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Very neat and practically clear design which is very friendly with data collector, especially in wide tick box.
In my opinion, I would remove weight in collecting demographic data, which I assume that it cannot be associated with the study.
Physical examination is well categorized including a detail section to provide its abnormal codes.
I am not sure what collected sample type means. If there is ID in the specimen or lab result, we can also add it.
More detail information about administrated vaccine can be added such as site of administration, route of administration, lot no, which would help in tracing back in finding out in manufacturing fault and adverse events.
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This week, we were introduced to the principles of ethics (general principles as well as principles for health informatics). Then, we were provided a case study of ethical and legal issues if an external person is outsourced for research. We also learned the 4-factor assessment for breach. Individuals have duties and obligations to be followed with ethical guidelines such as AMIA’s code of Professional and ethical conduct 2018. Entities have EU regulation in EU and Thailand regulation in Thailand respectively for personal data protection.
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I would simplify the normal way of encoding presence or absence as binary digits for pregnancy tests: 0 for negative, 1 for positive like in a physical examination where 0 is normal, and 1 is abnormal.
This could help in data analysis consistently with 0 as false, absent, or negative and 1 as true, present, or positive. -
The obvious benefit of having data standards regardless of any purpose is that they make data communication easier. Otherwise, data must be re-aligned for interpretation in multiple formats for different partners.
One simple example is the date format. Let’s suppose we are going to collect blood pressure including these indicators: systolic blood pressure, diastole blood pressure, and the date and time being measured. In this example, the date could be written in different ways: 2025-03-21, 21-Mar-25, 21/03/2025, 21/03/25, 21-Mar-2025, etc…
If a researcher has to combine all data from different sources that are unstructured/non-standard, he has to spend much time re-aligning the date format.
Therefore, data standards facilitate data communication faster and easier than non-standards for all parties.
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My team and I conducted the best way to manage computer systems to provide access systems and data internally and externally. The requirements include more than data quality and integrity with audit trial, user authentication, access control level, checks, data backup, and recovery support.
In scoping down for data management, we can think about it in two ways: database-only or system-wide database management.
For database only, enterprise-grade relational database management systems provide robust support for these features. The selected choices are Oracle Database, PostgreSQL, MySQL, and IBM Db2.
For the system-wide database management, it should be handled by a domain network system (with such Domain Controller, Active Directory). This can safeguard not only the database but also a wider scope from services running inside a computer to the whole network.
In my experience, normally spreadsheet programs can meet the requirements of study projects when it comes to data, however, they lack auditing standards, the ability to provide different levels of access, strong type checking, and data backup and recovery support.
But if we are looking for a specific field such as clinical trials, public health, and hospitals, we will have to look for specialized data management systems in adherence to standards and guidelines.
Ideally, study projects should be considered based on the nature of the study such as type of study (observational studies or interventional studies), study size and complexity (small pilot or large, or trials), collaboration needs (access control), the need for standards and compliance, project budget and so on. Examples are REDCap (Research Electronic Data Capture), OpenClinica (Community Edition), Castor EDC, etc…
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In my experience, the data collection and management process is very simple, involving setting up data type and validation, data collection, and data visualization. When it comes to data entry, there was neither double data entry nor single data entry with manual review. The given workflow also seems unrealistic for underserved communities with human resource shortages, where clinics and health centers are funded by donors or NGOs.
Nevertheless, based on the provided data management workflow, I would add protocol discussion to ensure everyone is on the same page with the agreed protocol. Second, I would implement database security and locking to prevent information leaks. Another significant consideration is a document sharing policy and to share the data in a public data repository during the project closure if its donors or owners allow so that it can support researchers to conduct future studies. However, if the project or program is executed in the enterprise or private hospital, I would follow the given comprehensive data management workflow.
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In Oct 2021, I conducted mental health pilot research on whether playing mobile games is one of their mental reliever, targeting young adults between 15 and 30 years old, experiencing the political unrest and civil war following the coup.
The primary data were collected quantitatively via an online form. There were not any problems regarding the data collection since it was digitally validated and provided with a lucky draw program, which does not influence or skew the results.
