What the Fourth Industrial Revolution can teach us about healthcare in poor countries
As with prior industrial revolutions, the question for those countries at a wealth or resource disadvantage is not if the Fourth Industrial Revolution will reach their people but rather when. At present, just as there are enormous disparities between the financial wealth of nations, there are substantial inequities in data production, storage, and access. Low and middle income countries (LMICs) can leverage their large populations and the falling price of technology to close the Big Data Gap. This requires effective and timely investments in scalable digital systems. Data on the health of individuals and populations is among the most valuable, yet such data are only minimally present in poor countries.
The fourth industrial revolution, representing the convergence of biological, social, and digital systems, will transform the public health and healthcare of rich and poor countries alike – again, the question is a matter of timing. When is of life-and-death consequence to citizens of poor countries; the number of years that separates the coming of the fourth industrial revolution’s changes to healthcare will relate directly with the amount of excess morbidity and mortality suffered. Brexit and the election of Donald Trump, partially driven by the politics of economic exclusion and inequity, further demonstrate that closing the digital equity gap are also of great importance in wealthy countries.
The power of big data for health is getting to, statistically, “n = all”. At present, in LMICs, planners and researchers alike have to rely on traditionally sampled populations to make decisions and test hypotheses about improving population health. The concept of n = all is that of the complete, continuous census, that every person who interacts with the healthcare system is integrated with other data points and incorporated into a surveillance system. The power of such a surveillance system is when data about household economics, geography, social factors, biology, illness, and genomics are all integrated.
The infrastructure that is needed for n = all healthcare systems builds off existing healthcare and data resources. The infrastructure required are two-fold: 1) professional community healthcare workers who continuously interact with a defined, complete population, in both health and illness; 2) simple, connected smartphone-based applications that connect to facilities-based data, including laboratories. Can such infrastructure be afforded? Yes – even now, at somewhere less than $5 per capita, such an infrastructure could be deployed. Indeed, we’ve already proved the feasibility in rural Nepal via a public partnership we’ve operated.
This infrastructure is necessary to rapidly improve health systems performance and achieve universal health coverage. Improvements can be made in disease surveillance, where laboratory data at the facility level could be combined with syndromic data at the community level for more timely and accurate epidemic response. This can prevent the enormous health and economic toll of epidemics. The prevalence of chronic conditions can be combined with the effectiveness of various known treatments to more adequately equip and resource healthcare providers. Entire populations can be enrolled and tracked to identify gaps in Universal Health Coverage. Overall, the healthcare system can become more adaptive and learning-centered.
Yet that infrastructure is Third Industrial Revolution infrastructure. The next opportunity, the one made possible by the Fourth Industrial Revolution, is to leverage rapid advances and cost reductions in machine learning, genomics, proteomics, and the microbiome and incorporate these into a personalized medicine on a population scale. We take personalized medicine to include both the social determinants of health – locally-specific data on water and air quality, food security, safe housing—and the biological factors – genes, proteins, microbes. All of these can be integrated, built upon the core infrastructure that continuously censuses citizens with community healthcare workers. LMICs that can establish the n=all public health infrastructure will be the ones best positioned to incorporate these technologies and encourage companies and other institutions to invest in their integration.
There are numerous privacy, security, and ethical issues posed by the kind of n = all personalized medicine we envision here. Citizens and governments will need to adapt laws and practices and protections to adapt. Indeed, this is where democratic governance is so critical – it is difficult to imagine a deployment of n = all healthcare systems that respects human rights in the absence of democratic institutions. The data architecture for public health surveillance should not be able to be used by states against dissidents, free speech, or personal privacy. This is why democracies like Nepal, where we work, need to lead LMICs in the coming Fourth Industrial Revolution and what it means for population and personal health.
Nepal is herself committed to being a leader in the innovation of affordable digital systems for the purposes of surveillance and science. We have laid the foundation for this via an innovative Public-Private Partnership that leverages Nepal’s evolving democratic governance tradition with that of our organization, Possible , which has a strong track-record in grassroots public sector healthcare delivery. Thusfar, we have created the basic elements of the platform – a continuous surveillance system by Community Healthcare Workers, a biometrics application, and a hospital-based Electronic Medical Record. The next steps will be iterating on our approach for scale, identifying new scientific applications, scaling up in Nepal, and shared learning and dialogue with other countries.
Together, we see great possibilities for LMICs to “leapfrog” many of the present barriers and be not merely passive recipients of Fourth Industrial Revolution technologies but rather be themselves contributing to the forefront of innovation. A key first step will be the core, comprehensive, and organized data architecture underlying healthcare delivery.
Duncan Maru, MD, PHD is a physician and epidemiologist at Harvard Medical School and a Schwab Foundation Social Entrepreneur of the Year. Honorable Minister Gagan Thapa is the Minister of Health of Nepal and a Schwab Foundation Young Global Leader.
SOURCE: World Economic Forum