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Speaker 1: On this episode of News World, has we come out

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Speaker 1: of weeks of stay at home orders and begin to

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Speaker 1: reopen businesses and bring our economy back to life. Tracking

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Speaker 1: COVID nineteen outbreaks and reporting those outbreaks in real time

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Speaker 1: is critical to keeping states open even if another wave

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Speaker 1: of COVID nineteen comes in the fall. State and local

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Speaker 1: efforts to monitor, control, and prevent the occurrence in spread

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Speaker 1: of infections and non infectious diseases are dependent on timely,

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Speaker 1: high quality data obtained from disease surveillance. The Centers for

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Speaker 1: Disease Control and Prevention is now required to collect additional

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Speaker 1: testing information such as demographic data, but they don't actually

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Speaker 1: have the capability to process it as of yet. I

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Speaker 1: was alerted to the important issue of real time disease

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Speaker 1: outbreak reporting by a health data management company called Percivia.

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Speaker 1: In fact, they have helped some states better collect made

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Speaker 1: sense of testing data and are recommending but a similar

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Speaker 1: system be deployed national to discuss this issue uninterrupted. Perciva

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Speaker 1: has agreed to sponsor this episode, so there are no

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Speaker 1: commercial breaks. I'm pleased to welcome my guest, doctor Mansour Khan,

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Speaker 1: a twenty year veteran of the software and healthcare industries.

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Speaker 1: He is the CEO of Perciviia. Doctor Mansur Khan is

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Speaker 1: a serial entrepreneur who has been developing advanced technologies and

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Speaker 1: cutting eggs software since the mid nineties. Over the years,

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Speaker 1: he's led teams that have developed technology and applications for

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Speaker 1: disease surveillance, artificial intelligence, quality management analytics, care management, and

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Speaker 1: cost in utilization management. Doctor Gannet's great to have you listen.

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Speaker 1: You have an amazing background of successfully understanding and developing

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Speaker 1: new technologies. Before we get into the details of this

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Speaker 1: particular great breakthrough and how it really related to the

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Speaker 1: pandemic and COVID nineteen, could you just find a min

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Speaker 1: two and explain how did you learn to be a

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Speaker 1: serial entrepreneur. It's a great achievement. Well, thank you, mute,

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Speaker 1: and as a pleasure of speaking with you, I'm not

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Speaker 1: sure I can explain how I learned to be an entrepreneur.

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Speaker 1: I think it's sort of been in my genes. I

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Speaker 1: remember during my college days at MIT, I was always

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Speaker 1: trying to come up with something to do. Who always

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Speaker 1: wanted to have my own business, always wanted to be

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Speaker 1: my own boss. And that combined with sort of the

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Speaker 1: high tech environment at MIT. It naturally led me towards

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Speaker 1: the technology field, but there weren't many failures too. I

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Speaker 1: probably tried five or six different things before my first

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Speaker 1: success at key Commerce. Like most anybody else, what looks

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Speaker 1: like a big success at the end is preceded by

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Speaker 1: many tries that teach you what you should and should

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Speaker 1: not be doing right. There's nothing like the real world

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Speaker 1: experience to learn the lessons of what work. From all

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Speaker 1: of that background, you then began to really look at

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Speaker 1: how we could have real time reporting for a variety

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Speaker 1: of illnesses. Talk from our particulation of people who are

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Speaker 1: now worried about a potential second wave. Why is real

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Speaker 1: time reporting so important when you have a pandemic? When

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Speaker 1: we think about how we're going to open up the

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Speaker 1: economy and get people back to work in small businesses

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Speaker 1: getting restarted, we really have to start thinking about how

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Speaker 1: do we manage both the ongoing crisis and the second

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Speaker 1: wave in the fall, which combined with the annual flu season,

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Speaker 1: is going to be a very confusing environment to deal with.