The finding concluded that mobile games offered a temporary distraction from the grim political situation in Myanmar rather than actual mental relief.
The challenge in this pilot research was in formulating research questions that deviated from the objectives of the research.
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I have learned case studies of whether AI assistance can hurt or improve their performance, balancing innovation and ethics, generative AI and medical ethics, and, more importantly, how to be ethically responsible in research and innovation. I have also learned what to consider in research with AI, including the regulatory aspect.
I re-organized this knowledge into three topics: Precaution, AI & The future of Healthcare, and AI Governance.
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Primary Health Care (PHC) is a whole-of-society approach to health that aims at ensuring the highest possible level of health and well-being and their equitable distribution by focusing on people’s needs and as early as possible along the continuum from health promotion and disease prevention to treatment, rehabilitation and palliative care, and as close as feasible to people’s everyday environment (World Health Organization: WHO, 2023). The primary care system assesses how early and convenient a sick person can get treatment. It is the first level of contact between individuals and the health system.
The availability of the primary healthcare system has been widened from centralized, urban-focused to decentralized, community-driven healthcare services in Myanmar. According to Grundy & et., al. (2014), the development of primary health care began after the post-independence period extending up to the 1988 uprising political event; the second period from 1988 to 2005 is when the country launched a free market economic model, but experiencing very low levels of national health investment; the third period (2005-2012) was the first attempts at health reform and recovery including national political reform and international politics; the fourth period was during the democratic transition period (2012-2020) which increased levels of development assistance for the health sector.
Before the 2021 military coup, the primary healthcare system (PHC) of Myanmar was handled by multiple stakeholders: private (for-profit), public (non-profit), ethnic health organizations (EHOs), and international development assistance. In the areas governed by ethnic armed organizations (EAOs), PHC services are provided by EHOs.
During the COVID-19 pandemic, the people-elected democratic government formed the National-level Central Committee on Prevention, Control, and Treatment of Coronavirus Disease 2019 to strengthen governance, scale up testing capacities, and coordinate emergency responses while maintaining essential services such as maternal and child healthcare (Lin et. al., 2023). Several health interventions were implemented at the primary level during the pandemic.
According to the case study by Lin et. al. (2023),_ Scaling up COVID-19 tests: With international support, Myanmar expanded its testing capacities from around 400 tests per day in April 2020 to around 12,000 tests per day by mid-September 2020
_ Expanding public awareness: The Ministry of Health and Sports (MoHS) utilized various media platforms to disseminate information about COVID-19. Celebrities and community leaders were also engaged in public awareness.
_ Telemedicine services: Teleconsultation services were provided by private hospitals and non-profit organizations.One month before the first case was detected, the government began active surveillance, trained health providers and professionals, and pre-allocated essential medical supplies. Nevertheless, Myanmar faced a shortage of human resources. The MoHS invited volunteers with medical backgrounds and community health workers. Volunteer support was critical in ensuring families received medical assistance. Intensive care unit beds and ventilators could not be provided. Generally, challenges included a lack of human resources when needed, limited testing capacities, weak infrastructure, and insufficient medical facilities. Additionally, there was no information and health services in conflict-affected areas.
While the democratic government was trying to control the COVID wave, the military seized power on 1 February 2021. From that point on, Myanmar’s healthcare system collapsed and corrupted. The consequences in the medical sector I encountered firsthand were brutal, for example, (1) a surgeon in Yangon refused to perform a medical procedure because a 2-week-old baby and his parents came from an affected state * * without even offering a COVID-19 test * *, (2) blocking the entry of life-threatening patient to a hospital * * denying them a COVID-19 test first * *, and (3) the military forcefully seizing oxygen cylinders meant for patients from private and public hospitals through armed threats. In the face of such adversity, people managed to struggle for their lives and built support mechanisms through community empowerment and coordination.
In my opinion, just as people were able to struggle through COVID-19 with community empowerment, and just as the people continue to resist the dictatorship system and fight for freedom with their own capabilities until now, Myanmar’s collapsed healthcare sector also has many opportunities to strive to achieve basic healthcare services for the public in rural or urban, or conflict areas or wherever they live, no matter what difficulties exist.
