Ep15 - New functionality calls for new data. Here’s how to find it. Key Tech POV

Show Notes

When you’re trying to add new functionality to existing technology, the data you need won’t be presented on a silver platter. Sometimes, you don’t even know what the right data is until you find it. This was the case with RevMedica, a start-up that’s developing a new hybrid robotic laparoscopic stapler technology.

Following Andy Rogers’ talk with RevMedica, he and electrical engineer Rachael Scott discussed the search for data when designing new functionality into existing platforms. 

Need to know:

• Cost is a driver in a hospital environment.
• Look at the competitive field and at other technologies that could enable your technology.
• Understand what your customers want and how they work; function and features define your architecture.
• Be ready to demonstrate the added value of the features you’ve designed in.

The nitty-gritty:

Clinicians want it all when it comes to medical technology, but they don’t want to pay for it all, especially in the highly competitive hospital market. So when you’re adding new functionality to existing systems – or even developing a disruptive new technology – you must pick and choose features that will make your product stand out without breaking the bank. 

There are a few ways to find and evaluate the data you need to successfully bring your product to market. 

1. Understand your customers. Listen to what they want and need, and understand how your product affects their workflow. Get user feedback throughout the development process. Clinicians perform many different tasks, so big changes can lead to non-compliance, which reduces efficacy.
2. Know when to hold ’em and when to fold ’em. Consider how far along the development path you can go before you pivot by setting milestones. And look to the data to make the “go or no-go” decision. For example, RevMedica’s original idea was to develop a full-on robotic technology. Months into development, it became apparent that a hybrid model was the way to go, and they had the smarts to make the switch.

1. Look for savings in manufacturing. Costs can be amortized for durable components, so deciding which components are durable and which are disposable is a critical decision. Seek ways to design multiple parameters into one sensor. This also affects the sustainability of your product – an increasingly important issue. In addition to opening up a new world of ideas, sustainability can be a competitive advantage in the marketplace.
2. Design for the outcome you want. Consider what happens when the task you’re enabling is performed correctly, and design to that outcome. Once again, this is where user data is invaluable. This helps you evaluate your feature set and ensures that you’re creating the right architecture for the platform. 

Above all, remember, there’s power in the data, and collecting the data as you develop new products helps inform another generation of technology.

Helpful Links:

https://www.revmedica.comHome | revmedica

What is MedTech Speed to Data?

Speed-to-data determines go-to-market success for medical devices. You need to inform critical decisions with user data, technical demonstration data, and clinical data. We interview med tech leaders about the critical data-driven decisions they make during their product development projects.

Welcome back to MedTech Speed to Data,

a Key Tech podcast.

This is Episode 15 with Rachael Scott,

Senior Electrical Engineer and project manager.

Rachael, welcome to the show.

15! You guys have been busy.

Yeah, 15, yeah.

Very exciting.

What anniversary is this?

Do I get silver? Gold?

What's the 15 year?

I don't know.

You're not near her

Majesty of a platinum jubilee.

So you got 55 more to go to platinum.

Okay. Yeah, yeah.

Thanks for hopping on the show, Rachael.

You've been at Key Tech for a while now.

I have.

You have a lot of lessons to share, I guess.

And also, you're a veteran of the podcast.

Thanks again for coming back on.

Yeah, absolutely.

Thanks for having me.

Yeah.

So today we're talking about

the last episode

or the theme from the last episode.

So the last episode was with RevMedica,

a startup company

in the New England area

with a powered surgical stapler,

a pretty cool architecture, hybrid architecture

where they have a powered,

battery powered sort of base and a disposable

stapler portion

that's pretty novel in that market.

And I think the common theme

from that episode, in my opinion,

was adding new functionality to platforms

that have been on the market,

you know, for a while

and trying to differentiate that way.

So thought you’d be a good guest

to bring on the show

to talk about some of your experience

in that area.

So let's just get right into it.

Let’s do it.

You don't get functionality and sensing

you know, for free, right?

So there's always this question

of how much more will buyers be willing to pay

for this added functionality.

So can you talk a little bit

about your experience of dealing with

the added cost

that comes with adding

sensing, adding functionality?

And I think your experience primarily

is on the disposable side,

but I know you've worked on,

durable and disposable architectures before.

Yeah, absolutely.

I think

what they said in the podcast

rings true, you know,

people want this functionality,

but they don't really want to pay for it.

And especially in kind of, you know, a hospital

kind of marketplace,

you know, cost is often king.

Like that is the driving factor for,

most people, especially in disposables

where you make millions of these

and that's, it's really hard to kind of see

any cost increase, even like cents.