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Speaker 1: We have to become much quicker in our responses, and

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Speaker 1: our responses have to be very targeted right now we've

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Speaker 1: been using a very blunt force kind of approach, basically

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Speaker 1: shutting down and our cities and our states and trying

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Speaker 1: to get tell everybody, And of course it doesn't quite

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Speaker 1: work that way. Right, It's what happens in downtown New

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Speaker 1: York City, or in downtown Boston or any major city,

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Speaker 1: there's nowhere close to what happens one hundred miles out

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Speaker 1: in the suburban and rural areas. We have to be

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Speaker 1: able to identify emerging clusters very quickly and then take

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Speaker 1: actions that are focused and targeted down at the neighborhood level,

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Speaker 1: so that you can quickly tell where things are starting

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Speaker 1: to change and then react to those changes in a

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Speaker 1: very targeted way. And you actually had an ongoing relationship

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Speaker 1: before the pandemic with both Iowa and Massachusetts. Is that right? Absolutely?

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Speaker 1: My previous company diagnosis one, we actually were building disease

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Speaker 1: surveillance systems. This was in the early two thousand after

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Speaker 1: the Stars and the Amtrak's cares and some of the

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Speaker 1: states were starting to understand that getting lab test data

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Speaker 1: very quickly and in an electronic form that was standardized

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Speaker 1: was very important to be able to respond. So we

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Speaker 1: built some of the very first systems in the country

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Speaker 1: to perform that function, and that those systems are still

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Speaker 1: operating in those states and they're still considered state of

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Speaker 1: the art, and those systems literally connect to hundreds and

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Speaker 1: hundreds of sites to collect data electronically and bring it

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Speaker 1: into the epidemiology folks so that the analysis can be

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Speaker 1: done very quickly. It's a capacity that we need across

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Speaker 1: the board as well as when we have a period

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Speaker 1: of intensive disease. Absolutely there's eight plus diseases that are

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Speaker 1: CDC called notifiable conditions, and COVID nineteen is just the

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Speaker 1: latest in that stream. So there's an infrastructure that's needed

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Speaker 1: across the country so that as any of these notifiable

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Speaker 1: conditions starts to appear, we can quickly identify them and

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Speaker 1: move on them. Now, in most cases they appear at

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Speaker 1: a much slower rate and much smaller numbers, but of

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Speaker 1: course COVID has changed that. Now on any time there's

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Speaker 1: any sort of a new infection, the first thing is

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Speaker 1: going to be is there's something that can explode and

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Speaker 1: really start impacting the whole country. So an infrastructure to

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Speaker 1: be able to collect data from anything that's notifiable and

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Speaker 1: very importantly new condition that arise to be able to

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Speaker 1: react to them very quickly is critical in Iowa. The

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Speaker 1: folks in Iowa Department's Public Health that use our system

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Speaker 1: told us that when COVID nineteen first came across their radar,

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Speaker 1: it took them thirty minutes to create the new COVID

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Speaker 1: nineteen condition information rules within our system so that the

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Speaker 1: system would know that data, how to grab that data,

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Speaker 1: what to do with it. Typically, that can take months

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Speaker 1: to weeks to do that in other systems. So, for example,

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Speaker 1: the CDC in their twenty twenty one budget justification state

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Speaker 1: that they've been working on a system and they have

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Speaker 1: managed to get the time down to enter a new

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Speaker 1: condition into the system they have been developing from months

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Speaker 1: to weeks, So literally from week in other systems to

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Speaker 1: thirty minutes in our system, that's how long it takes

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Speaker 1: to react. So why is it so hard to get

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Speaker 1: the government to look out to the private sector and

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Speaker 1: realize you're already doing what they're spending money trying to develop.

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Speaker 1: It may just be that everybody's so busy trying to

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Speaker 1: fight the fires in front of them that people aren't

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Speaker 1: quite thinking of how do you change the basics so

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Speaker 1: that you don't have to fight the fires, you can

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Speaker 1: keep them from actually lighting in the first place. In

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Speaker 1: addition to reporting within Iowa and Massachusetts, what are the

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Speaker 1: kind of clients does Persilia have? We support hundreds of

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Speaker 1: hospitals and many thousands of ambilitary practices across the country,

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Speaker 1: and we got into the business of helping healthcare providers

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Speaker 1: make the transition from FIFA service to value based care.

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Speaker 1: This idea that risk for treating a patient should move

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Speaker 1: from the insurance companies to hospitals and doctors is at

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Speaker 1: the heart of all the changes in healthcare that happening today,

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Speaker 1: and we provide tools that help the providers make that

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Speaker 1: transition from a feefa service to what's called value based care.

