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Ways to cope with challenges in big health data/EHRs:
1.Minimizing Data Inconsistency: Database Management Systems should include interchange and translation mechanisms to standardize data across hospitals and platforms. Furthermore, machine learning algorithms should be leveraged to extract meaningful diagnoses and investigation results from unstructured clinical text. This allows important information to become usable for analysis and decision-making.2.Improving Data Quality: EHRs should require mandatory completion of key CVD fields such as blood pressure, lipid profiles, ECG findings, and diagnosis codes before a record can be closed. This helps ensure clinicians capture essential information during routine care. In practice, smart alerts can gently remind clinicians when essential data are missing, making data quality part of daily workflow rather than an extra burden.
3.Building the culture of data ownership: a culture where clinicians and relevant persons feel ownership of the data. For instance, showing clinicians dashboards of their own patient outcomes or service performance can help them see that the data benefits their work, not just publications and research purposes.
4.Enhancing Translational Applicability: Utilization of machine learning can help standardize image interpretation, such as echocardiography, ECGs, and radiology images. This supports clinicians with consistent assessments. In addition, researchers should improve transparency by clearly describing dataset variables. When clinicians understand what the data mean, research findings become usable.
5.Reducing Selection Bias: In clinical research, stronger statistical methods should be applied, including hypothesis testing and appropriate sampling techniques. When possible, randomized controlled trials and population-based datasets should be combined with routine clinical data to reduce bias. This helps ensure that findings reflect real-world situation.
6.Investment in Training: Investment should be considered in formal training curricula in informatics, coding, data management, and advanced statistical tools. Training should not only target researchers but also clinicians who enter data daily. When staff understand how data are used, they become more motivated to collect high-quality information and apply research results in practice.
7.Solving Privacy and Ethical Issues: Updated cybersecurity and encryption systems are needed to implement ahead of cyber threats. Clear governance frameworks, consent procedures, and role-based access control help protect patient confidentiality.
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To cope with the challenges
1. Addressing Missing Data
Improve source data entry: Agree-on standardized variable and mandatory data entry field ensuring all essential data are filled.
Capacity building: sufficient training is provided for the assigned data entry staff.
Use alternative analyses for handling high missing values: for example, amputation techniques, mixed effects regression models, generalized estimating equation.
Use appropriate statistical methods based on the level of missingness (e.g. multiple imputation, mixed-effects models).
Conduct regular review sessions: data audits are regularly conducted to identify operational and systematic gaps to smoothen workflow and improve data quality.2. Reducing Selection Bias
Use advanced data analytic methods: including propensity score analysis, instrumental variable analysis and Mendelian randomization.
Use big data mainly for hypothesis-generation: always check and validate with RCT or triangulate multiple studies to be used for clinical practice.
Ensure transparency: about inclusion/exclusion criteria and participants characteristics.3. Strengthening Data Analysis Capacity
Build a team including experts with various skills for data handling: to handle very large datasets with multiplicity requiring multiple analyses to establish the significance of a hypothesis and identify correlations.
Build a multidisciplinary team: including clinicians, researchers, health informaticians, data scientists, statisticians and others.
Capacity building programs: for researchers including data science, health informatics, statistics and machine learning.
Standardize analytical protocols: to reduce multiple testing inappropriately and false positives.
Use validated algorithms and reproducible methods: for data analyses ensuring accuracy, transparency and ability to verify independently and therefore improve reliability of findings.4. Improving Interpretation and Translational Use
Early involvement of relevant stakeholders: for example, involve clinicians from the beginning of the study (designing to interpretation of results) to ensure clinical relevance and produce actionable results.
Produce results in clinical usable/meaningful formats: focus to provide actionable insights, not complex ones.
Enhance documentation: standardized essential data variables and documentation to be interpreted and effectively used.5. Managing Privacy and Ethical Issues
Data governance, oversight and data protection: regular audit trials with access control, encryption. Clear laws and policies should be in place to mitigate the breach of personally identifiable information.
Anonymize data: to reduce identification risks.
Minimize data: only necessary information should be provided and used by researchers to reduce data breach.
Ethical Board consent: to request consent from board members to balance privacy with public health benefits and ensure research is conducted ethically.6. System-Level and Policy Solutions
Data sharing policy: ensure responsible data sharing across organisations and departments with clear regulatory and ethical safeguards.
Data standards: develop national standards for EHR to enhance interoperability, data quality and health information exchange.
Digital infrastructure: safeguarding infrastructure according to minimum standards to prevent cyberattacks and data breaches.In conclusion, many adjunctive and robust procedures should be planned and implemented at each steps of data processing to make the greatest possible use of big data and improve public health.
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To cope with the challenges
1. Use standard formats
Use common rules and coding systems so data from different hospitals and systems can be easily combined and compared.
For example, public hospitals and private hospitals in Myanmar should use the same diagnosis and reporting formats so patient data can be shared nationwide.
2. Improve data quality
Check data regularly to remove errors, duplicate records and incorrect information.
In Myanmar hospitals, regular review of electronic and paper-based records can reduce mistakes caused by manual data entry.
3. Manage missing data properly
Use suitable statistical methods to deal with missing information instead of ignoring it.
This is important in Myanmar, where some patient records may be incomplete due to limited resources or emergency situations.
4. Link data from different sources
Combine data from electronic health records, medical devices and registries to get a complete picture of patients.
For example, linking hospital records with data from community clinics and screening programs in Myanmar can improve disease monitoring.
5. Train healthcare staff and researchers
Teach doctors and researchers basic data analysis and computer skills to handle big data better.
Training programs for Myanmar healthcare workers can improve confidence in using electronic health systems.
6. Use advanced analysis tools carefully
Apply machine learning and computer models to analyze large datasets but always test and validate the results.
These tools could help Myanmar researchers identify trends in common diseases such as hypertension and heart disease.
7. Protect patient privacy
Use strong security systems and follow laws to keep patient information safe and confidential.
This is especially important as Myanmar moves from paper records to digital health systems.
8. Clear rules for data sharing
Create clear legal and ethical guidelines so data can be shared safely for research.
National policies can guide how hospitals and universities in Myanmar share health data responsibly.
9. Reduce bias in data
Use proper methods to reduce bias and make sure results represent real patient populations.
Including data from both urban and rural areas in Myanmar helps ensure fair and accurate results.
10. Apply results to real healthcare
Make sure research findings are easy to understand and can be used by doctors to improve patient care.
For example, research findings can help Myanmar clinicians improve prevention and treatment strategies for cardiovascular diseases.
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