ATTENTION RADIOLOGISTS: AI Is Not Going To Replace You Anytime Soon

I read this article in IoT World about connected health trends to keep an eye on for 2019. It talks about use cases for AI such as wearables, patient dashboards, surgical robots. It talks about some VS guy who a few years ago said that AI can do 80% of what doctors can do and that radiologists would be obsolete by 2022!

The overall progress of AI adoption in healthcare has been really slow. It seems to me that so many companies are trying to develop AI solutions for applications and use cases that are just too complicated or rely on an ecosystem of other technologies to make them happen.

We need to stick to the basics. No tech project or initiative will be successful unless there’s a business case to drive it. And in healthcare, the obvious business case for AI starts where IoT did: service and support.

Companies trying to deploy an IoT technology so they can remotely support their devices in the field are certainly still facing challenges today. More and more of their end users, particularly overseas, are resisting their attempts to connect to their devices on their network because of security concerns. But the business case for remote service and support still is strong.  Reducing downtime, reducing 2nd service calls, improving MTTR and FTF rates still translate right to the bottom line for OEM’s and their customers.

AI for healthcare should start here. There are AI technologies available today that can help service and support organizations improve their support capabilities even further. Some of the benefits that you can get from AI today:

-Upskilling the service organization by making your service rookie as smart as your veteran;

-Automated dispatching so that experienced techs don’t have to spend time on the phone;

-Enabling connectivity to devices without relying on the customer’s network

-Dramatically reduce 2nd service calls

 

There are many others.  And if you’re a radiologist, don’t worry – your job is safe!

How to Get Organized to Evaluate RevTwo AI

So you’re thinking about trying out RevTwo’s AI technology? We are currently working with several potential customers on proving out our technology for them and would love to work with you.

IDENTIFY KEY ISSUES

To get started, talk to your service and support teams and identify 3-5 common service/support use cases that usually result in having to send a technician onsite to resolve it.

Sometimes our customers may struggle to come up with the right issues they want to address.  One way to get your team thinking is to consider issues with one of these characteristics:

-An issue that happens often

-Something that was expensive when it happened

-Something that everyone in the organization would know about

It also doesn’t have to be something cut and dried like “the water pump failed”. Most complex issues are not that straight forward and usually have multiple root causes and symptoms. For example, it could be “my system is running slowly”.

HOW DID IT IMPACT THE ORGANIZATION?

Here it would be great if you could identify the cost of this issue, both to your organization and to your customer. It will be important to be able to show real business value if RevTwo’s AI could help you avoid or at least drastically reduce the expense of dealing with these issues. As I’ve written before in other posts, the technology doesn’t matter if you’re not solving a business problem.

One example would be to use reduced resolution time as a business case. In that instance, you look at each issue and calculate its resolution cost:  Cost = resolution time * labor rate. Then in the POC we would observe a much lower resolution time which would translate to cost savings.

WHAT IS THE ROOT CAUSE?

Ultimately, what was the root cause? Some use cases have many root causes, making them more complicated to diagnose, some do not.

HOW WAS IT DIAGNOSED?

How was the root cause identified? What things were asked about, what was looked at, tested, etc?

What would also be extremely helpful is to list out the diagnostic indicators that were used.  Examples would be sensor readings, log files, registry settings, and where they are located (ie file, table, etc).

HOW WAS IT FIXED?

What steps were taken to fix the issue? If you have a knowledge base, where is the article that explains the solution? View only access to your KB would be helpful as well.

WHO IS THE EXPERT?

All organizations have that one person (or many of them!) who has been around for a long time and has seen it all. Ultimately, you want the RevTwo AI to be as smart and knowledgeable as that person so that the entire service and support team can have access to that know – how.  We often spend some of our time talking with that person in order to make sure that the best solution set gets input into our AI database. So access to this person would be important.

 

If you want to learn more about how RevTwo AI can help your service and support organization reduce triage and resolution time, email me at dave@revtwo.com