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Speaker 1: When you have a new client, are you walking in

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Speaker 1: with a cookie cutter or are you able to use

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Speaker 1: your various tools to try to be directly responsive to

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Speaker 1: what they're trying to measure. So that's one of the

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Speaker 1: big strengths that we have. There's three things that anybody

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Speaker 1: who makes this transition needs to do. They need to

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Speaker 1: manage qualities, they need to manage care, and they need

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Speaker 1: to manage cost. But there are many, many different business

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Speaker 1: models that the different insurance companies, including Medicare, is trying

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Speaker 1: to figure out what's the best way to make this transition.

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Speaker 1: You need a tool set that is highly adaptable, and

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Speaker 1: one of the things that we have done is that

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Speaker 1: we've been working on an AI system since two thousand

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Speaker 1: and five, So that AI system is at the heart

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Speaker 1: of everything that we built. That allows a very adaptable

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Speaker 1: approach to each client and to each environment and to

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Speaker 1: each business model that that client is involved in. If

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Speaker 1: you're walking to say even a medium sized health system,

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Speaker 1: you're going to find that they're involved in many different models.

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Speaker 1: They have feefa service, they are an accountable care organizations,

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Speaker 1: they have bundle payment, they have commercial risk arrangements, they're

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Speaker 1: in Medicare advantage, they have Medicaid risks. So to manage

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Speaker 1: all of these different programs and business models is very,

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Speaker 1: very tough, and typically you might have six or seven

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Speaker 1: different point solutions that they're using, and when we walk

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Speaker 1: in there, we replace six solutions with our platform and

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Speaker 1: bring in the AI to help manage that so that

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Speaker 1: when a patient walks into a facility or walks up

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Speaker 1: to a doctor, the doctor doesn't have to worry about

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Speaker 1: what the business rules are they can just take care

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Speaker 1: of the patient, and the system helps identify and manage

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Speaker 1: those rules for them. How does the artificial intelligence component

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Speaker 1: of this make it different from the way you might

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Speaker 1: have tried to design it before we hit AI. That's

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Speaker 1: a really interesting question. If you look at the IT

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Speaker 1: systems that helpcare provided US. Systems like epic Concerner and

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Speaker 1: Afena and ecwt C, these were all built in the

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Speaker 1: nineties or even earlier. They were built before there was

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Speaker 1: really even any realistic concept of AI. When we first

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Speaker 1: started working on that, we thought we would build an

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Speaker 1: AIR system that could work with any EHR. It turns

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Speaker 1: out that is very difficult to do because the AI

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Speaker 1: needs to be at the heart of all of the

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Speaker 1: data flows so that it can monitor those data flows.

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Speaker 1: If it's outside the system, it's very hard for it

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Speaker 1: to be able to tell what's going on with the patients.

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Speaker 1: So when we started building our system, we put that

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Speaker 1: at the heart of our system. So all the data

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Speaker 1: flows go through the decision support engine that's at the

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Speaker 1: heart of our AIR system. It allows the patient's information

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Speaker 1: to be analyzed in real time, and so if there

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Speaker 1: are quality issues, if there are care issues, around any

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Speaker 1: given patient and any given point in time, the system

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Speaker 1: immediately identifies those in real time and then generates alert

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Speaker 1: guideline within the system. But at the same time, it

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Speaker 1: allows you to do things like take a population of

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Speaker 1: patients and then risk gratify them so that you can

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Speaker 1: deal with high risk patients in a different way than

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Speaker 1: you deal with media risk and low risk patients, and

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Speaker 1: you can design programs. So maybe for the high risk patients,

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Speaker 1: you want to have care managers that go to the

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Speaker 1: home at least once a week, they reach out to them,

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Speaker 1: maybe even daily their data coming in from home devices.

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Speaker 1: But your lower risk patients you have a less heavy

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Speaker 1: touch approach. And for the really lower risk patients, maybe

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Speaker 1: you communicate with them most electronically and really don't dedicate

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Speaker 1: any direct resources for them. So to the ability to

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Speaker 1: manage your resources efficiently and make sure you're not overspending

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Speaker 1: where you're not getting value and you're getting rid of duplication.

