From the article, I agree with all the challenges provided about Big Health Data for improving the treatment and outcomes for cardiovascular patients. There are some suggestions based on my opinion for coping with those challenges, described as follows:
1. Missing data: Missing data can compromise data integrity and lead to misinterpretation. A common issue I have encountered in data collection is patients not answering every item on a questionnaire for various reasons, such as concerns over privacy, time constraints, or ignoring. In my experience, assuring patients of data security, explaining the use of their health data, and highlighting the research’s potential benefits to others can enhance participation. Additionally, data quality derived from Electronic Health Records (EHRs) is significantly influenced by healthcare professionals’ involvement. Implementing strong indicators to assess the completeness of data entry by healthcare professionals in EHRs, along with regular internal audits, can strengthen health data quality. This approach may require a cultural shift within the organization, emphasizing collaboration among healthcare professionals. Moreover, a robust information system can mitigate data missingness. Features such as alert messages can prompt users to complete missing data fields. If missing data cannot be resolved at the source, statistical techniques may be employed for handling large volumes of patient health data. In such cases, education and training for staff who regularly interact with health data, such as informaticians, statisticians, or healthcare professionals skillfull in data analysis, become essential.
2. Selection Bias: The results of a research that contains the selection bias can also reflect to the misguided information for doctors and it is also serious. The bias can be in various forms as mentioned in the paper; geographic, insurance, medical history. To overcome this challenge, use a diverse range of data sources to capture a more comprehensive representation of the population can help eliminate the selection bias. Incorporating data from multiple healthcare settings, regions, and demographic groups can also help mitigate bias. However, if there might have potential bias in the study, transparently report the limitations of the data set, including any potential sources of bias, clearly communicate the demographic and historical context of the study population, enabling readers to interpret findings within the appropriate context is important.
3. Data Analysis and training: As previously mentioned, skills in statistics, informatics, or analytics are essential for researchers and healthcare professionals when they are dealing with the big health data. Considering the limited availability of skilled staff in these areas, an effective alternative approach is to utilize existing organizational resources for assistance. Engaging with statisticians in the clinical research center or seeking support from staff specialized in these analytical fields can be highly beneficial.
4. Interpretation and Translational Applicability of results. It is always difficult when it involves the application to human’s life such in context of cardiovascular practice. To help reassure the results will be benefit for human, the results’ quality and reliability is needed. Collaboration between interdisciplinary and request for the expert opinion can ensure that research questions are relevant and that findings are practical and applicable. Moreover, learning from the previous outcomes can help in understanding the effectiveness of translational efforts and guide future directions.
5. Privacy and Ethical issue: It’s essential to strict data protection regulations to ensure confidentiality and security. Informed consent should be obtained from participants, clearly explaining how their data will be used, and allowing them to opt out if they wish. Furthermore, using anonymisation of data can help protect individual identities. Ethical review boards must take part in every human related research to ensure compliance with ethical standards and to address potential risks and benefits. Additionally, implementing robust cybersecurity measures can prevent data breaches and unauthorized access.