So when you're suddenly adding

sensing technology, which often regards

your processing and battery and,

you know, PCBs and

in like how you're connecting to your sensor,

suddenly it's

trying to keep it down is

is really, really challenging.

So that's something you know, that

we have like

I've worked on a couple of projects where,

you know, we're trying

to find really unique ways to keep costs low

and sometimes, you know,

in one application, you know,

we really we ended up pursuing like

an implementation of a very simple technology

but like tried to kind of

work it into a manufacturing process

that already existed

because every cent counted.

And so we were kind of

we worked up

from all the things

already existed in the industry

and tried to build

a kind of new application around that sensor.

But the cool thing about like

what you know, RevMedica is doing

and some of the flexibility

that having a durable offers

is it allows you to abstract, you know,

some of your big cost players

which are often battery and processing

and make that reusable,

which gives you a lot more flexibility

in the feature set.

You can offer something

because you have that,

you have this box that is the instrument

doesn't need to be replaced.

So you get a lot more freedom, as it were,

to kind of play around

and have more functionality.

That's great.

I mean, there's always this question of

of what goes on the durable

and what goes on the disposable.

Yeah.

It reminds me a lot of the diagnostics market

where you're trying to get that razor blade

cost down really low.

But I thought what was interesting with

with RevMedica is

they were working backwards a little bit from,

you know, the

I mentioned the hybrid architecture,

so their competitors

had a few other disposable products.

And, you know,

there are also some reusable platforms

that have challenges with sterilization.

So I just think it's interesting to kind of

look at your competition,

look at the technologies

that will enable added functionality

and kind of try to fit your product

in between your competition.

And I think they're

they're going to get there.

Yeah. Yeah.

It's a really smart way

of kind of approaching that,

especially from kind of that

that cost aspect

and being able to see

what your competitors have and, you know,

where they might be limited by cost

more of like, by disposable cost

or by functionality, by,

you know, not having these smarts

bridging that gap allows them to try

and be the best of both worlds,

which is, you know, is true in the cost market.

So I also think RevMedica did a really good job,

you know, as they should have,

really understanding their customers.

They were part of the I-core program.

And part of that is just doing surveys,

and just paper studies, really

Yeah.

Not even building a prototype.

We don't talk enough about that.

I think here at Key Tech

and even with our customers

like what statistical data

do you have from a survey

that says this feature is viable?

Right.

You can get that for a lot cheaper

than building five prototypes

and getting results that way, so clear there.

But I thought it was interesting that,

you know,

when you're looking

and this is common across

a lot of our customers,

they're adding functionality.

When we asked them

you know,

how does this fit

or how are you improving the workflow?

And the response was,

we are not changing the workflow

really at all.

I mean, they are

you know, requiring some

some wipe down and whatnot.

So they're modifying it some.

But I think just the general mantra of

stick with the workflow is an important one.

So in your experience,

how has that sort of thinking of

don’t change the workflow,

driven some of the product development you’ve done?

It's a huge driver

especially in kind of some of the realm

that, you know, RevMedica is in as well.

And some my experience there,

hospitalists and clinicians are doing,

you know, a thousand things a day

and they really,

you know, and a lot of them

are little things that like

still are so critical in patient safety.

And the way that you start to interact

with those thousand little things,

if you make even a few little changes,

you're kind of disrupting this flow

that people have gotten into for years.

And there's a reason that they work that way.

So it can be a really hard challenge.

And we are regularly kind of faced with

that dilemma of, you know,

like we should

not we're trying not to change it.

We're trying to.

But it does constrain you

when you're truly adding battery and smarts.

There are real,

like real changes to your workflow there.

So it is really about like

how can you do that smartly?

In one application that we worked on,

We kind of investigated actually actively

changing the workflow, in order,

like adding an actuator to a disposable

that kind of forced them, like they couldn't

the user were no longer able

to access something critical

without actuating this thing.

So it kind of forced them to take that step.

But there was a lot of challenges

and we appreciated that battle

we might be up against.

But what we found that

that kind of method,

it did inherently improve the process,

but we didn't know that we like

we thought it might,

but we had to do a lot of work

to kind of justify that change.

And it was a small change.

We're talking,

you know,

a button as opposed to just,

you know, accessing something.

So even something as small as that

you know, we had different reactions

and we had to pay a lot of attention

to how people interact with this device

and what their workflows are.

And that's different across

people, physicians, hospitals,

it's a whole world.

So not changing it is sometimes,

a smoother approach, but it does constrain you.

Right. Yeah, I agree.