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Speaker 1: We talk about thirty percent of healthcare spend being non

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Speaker 1: value added. That's literally trillions of dollars, and so that's

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Speaker 1: what the AI is really good as is helping you

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Speaker 1: identify those non value added activities and try to get

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Speaker 1: them out of the system. It leads to a leaner

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Speaker 1: and leaner and faster and faster system. Absolutely, but very importantly,

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Speaker 1: a system that can deliver higher quality at reduced costs

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Speaker 1: and deliver it in a way that makes the burden

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Speaker 1: on the care provider less. You're gathering huge quantities of data,

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Speaker 1: processing them through a system that is self evolving as

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Speaker 1: the artificial intelligence at the heart of it learns more

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Speaker 1: and more about what to look for and what to

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Speaker 1: deliver and how to relate things, so that you're going

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Speaker 1: to have an ability across all of the reportable disease

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Speaker 1: states the CDC is looking for. You'll have an ability

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Speaker 1: for a hospital or a lab or a state government

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Speaker 1: to provide this in very close to real time, as

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Speaker 1: you said, instead of taking a week to begin to

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Speaker 1: gather the sub you'll be able to do it like

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Speaker 1: in twenty minutes. Is that a reasonable summary. Absolutely. The

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Speaker 1: hospitals need to know that somebody shows up in any

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Speaker 1: are whether or not they're a COVID case. And if

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Speaker 1: you're doing testing at the point of care, if you're

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Speaker 1: going to test thousands of people in the drive through location,

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Speaker 1: you need to be able to take that data very quickly.

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Speaker 1: You can't take a week after that to figure out

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Speaker 1: if somebody but positive. You need in minutes to know

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Speaker 1: if they're positive and then be able to quickly contact

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Speaker 1: raise them to say, oh, who have you been in

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Speaker 1: contact within the last two days, and then test those

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Speaker 1: people and quickly identify and even in cluster and put

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Speaker 1: a wall around it. Percivia had been an average state

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Speaker 1: on January one. How big a difference and speed and

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Speaker 1: an understanding whether have been compared to what actually happened.

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Speaker 1: So I'll give you an example today. If we're doing

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Speaker 1: testing in nursing home, which is a huge concern in

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Speaker 1: the country today, and so nursing homes do a test,

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Speaker 1: they send the specimen out. In many cases it goes

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Speaker 1: to a state lab or some large lab. The lab

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Speaker 1: does the test and then typically they'll mail it back

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Speaker 1: when it takes a number of days, maybe as much

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Speaker 1: as a week for that cycle to occur. If our

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Speaker 1: system is in place, once the test is done in

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Speaker 1: thirty minutes or so, the nursing home would know whether

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Speaker 1: that's specific patients positive or not. And if you deploy

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Speaker 1: a point of care testing device or some sort at

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Speaker 1: each nursing home, which is not that expensive to do.

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Speaker 1: You would know within twenty or thirty minutes, depending upon

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Speaker 1: the device. That nursing home would know, but very importantly,

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Speaker 1: the epidemiology of the state. Folks that need to react

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Speaker 1: to that would know, so they could take action very quickly.

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Speaker 1: We're talking going from weeks two minutes in terms of

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Speaker 1: your ability to react to these sorts of events. Because

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Speaker 1: the system has been deployed for years, you could literally

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Speaker 1: deploy it at a national level in a week. So

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Speaker 1: if the CDC said tomorrow, here's a contract, go do it.

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Speaker 1: A week from now, we would have the system up

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Speaker 1: and running and start connecting sources to it. Doctor Redfield

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Speaker 1: at CDC testified to the House Appropriation Security on June fourth.

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Speaker 1: He said a quote, I have stags that are still

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Speaker 1: collecting data on pen and pencil. Isn't it something that

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Speaker 1: we could leap frogs into the twenty first century pretty rapidly. Absolutely,

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Speaker 1: we have been trying to get attention. There is a system,

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Speaker 1: it's there, it's working, The people who use it love it.

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Speaker 1: Every state in the country should have this system. This

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Speaker 1: is a very solvable problem. We have a capacity to

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Speaker 1: really be dramatically better, faster, more accurate, and more capable

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Speaker 1: long before a second wave of COVID. It is, but

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Speaker 1: it takes the right investments in the right decision. Now

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Speaker 1: you talk about deploying a nationwide electronic laboratory reporting service,

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Speaker 1: thinking about the potential for a second wave of COVID,

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Speaker 1: how much different would we be by September October if

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Speaker 1: we truly had a nationwide electronic laboratory reporting service. The

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Speaker 1: difference would be very big in a couple of areas.

