A deep understanding of access drivers and barriers is crucial to biopharma market access success.  In this bizi blog post, we explore three major market shaping trends impacting pharmaceutical US market access and their implications on access competency development. 

Trend #1: Consolidation within the US healthcare ecosystem is creating a new breed of sophisticated customers with increased purchasing power and centralized evidence-based decision-making.  Healthcare reform and declining reimbursement coupled with historical tensions between key players within the US pharmaceutical supply chain have resulted in massive consolidation among the US delivery sector and more recently led to a more integrated and concentrated payer landscape (e.g., DOJ preliminary approval of CVS and Aetna merger, Cigna’s acquisition of Express Script). 

Fewer, larger health systems and payers not only result in increased purchasing power by these large customer groups but also shift treatment decisions away from individual physicians to enterprise administrators through formulary control and clinical pathway development emphasizing product value.

This structural shift requires manufacturers to re-assess not only their “Key Accounts” but also their Key Account Management (KAM) capabilities.  Historically, it’s been all about selling to doctors via features and benefits detailing.  However, value-based selling to C level non-clinician administrators requires markedly different skills and backgrounds.

Key Account Management is about creating value for the customers and the company.  As such, a deep understanding of both the customers’ goals and needs and how one’s own company makes money is key to success.  This demands Key Account Managers who are strategically minded, number savvy, capable of establishing common ground creating win-win solutions for the customers and the company, and able to articulate value messages (clinical and economic) to address specific stakeholder’s specific needs.

Successful key account relationships are not built overnight.  As such, companies should consider structuring the KAM role as a career role to provide stability between the customers and the company rather than as a stepping stone to greener pastures.


Trend #2: US payers increasingly use tools to manage risks associated with products’ uncertain clinical outcomes limiting patient access driving down pricing.  Tools such as requiring prior authorization and/or companion diagnostics, step therapy, developing clinical pathways, reforming payment models (e.g., pay per episode rather than buy-and-bill) and entering into risk sharing agreements with manufacturers are on the rise in the US especially for high per patient cost specialty products including orphan drugs (e.g., starting Jan 1, 2019, Medicare Advantage health plans can apply step therapy to Part B physician administered medications.).

In addition, consolidation in the payer sector is breaking down the pharmacy and medical benefit management silos subjecting products traditionally covered under the “medical benefit” to increased “value” scrutiny.    

To address payer uncertainty and demonstrate “better value for money”, biopharma firms need to evolve its business model to support an end-to-end evidence-based approach to product development and commercialization.  This requires “Access Leaders” capable of connecting technical access domains to the rest of the business effectively integrating access considerations into clinical, medical, regulatory and commercial.  In particular, Access Leaders need to work cross-functionally to align evidence strategy and execution with payer requirements and apply relevant clinical and health economic data to support pricing and reimbursement negotiations.

In the shift to value, biopharma firms will also be increasingly assessed on their ability to create value for the healthcare ecosystem overall.  Patient services “beyond the pill” solutions are no longer optional.  According to a recent survey of 200 patient services executives across the US and Europe, patient disease education and medication delivery and support are driving above-average business impact.  However, the study also revealed to sustain success companies must establish a clear ownership for patient services rather than spread it across organization silos diffusing accountability.  What’s more, companies need to figure out how to measure the impact of patient services on patient outcomes, the main goal for investing in patient-centric capabilities.


Trend #3: Increasing pressure on pharmaceutical pricing transparency highlights the importance of policy and health economics capabilities.  22 States across the U.S. have introduced bills focused on ensuring greater pharmaceutical price transparency around maximum allowable price, net price disclosure and list price increases.  This legislative trend requires manufacturers to closely monitor relevant policy trends, identify key policy stakeholders at state and federal levels, and invest in “policy” and “health economics” capabilities to both positively influence policy positioning addressing access barriers and to justify drug prices in a more transparent environment.


About bizi
bizi is a specialist Market Access firm dedicated to working with life sciences manufacturers.  We offer one-stop Access capability build services to Chief Commercial Officers and Access Domain Leaders.

Our consulting platform based on a unique delivery model connects proven independent consultants to Access projects across the entire product development life cycle offering better value for money.  Our consultants often work as an integral part of a client’s team serving as the client’s in-house domain Subject Matter Experts (SMEs).

bizi's services are powered by our back-end proprietary database platform covering 8 Access Competency Modules tracking 1000+ life sciences firms and thousands of pre-screened Access candidates.

