Dr. Rupal Malani

So you could imagine things like virtual triage with AI support, omni-channel interactions to support a patient through the full life cycle of a medical event, but to do so seamlessly.

So I think there's a lot of opportunity.

Those are, again, two where I think there's perhaps significant and nearer term opportunity relative to others, but that I'm very excited about.

What we see separates those that are successful from the rest, frankly, in healthcare, but also otherwise, is a few things.

So first is we would say it's critical to take the function and transform it end to end, whether that's supply chain, OR, or something else that's particularly important for the organization at the moment.

And this is because outsized value comes from multiple solutions working in tandem.

All too often, we'll see point solutions that actually create a more frictional experience for the end user, whether that's someone on the administrative side, the clinical side, or the patient.

It's much better if you can kind of see those systems working in tandem and synergistically.

Second, the transformation really needs to be owned equally by business leaders as well as their CDO.

Successful tech transformations really do solve a core business problem, and they're not nearly as successful and often we would describe as having failed in a meaningful way if they're done to the business.

So that means we also need dedicated teams that include business leaders as well as data scientists, data engineers, designers, etc.,

sitting together and the ability to translate the business problem into the potential technological solution.

And then also from the technological solution back into the operational workflow.

So it works seamlessly day to day for the people that have to incorporate it.

The third thing is, you know, the data and technology foundation here is obviously critical.

But sometimes we see that organizations are trying to completely perfect that foundation before they get going with use cases.

We find that if you fix those data gaps in parallel to pursuing valuable use cases, then you start to get that flywheel of impact going.

If you sequence, it's very hard to get going.

Maybe the last thing I'll just say is that building a scaling engine is both an operations and a funding problem, right?

Because once you've shown value against a minimum unit and you have a change in implementation plan, you can roll it out.