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For the record,
1. My name is Cing Sian Dal. You can call me Cing.
2. My background: Bachelor of Dental Surgery + Bachelor of Computer Science
3. Related to informatics, I am responsible for developing health information systems for underserved communities in Myanmar
4. From this program, I expect to gain a depth and breadth of knowledge of health information systems and public health, as well as how to integrate them. -
Most organizations I’ve worked with had a small IT and engineering department team of less than 8 people and were budget-constrained. I will describe a disaster recovery plan based on those criteria and the on-premise information system (physical system, not cloud) and recommend suitable technologies based on the plan.
(1) Risk assessment and analysis
_ Identify potential risks and threats to the information system including natural disasters, cyber-attacks, hardware failures, theft, and fire.
_ Classify these risks based on their likelihood and impact on the operations
(2) Disaster Recovery Committee
_ Ensure that everyone understands their roles in case of disaster
(3) Notification procedures
_ Establish a clear communication to simultaneously (not a call tree) notify all team members in case of a disaster so that everyone is informed promptly.
(4) Recovery procedures
_ Outline step-by-step procedures for recovering each component and analyze prioritized critical component
(5) Reconstitution phase
_ Plan for the restoration of normal operation while/once the risks are eliminated.
(6) Ongoing maintenance and testing
_ Regularly inform the disaster recovery plan
_ Conduct mock drills to identify any weaknesses and make necessary improvements
Given the small size and budget constraints, the following technologies are recommended:
(1) Backup solutions
_ Cost-effective backup solutions like Google Drive, Dropbox, or specialized cloud backup providers can be used. For on-premise solutions, secondary RAID storage or external hard disks can be utilized.
(2) Cloud solutions
As secondary solutions for some services such as server or storage, cloud services (e.g., EC2, S3, Azure Blob) could be used.
(3) Redundant Systems
_ Implement inactive redundant systems which will be activated once the primary systems fail, in fiber optic internet, server, backup, power supply, and so on.
(4) Automation
_ Tools like Acronis, Veeam, or Windows Backup allow to back up at scheduled intervals.
_ Automate to re-route to the cloud server if a physical server fails.
_ Automate to start generators or supply with UPS if power is cut
_ Automate the inbound and outbound internet traffic
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The benefits of high availability will be evident when the system becomes unresponsive and experiences a failure. For the patients and hospitals, all operations will be stopped, which will delay treatments (in turn affecting patient outcomes), inefficient hospital workflows, and affect the quality of care (disappointing patient experience).
The benefits of high availability are:
– improved patient experience including doctor experience, allowing them to access up-to-date information and providing healthcare services seamlessly
– improved the quality of healthcare: the digital system can support their workflow/operation faster without any delay or service disruption
– reduced costs associated with system downtime and system recoveryIf the hospital relies on the digital system without a secondary solution such as a paper format or secondary server, then the High availability is not optional. In that case, as a health informatician, you must determine the level of High availability balancing with the implementation and maintaining cost.
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Your presentation offered valuable insights and expanded my knowledge of malaria and its surveillance.
A question that doubts me is:
If a patient visits a healthcare facility (e.g., hospitals, clinics) for medical attention and the consent form doesn’t explicitly mention disease surveillance, can we still use the data collected from that facility for surveillance purposes? -
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Your presentation is insightful and I’ve never thought that weak case definition would be helpful and useful in detecting potential cases earlier than confirmed cases for a more proactive response. I think that you’ve already been familiar with the flow of how a surveillance system works since the data flow diagram is impressive, I am curious to learn about how the analysis algorithms work. Regarding the weak case definition, I wonder what epidemiological factors or clinical features in identifying weak cases for dengue are.
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A company I worked for saw an employee gradually decreasing performance, affecting the team’s progress. He was then given a chance to improve it within two quarters. After two quarters, he could not meet with the goal. He was then terminated with both consensual agreements.
After two weeks, the company saw an increasing number of dummy data in the existing records. As a consequence, it affected on data of partners and their reporting mechanism, thereby harming the company’s credibility. Thanks to its backup policy, the company could recover from it easily.
Then, to mitigate such attacks in the future, the company reviewed its offboarding practices and CIA triad. Some of the CIA triad lists were as follows:
Confidentiality – (1) Updating username and password before offboarding, (2) Implementing 2FA in every authentication and authorization layer, (3) Updating access control list before offboarding, (4) Rotating SSH key (encryption key) before offboarding. The most important thing to be noticed here is to do it before offboarding.