In conclusion, Myanmar’s primary healthcare system demonstrated resilience and innovation by government and non-government organizations including community empowerment during the COVID-19 pandemic despite political unrest. Continued support in community-based healthcare infrastructure from overseas development assistance is essential for maintaining primary healthcare access and ensuring health equity in Myanmar.
References:
World Health Organization: WHO. (2023, November 15). Primary health care. https://www. who.int/news-room/fact-sheets/detail/primary-health-care
Grundy, J., Annear, P., Ahmed, S., & Biggs, B. A. (2014). Adapting to social and political transitions – the influence of history on health policy formation in the Republic of the Union of Myanmar (Burma). Social science & medicine (1982), 107, 179–188. https://doi.or g/10.1016/j.socscimed.2014.01.015
Lin, A. M. H., Tin, N., & World Health Organization. (2023). Myanmar: a primary health care case study in the context of the COVID-19 pandemic. World Health Organization. http s://iris.who.int/bitstream/handle/10665/372726/9789240058811-eng.pdf -
Please share about the work towards the UHC scheme in your country, what works and what needs to be done to make it work, and its strengths and weaknesses, for example.
Universal Health Coverage is about ensuring all people get the quality health services they need, without experiencing financial hardship (World Health Organization: WHO, 2019). Universal Health Coverage (UHC) is not a realistic statement; however, it is a powerful concept that drives responsive governments to accomplish it.Having said that, public health services are not provided for free by governments. Instead, they manage to secure the required budget through various mechanisms including national health insurance plans, tax-funded systems, and employer-based contributions. In the absence of other funding options, philanthropic health NGOs become the exclusive providers of primary healthcare.
#_ The work towards the UHC scheme
Myanmar relies on an out-of-pocket scheme. Myanmar National Plan 2017-2021, based on the UHC concept, was introduced before 2017. The plan focuses on extending access to a Basic Essential Package of Health Service (EPHS) to the entire population by 2020 while increasing financial protection (Ministry of Health and Sports, 2016). In terms of the UHC dimension, it focuses on the population with basic healthcare services while reducing out-of-pocket spending on health.
#_ What works
As a result of this plan, according to the report by The Lancet. Global health (Han et. al., 2018), coverage of health services ranged from 18.4% to 96.2% nationally, however, most indicators such as skilled birth attendance, and the institutional delivery rate fall below the 80% UHC target. In terms of quality of care and services, the progress is limited to success due to underfunded township health facilities and a lack of standardized training for ethnic health workers (Community Partners International, 2024). Regarding cost reduction, 2% were impoverished by healthcare costs while 14.6% of households faced catastrophic health payments (Han et. al., 2018).
#_ What needs to be done
As a solution, increasing funding for township-level health facilities and expanding community-based health workers (CBHWs) to rural areas could strengthen community health systems. According to the World Bank report (2017), one suggestion includes governance reform to increase accountability and eliminate inefficiencies. The Ministry of Health and Sport underspent 15.26% of its budget allocation in 2016–2017(The Ananda, 2018)._ Strength and Weakness
While this plan possesses strengths, it also exhibits weaknesses. Some of the positive impacts are (1) covering more area and more population and (2) reducing out-of-pocket expenses and eliminating financial hardship. Some of the challenges in executing the plan are: (1) lack of human resources and increased medical staff turnover rate, and (2) underfunding leading to health disparities.
However, the military coup in 2021 exacerbated weaknesses. It has devasted healthcare infrastructure leaving the population with severely limited access to basic essential healthcare. Myanmar’s UHC index declined from 52 in 2015 to 52 in 2021 (ASEANstat, 2024). The score is now anticipated to decrease significantly more than in 2021.
