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.