Integrity – A background monitoring system is implemented, which triggers watching data integrity only when suspicious traffic is detected.
Availability – Although the underlying infrastructure was not affected, to ensure the availability of data and system operation, the infrastructure was further isolated and divided into different network segmentations so that unauthorized access could be prevented.
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A project plan, in the traditional way, is a paper essay written by imagination. Therefore, when reality kicks in, the plan is always constrained by time, cost, and quality. Fortunately, most projects I worked on were non-linear and progressed with the feedback loop (incremental development). In my experience, there was not even a project document to initiate a project. I like and prefer that way. However, for a big project or project proposal, it is mandatory to follow at least 12 steps of project management.
In my case, most projects are software development, and initiated by a program manager. Accountability for the project control and ownership rests with me. Usually, the project definition would be the problem statement – what problem are we trying to solve? Based on the problem statement, I gathered their expectations and transformed them into feasible digital solutions which were then broken down into an ordered list of tasks using a Gantt chart with Excel. The Gantt chart allows me to monitor the progress, productivity, and performance, and adjust priorities. The progress and quality of the project are improved by the iterative process of gathering feedback and review. Resources, risk assessment, costs, and safety margin were handled by a program manager. However, this workflow is suitable only if there are one or two people involved in plan execution.
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In my opinion, it is vital to be a team in harmony with unique personalities and characters before forming a team. For example, if a team of introverted leaders was formed for a social community, it would be detrimental to the community.
Before I was elected as the president of a civil society organization, the former leaders carefully picked up candidates based on diverse sets of personalities and characters possessed by each individual and how a team would look like in combination.
Fortunately, the general secretary I worked with was charismatic, socially attractive, friendly, and welcoming in her personality traits. As for the organization, I would be like the father and she would be like the mother of the organization. So, I was like a navigator of the team and organization while she was like a driver of the team and the organization.
For the motivation of a team, I always co-delegate to my partner and myself because she is more effective than me in that case. Yes, there are many ways to motivate the team, such as recognition and appreciation, rewards and incentives, encouraging and empowering, and paying attention and respect. It is important that, as the primary leader, I must express these even if she could articulate them perfectly. Because some people love to be praised and rewarded by people with the highest position, making them feel highly superior or motivated.
Because the leading team members must be individuals with diverse personalities and characters, it will make it a lot easier not only for motivation but also for problem-solving, conflict, and resolution, facilitating dialogue, fostering relationships, and clear communication.
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Recalling details and understanding the big picture would be listening skills that I need to improve because whenever someone told me something important, I could not grasp the context and retain important facts. I simply end such conversations, with “Okay”.
In such situations, I would rephrase the way I understand whether I miss the details or not; I would further ask and clear my misunderstandings whether I’ve accurately comprehended their message. In doing so, there could be a challenge with people who are impatient with being asked again and again, taking much time or providing more details. In that case, how do you manage it?
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Normally, my strongest component is empathy without outward display. I could recognize emotions in others, avoid judgments, and convince others to understand others’ emotions sometimes but not respond appropriately most of the time.
To express my empathy requires my expression or interaction with them appropriately when needed, like saying “hi” when someone is feeling down. However, I limit myself not to express it because my core belief is eventually people or everything will be okay whether I express it or not. Additionally, it takes a lot of energy to express myself as I am an intrinsic and introverted person. So, my action plan for expressive empathy will be limited to close relationships like family, teammates, classmates, friends, and so on.
Usually, I am weak at self-regulation when I become emotional. While I am angry at something or someone, it also affects other things or other people. Emotions expand. This also affects my health, causing hypertension.
I have no idea about this action plan. The realistic ways that work for me are playing attention-intensive games such as Mobile Legends, walking away from the source of emotion, and listening to music with headphones on.
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Generally, a government’s system and political dynamics affect the nature of its leaders’ speeches. In Singapore, with its localized politics and de facto one-party state, leaders can deliver speeches focused on a single, unified message. Conversely, in the US, with its complex, internationally engaged political system centered around diverse people, leaders often need to address multiple issues in a single speech, which was also significantly shaped by the 2020 presidential re-election.
When measured against the six principles of CERC, the Singaporean leader, Lee, is a skilled communicator while the US President Trump focused more on reassurance, optimism, nationalism, and personal branding rather than risk-focused guidance.
Be First – Both leaders addressed their nations promptly; however, Lee shared Singapore’s actions and regular updates while Trump initially addressed travel restrictions and the pandemic global spread and portrayed the crisis as under control without evidence.