In conclusion, the National Health Plan (2017-2021) delivered primary healthcare services more or less than 80% UHC target. The military coup in 2021 jeopardized Myanmar’s healthcare system, undoing years of progress.References
World Health Organization: WHO. (2019, October 3). Universal health coverage. https://ww w.who.int/southeastasia/health-topics/universal-health-coverage
Ministry of Health and Sports. (2016, December 15). Myanmar National Health Plan 2017 – 2021. https://www.mohs.gov.mm/page/5832
Han, S. M., Rahman, M. M., Rahman, M. S., Swe, K. T., Palmer, M., Sakamoto, H., Nomura, S., & Shibuya, K. (2018). Progress towards universal health coverage in Myanmar: a national and subnational assessment. The Lancet. Global health, 6(9), e989–e997. https://d oi.org/10.1016/S2214-109X(18)30318-8
Community Partners International (2024, September 13). Universal health coverage in Myanmar: The way forward – Community Partners International. Community Partners International. https://cpintl.org/type/impact-story/universal-health-coverage-in-myanm ar-the-way-forward
Moving toward UHC : Myanmar – national initiatives, key challenges, and the role of collaborative activities. (2017). World Bank. https://documents.worldbank.org/en/publicati on/documents-reports/documentdetail/991991513148339321/moving-toward-uhc-myan mar-national-initiatives-key-challenges-and-the-role-of-collaborative-activities
The Ānanda. (2018). Giving but not receiving it. https://www.theananda.org/en/blog/vie w/underspent
ASEAN Secretariat & UN Global Database. (2024). ASEAN Health Worker Density (per 10,000 population), 2022. In ASEAN Health Worker Density: Vol. VI. https://www.aseanstat s.org/wp-content/uploads/2024/03/ASEAN-Statistical-Brief-March-2024-FINAL.pdf -
About 84% of Myanmar’s population with lower socioeconomic status resides in rural areas. Most rural areas lack electricity, safe drinking water, and poor sanitation. This contributes to poor health outcomes. For basic health services, there are NGO-established village health centers. However, more serious conditions require travel to the nearest city hospitals.
The political unrest since the military coup has further caused health disparities. Healthcare has become a battleground nationwide except for the urban centers particularly, Yangon and Mandalay.
In rural areas, the quality of care cannot be expected while struggling for basic care. Essential healthcare is facilitated by internally displaced health professionals within the affected communities. Access to medicine and required equipment is obtained through neighboring countries with external support from various countries. In the military-controlled areas, of Yangon and Mandalay, primary, secondary, and tertiary healthcare services are easily accessible.
Addressing these gaps is a complex undertaking. Political considerations matter in this context. Peace is the only medicine that heals wars. However, practically saying, conflict is a transition phase to eternal peace or hell depending on the conqueror.
Within the context of healthcare, continuous external support from people around the world through the neighboring countries and the presence of available human resources would stabilize the current phase of health service (basic service). Sadly, it would not be feasible to advance to a higher level of healthcare in conflict-affected areas since hospitals are easily vulnerable to military airstrikes.
In conclusion, access and quality of care continue to widen between rural conflict areas and military-controlled urban centers. Although there is no way to fill the gaps, maintaining basic health services in rural areas requires external aid.
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After the Feb 1 coup in 2021, the majority of the functioning health workforce exists in major cities: Yangon, Mandalay, and Naypyidaw in Myanmar. The workforce has already been extremely maldistributed across the country before.
The recent suspension of USAID funding in Myanmar has had a significant and devastating impact on a wide range of health programs such as HIV/AIDS, TB, and malaria, and has the potential to increase the risk of disease outbreaks. There are a large number of internally displaced people in the conflict-affected areas and the suspension leads to the difficulty of obtaining access to essential healthcare services (Imagine you were sick and there was a shortage of medicine, in a few days, you died — is the world just?).
In terms of health workforce shortage, urban centers like Yangon, Mandalay, and Naypyidaw have a moderate number of healthcare professionals as most people from other areas which are in conflict, migrated to the major cities. In contrast, areas like Chin, Kachin, Rakhine, and some parts of Shan experience catastrophic shortages, with low sustainability. Other regions, like Ayeyarwaddy, Bago, Magway, Sagaing, and Mon face extreme deficits.
So, what can we do for areas with critical deficits?
If we talk about establishing a healthcare workforce in war zones, it would be an extremely risky undertaking. So, the realistic solution is to employ internally displaced healthcare professionals (doctors, dentists, nurses) from within the affected communities. Even if they can function to provide healthcare services, multiple challenges remain. Among these, access to medical products and equipment is a major obstacle.