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Speaker 1: Once the system is deployed, your ability to identify positive

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Speaker 1: cases goes up by literally orders of magnitude. We're going

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Speaker 1: to have tens of thousands of locations where testing is

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Speaker 1: going to be performed. So the data problem is bad.

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Speaker 1: Now it's going to get really, really bad. You're going

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Speaker 1: to have literally millions of test results coming in to

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Speaker 1: multiple places. The ability to sort through all of that

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Speaker 1: data quickly, When I by quickly, I mean minutes and

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Speaker 1: identify what you should be focusing on, who you should

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Speaker 1: be focusing on, where you should be focusing on is

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Speaker 1: going to make a day and night difference. If I

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Speaker 1: know that this person at this time that was tested

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Speaker 1: was positive and within the next half an hour, I

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Speaker 1: have a list of the people that they have been

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Speaker 1: in contact with over the last two days. But whatever

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Speaker 1: time period be select, then my ability to quickly zero

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Speaker 1: in on those quarantine a small number of people and

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Speaker 1: not have to shut down half the city is going

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Speaker 1: to make a huge difference in terms of the impact

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Speaker 1: that it has on people. And that's really what it's

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Speaker 1: all about in the end, right, this is not about technology,

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Speaker 1: this is not about money. It's about people's lives being disrupted.

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Speaker 1: Small business is being destroyed. So the ability to keep

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Speaker 1: the country from another shock, that ability is going to

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Speaker 1: be critical should we hit a second wave. Because we

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Speaker 1: were faced with the intensity of COVID nineteen, we're going

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Speaker 1: to have real breakthroughs in healthcare. We've already had it,

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Speaker 1: for example of tele medicine. We're almost certainly going to

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Speaker 1: find a way to get to real time reporting, and

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Speaker 1: there a whole range of these things that would probably

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Speaker 1: taken another generation to get to. But the sheer pressure

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Speaker 1: the current situation is going to lead this innovate. Absolutely,

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Speaker 1: healthcare is now changing in a way that it's not

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Speaker 1: going to go back. People are getting used to virtual healthcare,

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Speaker 1: doctors are getting used to it. Systems are getting used

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Speaker 1: to it, and one of the things that we noticed

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Speaker 1: this was that the old FIFA service model really took

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Speaker 1: a hit. So I think it's also going to accelerate

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Speaker 1: this transition from FIFA service to value based care, and

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Speaker 1: that's going to have a huge impact on our ability

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Speaker 1: to deliver better care for lower costs as we go forward,

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Speaker 1: and as you know, healthcare cost is one of the

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Speaker 1: key drivers of the financial issues we're going to face

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Speaker 1: over the next decade or so. Well, listen, thank you

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Speaker 1: very much. I think this has been an extraordinarily informative

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Speaker 1: conversation and I think that hopefully it's going to be

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Speaker 1: a step towards us developing the kind of nationwide electronic

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Speaker 1: lab reporting system that will really make a huge difference

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Speaker 1: if COVID does come back. Thank you, Newt the pleasure

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Speaker 1: speaking with you. Thank you to my guest, doctor Mansur Khan.

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Speaker 1: You can read more about real time data reporting of

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Speaker 1: COVID nineteen and perseivious technology on our show page at

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Speaker 1: nutsworld dot com. News World is produced by Gamer Sweet

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Speaker 1: sixty and iHeartMedia. Our executive producers deVie Myers, and our

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Speaker 1: producers are Garner Sloan and Joe De Sanis. The artwork

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Speaker 1: for the show was created by Steve Penley. Special thanks

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Speaker 1: to the team at Gingwich three sixty. Please email me

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Speaker 1: with your questions at Gingwich three sixty dot com slash questions.

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Speaker 1: I'll answer them in future episodes. If you've been enjoying Newtsworld,

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Speaker 1: I hope you'll go to Apple Podcast and both rate

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Speaker 1: us with five stars and give us a review so

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Speaker 1: others can learn what it's all about. I'm new Gingwich.

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Speaker 1: This is news World.