Be Right – Lee explained Singapore’s response and the nature of Covid-19 including differences from SARS to set realistic expectations. However, Trump covered broadly travel restrictions and economic aid overemphasizing his government’s plans and downplaying the potential impact of the virus.
Be Credible – Lee outlined ongoing efforts and possible future steps while Trump emphasized America’s superiority and economic strength and was overly optimistic during the time of crisis.
Express Empathy – Lee addressed concerns about health and supply shortages and encouraged calmness while Trump expressed sympathy but was more focused on control over the situation.
Promotion Action – Lee shared practice steps for the public such as hygiene, and visiting the doctor when unwell while Trump emphasized national-level economic measures.
Show Respect – Lee respected his audience by highlighting the contributions of Singaporeans while Trump’s emphasis was more on national strength and leadership – this would feel less personal and less connected to individual efforts.
Based on these principles, Lee addressed the issue with transparency and empathy while Trump focused on national strength and his administration.
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As for Myanmar, following the Feb 2021 coup, at the national level, it seems impossible to have a collaborative team, build transparency and trust among ethnic groups, utilize technology for an intended purpose, provide access to information technology, embrace digital inclusion, and empower digital health transformation.
In terms of team collaboration, it can be strengthened at the ethnic level or federal-state level. However, when it comes to field operation settings, the team should be localized meaning that the members of the operation should be state residents they trust.
Trust can only be earned over decades because trust has been broken for over five decades between ethnic groups and the national government. Transparency will not work until trust is earned. In my experience at work, we have leveraged focal persons from digital literacy to the ability to handle server administration, and given the complete handover of server infrastructure (full ownership, full control, and access, therefore, full transparency), the project stakeholders are also non-state actors, even so, they abandoned it within one month after the handover.
In my experience, the underlying technology infrastructure is not something that ethnic groups or state actors lack. It is more related to their issue with continued adoption due to one main reason of feeling and belief: “Technology tools are obstacles” even after we resolved all of their feedback and experience. The internal research found that it depends not on ethnic groups or the state actors but rather on collective familiarity with technology, specifically saying, that if there are at least one or more senior focal persons familiar with technology, any digital health project will succeed.
The tech-equity is a broader topic and the underlying infrastructure (i.e., internet access, mobile access) should be provided or turned on by the state or national government. As of now, mobile networks and internet access are turned off by the national government (while the mobile network infrastructure already exists).
Digital transformation involves multiple factors. Even if the project requirement for transformation satisfies with multiple factors, it is still challenging. Although digital health is the only possible way for data collection, analysis, and research in conflict areas, the state actors themselves are not willing to continue to adopt it due to trust issues, pessimistic beliefs, and feelings even if Starlink for internet access, funds for management and human resources, required hardware and facilities such as portable power station, power bank, mobile phones, laptops for each individual, solar powered battery and inverter, custom-engineered data collection app with offline first capability, dedicated cloud servers, etc are provided.
In conclusion, as for now, to prepare for disease outbreaks, it is only possible to retrieve information from the organizations each state relies upon and trusts.
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The World Health Organization has declared several outbreaks as the Public Health Emergency of International Concern (PHEIC). They are:
(1) H1N1 in 2009 in Mexico
(2) Poliomyelitis declared in 2014 and ongoing
(3) Ebola outbreak in West Africa declared in July 2016
(4) Zika virus epidemic declared in Feb 2016
(5) COVID-19 pandemic declared in 2020
(6) Mpox outbreak recently declared in August 2024
Reasons for such concerns are based on these four factors:
(1) seriousness such as death or severe illness affecting many people
(2) unusual or unexpected nature such as harder-to-control disease
(3) International spread risks such as the spread across countries and continents quickly
(4) Travel and trade restrictions such as outbreaks leading to a negative impact on economies and daily life
Diseases that are resistant to drugs such as antimicrobial resistance have the potential to be a future PHEIC because people could misuse antibiotics, and most countries do not have regulations over antibiotics, offering them as over-the-counter medicine.
Another future PHEIC could be diseases (e.g., Nipha virus) that arise from the evolution of zoonotic viruses which can jump back and forth from one or more series of animals to humans.
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Despite its high positive predictive value of 94.4% and data quality in the Korean hepatitis B surveillance system, the two key issues for potential improvement are the misreporting of chronic hepatitis B cases and the limited usefulness of the surveillance system.
Misreporting of chronic cases: The complexity of diagnostic criteria (i.e., various tests such as HBsAg, IgM anti-HBc, 60% of asymptomatic characteristics, etc.) leads to this situation and it becomes complex when many symptoms (i.e., jaundice, fatigue, malaise, etc.) overlap in acute and chronic infections.