If we analyze it on the map, we can see where the solution to delivering medical products and equipment lies, that is simply, from neighboring countries. Remote conflict areas need external support from neighboring countries. Without aid, diseases know no border and will spread relentlessly. Providing essential medicine to border conflict zones protects neighboring countries from disease outbreaks.
Looking at the map, logistically, medicine supply routes could be established from China to Shan and Kachin, from Thailand to Karen and Karenni (Kayah), and from India to Chin and Rakhine. Unlike the logistic delivery, since the delivery is without the involvement of bilateral governments, it is a life-threatening delivery, that is, air strikes, snakebites, drowning and landmines are very likely, unlike normal circumstances in other areas.
In summary, the situation in Myanmar is dire with widespread instability in every sector. There is no other way to provide essential healthcare services in conflict areas in Myanmar except by relying on healthcare professionals from internally displaced communities and external support from neighboring countries to streamline medical products and equipment.
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I was a part of transforming from paper-based medical records to a digital system for underserved communities.
There were no engineering obstacles to digital transformation such as implementation, security and privacy, infrastructure, and resource requirements. However, there are major challenges in user perspectives.
_ Users enjoy writing records on paper more than data entry into the software system.
_ Users spend more time on digital data entry than paper-based recording for a single record.
These two facts remain valid after two years of use.
On the bright side, there are many benefits of using EMR in conflict-affected areas.
_ Data are protected against physical destruction of records.
_ Data are accessible anywhere anytime (stored on the cloud).
_ Displaced patients can access their records at different health facilities.
_ Healthcare providers/donors can review aggregated accurate data summary
_ Assist in disease surveillance and investigation
_ Assist in effective management and provisioning of vaccines and medicine
While offering many benefits, two key disadvantages mentioned above present a significant risk of a domino effect, impacting all aspects of the situation. No data flow, no users – the software is essentially dead.
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The article focuses on utilizing Big Data to solve cardiovascular diseases and mentions the challenges of practically approaching it.
# (1) Missing data: It is due to the data being omitted by clinicians, considering it unnecessary, patient refusal, disagreeing with data collection, and unsolvable missing data. As a result, there is less than 10% manageable missing data, 10-60% unmanageable missing data giving different results among methods, and 60% of missing data does not have a valid statistical solution.
The paper mentioned different solutions: (a) Complete-case analysis, (b) Available-case analysis, (c) Imputation techniques, (d) Mixed effects regression models, (e) Generalized estimating equations, (f) Pattern mixture models and selection models.
In addition to that, my suggestion for this missing issue is that data fields should be validated during the process of submitting if the data collected is aimed at further research. There should also be a beneficial program for the patients such as providing a monthly lucky draw.
# (2) Selection Bias: As the patients differ in their geographic profiles, insurance coverage, and medical history, this results in different variable distributions in different treatment groups. Consequently, a large volume of data no longer ensures a representative sample, preventing making any valid inference, and generating several false positive results.
The paper suggests (a) Propensity score analysis, (b) Instrumental variable analysis, (c) Mendelian randomization for genetic studies, (d) Considering results as hypothesis-generating, and (e) Validating through RCTs.
My opinion on this issue is the same as the Propensity score analysis which matches patients with similar characteristics across treatment groups to reduce bias.
# (3) Data Analysis / Training: Lack of formal trainings in informatics, coding, data analysis, large database handling, inefficient algorithms leads to this complexity, resulting suboptimal analysis and inefficient data processing.
In my opinion, this could be easily improved by providing formal training programs, and collaboration between clinicians and data scientists. Analyzing singly-handed could lead to doing the right thing in the wrong way.
# (4) Interpretation and Translational Applicability of Results: Studies being complex and not self-explanatory with poor variables description, subjective assumptions in analysis, questionable data quality could contribute to unclear conclusion and biased interpretation.
To improve the interpretation and translation applicability of results, firstly, the variables and metadata in the datasets should be consistently well-defined to make it easier to interpret or use across studies. Standardization will address this issue. Secondly, validating through independent studies should be established whether replicating the same studies confirms the same results.
# (5) Privacy and Ethical Issues: Medical servers can be targeted by cybercriminals and there could be a risk of identifying individual information. As a result, it compromises individual privacy.
