Reduce costs, save lives: how healthcare data can help emerging economies

Emerging economies are projected to surpass mature economies in overall generation of data by 2020. The volume of healthcare data, one of the fastest growing segments of data, is estimated to be increasing by 48 percent each year.

Where is the data coming from?

Technology-driven advances are having substantial impact on healthcare delivery models, including the wider adoption of genomics, cloud and edge computing, drone delivery systems, artificial intelligence and Internet of medical things (IoMT). These innovations are contributing to exponential growth in data proliferation, particularly in the healthcare sector.

This rapid growth is leaving the healthcare sector with the challenge of managing increasingly large volume and variety of data. A less visible problem, however, is the risk that emerging economies will be “data-rich but information-poor,” a paradox where all this data does not necessarily result in actionable insights to improve patient outcomes.

How do emerging markets avoid this trap? There are three key considerations for emerging markets, like Rwanda, to leverage the exponential growth of healthcare data and leapfrog the developed markets: developing a skilled workforce, breaking down siloes of data through building for interoperability and setting forward-leaning data policies

Health data is generated in a dynamic and complex ecosystem, which involves ever expanding sources of data, stakeholders and capabilities. The workforce needs are also evolving by necessity, to adapt to the new digital health models and patient needs. A workforce development strategy should start by surveying the existing clinical and non-clinical staff and their functions, then analyze how technology-driven advances are likely to impact the roles and functions of healthcare professionals over the next 10-15 years when many digital technologies will be widely deployed in the health sector. New skills will be required to increase digital literacy of the healthcare workforce, with a focus on specific areas like genomics, telemedicine and other remote patient care pathways, artificial intelligence and big data. The changes in skills required will have implications on curriculum design, training or re-training and development programs for existing and future healthcare staff.

Emerging economies are particularly willing to embrace change and become early adopters of innovation across many sectors, an advantage in adopting new ways of training the workforce. For instance, Rwanda has attracted leading science and technology institutions looking for innovative environments in emerging economies. Notably, the African Institute of Mathematical Sciences (AIMS) and Carnegie Mellon University (CMU)- Africa both have campuses in Rwanda. These institutions are contributing to developing a critical mass of brilliant digital leaders focused on solving context-specific challenges in Africa and beyond. Countries like Rwanda can leverage these institutions to develop curricula and programs that respond to emerging workforce needs in healthcare including genomics, big data analytics and bioinformatics. Health education entities can facilitate upskilling or new professional roles that fill existing gaps in the workforce while building a critical mass of appropriately trained health professionals.

Although most health systems use Electronic Health Records (EHRs), only a small percentage have interoperability between various providers and health facilities. Interoperability between healthcare records systems is the main barrier to functional digital health systems and a seamless patient experience and efficient payment and reimbursement. A major hurdle has been the lack of a comprehensive and widely accepted standard that can be used to transmit data from one information system to another.

The FHIR (Fast Healthcare Interoperability Resources), a standard for exchanging healthcare information, has gained considerable attention as it uses an internet-based approach to allow access to data across EHR operating systems. It means a patient who is treated by multiple healthcare providers will not deal with traditional hard-copy archives or siloed clinical records, but rather, will benefit from a comprehensive personal health record that consolidates medical history, laboratory test results and imaging, hospitalization records and medications.

The FHIR standard is an important step towards widespread interoperability and integration. The latest development of FHIR standard is reducing technology barriers and specifying a better data model and workflow.

Many countries and institutions are developing or updating their eHealth and digital technologies strategy. Emerging countries in early adoption or building up digital health can adopt interoperable systems from the start rather than replicating today’s data environment of siloed and fragmented data in entrenched and legacy systems. These countries can adopt a standard like FHIR, which lowers technology barriers, reduces the need for extensive training and increases information exchange for better care management at a lower cost.

The increased volume of health data has raised concerns around data governance, privacy and security. Data governance involves striking a balance between the need to collect and protect individual-level information and the need to share the information to create value for the individual and population. Many countries are reviewing their policy and regulations to address challenges around data access, privacy and security, which have significant implications given the sensitive nature of healthcare data. This is particularly relevant for genomic data, one of the most sensitive types of information linked to a person and their relatives.

The leapfrogging with precision medicine project being piloted by the Government of Rwanda in collaboration with the World Economic Forum provides an example of governance protocols and policies around genomic data. The project aims to show how emerging economies can bypass legacy systems and accelerate the adoption of precision medicine.

As countries develop national data governance protocols and policies, there is a need to consider harmonization across sectors with a view towards alignment with regional or international standards. For example, the General Data Protection Regulation (GDPR) sets legal requirements for the collection, storage and processing of personal data, including health data, within the European Union. A significant number of non-EU countries are adopting “GDPR-like” regulations as a result of trade or collaboration agreements with European countries or organizations. The careful attention of policy makers will be key to ensure appropriate alignment internationally while addressing national concerns around healthcare data.

The rapidly expanding healthcare data landscape requires emerging economies to navigate constraints in financial and technical resources, and a range of legal and ethical issues, while building trust in new systems. Rwanda may be considered a smaller emerging economy, but the country is forward-looking and tackling these problems right now. Rwanda is increasingly recognized as a place where innovators feel comfortable working to pilot the best ideas and technologies, and where policy environments support the appropriate collection, utilization and experimentation with healthcare data. The country is making the right investments now, in new digital health models at a fraction of the costs borne by entrenched and legacy health systems.

Although there is no universal answer on how to manage the exponential growth of healthcare data, laying the right foundations and tackling current barriers will pay off as people are able to access high-quality services that reduce costs and save lives.