To fulfill this gap, their diagnostic criteria should be enhanced by involving liver enzyme tests as mandatory indicators for diagnosing acute hepatitis B similar to practices in other countries such as the United States because chronic hepatitis B, typically presents with persistently high liver enzyme levels over a longer period whereas acute does not. Additionally, conducting effective training workshops, and updated and clear guidelines for recognizing and reporting acute and chronic hepatitis B cases accurately would reduce this misreporting rate.
Limited usefulness: Although the system claims to provide high quality and timeliness, it lacks usefulness beyond monitoring in acute cases. It should extend into supporting effective intervention strategies in assisting the elimination of viral hepatitis.
To assist in intervention, developing the system to be interactable (data sharing) between the surveillance system and public health agencies would facilitate rapid response to outbreaks. The system could also have an opportunity to broaden to include chronic hepatitis B. This could reduce the disease burdens of hepatitis B in Korea. This could also help in planning the national strategies in eliminating by providing actionable insights from the system.
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The outbreak investigation involved several steps and health professionals such as clinicians, researchers, and analysts. Information technology could be applied to streamline data collection, analysis, and communication during the outbreak investigation process. There are several unique tools dedicated to one purpose and general purpose.
Verification and preparation: In establishing the existence of an outbreak such as foodborne outbreaks, PulseNet provides a laboratory network of DNA fingerprints to identify food-borne outbreaks. In verifying the diagnosis, a laboratory information system like LabWare or STARLIMIS can be utilized to facilitate sharing of lab results to ensure timely and accurate diagnosis verification. In preparation for fieldwork, there are several software to assist in team management such as Trello or Jira to organize tasks, assign roles, and track their progress for field investigation.
Describe the outbreak: In constructing a working case definition, tools like REDCap allow researchers to create surveys to define case criteria systematically to ensure definitions of cases, symptoms, and patient characteristics. In finding cases systematically and recording information, electronic health record systems such as Epic or Cerner allow healthcare providers to systematically record patients to make it easier to identify cases during the outbreak. In performing descriptive epidemiology, software like Tableau or ArcGIS can be used for data visualization and mapping which helps investigation and analysis effectively.
Hypothesis development and testing: In developing a hypothesis, statistical software like R or SAS can help in identifying correlations and hypothesis formulation. For the analytical studies to test hypotheses, simulation tools like EpiSim, and GLEAM can be tested to simulate outbreak scenarios. For special studies, for example, environmental studies, environmental monitoring tools like QGIS allow spatial analysis of environmental factors related to outbreaks.
Response and action: For the implementation of control measures and follow-up, there are automated alert systems such as FirstWatch, SORMAS, and Healthmap can be used and tools like OpenLMIS can also assist in medical logistics and supply chain management. For communication between health professionals and people, social media platforms can be effectively used. Between health professionals, their preferred platforms such as Slack, and reporting tools like Tableau or Power BI can be used to share real-time updates on outbreak status.
Although there is a wide range of tools made for specific purposes and general purposes, factors like cost, user experience, and familiarity with users should be considered.
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Among digital tools used in COVID-19, I like telemedicine the most because it stands out for more significant health outcomes for individual and community health than any other advanced tools. I will detail how telemedicine aids in COVID-19 surveillance and response, and its significance.
During the pandemic, the first challenge in population is a medical visit to healthcare providers due to the government’s restriction on social distancing and isolation. There was no sufficient number of healthcare professionals to handle the large number of infected populations. The last solution driven by the government was stay-at-home quarantine to reduce the spread of infection. However, this led to the demand for healthcare services, and medical supplies and equipment such as oxygen concentrators (while most people could not afford them). For healthcare services, while people were living with stay-at-home restrictions, the only way to provide healthcare services was through telemedicine.
Telemedicine serves as a communication channel between healthcare providers and patients via video calls, phone calls, texts, or secure messaging platforms providing consultation and medicine orders with the ability to choose a wide range of competent medical professionals.
Telemedicine apps can facilitate a faster COVID-19 response by providing easy access to doctors and medicines. A more advanced telemedicine app such as HealthVault allows you to track vital signs and send data to your doctor allowing continuous monitoring without in-person visits. It is extremely useful for patients with chronic conditions.
Although telemedicine apps could act as surveillance data points, privacy laws may limit and hinder their use this way. However, if people voluntarily provide information via telemedicine apps, this could help public health officials track disease outbreaks and implement effective prevention measures.