The paper discusses (1) using broad consent models, (2) implementing a “social contract”, (3) continuous improvement of data security systems, and (4) balancing privacy protection with community benefits.
My concern is about balancing privacy protection with community benefits. If it aims for benefits primarily, it is open to abuse such as corruption and public manipulation. Even if there are no benefits, ethical standards, and privacy policies should not harm people.
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I will pick the first misinterpretation: “The P value is the probability that the test hypothesis is true” (Greenland et al., 2016, p. 340). I will elaborate on it with an example of the coin example.
The coin is fair either P(H)=0.5 or P(T)=0.5 which is H0 (null hypothesis)
But if you believe that the coin is biased towards the head, getting more heads, then P(H) ≥ 0.5 which is H1 (alternative hypothesis)
Now, you experimented 100 times and got (observed) 60 heads of 100 times.
You calculate the p-value and get ~ 0.028 (2.8%).
Here, you cannot misinterpret 2.8% as the probability that the coin is fair or the probability of getting more than 60 heads.
Instead, the p-value indicates that there is a 2.8% chance of seeing 60+ heads by random chance under the assumption that the coin is fair.
Because the p-value will randomly vary with each experiment and each flip under the assumption that the coin is fair.
Reference:
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. *European Journal of Epidemiology*, *31*(4), 337–350. https://doi.org/10.1007/s10654-016-0149-3
I might be wrong. Please feel free to correct me.
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Based on the four recommendations points, as a public health professional, one can participate actively in building consensus on corruption’s scope. If it does not exist yet in his organization, he can give advice to the organization he’s working at if they are open to ideas.
Second, in agreeing on the consensus, it might need to define the acceptable form of corruption because it enables dysfunctional system works, for example, bribing officials could allow to access medications for a patient with a rare disease needing affordable drugs which is available in another country, but is refused by the government due to political reasons. However, when it comes to the issues of the organization, it must be rejected. For example, a hospital has a long waitlist for a critical surgery, and paying a bride could move a patient up the list. In this case, their management team should review their operation whether it needs more technical efficiency, or hiring more high-performance surgeons to speed up the queue. As the paper suggests, it is important to concentrate on what matters most.
Third, as a public health professional, it is vital to contribute to the corruption issues in the research communities and share data on anonymized corrupt practices to build evidence for effective interventions and increase the visibility of corruption in the health research databases.
Fourth, the research should not be limited to the health sector because corruption is a systemic problem not a localized problem. Therefore, an interdisciplinary approach is necessary to enable a holistic view of the problem.
I agree with these four recommendations, however, corruption could be either a social norm or unavoidable, especially where the rules of laws are not effective and the government itself is corrupt. In this case, the boundary of corruption should be defined. On the other hand, all these recommendations can only be exercised but limited within the organization.
In addition to these recommendations, there are some suggestions I would like to propose. First and foremost, each individual should be ethical himself first and be responsible for reporting corrupt practices if the doors are open. Secondly, but of equal importance, the government should strengthen anti-corruption and enforcement mechanisms because the government affects more or less every facet of public life. Finally, public health professionals should promote external audits from a small project to a large program to be implemented across all health organizations.
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Hi Kedsarin, your infographic is super creative and includes most of the discussion points in the webinar. If there is a legal case from using a teleconsultation app, not only doctors but also the platform could be held responsible in places where the platform is not required legally to have a physical hospital and services.
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Hi Wannisa, thanks for sharing a summary that covers everything we’ve learned and discussed this week. I think the challenge of “legal issues when a problem occurs” could be simplified when a teleconsultation service via an app requires physical hospital and healthcare services, as in Thailand. Otherwise, as you said, legal issues could be debatable when a problem occurs.
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Very detailed and concise infographics including our discussion points. As Jumbo discussed in the webinar, there could also be a case in which employees sometimes do not differentiate between ethical and unethical actions. In that case, as you mentioned in the graphics, reinforced training would be definitely helpful.
