The Asia-Pacific region (APAC) is a leader in investing in digital health infrastructure and patient-controlled access to personalized health data. While Taiwan has become the benchmark for this approach, governments throughout APAC are quickly following its example. Large-scale government investment in digital health infrastructure across APAC is creating new opportunities to improve patient outcomes and expand access to insurance protection for underserved people.
The development of digital health infrastructures in APAC
Current research demonstrates that APAC is on the cutting edge of using digital health infrastructure and personalized health data to improve patient outcomes. Getting to Personalised Healthcare in APAC: Findings, Insights, and Recommendations, a recent report by FutureProofing Healthcare and the Copenhagen Institute for Future Studies, measured APAC countries on the control, sharing, and interoperability of health-related data and the digital infrastructure that supports them. These benchmarking criteria represent the primary trends we can expect to see in the approach to digital health data throughout APAC.
- Use of electronic health records (EHR)
- EHR strategy
- EHR implementation
- Digital infrastructure
- Patient data control
- Cancer registries
- Health system data
As shown in Table 1, Singapore, Taiwan, and Thailand achieve the highest scores in APAC for their extensive use of digital health information.
Providing patient control of personalized health data in the form of EHRs and digital infrastructure is a critical element for health systems in APAC. The proliferation of personalized data means that patients can receive customized care to meet their specific needs.
EHRs can provide healthcare summaries containing information such as test reports and imaging, prescription drug information, and discharge summaries that can be shared among service providers to ensure consistency and data-driven decision making by healthcare practitioners. Artificial intelligence (AI) and machine learning (ML) applications can use both historical and real-time data collected from wearable devices to prescribe preventative interventions, prevent the threat of chronic disease, and eliminate repeated medical procedures that increase patient risk. Digital infrastructure supported by mobile applications also allows patients to book in-person and telehealth appointments easily. These solutions not only contribute to better patient outcomes, they contribute to overall efficiency within the healthcare system by reducing congestion and predicting the best allocation of medical resources for each patient.
Case Studies: Key elements in digital health infrastructures
As demonstrated by the research, Singapore, Taiwan, and Thailand are leading the way in digital healthcare innovation in APAC. However, we can also see that many other regions are quickly following their lead, with government investment in digital health infrastructure increasing dramatically throughout APAC. The proliferation of personalized data is also set to transform the way insurers approach health risk management. While traditional insurance approaches to patient risk primarily consider factors like age and gender, applying AI and ML solutions to more extensive patient data can predict the likelihood of chronic disease and hospital admission, which will give insurers new ways to assess risk for patient coverage.
According to the research in Getting to Personalised Healthcare in APAC, Singapore, Taiwan, and Thailand are making considerable progress in establishing the necessary conditions for transforming digital healthcare and health insurance. As a result, all other APAC countries will be able to emulate the best practices and example frameworks provided by Singapore, Taiwan, and Thailand.
Table 2 summarizes several of the important elements that make Singapore, Taiwan, and Thailand the benchmark countries for digital transformation of healthcare in APAC, including:
- Policy: Government policies and projects that encourage and promote digital transformation in healthcare, including regulatory sandboxes that support innovation.
- Systems: Digital systems that support health infrastructure.
- Data-Supported Solutions: Digital solutions built on the health data infrastructure.
Government investment in digital healthcare throughout APAC is helping to create better patient outcomes through easier access, increased efficiency, and predictive medicine. However, the infrastructure on which these innovations are built is also opening up significant opportunities for insurance inclusivity, which will not only meet the needs of millions of people throughout the region, but will allow for the introduction of new, AI-based product types.
Digital health infrastructure’s impact on insurance
Government investment in digital health infrastructure is opening new data sources that insurance companies can use to enhance customer experience and improve insurance inclusivity. The predictive power of personalized health data helps insurers serve evolving customer needs by creating more diverse and inclusive health insurance products that transform traditional underwriting approaches to risk.
By using personalized health data that support accurate health predictions, insurers can offer fluidless underwriting for remote sales and new, more inclusive products that integrate prevention as a core component. This would not only allow customers to make more informed decisions about the type of insurance they purchase, it would expand coverage to populations that were previously considered substandard. It would also extend the eligibility age for seniors, which is critical for supporting an aging population. These products could include annual health checkups and unique benefits at point of sale to incentivize customers to engage in continuous health improvement to prevent the onset of chronic disease and encourage them to consent to providing their data.
New data sources derived from digital health infrastructure can help transform health insurance by augmenting underwriting and facilitating product design. These data sources will enhance risk assessments and accelerate underwriting practices by reducing the need for medical exams and manual data collection. Machine learning models take seconds to analyze the risk indicators from digital health data instead of the months that are required to complete medical exams. Underwriters and actuaries would also be able to validate machine-learning predictions as meaningful risk indicators.
Privacy, Security, and Quality: The Importance of Data Governance and Consent
For customers to share their personalized health data from their EHRs, insurers must guarantee privacy and security. Customers must be able to trust the security infrastructure that governs their data and to maintain control over who has access to their data. In addition, insurers must provide value to their customers to convince them to grant access to their data. A version of this trend is already taking place, with insurers providing incentives like discounts or reward points redeemable with partners for customers who consent to providing wellness data, such as step count, from fitness trackers. Fitness data, however, has limited predictability for AI. Insurers must therefore provide more powerful incentives to convince customers to consent to personalized health data from EHRs. Innovative AI solutions that can help customers predict and understand their health data is an example of such an incentive.
References
Personalized health data is transforming healthcare in APAC. When customers consent to provide access to their data, the opportunities for preventive care and enhanced insurance coverage are endless.
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