The author is a Senior Research Fellow at the Center for International Innovation Management and Director of Artificial Intelligence and Marketing Innovation at Microsoft.
As digital technologies become widespread in our lives, they have a huge potential to affect health and well-being. To get the most out of them, we need a new form of innovation targeted at those who face the toughest obstacles. That is to develop these technologies with historically marginalized people, not for them.
History shows that the effects of inventions usually manifest themselves after their initial adoption. Over time, we begin to see opportunities: touchscreen cell phones; An Internet that provides important information and services and connects people around the world; cars that can run offline.
But inventors cannot always foresee the consequences of their inventions and whom they can leave behind. New technologies are often expensive, at least at first. They need infrastructure such as electricity, networks and roads. They repeat, and in some cases exacerbate, inequality in the society from which they emerge.
Later there are rules: seat belts in cars, protection of privacy of Internet users, a moratorium on face recognition technology and so on. While regulating technology is essential to the public good, it is designed to protect people from measurable harm, not to predict or prevent it in the first place.
We need to expand our approach to responsible innovation to cover methods that are inclusive by default and enable all people.
This means not only expanding technologists ’understanding of the implications of what they create. It also means co-creation with rather than build for marginalized communities and adapt existing methodologies – or develop new ones – to reflect the needs, voices and experiences of people who are often excluded from the innovation process.
What will the model of empowerment for health innovation look like? Here are three tips.
First, empower patients, community organizations and vulnerable groups. The Covid-19 pandemic has made it clear that health systems are often ill-equipped in order to serve the people most in need. Community health workers and organizations are building a bridge between health systems and marginalized community members.
There is an urgent need to maintain relationships between people in need, healthcare professionals, partners, organizations and sponsors – and to ease the administrative burden to focus on direct care to patients. To do this, my colleague Mary L. Gray co-founded Project Resolve with the Healthy Community Hub, a coalition of black and Latin American NGOs in North Carolina.
Project Resolve uses open source software and systems to support two approaches: case-focused care focuses on services for each person, such as helping someone with housing instability; while service-oriented services offer specific interventions – such as a vaccine clinic – for a group of people.
Second, adopt interdisciplinary approaches. Healthcare is highly dependent on data: laboratory outcomes, patient histories, and other factors important to understanding human and population health. But they often lack an important context, such as people’s access to fresh food, insurance, transportation, or stable housing. These factors may primarily determine a person’s ability to afford medication, undergo treatment, or access care.
The more we build digital systems to inform the future of health, the more important it is to incorporate the broader expertise of groups such as anthropologists, economists, linguists, medical, security and privacy professionals, data scientists, engineers and community organizers who deeply understand that can determine good health outcomes.
Third, supplement the “big” data with “small”. One of the biggest challenges in healthcare innovation is “small” or “sparse” data – data that is often lost, skipped, or stored in multiple disabled tables. Examples include patient stories scattered across public health facilities, emergency centers, pharmacies, and emergency departments.
New privacy methodologies are critical to responsibly collecting, storing, and studying “small” data and combining it with large-scale data to create a more inclusive and accurate database.
Pollution monitoring is one example where both big and small data play a role. Satellite images can provide a wealth of data on air quality at the planetary level, but they cannot tell us much about conditions and trends in a particular area. The Eclipse project from my Microsoft colleagues includes data from local sources collected in collaboration with conservation partners across Chicago. The aim is to enable these groups to monitor the environmental conditions that directly affect their communities.
This approach to innovation is a step towards changing the way we conduct research and develop technology. But, most importantly, it requires humility to appreciate and learn from the experiences of the people he is to serve.