In summary, telemedicine has significant effects on health outcomes quickly recovering from COVID-19 and the ability to function as a surveillance data source for public health officials.
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How can surveillance help to detect and control the disease?
Surveillance plays an important role in public health, especially in detecting and controlling diseases by collecting, analyzing, and interpreting data related to health events and data sources such as hospitals, facilities, and communities.
After systemically collecting data, the context behind the data can be interpreted through visualization using lines, charts, graphs, and so on. For example, suppose the line graph suddenly increases in cases or unusual geographic distributions. In that case, it allows public health professionals to quickly identify outbreaks and control the spread of transmission by implementing necessary interventions.
Should we conduct active or passive surveillance or both for the disease, why?
Conducting both active and passive surveillance promotes its effectiveness. Active surveillance enables actively finding cases when a disease like monkeypox can spread rapidly, while passive surveillance is also very useful and important as a baseline system to detect cases.
Which method should be best to identify cases, and why?
– Cases in Medical Facilities vs. Community
– Sentinel vs. Population-based surveillance
– Case-based vs. Aggregated surveillance
– Syndromic vs. Laboratory-confirmed surveillanceThe combination of all methods is useful when the incidence rate increases uncontrollably.
Regarding cases reported from medical facilities and communities, both are useful because patients with severe cases will come to hospitals while those with mild cases remain unnoticed within communities.
Between sentinel and population-based surveillance methods, the former helps gather detailed data in surveillance at key sites while the latter allows for calculating incidence rates across a defined population.
Among case-based and aggregated surveillance, the former is deployed either after the trend report from aggregated surveillance is unusual or when a case-based study for monkeypox is required such as contact tracing and understanding transmission patterns.
Laboratory-confirmed surveillance is required for definitive diagnosis while syndromic surveillance provides early warning symptoms of potential cases.
If I had to select only one specific for monkeypox, I would select case-based surveillance because it provides detailed information and real-time data (faster than others) on disease spread which can help public health officials allocate resources effectively.
What dissemination tools will you choose to disseminate monkeypox surveillance information? Why do you choose this/these tools?
I would recommend the following dissemination tools:
– On-going and real-time dissemination tools like ProMed and HealthMap to rapidly share case information and outbreak alerts
– For quickly disseminating confirmed disease reports and information like the Health Alert Network (HAN)These tools allow for the rapid sharing of time-sensitive information allowing immediate outbreak response.
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Sharing the detailed data to a third party is a must “no” even if the benefits of their research outweigh the risks of privacy.
Even if providing the data to them is mandatory, first we have to rationalize the need for data in detail at an individual level for malaria disease prevention and control. Second, we have to question the accuracy of the disease trends and patterns with and without providing sensitive data, and the ability to prevent and predict from the calculated accuracy.
Nevertheless, I would anonymize data by removing personally identifiable information (PII) such as home address. I would also minimize data indicators (providing only relevant information) based on the balance between beneficence and non-maleficence for every data indicator. Instead of providing individual details, the aggregate data can be provided at the village level or township level. I would prepare and negotiate with the research team for a data use agreement (DUA) that adhered to legal and ethnic standards as well as local and cross-border regulations. In this way, we can facilitate the research by respecting the principles of privacy, autonomy, beneficence, and non-maleficence.
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A short answer to these questions for this HIV case is that I will not disclose the patient’s information to my friend by respecting the patient’s self-determination, beneficence, and non-maleficence, and above all, by following legal and professional obligations to maintain the confidentiality of all patients. However, I would encourage the patient to inform his spouse, to act responsibly, and to consider the well-being of his spouse. As a health information professional, the best is to refer to consult a healthcare professional for further guidance to disclose this information safely.
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The example I provided in the previous discussion primarily stemmed from the startup’s (developers) decision not to fully operate the business after its launch. On the flip side, there is also a need for change management in users/customers even if the developers operate their business.
Regarding awareness, while the startup looked for dental clinics where the change for a software system was needed in the beginning before the software development was initialized, some users were not aware of features included in the software to solve their business problems such as monthly revenue reporting. This is because there was no effective communication in conveying the functionality of the software with their business problems.
As for the desire, some users reported that they were pushed back to the traditional system based on paper. While the clinic founder had the desire to digitize the solution, their clinic staff instead reverted to their habit of recording on paper.
Concerning knowledge, most users are not fully aware of the features included in the software. A comprehensive onsite hands-on training will be required.
As for the ability to practice, they should be encouraged by the clinic founder or manager to get familiar with the system.
