbreak detection, easier response, and control effective.
1. Allam Z. The Rise of Machine Intelligence in the COVID-19 Pandemic and Its Impact on Health Policy. Surveying the Covid-19 Pandemic and its Implications. 2020:89–96. doi: 10.1016/B978-0-12-824313-8.00006-1. Epub 2020 Jul 24. PMCID: PMC7378493.
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According to the background, I believe I have basic knowledge and skills in the following disciplines:
1. Information Science – I have professional experience in data and information management. These experiences enable me to collect, organize, store, manage, and disseminate the data and information. However, in relation to big data and statistical analysis, I need to enhance my capabilities in these fields.
2. Computer Science: I have basic knowledge in computer science. But, I have low knowledge in algorithms, data structures and programming languages, AI, and machine learning.
3. Management: I have experience in human resources management, strategic management, project management and information management. But I believe my knowledge and skills in management settings are basic.
4. Organizational theory: I am familiar with my organizational structure, strategic directions, behaviors, and change.
5. Public health, epidemiology, and statistics: As my academic background is in community health, I have basic knowledge in public health, epidemiology, and statistics.In conclusion, I need to enhance all disciplines. Additionally, I believe this program will enable me to enhance my existing capabilities and learn new things, especially in information science, statistics, public health, epidemiology and political science, and computer science.
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Hello everyone,
My name is Myo Thiha, but you can call me Leon or Thiha.
I have a professional background in Monitoring, Evaluation, and Learning (MEL), including research and experience in health system strengthening and overseeing the development of electronic health record systems. I am excited to join this program to enhance my skills and competencies. I look forward to learning, sharing, and networking with all of you. It’s a pleasure to meet you! -
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