I wouldn't want to invest in a company

that says, oh, we're changing the work,

we're doubling the workflow, or,

oh, all you have to do is rely on the clinician

or nurse to,

you know, twist these two knobs

and all of a sudden

your outcomes are improved 20%.

But you have to stick with what people know

because they're going to try to shortcut it.

Right, and then

you have poor acceptance into the market

or you have, just people don't do it.

So, you know,

they don't do the things

that you've implemented.

And so you don’t

get the impact that you're hoping for.

So it's a really tough challenge.

And especially things like batteries

when you're adding smarts

to disposables and to durables

and really changing that process

like fundamentally a battery

kind of forces a workflow change in some way

or if a gear in a purely disposable,

you're kind of thinking about like,

how can I make it?

So the battery is not defining

when the person throws this away

or when the person has to do something

because you don't want your battery

to be defining that.

But in like the durable world,

kind of with what RevMedica is doing,

you have to start thinking about like, OK,

like how often does my battery need to last

for maybe a surgery

or where does this charging station live

and how does that disrupt the normal

workflow of just throwing something away?

So, you know,

that part is like kind of its own

separate workflow analysis.

And sometimes that drives

your battery decisions

even more than the features.

So let's switch gears a little bit here.

So you're talking about RevMedica.

They're adding sensing,

which is just specifically

they're sensing the thickness of the tissue.

And then

they're claiming to apply the stapler force

as a function of

that tissue thickness, which is a novel

is novel functionality for stapling platforms.

But, you know, in your experience,

how have you

founded a metric

you know, for a feature

where that hasn't existed before,

you know what I mean?

Like, how do you know what's good enough

Yeah, that's the question

we come up with every time.

You know,

we are trying to add smarts to a platform

or a technology.

It has existed in the market.

You know, you're adding smarts,

but these smarts haven't existed yet.

So you're simultaneously collecting data

and trying to use that data to help

help a clinician make an informed statement

like give viable and helpful feedback,

you know, no one wants to hear.

So you suddenly you're

you're doing exactly what you said.

It's really challenging.

You're saying OK, this force is applied,

but how do you know that force

being applied is acceptable?

Like that data doesn't exist previously.

So it's a really hard challenge.

And we've approached that by,

huge amounts

of trying to get honestly kind of prototypes,

just data

collection prototypes that on already existing

units and just kind of measuring that data in,

you know, in different ways,

like getting it in the hands of users,

getting it in the hands of

even just people to kind of

doing that action

and finding ways to assess that.

But then kind of the other side of the coin is

you do need a professional to assess

whether or not

they would have deemed that good enough

because there is that qualitative element

a lot of the work,

the things that these,

you know, interns are doing

are qualitative in nature.

So it's kind of correlating those two.

So you really need an expert to help you assess

whether or not something was good enough.

That's great.

So let's talk a little bit about adding more

and more sensors.

And I know you worked with a customer that

clearly cost was king.

And the request was let's sense

multiple parameters,

I guess with one sensor,

you know, again, driven by cost.

Can you talk about

doing that or what you think

what your thought process is

when you're trying

to define the right architecture

with clear cost constraints,

with trying to sense multiple things

with one sensor?

It was, I feel like I said this a lot,

it was a real challenge.

But it really is,

I think, you know, kind of understanding

where you are starting.

And I think when you start

with that technology exploration,

it does feel a bit like,

you know, the world is my oyster.

I have so many technologies

that can do, that could accomplish this goal of

this sensing goal

or kind of like the higher level

what you're trying to do.

But where you start to really narrow in is

what of that kind of breadth of technologies,

pressure, optics, magnetics, RFID,

you know, force?

Like, there's so many, you know,

ultrasound, there's

just a myriad of sensing technologies,

you start to get really narrowed down

and it's like which one of those

can kind of cross

into these other sensing goals

that they might have.

So, you know, for example, like,

maybe they want to know

when a device

when someone wants to know

when a device is being used.

But then they also want to know something,

that it's doing its job correctly.

So can you frame those questions

into what is actually happening

when it does its job correctly?

Is there a pressure change?

Is there also like a physical optical barrier,

does like fluid leave

and so you could see a difference in that.

And there's a lot of ways

that like that higher level sensing goal

can be kind of nitpicked down

into different ways you could view it.

And then you start to get this overlap

and you start to see, OK, this technology's

really only going to be really good

at this one thing.

And this technology

is only really going to be good

at this other thing.

And then you start to find the one technology

that might be good at all things,

but you do start to have to assess

your feature set

when you're that cost constrained

because it's really hard

to find one size fits all and does it reliably.

