It’s no surprise that healthcare – an industry full of data, highly dependent on partnerships, and in need of connectivity – is leaning into platforms. Our ability to take advantage of them, however, is largely dependent on how we approach the innovation.
In 2011, when Marc Andreessen wrote “Software is eating the world”, it was a pretty provocative statement. Now, most people accept it as truth. Indeed, beyond Silicon Valley, and in sectors ranging from automotive to healthcare to financial services, companies are evolving their businesses to mirror the digital age. The latest wave of innovation, however, is challenging businesses to not just consider who they are but also what they do and how they operate. I’m talking about the platform economy – an era defined by fundamental changes to existing business models. Companies are shifting from providing traditional products and services to building networks to exchange services, spur innovation and make connections. In 2018, seven of the 10 most valuable companies globally were based on a platform business model.[i] A McKinsey report suggests “ecosystems” will account 30 percent of all global revenues by 2025.[ii] Given these statistics, it’s no surprise that healthcare – an industry full of data, highly dependent on partnerships, and in need of connectivity – is leaning into platforms. Our ability to take advantage of them, however, is largely dependent on how we approach the innovation. At re:MARS this week, I will discuss why healthcare needs a strong AI platform and what GE Healthcare has learned during the development of our intelligence offering, Edison. Here’s a preview of the insights.Kieth Bigelow is the SVP, AI and Analytics of GE Healthcare. This post originally appeared here.
- Break down data silos. Healthcare has become good at generating data but bad at using it. A good healthcare platform must combine globally diverse data from across modalities, vendors, healthcare networks and life sciences settings.
- Secure experts. Data volume and variety come when silos are broken, but data veracity will only be realized when information is reviewed by top clinicians and researchers. Expert annotation helps ensure you’re training algorithms with accurate information.
- Build an ecosystem. Like in other industries, a platform is only as strong as the network it fosters. In healthcare, this network defines the quality of the algorithms as well as the ability to rapidly validate and scale the work.
- Make ethics central. The ecosystem should be connected by not only technology but also a shared commitment to compliance and ethics. Ingraining compliance and ethics into the platform itself prevents development from veering outside these standards.
- Deploy agnostically. When algorithms can be deployed via edge, cloud, smart devices or intelligent applications, AI is enabled at the point of maximum impact for better care.