The author is a Senior Fellow at the Center for International Governance Innovation and Director, Artificial Intelligence and Innovation Marketing at Microsoft.
As digital technologies become more pervasive in our lives, they have tremendous potential to impact health and well-being. To get the best out of them, we need a new form of innovation that focuses on those who face the biggest obstacles. And that means developing these technologies With historically marginalized people Pro You.
History shows that the implications of inventions tend to emerge long after they are initially conceived. Over time we see the possibilities: mobile phones with touch screens; an Internet that provides important information and services and connects people around the world; Cars that can drive autonomously.
But inventors cannot always foresee the impact of their inventions, nor who they leave behind. New technologies are often expensive, at least initially. They need infrastructure such as electricity, grids and roads. They replicate and in some cases reinforce inequalities within the societies from which they emerge.
Regulations follow later: seat belts in cars, data protection for internet users, moratoria on facial recognition technology and so on. While technology regulation is essential for the public good, it aims to protect people from measurable harm, rather than foreseeing or preventing it in the first place.
We need to broaden our approach to responsible innovation to include practices that are inclusive by default and empower all people.
This doesn’t just mean expanding technologists’ understanding of the implications of what they create. It also means co-creation With instead of building Pro marginalized communities and adapting existing methods – or developing new ones – to reflect the needs, voices and expertise of people who are often excluded from the innovation process.
What would an empowerment model for healthcare innovation look like? Here are three recommendations.
First: Disenfranchise patients, community organizations and vulnerable groups. The Covid-19 pandemic has highlighted that healthcare systems are often ill-equipped to serve those with the greatest need for care. Community health workers and organizations create a bridge between health systems and marginalized community members.
There is an urgent need to support relationships between vulnerable people, health workers, partners, organizations and funders – and to reduce administrative burdens so that the focus can be on the direct care of patients. Founded by my colleague Mary L Gray project resolution with the Healthy Community Hub — a coalition of Black and Hispanic community-based organizations in North Carolina — to do this.
Project Resolve uses open source software and systems to support two approaches: Case-centered care focuses on services for each individual, such as: B. Helping someone with an unstable home; while service-oriented care for a group of people requires a specific intervention – such as B. a vaccination clinic – offers.
Second, pursue multidisciplinary approaches. Healthcare is highly dependent on data: laboratory results, medical histories, and other factors critical to understanding the health of individuals and communities. But they often lack an important context, such as people’s access to fresh food, insurance, transportation, or stable housing. These factors can determine a person’s ability to afford medication, seek treatment, or even have access to medical care.
The more we build digital systems to inform the future of healthcare, the more important it is to incorporate broader expertise from groups such as anthropologists, economists, linguists, medical, security and privacy experts, data scientists, engineers and community organizers who do this deeply understand factors that can determine good health outcomes.
Third, you supplement “big” data with “small” data. One of the biggest challenges in healthcare innovation is “small” or “sparse” data—data that is often lost, overlooked, or stored in multiple, separate spreadsheets. Examples include medical histories scattered throughout community health facilities, emergency rooms, pharmacies, and hospital emergency rooms.
New privacy protection methods are critical to responsibly collecting, storing and learning from “small” data and combining it with large data to build a richer and more accurate evidence base.
Pollution monitoring is an example where both big data and small data play a role. Satellite imagery can provide a wealth of data on air quality at the planetary level, but they can’t tell us much about the conditions and trends in any given neighborhood. Project Eclipse, by my Microsoft colleagues, includes locally sourced data collected in collaboration with neighborhood conservation partners across Chicago. The goal is to enable these groups to monitor the environmental conditions that directly affect their communities.
This approach to empowered innovation represents a fundamental shift in the way we conduct scientific research and develop technologies. But most importantly, it requires the humility to appreciate and learn from the lived experience of the people it is designed to serve.