Right, I mean, clearly there's

a lot of applications

where just using cameras, for example,

will sense a lot of things

but you know,

is that the right architecture for,

your workflow, for your cost model,

for your platform?

Yeah.

And then on the flip side of that,

I've also seen

you may recall the GenMark project.

We put a white paper out about using resistors

that are used to sort, of as part of PCR.

You can also use that

for temperature sensing,

like dual modes sort of

electrical components, I guess,

it was one way to do it there.

Yeah, and like one common thing that,

for just an example is like capacitive touch

is something, you see in every single iPhone it

you to detect that someone is using it.

But you know, there's also very commonly

for like liquid level detection.

So kind of looking at

how could you

possibly interact

that single sensing element

to do very different tasks

and that you can kind of apply that process

to all the different type

of sensing technologies.

So our audience is in the business

of commercializing products.

They're short on money,

have a lot to do, RevMedica is no exception.

And one of the themes from that

last interview was needing to pivot

once they started, you know,

getting more market data and end user data,

What's been your experience with pivoting?

When have you had to pivot

or a client has had to pivot

and what are you looking out for

to help make those decisions?

It's really yeah.

What they kind of what they said resonated

and I know from working on a number of projects

where you kind of you're

trying to hit a prototype goal

and you hit that prototype

and then you kind of start to reassess

where you want to go from there.

It can be challenging to do that efficiently

and, you know, cost-effectively.

But I think in my experience,

you know, in order to get to a prototype

you have to make assumptions on date.

You have to make assumptions at the beginning.

You just don't have the luxury

of having every bit of information or,

you know, you're doing research

and development for indefinitely

and you just don't have that luxury.

So you have to make assumptions to hit

to get to that prototype,

to get to that milestone.

And that's important.

That milestone is important

and will teach you a lot of stuff.

So what happens often is

you get to that milestone and you learn a ton.

And sometimes often

you learn that

some of your original assumptions

were not valid

or they're not valid anymore,

or market requirements change,

or kind of as you're learning.

And I think just being open to saying, OK,

I've learned this, I've learned a ton.

And then really revisiting those assumptions,

not continuing to kind of say,

I will modify this prototype

to fix those assumptions at some point

your device can be

one of your biggest constraints.

And I think that's when

sometimes you feel like you've gone

you could have gone too far.

And it's a really hard thing

to make that assessment.

But when, you can often

leverage that knowledge to kind of go

in a different direction

just as quickly

as kind of improving that prototype.

So it's about reassessing that

in my personal experience

I've done, I've kind of

I really like to keep that open mind

and in all projects

because I think it's important

to get to the product

that really is going to be effective.

But one kind of specific example,

you know, in this disposable

unit that we were working on,

we had made an assumption

at the beginning on this technology

that we were going to use to implement

very cheap sensing.

And it would, based on this idea

that they already had this

this technology in manufacturing,

it's used all over the place

and it's used cheaply.

But what we didn't recognize

until we were going into,

you know, feasibility prototyping

and going down that line was that

our very specific application of this

was kind of first of its kind.

And that came with a lot of risk.

It was really just

no one could give us the answers we needed

because it didn't ever been done before.

And there was a lot of R&D

that would have to go into it to

to get to a point where we felt confident.

And I really do

believe we would have gotten there with that

same technology.

After six months

and prototyping and learning

and getting this in the hands of users,

we found there was an alternate

that was relatively inexpensive.

It was a new process to their manufacturing,

but it was not as expensive as we had thought

it could be in the beginning.

So we did end up

pivoting to kind of approaching it that way.

And I think it was the right approach

over kind of

trying to continue down this path

based on the original assumption we had made.

I think the best way to that, I think about,

pivoting or,

this game of product development

and speed to data is

I think you really have to be disciplined

at setting a timeline

like at the end of Q1

we are going to make a decision on the features

we want to explore in the in the next quarter.

You know, like if you're

if you're not disciplined about that, things

just keep bleeding on

and then you're losing track of like, well,

what are we what are we aiming for?

So a lot of times our projects are kind of

artificially sort of limited by,

the scope that we propose in the budget

our clients have.

But I think that's also,

you know, that helps, to really drive

to the data that they need to decide

whether should they pivot

or should they keep going,

should they go to their management and say,

look, this is looking like it will work.

We want to do another three months,

six months, nine months,

assuming this architecture.

Absolutely.

And I think

that's an important

kind of thing that we've learned.

And our clients

or a lot of the projects we worked on yeah.

It's appreciated that, you know,

we can kind of

as a group collectively direct

in each of those directions

just because, you know,

depending on where you need to be.

as you said, like

we got to make decisions at some point.

