On December 22, 2022, the Peking University Global Health and Development Forum 2022 was held with the main theme of Digital Transformation and Development Divides. Co-organized by the Beijing Forum, Asian Development Bank and PKU Institute for Global Health and Development, this Forum brought together world leading scholars, policy researchers and industry leaders from both China and international communities to share their insights and recommendations on the thematic topics, attracted over 10 thousands online viewers participated in the event. Victor J. Dzau, President of the U.S. National Academy of Medicine (NAM), Vice Chair of the US National Research Council delivered a speech at the Opening.

Hello Dr. Liu and good morning to every one of you. I bring you greetings from the US National Academy of Medicine. It's a great honor for me to speak at this Global Health and Development Forum 2022 and to follow the honorary speakers that are on this panel.

My topic today is going to be on using digital technology to improve health and population outcomes.

The world face major health challenges, they face challenges in healthcare where there's access and affordability challenges, there's fragmentation of the healthcare system and its delivery of healthcare, the lack of continuity, problems with follow-up and of course workforce shortages. Also, we have major diseases such as infectious disease outbreak, pandemics, COVID-19 and of course non-communicable diseases.

Finally, we have challenging trends that is the population in the global aging and particularly in China. And there's also climate change that affects health and the importance issue of health and health inequity.

Now these major issues are confronting us and I do believe that digital health and data are possible solutions, for example, in areas of healthcare access, improving quality and affordability, fragmentation, integrating health, public health and community care continuity. Being able to coordinate across the different providers and workforce by using digital technology is possible to reduce staff burden and being able to infect better staffing needs.

And as far as major disease is concerned, if you look at pandemics, digital technology and data can be used to look at prevention, detection and responding to outbreaks. And for NCDs, certainly digital technology and data can strengthen scale of public health and community health enable health promotion and disease prevention. And finally looking at the challenging trends, certainly about these technology can help aging in place and promote healthy longevity. As the climate issue, the use of these technologies can improve systems, sustainability and resilience.

And finally, the ability to access data and digital technology can connect people in remote areas and increase in fact and improve equity. I think that digital health and data can address health systems, care delivery diseases and demographic changes, can improve health equity and use disparities.

Let's look at how digital technology can be helpful in healthcare delivery, augment performance, decision support, transforming data into actionable information, improve speed of prediction and diagnosis. It can ensure continuity of care, using interoperability between systems and external data, one can bring these things together, one can also use it to care coordinate across multiple providers. And looking offsite patient management, remote monitoring, telehealth and extending the acute and specialty care to patients at their home and also remote communities.

Finally, partnering with patients to enable self-management, reduce waste and reducing errors in improving clinical risk stratification. But beyond healthcare, when healthcare only accounts to 10% of outcomes, but the current system are built around individual treatments. But you need to go into population health, whether it's large-scale approaches to on health promotion disease prevention, and whereby you can focus on entire health or entire community and therefore being able to identify high risk groups, target prevention and target care.

And finally, it's important to bring in the social determinants, other data and address social equity. As by Abernathy in a published paper "As digital health tools become increasingly sophisticated and capable of capturing social, behavioral, environmental determinants of health, clinicians and caregivers can learn more about the individual in the context of their daily lives… such as digital health applications have the potential to help prevent, mitigate, and reduce disparities in access and care."

In this context, we need to think about social determinants because as I show this, I said before in this paragraph on the left-hand side, healthcare accounts for 10% of health outcomes. There are genetic, behavioral factors, social and environmental factors. And this is where digital technology can meaningfully impact social determinants by actually measuring non-medical and social factors, collecting better standardized, integrate them to help in fact address some of these issues.

And the new cases which are under development include environmental factors like climate change and air pollution, coordination of care during natural disasters and support behavioral change, self-management, and importantly encouraged adherence to treatment plans.

Finally, when you look at healthcare and public health, the real important opportunity is to do data integration. Last decade we seen massive growth in data health and health related data shown here, patient system data, public health data, social environmental data and research data. When you bring all these data to be integrated, it can allow you to have more public health strategies to precisely examine more risks and customize engagement to subpopulations.

Also, community data allows bring together the understanding of the environment patients live, the social factors and systems in which they function, whereby there's greater opportunity to understand where to act and how to act. So there's a need to bring together data all together to understand challenges and various opportunities. And of course providing data from all sectors allow you to have real world evidence where you live, how you live, and how's affecting your health. So real world data and the generation of evidence.

