Automate car dealer software for Life Sciences

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hello everybody i've got josh with me today and we are going to be talking about intelligent automation and digital transformation within the life sciences space but before we get into the questions that i have for you would you mind giving everybody an introduction hey everybody um josh noble um i've worked uh with blue prism for about five years or so at this point uh the majority of the work that we've done across fairly highly regulated industries healthcare life sciences the pharma space i've been involved with for that that for years in fact have had a chance here to work with i think about 60 percent of all pharmaceutical companies over 10 billion dollars in revenue so um learned a few lessons on that side from the uh you know the compliance regulatory side of of things from from health care and life sciences and hopefully can share a little bit here here on uh on emma's podcast perfect that's what we're going to dig into but to get us started i just kind of want to level set with the idea of you know as we talk about digital transformation and intelligent automation from your perspective where is the value coming from for life sciences companies to look at investing in these types of technologies yeah so um of course the easy place that everybody starts uh is still applicable to the life sciences space so we look at shared services historically finance hr generally into into i.t but if you want to get a little more narrowed into say the life sciences area we can look at say merger and acquisitions there's a ton of work that goes into mergers and acquisitions and how it's how a lot of pharmaceutical and like sciences companies grow and if we look at say just rules driven repetitive automation like rpa alone we can get into a lot of say one-time transition work where maybe it doesn't make sense to hook together deeply to different systems one-time transition work offer letter generation re-keying of data from payroll systems last year i was involved with a learning management system migration that involved porting records from 30 000 employees to a new system and that manually would take a lot of time there's a lot of checks that need to go in place to make sure that's accurate but building back-end integrations is maybe not the right way to historically go about that we're seeing companies that are deploying intelligent automation across a whole lot of different departments and as they build up those objects or those reusable lego blocks they can start to string together processes that bring together many different departments even again on the rules driven side so for example taxable asset tracking so if anybody's a u.s citizen involved with a clinical trial well we might as well go find out all the assets that are related to that that person and the potential tax write-offs but that involves multiple different departments and then as we move up kind of the cognitive scale we can get into things for example in the legal area where you're pairing rpa plus natural language processing to look for say contract terms that need to be renegotiated um if we look at say larger pharmaceuticals and biotechs i mentioned they tend to grow a lot through merger and acquisition and that involves a ton of just re-keen where people are acting like human photocopiers for a good portion of the day so when uh when a company acquires another company they're also acquiring their various different systems and their data that comes with it and so you've got two choices at that point if you're the acquiring company you can merge all of that data into your systems or you can have both systems running in parallel and the burden of that historically is either generally landed on it and it's generally already overloaded before that type of activity occurs so um integration of two companies ends up taking a couple years it's a big problem particularly and again the life sciences space where you see all that integration going on all the time so nowadays we have these no code or low code intelligent automation solutions that can be governed by it it's definitely still involved very very critical i.t must be involved with this please don't go about that route but they can be they can be governed by it but owned by the business so that you can then glue together these various different systems or say even map that one time transition transition without getting into heavy coding i.t historical resources the other other big area to keep in mind aside from the straightforward data movement data parsing gluing together these systems is that unlike say other industries where say insurance area may look at savings being around work reduction reducing the number of hours required to go through a particular piece of work saving time life sciences time companies tend to value a bit more towards higher volume and consistent so doing work right the first time eliminating human error and checking for human errors tends to be a higher value there than say just the body count type of type of work so um that also feeds into compliance but and i think yeah another question about compliance you fed into it perfectly because that's going to be the next question and i started smirking over here because i was like oh he's doing my job can you tell he's a pro um so when we look at compliance you mentioned this idea that um you know we're looking as that value bring brought forward by intelligent automation is doing work right the first time without human error so let's talk a little bit more about this idea of how intelligent automation can support compliance within life sciences all right so um you know i just mentioned accuracy a second ago but if we remember back from say basic stats class we probably remember that you know in general a larger sample size more data is generally a good thing but for companies that's only a good thing if you can do something with it if you can make any sense of it and so on the compliance side intelligent automation even down to straightforward software robots can collect data from all sorts of different systems aggregate that data together and where that becomes beneficial is let's say you need to review communications um you can scan through emails chats ivr