While reinforcing to utilize it, the startup should collect their barrier in using the software so that they can incrementally get the updates.
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A group of my dental friends, who were programming enthusiasts, decided to develop the Dental Patient Management System software. It took almost six months, but it failed due to two key reasons: operation, data, and people.
It was built very strong in other areas of design, and cost.
Before the software development started, they researched the possibility of using the LEAN methodology. They gathered user expectations designed a porotype and re-iterated the process until users felt worthy of paying the software licenses for their clinics. It was designed perfectly based on user experience. Most developed with strong passion and commitment including their financial investments such as dedicated desktop setup, and thermal printers for receipt.
Despite its good design and investment, there are problems in its operation, data management, and people.
As for the operation, there is no technical support, education, and software training, inability to provide on-call when the system is down or slow, and unprofessional customer service.
Regarding data management, users duplicate patient identity registration (as a consequence of lack of software training), do not rely on the budget reporting built-in feature (continue relying on Excel), and data unavailability when the system is either down or slow.
Concerning the people factor, there are issues for both users and developers. Users became permissive towards the system due to the poor communication between users and developers; day by day the actual users grew dissatisfied towards the system.
In this case, we can learn that inventing things is easy, but maintaining things is extremely hard. My dental friends began with a small dream of inventing it but their dream stopped and did not go further as a long-term vision.
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Unlike decision support systems used in developed countries, my experience involved dealing with underserved communities. The decision support system is processed with qualitative and qualitative approaches instead of streamlining with the digital system. The system could not support individual healthcare but it is aimed at supporting public health only, for example, disease surveillance and early detection of outbreaks.
Focal community workers (who lived there or clinical practitioners) record the number of events (existing disease, new cases) in their clinic logbook. At the end of every month or biweekly, they take pictures of their logbook and send it to their funded or supervised organization via their preferred messaging platform like Viber, or Signal. For further issues with their data such as abnormal data, they call for details in the qualitative method.
This non-digital approach has been working well with the ability to provide early detection of disease outbreaks, intervention planning, and resource allocation. This non-digital approach can be able to replicate in other areas similar to this. There are no factors influencing in implementation of this kind of decision support system, however, its viability depends on data collected from clinics.
However, despite its early detection, when implementing resources (especially medicines), the majority of problems exist in logistics and supply chain, such as the inability to order medicines in bulk (due to policy and regulation of purchased countries), transportation issues in armed conflict area, inability to maintain temperature for temperature-sensitive vaccines.
In summary, this non-digital approach works well with digitally challenged people and brings convivence for data collection, however, its vitality and the accuracy of prediction depends on cooperation with focal clinics.
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There might be several reasons behind why the hospitals in the country do not use the ICD standard (or even any kind of standard). It may be their focus on treatment rather than data collection for any kind of purpose. It may be their familiarity and efficiency with their own format that does not hinder their research. It may be due to no data sharing policy no data collection in their operation or budget constraints to support information systems. If hospitals prefer their sole existence focusing on the treatment outcomes without the need of data exchange, there won’t be any problem.
However, when it comes to long-term local and international cooperation for any kind of purposes such as research, public health, disease surveillance, and insurance, the problem will arise:
Delay in everything: First and foremost, hospitals will have to spend a lot of time and budget on conversion from their format into a standardized format. It includes local and international research collaboration and public health research. Insurance companies are less likely to cooperate due to the difficulty in understanding their data, and inaccurate characterization of diseases. It can also delay tracking disease surveillance when epidemics happen.
Difficulty in all aspects: The hospital won’t be able to provide data used in tracking disease trends to the government and Ministry of Health or relevant agencies. The hospital has to invest more in time and budget to manage standardized systems. It may be also challenging to communicate data between healthcare providers.
In summary, delay and difficulty are the main consequences if the hospitals do not use the ICD standard.
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My first reaction to this paper is that it does not make any sense, provoking me to ask so many questions. Why are physicians required to do such responsibilities in the first place? Shouldn’t they assign to other professionals such as a Data Entry Officer? When I read the consequences of physician burnout, it is a bit of a joke that it leads to substance abuse. After reading the paper, I concluded that it is rather an HR management issue, injecting irreverent responsibilities into the role of physicians.
What do you think about this finding?
Regarding this finding, I totally agree with the causes and consequences of physician burnout. Having said that, I am curious to see how it could lead to substance abuse (probably arising from mental health issues) while others do not seem to suffer from it.
Have you ever heard any complaints from health officers (or yourself) about using EMR?