I think data technology and digital technology have great impact on research and in the future. It can move from a reduction of approach to system level approach, integrate from molecules to population health, hence position public health. We should invest and leverage data development, convergence signs and closely work the public health and communities. That's what I wrote in Lancet Paper 2021.

So these are the three areas I'd like to spend a minute on, convergence, what is it? It's bringing together different disciplines and different expertise, integrating knowledge and methods and expertise to look at new frameworks to address the issue of health. So that is social science, behavioral science, economics, law and health sciences all together to address this issue. And digital and data can do a big job in linking these things together.

The ability to integrate clinical care and research was really, really validated and shown by the discovery trial of United Kingdom during COVID-19, where they can link the data from the healthcare duty system, call NHS the National Health System services, along with in fact looking at research so that there's real time enrollment of 40,000 patients, plus 175 hospitals and 13 treatment arms versus non-treatment and get real data. And as you know, recovery data showed the evidence for use of dexamethasone, the use of vaccines and of course of antivirals.

Finally, as I said, if you use multiple sources from health and non-health data, you're now looking at the world in which a patient lives, the real world. Whereby you can look at what happens to that patient. So bringing together electronic health record claims and billing data, disease registry and personal devices, et cetera, can create real world evidence enabling to intervene in the surroundings of the individual patients.

Now it's important to recognize because all this inter collection using digital technology and data collection can be greatly enhanced by artificial intelligence and machine learning because it can create operational efficiencies to make life providers and patients much easier and assist in determining the most optimal health pathway and therefore the future will be defined by leveraging data for value efficiency by using AI and machine learning.

So on the right-hand side the list of what AI machine learning can do, improving diagnosis accuracy and speed. Targeting potential treatments, monitoring access to patients, reducing variability in care, helping look at lifestyles and helping make decisions for patients, augment or automate the workflow.

Now all of this is very exciting but we are not there yet. There are many issues we need to address before the widespread use, everyday use of these technologies. For example, in the area of data integration, you need interoperability, effective platforms to exchange data information across different settings. Data sharing, a commitment to share data across all stakeholders. And you have to minimize the risk of releasing sensitive health and personal data, the issue of privacy. And finally, AI machine learning, we got to be careful looking at the data sets because the data sets will be generalizable and avoid biases so that we can create algorithms that's in fact is biased and not generalizable to everybody.

Now I think all these technologies have to be available and accessible and the big concern is of course the gaps in digital technology access and of course the cost of these technology. Therefore, it can even aggravate inequities. Finally, these data have the evidence-based, technology has been based on evidence that there is actually good outcomes' measurement. Here's where in fact you need really scientific studies to demonstrate the usefulness and the oversight regulation to be sure they approve for use and they in fact have meaningful governance and corporation.

So this means, we talk about the last piece of caution, which is called the digital divide. And here you can see that in the right-hand side there's a map of the world which maps on the use of internet. Dark colors show more up to a hundred percent and light colors shows less up to zero in white colors. And you can easily see for you that the concentration of places like US, North America, Asia, China and others have great use of internet, but not in many of the developing nations and the least developed countries.

The numbers as you can see is 87 of developed world are connected, but 47 in developing nations and only 19 in the least developed nations. But that's not only happening globally, it's happening in our own countries. United States is well known that in fact people living in remote areas have much less broadband access. People who live in poor areas and under-resourced areas have less ability to have devices and broadband access. In fact, in populations like Black, American Indians and Alaska native populations, there's more poverty and then in fact there's less availability of these technology. And I think it happens in China too, given to vast geography. And there are many different neighborhoods and different communities. There's also an unevenness in terms of availability of these technologies.

This digital divide can create a growing gap between those who have and those who have not. Those who don't have broadband, those who cannot afford these technologies, those who have low digital literacy and of course they're also language barriers in the use of this technology. I think the next phase is really important to invest in infrastructure and digital education. We have efforts on communication and collaboration to include those who are hesitant to adopt this technology, when you enhance the knowledge and resources for utilization. And importantly to make sure it's affordable across the entire population.

I think the whole field of digital health is very exciting and I think going forward in this innovation space we can see that in order to achieve the full potential we need to do the following, bring technology development based on valid reliable scientific evidence. Second, we need cooperation between health industry, the government and the public. We need to leverage individuals, fully engage individuals in the management of loan care using digital technology, embed equity transparency as primary principles and payment systems so that we can pay for this technology. And high value care ensures seamless integration, interoperability and of course enhance cybersecurity. And finally, really take advantage of AI machine learning in algorithm which must be validated and generalizable and real-world testing.