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Thank you for sharing your experience, Aung. My valuable lesson from the lecture is that determining the data collection method should be based on the familiarity of people at the operational level because my experience with communities suggests that electronic data capture (a method unfamiliar to data collectors despite being trained for several months) can slow down their data entry causing less data than aimed if it is validated excessively while moderate validation can lead to invalid data. I agree that digital survey or mobile-based data collection is the best for us who are already familiar with it. Regarding data, I believe that making de-identifiable data public (if permitted) would be highly beneficial. This also requires planning at the beginning of the project. The value of data is realized through sharing and analysis. Archived data is dead and useless. On the flip side, in the Myanmar context, de-identifiable data could be re-identified by the authoritarian regime for strategic misuse, such as the total population in a specific area and resource distribution.
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I agree that community-based surveys can not satisfy this workflow standard. Data checks are also essential before processing data for analysis if paper-based data collection is used. Additionally, I think that we could also add single or double data entry with manual or peer review to ensure the quality of data.
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I agree that field-based and remote-based experiences are different. Not only are data skills important, but also communicating between multiple stakeholders (focal person, research team, technical team) requires a lot of soft skills. Because the way we communicate can impact the result (I would call it communication bias)
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I agree that people in rural areas do not traditionally receive quality care with being in a low priority. If basic health care is not managed to be distributed equally to the entire population (or state/region/division) of all races, health equity is a fancy term used by public health professionals.
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I like the idea of giving more incentives to work in rural areas. According to the law of economics of scale, people in rural areas are not financially viable, therefore, those places cannot scale up many resources and infrastructures. Regardless of those factors, doctors should be assigned enough for patients. If assigning doctors to where they have to work is determined by themselves, providing more incentives could be an attractive factor.
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Thank you for sharing SSS and CSMBS in Thailand. I understood that the difference between them is that they include the benefits of family members for CSMBS. However, from a different perspective, to be fair, employees working in private sectors are assumed to earn more than civil servants. With this assumption, I think that the benefits are skewed into civil servants.
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Your discussion gave me a new perspective. I think the resource challenge in medicine, particularly the demand for doctors and nurses, comes from how medical schools teach. Frankly speaking, schools treat all medical knowledge as crucial, even though much of it fades away after graduation. We need to re-evaluate both the teaching methods and the entire process of becoming a doctor. For example, instead of teaching basic sciences (biology, chemistry, physics) in the first year (which have been already taught in high school education), pre-clinical studies (anatomy, physiology, pathology) should be introduced in the first year.
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I agree that EMRs are the key drivers of healthcare. On the other hand, EMRs should be managed not to be easily accessible by everyone at healthcare provider. To prevent accessing patients’ data by senior officers or higher authorities of healthcare providers, it could be protected by implementing an Identity and Access Management policy either on-premise infrastructure or cloud or both.
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It’s interesting that I have never thought that spending office hours for personal tasks as corruption but abusing while spending office resources is indeed a corruption. Sometimes, employees may lookup information such as travel and tour, local news for personal interest, on the other hand, as per traditional company policy, everything we do on the company laptop belongs to the company, so it’s risky if we have personal photos and videos.
I agree that network analysis is useful in uncovering corruption. In banking sector, they use a graph database (a network of transaction from one person to another) to find out fraud. In health sector, I believe that if we check a network of transaction (if digital record), it will be easier to find out.
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Your discussion is thought provoking. Although a hospital uses a health data standard while another hospital uses a different standard, then can we call it a standard? In the end, it seems to me that the universal standard is just paper format. The possible solution to a universal standard is to be enforced by the government. It should be nationally determined although it kills innovation or freedom.
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The six building blocks are useful when strengthening the health systems. As the terms explain, it is the building block, we can build it upon brick by brick / blocks by block. Of all blocks, focusing on the essentials such as service delivery is the realistic approach. In my opinion, I think that a sustainable solution needs financing to keep it up running in the long run.
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Providing free treatments for cancer patients under the UCS is the first significant step towards healthcare equity.
Regarding the long waitlist, I think that prioritizing case by case (urgent cases) would be a fair system rather than a first-come-first-served order. Or, it means that more specialists are demanding.
I agree that there should be a nationally monitored committee for this, and simplifying policies promotes trust and reliability while complicating policies is deceitful.
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