In my experience, in Myanmar, EHRs are handled by office staff. Physicians prescribe and take notes on paper. Those notes and prescriptions are digitized by office staff. Due to the EHRs, the consequences are affected on patients, not physicians. Basically, as a patient, it tests the growth of our patience. The major complaint from patients is longer waiting time and the complaints from officer staff (not physicians in this case) are the same as the consequences described in the paper.
Any suggestions to avoid or reduce this problem?
Until the causes are solved, eliminating the consequences will be ineffective or temporary with recurrences. Among six main causes, I’ll discuss four cases related to e-Health:
EHR’s documentation and related tasks: Automating documentation process with offline-capable AI model from paper records or assigning patients to record their documents in the application by themselves by prioritizing treatment first.
EHR’s poor design: There is no one-size-fits-all design that will satisfy all hospitals, clinics, and industry. The user interface cannot be defined without understanding the backbone of the operation behind it and how users will experience the use of the app design while in operation. The objective of EHR and its design should rather accelerate their operation than be a barrier to them.
Inbox alerts and alert fatigue: The system will trigger alerts in non-humane ways. The humane way is to call physicians if it is actually something very urgent, very emergent, and worth noticing. If the alerts are coming from outdoor patients, it is better to be responsible for the patients themselves. If something bad happens, as time is critical, the nearest doctors or hospitals can save lives. Additionally, the system should not handle alert-level messages but notification-level messages for physicians even if it is an emergency.
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This app is a significant healthcare tool, particularly for remote areas. The integration of prescription delivery would greatly enhance user convenience.
Your discussion explored comprehensively the app’s domain coverage and impact.
As an international student, I am curious to know if the app offers telehealth services to local residents and foreigners. -
This is an interesting project. Traditionally, diabetic records have been maintained on paper or through recording apps [not integrated with glucometer]. However, this new approach [app integrated with glucometer] directly transmits measurements to a database with accurate timestamps, ensuring data integrity and reliability. This makes it particularly valuable for research purposes due to its immutability.
Your discussion comprehensively explores the potential impacts of this project on current healthcare practices. I would like to know if this initiative is ongoing or discontinued. -
According to a paper by Shaw T, et al. on “What is eHealth (6)?”, there are several definitions that can explain “eHealth”. In your opinion, what should be a definition of eHealth?
The paper introduced that there are several definitions, however, those lack sufficient conceptual clarity. Shaw T, et al. defined it based on three domains that can be interconnected (2017):
Health in Our Hands: This domain emphasizes the use of technologies to monitor, track, and inform, for example, fitness tracker apps, personal health record apps, glucose monitor app
Interacting for Health: This domain focuses on leveraging technologies to enable health communication between patients and health professionals, for example, telemedicine platforms
Data enabling Health: This aspect involves the collection, management, and utilizing health data to enhance healthcare delivery, for example, the clinical decision support system
From my perspective, this definition by Shaw T, Et al. concentrates on categorizing the use of eHealth to cover all aspects. I would define “eHealth” as Electronic Health or the use of technologies to improve healthcare. It seems to me that the term does not need over-definition as the term “electronic health” has already provided a clear framework for understanding in science. Nevertheless, I anticipate that the wisdom of this precise definition will be revealed as we explore the topic further in the following chapters.
References:
Shaw, T., McGregor, D., Brunner, M., Keep, M., Janssen, A., & Barnet, S. (2017). What is eHealth (6)? Development of a Conceptual Model for eHealth: Qualitative Study with Key Informants. Journal of Medical Internet Research, 19(10), e324. https://doi.org/10.2196/jmir.8106 -
Before explaining Big Data characteristics, I would like to mention the evolution of humans first.
In the Stone Age, humans lived a hunter-gatherer lifestyle. It didn’t stop there, humans evolved to the agricultural revolution where humans began the development of agriculture and domestication of animals. Humans didn’t end there. It continued to the Industrial Revolution where the invention of machines, and electricity led to rapid urbanization and industrialization. Human evolution did not end there; they kept getting better into the information age where the development of computers and the internet transformed communication and global connectivity. Humans continued to advance into globalization and now artificial intelligence. Here, you can see the characteristics of humans evolving through different evolution
Similarly, the characteristics of Big Data started with 3Vs and progressed to 5Vs, 7Vs, and then 10Vs. Initially, the definition of Big Data satisfied with three characteristics: Volume (the large quantity of data), velocity (the speed of data processing), and variety (various forms of data types); however, eventually, it was no longer true. It transitioned into the need for two additional characteristics: Veracity (the trustworthiness or accuracy of the data) and value (the usefulness or benefits of the data). Over time, Big Data possessed two additional characteristics: Variability (the inconsistencies or changes over time), and Visualization (the ability of data presentation). As time goes by, Big Data has grown into 10Vs in which the new three characteristics are Validity (the accuracy of data for intended use), Volatility (the lifespan of data or data relevance), and Vulnerability (the security and privacy of data).