transcripts and potentially flag for adverse events things that maybe a human didn't notice in those communications but software robot that can dig through those meticulously can then once you've aggregated that data together you can apply something like data robot to potentially identify patterns between that data now where that becomes very tricky is the flip side of that aggregating data collecting data can be a very good thing what the rules get a little weird is doing something with that data especially externally so for example uh turning around without human intervention and following an individual case study report maybe might not be the safest thing today and the reason is because that starts to bring in things like natural language processing and various different cognitive solutions like that get into factoring in probabilities versus just very very defined rules and the industry in at least in life sciences is still a little unclear as to the regulations and the rules of how those probabilities can be used if you check back i did a webinar with avv ppd and j and j back in july of i guess it's 2020 at this point and they talked about their experience with this provide a bit of knowledge and those industry groups can be really powerful because just because the fda for example doesn't have exact rules the industry bodies as a whole various different companies can come together to form that point of view and they talk about gxp versus non-gxp compliant processes and how they handle that and also keep in mind that not all systems are friendly to intelligent automation i mean if you think about maybe the most newsworthy point on that as is outside of the life sciences area but think about the us small business administration issues around the ppp loans this past year the government went so far as to blame robotic process automation for overwhelming their website and despite your opinions on this kind of scapegoat excuse it did trigger the us government to open up apis to access for smaller financial institutions and level the playing field so even though rpa now it wasn't necessarily the right solution for submissions it did have a positive benefit overall by level of playing field and in the life sciences space huge vigilance over in europe went through a similar thing maybe a little less newsworthy but huge vigilance is the system that handles electronic submissions for pharmacovigilance information so if you're doing uh clinical trials in europe or you're say marketing um medicinal process products for investigational applications you need to file that through youtube vigilance and a few years back huge vigilance was only providing five logins to companies so big companies had to be extremely efficient with how they were using those logins to this government site nowadays youtube vigilance provides the option for edi uploads but that requires a life sciences company to ensure that their local safety pharmacovigilance database is compliant with huge vigilance's messaging format their terminology their database structure there's a lot of testing that goes involved with making that work and just like the u.s sba e-trans system for ppp loans user vigilance may not be real happy with companies hammering their platform through user interfaces so even if you however can't do the final filing through user interface see through rpa against huge vigilance and you're taking that edi upload approach there's still a lot that has to go on within your walls before you do that upload so there's a lot of data aggregation manipulation of data verification of steps that go in before you're submitting to those portals and keep in mind one of the benefits of intelligent automation versus again your historical kind of edi uploads is the amount of logging that can go in you can track every single step that's gone into your processes and report out on that versus just saying we're firing off this bulk file intelligent automation can also do the qa testing that's involved with making sure your database and terminology and everything there is going to match up with you to vigilance so benefit there um one other area to or there's two other areas to mention around compliance one is that you can use intelligent automation to keep ahead of constantly changing regulations so for example drug labels um those change all the time the requirements year to year from every government agency in the world tends to change and so you can use intelligent automation to actually update your drug label artwork again around the globe and that's particularly important when you're talking about smaller companies that need to operate in many many different geographies many different countries and sanctions and then finally tangential to compliance we get into the security side of things we're talking about life sciences companies that are doing clinical trials that are dealing with health care information so you confidentially confidentiality pii phi data protection is key here and every human that's seeing or touching health care data adds risks about a year and a half a decade and a half ago i worked at an er and i can tell you that there were plenty of nights at 3 a.m that we were laughing about you know scans that we were seeing medical scans that we could access that were from weird things and there was a lot of water cooler talk that occurred that probably wouldn't be kosher now and nowadays but even though not malicious that could be a problem that you're having humans being able to see that information and it has led to unintentional data leaks through say bpo organizations that's the type of thing that if all we're doing is feeding that information through software robots without a human looking at those screens that can be beneficial and finally on that that point that's where the the aspect of citizen developer kind of topics and this going back to the days of macros for everybody and everybody automating their own things can start to get a little bit uh iffy when we get into life sciences companies because while vendors are selling that as quick and easy it's can compound to again that information coming up on screens that maybe we didn't intend to leave up on screens when we're walking away um and expensive mistakes if we're accidentally linking that information or fat