And sometimes it's really hard.

You have to make those assumptions.

So it's just kind of keeping track of those

and being able to revisit those is really,

really valuable

to the overall trajectory of a project.

Great, all right.

Let's talk about something

very near and dear to your heart.

RevMedica

does claim to be a much more sustainable

platform than their competitors,

with their usable battery power pack

and you're not throwing away

something, you know,

with batteries in it, for example.

Can you just share any of we didn't

talk about that much in the episode, but

can you just

you lead the sustainability

sort of initiative, Key Tech Green.

Can you just talk a little bit about,

either what's going on here at Key Tech,

or kind of just your thoughts

as you look at projects

when thinking about sustainability.

Yeah, absolutely.

It's certainly near and dear to my heart.

So, you know,

Key Tech Green, which is something I kind of,

a fellow,

you know,

group of people at Key Tech right now

is we're trying to look across the board

at sustainability both, you know,

how are we ourselves

wasting a lot and shipping too much

and do we really need

overnight shipping

every time we get a McMaster box

to also like how can we encourage

and build designs

that are just as effective

but not creating as much waste?

And that's something that

we're starting to think about more

and I feel like

I have noticed

as I've been looking at newer projects

in recent years, I have felt this kind of shift

a little bit more into that is a

that is a question on people's minds.

And I'm really excited about that.

I'm really excited that this seems to be,

you know, not just,

oh, it would be nice, but like,

some people are coming to us with

this must be a durable component.

I can't move to a disposable

and things like that

to try and reduce this waste.

And so I'm just really excited

like this is a kind of a

an input that are coming to us from clients

more and more.

So it's really exciting to see RevMedica

kind of take that and, you know, build

that as a platform pro,

you know, and a selling point.

And I do see kind of,

that's a really exciting thing for,

the medical industry, which is,

got a lot of disposables across it.

When I hear sustainability,

I put my entrepreneur hat on

and I think like,

you know, any product out there,

how can you make this product, this workflow,

this user experience

more sustainable, you know, sustainable,

like the real definition of sustainability,

like environmental sustainability,

not necessarily

a sustainable,

you know, burn rate or something like that.

But if you look at any product with that lens,

all of a sudden

this whole world of sort of ideas,

come about.

I think.

So I think we’re going to be seeing more platforms

that are pushing sustainability like RevMedica

Maybe it's not necessarily a step change,

but it's an equal product

but if you can show that, or prove that,

the approach is more sustainable,

I think you will get more customers.

And it's not just the product

it's the whole supply chain.

Yeah, to get there.

That's my challenge to all

12 listeners of you out there.

Is to think of products

in a more sustainable light

and you might find some ideas there.

Yeah.

Well, hey, any other any other

high level thoughts on

adding functionality to on market products?

I mean, that's

where innovation comes from, I guess.

But just any other thoughts.

I think kind of covered a lot of them.

But really that it

it feels like

there's so much power in knowledge

and there's so much power in that feedback

that, you know,

we're just starting to touch the surface,

like what RevMedica

is trying to do

with providing feedback to users.

There's just so much power in it

and our processes and things.

But that kind of path to the market

and the kind of the,

the major things is

like getting it into workflow

that people already feel comfortable

with or disrupting the workflow

in only positive ways

is a really big challenge and then,

you know,

having something to compare to

is really hard.

So I think those are just the main takeaways.

But it's really exciting to see.

And I think you know,

the more we have

that capability to just

see what's going on,

I think it can only be better.

Yeah.

I mean, I think that it's a similar story

to like the surgical robots that are out there

where you're getting these products

with additional sensing out in the field

and there's a market for it.

But what they're really doing

is that, you know,

they're collecting all this data

that will then inform

even more features

and better outcomes and things like that.

So I think we're like,

I’m trying to think of a not a web 3.0 analogy,

but, you know, product development

probably 5.0 at this point.

But like, you know what I mean?

We're getting the electronics out there

that didn't exist before.

And then there's going to be

this whole other wave of,

you know,

machine learning,

artificial intelligence layered

on top of these products, guiding users

to perform their procedures,

you know, just better.

Yeah, no.

And that's a really awesome direction.

And it's just it's going to take time,

as you said, to kind of

we're getting the sensors in there

to collect the data.

And even if these first implementations,

because we are trying to do both,

assess and provide the data,

I think iteration is going to be important.

But yeah, that next gen is coming

and it's going to come in

and it's going to,

you know, really help,

I think with that quantitative measurement also.

Awesome.

OK, thanks, everybody.

That's it for episode 15. Talk to you soon.