As an example, let’s suppose that we own a private hospital. As an earlier stage, we implemented electronic health records which require:
Volume: The massive amount of patient data including past medical history, lab test results, and treatment plans
Velocity: EHR data are constantly being updated as patients receive treatment, tests are performed, and diagnoses are made.
Variety: EHRS data contains structured data (vital signs, blood pressure, blood type, etc.) and unstructured data (physician notes, etc.)
Then, our hospital has advanced into integrating with wearable health devices for specific patients. In this case, the following characteristics can be seen:
Veracity: Data collected through wearable devices are made sure to be reliable and accurate
Value: The insightful values such as early disease detection and personalized health recommendations are given back using the collected data.
As time goes by, the hospital has incorporated genomic medicine which involves:
Variability: Genetic data are greatly varied with individuals and populations requiring a sophisticated analytic system.
Visualization: Without visualization of genetic data, it would be quite impossible to identify the patterns and their relationships.
Over time, the hospital tried to develop the analytic platform to enhance the clinical decision support system that demands:
Validity: The data will need to be made sure that the retrieved data is correct and accurate for decision-making, analysis, research purposes, personalized medicine recommendations, and so on.
Vulnerability: At the same time, sensitive patient data are protected from data breaches, unauthorized access, and attacks
Vlatility: The duration of data storage and managing long-term storage should be considered based on data retention laws or policies.
In conclusion, different characteristics of Big Data can be seen as it evolves like a baby growing into an adult and then an elderly.
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That’s frustrating, infuriating, and very unethical. Every organization dealing with medical data must have a data sharing policy and data governance policy. Usually, at the enterprise level, data handling is automated by using enterprise software such as BlackBerry Workspace, and Microsoft Purview Information, Vera.
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It’s truly difficult to assess risks and make a contingency plan for me as well. The only way is, as you mentioned, to have a team meeting and brainstorm together because it’s never been a solo plan by nature since the beginning of the project. It also takes a pearl of wisdom to filter out useful ideas while brainstorming. In that case, the Risk Matrix introduced in the course, is truly useful in determining risks and their impact.
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We are programmed to respond since birth whenever we have conversations. Everybody can hear, but nobody can easily listen. Listening takes energy and attention. It’s more difficult when it involves non-verbal communication because it can express the opposite, for example, smiling while offering condolences. I agree that maintaining eye contact is helpful because it improves concentration to some extent but it does not work if it becomes a long conversation.
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It would be quite challenging if a doctor is unwilling to cooperate with you if it takes much time, especially with such frequent follow-up questions. To improve recall, I think memory games like listening to weather forecasts without watching and jotting down what you remember could be more effective in practice.
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Obviously, you are indeed a well-regulated person not only by emotion but also by discipline. Your promptness in discussion proves us all. And, I admire your self-discipline.
When it comes to empathy, I understand it in a different way from recognizing emotions. For me, empathy involves the experience of putting yourself in another person’s shoes. I believe that if we have a similar experience to others, we can easily recognize emotions in others quickly.
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It is interesting and thought-provoking whether HIV can be a future PHEIC. As far as I researched, although HIV seems to be met with four criteria, the virus is all based on individual behavior (i.e., multiple sex partners, shared needle injection), unlike air-borne disease. Therefore, based on the situation in Myanmar, it’s fair to say that AIDS/HIV is one of the critical public health concerns affecting around 240,000 people according to a WHO progress report and it has a high prevalence. It is a serious issue nationally.
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Thank you for sharing the insightful discussion. We may assume that everything is okay if a patient is not admitted to a clinic or hospital but this could be the opposite – diseased or self-medicating at a chronic stage. There could be several reasons. The follow-up should be implemented after a considerable amount of time.
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I agree that IT plays a crucial role in outbreak investigation because it’s hard to imagine an outbreak investigation without technology. Manual processes would be error-prone and slow down response times. On the other hand, using software incorrectly can also delay operations, especially with poor user interfaces.
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I agree with the fact that case-based surveillance is a recommended option because basically, it is cost-effective and also serves as a point of trace or upcoming route of trace. As for health professionals, the Ministry of Health is a reliable source, and news and social media platforms are easily accessible sources of information for the general public.
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