friendly an extra zero an extra thousand times because individuals automated their own work so anyway digress into a whole lot of different areas we might need an episode number two on this um the last question that i have for you kind of goes back to something that you mentioned in your first response and it's the idea of how intelligent automation can help companies that are growing through acquisition manage some of the activities that come along with that but also kind of looking at how can we leverage intelligent automation to scale in life science or especially when we're looking at like startup companies and the like um in the current environment how can they grow using intelligent automation in ways that maybe we couldn't 10 years ago yeah so as you hit on and i mentioned at the beginning the merger and acquisition part is is big but let's take this from uh from a slightly different direction so if you look back a decade ago start of 2011 there were about a hundred thousand registered studies on clinicaltrials.gov if you jump ahead a decade to the start of this year start of 2021 there's about 360 000 registered studies on clinicaltrials.gov and in a real life scenario if you take cell gene for example before they were acquired by bristol myers squibb the average number of clinical studies that they were doing was increasing by 20 year-over-year but their head count was only increasing by four percent year-over-year so heck of a lot more work without a matching head count and intelligent automation can be a force multiplier in that that scenario there's a quote i use from alice in wonderland all the time in various different presentations and i'll butcher it if i don't read it because it takes all the running you can do to keep in the same place uh if you want to get somewhere else you must run at least twice as fast as that and in the life sciences industry if we use that cell gene example you actually have to run four times as fast so we need something else to help our people be able to keep up with that much data process it handle it accurately and move forward you got to do more with less and make sure that work is done right the first time and again intelligent automation would be a force multiplier for that to aggregate data between you know consolidated various different presentation layers for example just in the contact center alone imagine one person has to deal with 20 different applications when you call call them to help out with something or to turn around and do the reverse of actually inputting data one time and then duplicating that data into many different systems and you know that's big but it's a bit straightforward now let's factor in the intelligence factor remember where this space was at about a decade ago i mean the iphone 4 was just being released uh it was the first iphone with a front-facing camera and it'd break if you held it the wrong way so now we have iphones with three front facing cameras and lidar to turn yourself into a 3d modeled chicken if you want to i mean this technology has taken a huge leap even in the consumer space and we can apply that to the commercial space just two years back one of my colleagues and i jason salmani and i built an automation demo that was allowing for pill identification to be run using an image and it would actually take a picture of a pill and tell you what that medication was and in fact now two years after that while that was just a demo at that point two years after there's a big systems implementer selling an application to do exactly that and we've reached the level of trust of that type of technology for that accuracy to you know be at a commercial scale heck half of clinical trials are still relying on paper documents so we have the rise of much more intelligent ocr icr technologies over the last decade that also get into things like handwriting recognition that are using crowdsourced data samples so technology is moving extremely fast the technology accuracy is equally important that's improving at an exponential rate which is important again when we're dealing with pii phi life and death types of situations here we're training those intelligent automation solutions from aggregated cloud data sets and then finally i think probably the biggest change that everybody can see out there beyond my kind of diet drive on this stuff is we're not just using the term artificial intelligence anymore the way we were a decade ago as soon as somebody comes to me and says josh give me a use case for xyz software plus intelligent automation or ai and i start to describe a use case to them they say no no that's that's rpa plus a chatbot or that's rpa plus a natural language processing as soon as we define the case now there's other words we're using instead of the this replacement of this broad artificial intelligence magical marketing term so that's a huge change that we're seeing within the market and defining what those actually look like so hopefully that was helpful i know this is probably a little on the on the longer side but um you know i'll pass it back to you i know the the game of semantics within this industry is something that's a little bit of a pet peeve of mine because it is once everybody gets used to a term used to a thought process we rename it and we look towards something different um how we're going to talk about it but at the end of the day just like you were talking about the ultimate goal of any of these tools coming into an organization especially within life science is to help you manage the parts that maybe humans aren't great at managing and can be better suited with a bot or some sort of technology aggregating data and helping you scale your business to do more with less and so that's kind of the biggest takeaways that i've got from our conversation and i really appreciate you taking the time to sit down and talk with me a little behind the scenes for everybody josh has been wonderfully patient with me with all sorts of technical difficulties today so an extra thank you for that um but i want to encourage everybody who isn't already connected with josh to make your way over to his linkedin and reach out to him there but thank you very much again for your time today and have a wonderful day everybody if 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