Vendor View: Adopting AI in healthcare system? Build data frameworks first
Experts offer keys to building robust data frameworks for healthcare data and stakeholder engagement in the Asia Pacific region.
As artificial intelligence (AI) gains ground in the healthcare industry, tech advisory services firm, International Data Corporation (IDC), predicts that by the end of 2026, more than half of healthcare organisations in the Asia Pacific will establish data governance frameworks.
The framework, IDC said, will prioritise ethical and explainable use of AI for predictive and preventive care.
But what factors should healthcare providers consider when creating such frameworks? What challenges will software developers face?
Healthcare Asia consulted industry experts on the matter and they raised very compelling insights and points that require attention.
Eric Dulaurans, Growth Digital Leader - Intercontinental at GE HealthCare
Instituting a well-defined Data Governance Framework (DGF) is particularly important in healthcare, given the sensitive nature of patient information and the complexity of healthcare data management.
A robust DGF can help healthcare organisations manage data more effectively, reduce the risk of data breaches, improve data quality, and enhance the overall quality of care. It can also help organisations comply with evolving regulatory requirements.
DGFs ensure that there are guidelines for all facets of data governance, such as the collecting, storing and processing of sensitive data, the roles and responsibilities of data stewards, and policies and procedures for protecting healthcare data from unauthorised access, use, or disclosure, amongst others.
Whilst this region is no stranger to governance regarding data privacy and security, it is also one that is culturally very rich and diverse in both the dynamics of its healthcare systems and their respective regulatory requirements. This richness and diversity can significantly impact the collection, management, and use of healthcare data.
When building data governance frameworks in healthcare in the region, healthcare providers must consider specific cultural considerations associated with healthcare data, such as patient privacy, confidentiality, and cultural attitudes towards data sharing.
Therefore, when developing software that harnesses the use of sensitive data, such as the use of AI and predictive care in precision healthcare or virtual health assistants, active steps must be taken to enhancing data security, thereby reducing vulnerabilities to data breaches, and enabling better data sharing to ensure more efficient collaboration on patient care.
GE Healthcare creates a set of ethics around how it will and won’t use AI in healthcare. These ethical considerations focus on four issues:
Ensuring the safety and privacy of patients.
Being a trusted steward of data
Guarding against bias
With careful planning, stakeholder engagement and proper change management, such initiatives can be put in place without incurring challenges such as added layers of organisational complexity, bureaucracy and even data silos.
To accelerate healthcare transformation, software developers can lead the charge and guide healthcare providers to not only define what to do, but also guide how to implement a tailored DGF for their customers’ needs, over time, and with measurable and tangible positive outcomes.
By prioritising data integration, data security, and analytics, software developers can help healthcare organisations improve patient care and outcomes whilst ensuring compliance with in-market regulations.
Professor James Pang, Associate Professor of the Department of Analytics & Operations at NUS Business School
When building data governance frameworks in healthcare organisations in the Asia Pacific (APAC) region amidst the rise of AI and predictive care, several key factors need to be considered.
Data Privacy and Security: Implement robust measures such as encryption, access controls, and audit trails to safeguard patient data. Develop comprehensive policies and procedures to protect sensitive health information, mitigate unauthorised access, prevent data breaches, and counter cyber threats.
Regulatory Compliance: Understand and comply with local and regional regulations governing data privacy, security, and healthcare practices. The regulations include the Personal Data Protection Act (PDPA), the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and other relevant regulations specific to each country.
Data Quality and Integrity: Establish data quality standards to ensure accurate and reliable data for analysis. Implement data validation and verification processes, data cleansing techniques, and data integration protocols to maintain data integrity and prevent bias in AI algorithms.
Consent Management: Develop clear policies and procedures for obtaining informed consent from patients for data collection, storage, and analysis. Ensure transparency in explaining how patient data will be used and shared, particularly in the context of AI and predictive care.
Ethical Considerations: Address ethical concerns associated with AI and predictive care, such as potential bias, discrimination, and fairness in algorithmic decision-making. Incorporate ethical guidelines and principles into the data governance framework to ensure responsible and equitable use of AI technologies.
Data Sharing and Interoperability: Enable interoperability between healthcare systems and encourage secure data sharing for research and collaboration. Implement standards and protocols for data exchange, integration, and sharing, whilst ensuring compliance with privacy regulations.
Should this challenge the software developers of healthcare providers? Yes, the development of data governance frameworks in healthcare organisations indeed poses significant challenges for software developers.
They must ensure data security, comply with complex regulations, address ethical considerations, facilitate interoperability, and prioritise user experience.
Developers need to possess expertise in secure coding, data governance, privacy regulations, and emerging technologies.
Collaborating with data governance experts, healthcare professionals, and stakeholders is crucial. Adequate investment in skilled development teams, training, and fostering a culture of data governance and ethics is essential for healthcare providers to meet these challenges effectively.