Leveraging our database platform, bizi’s executive search services can cut short listing time to hours while improving hire quality.  bizi has successfully on boarded VP market and patient access, head of HEOR and field teams for both pharma and device firms.

This Cutting Edge report examines market access staffing trends for four sub-groups based on surveyed market access teams across the US, European (EU) and Emerging Markets (EM):

• Outcomes-Based (HEOR, PRO, CER)
• Strategy-Focused (pricing and reimbursement)
• Relationship-Focused (account management, government affairs and launch sequencing) and
• General Market Access (anything not included in prior 3 sub-groups)

Here are some key takeaways:

• Relationship-focused personnel make up the largest percentage of the market access team globally while strategy-focused personnel make up the smallest percentage.
• Allocation of relationship-focused personnel dedicated to Government Affairs and Launch sequencing has increased considerably in the US from 1% in 2013 to 8% in 2015.  Emerging Markets have the highest percentage of government affairs and launch sequencing FTEs, at 12% in 2015.  
• Percentage of Market Access staffing dedicated to pricing has more than doubled to an average of 15% from 2013 to 2015 in EU and EM.  US is the only surveyed region decreased pricing FTEs from 7% to 6% during the same period. 
• Outcomes-Based staffing has increased from 1% (2013) to 6% (2015) among surveyed US Market Access teams.  Yet US remains the lowest in FTE percentage for Outcomes-Based staffing comparing to EU (12%) and EM (15%). 

However, it is unclear the sizes of the firms included in the Cutting Edge survey and if the report has taken into account Outcomes FTEs housed outside Market Access such as those reside within Medical Affairs or standalone. 

Based on bizi’s proprietary executive search database for market access, among firms the database  regularly tracks, in the US, Outcomes-Based FTEs vary significantly by company size and even among companies of similar size.  Inclusive of field Outcomes personnel, large biopharma firms (1001+ employees) collectively employ more than 70% of the Outcomes FTEs.  And only about 9% of late phase small biopharma firms (less than 500 employees) have dedicated in house Outcomes infrastructure.  Device and diagnostics firms except for a few exceptions are lagging on HEOR infrastructure investment comparing to their biopharma counterparts.

One of the first books I’ve read when I started bizi is Jack Shostak’s SAS Programming in the Pharmaceutical Industry.  I had the privilege to get on the phone with Jack on a sunny Saturday morning and got him talking about his book and much more.  

Kelly:     Jack, what are some of the common attributes shared by the top statistical programmers you’ve worked with in the past?

Jack:    The number one attribute is something you don’t necessarily pick up in school, or get from books.  It’s just something you are going to have, and that would be attention to details.  The work we do is varied and dynamic.  You can’t necessarily make people more detailed.  It’s an inherent thing. If somebody has good attention to detail in their work, then all other things can be overcome. They can learn whatever they need to learn.  So, attention to detail is probably the number one thing I would say.  And that’s really hard to pick up on a resume or judge by what classes they’ve taken.  It’s something you find out when you work with them.
 
Kelly:    How about the ability to learn new things then, Jack?  As you alluded to, the nature of the work is dynamic; the industry is constantly changing as well from regulations to new data standards. 
 
Jack:    Well, it’s like with all things, you have to keep learning.  People come to statistical programming historically from all kinds of disciplines.  Maybe they had some SAS in school, took a SAS certification class, or dabbled in analytics.  But things have changed over time.  

For instance, SAS is kind of the Swiss army knife of statistical programming but it’s not the only tool in the arsenal you can use.  R is coming up fast.  So, to stay versatile, statistical programmers may want to learn R.   

Aside from the statistical tools, now really to be successful, statistical programmers need to also be fluent in data standards, they need to understand and develop expertise in CDISC SDTM and CDISC ADaM. 

So things are changing and you always have to keep learning.  You have to watch the horizon, and decide what it is you want to specialize in.       
  
Kelly:    In my working with statistical programming managers, I experienced two schools of thoughts.  One school of thought believes statistical programmers should consciously develop their clinical knowledge and skills so that they can dialogue more effectively with statisticians and clinicians.  The other school of thought deems “clinical knowledge” at best a nice to have for statistical programmers. 

So, how important is clinical knowledge to statistical programmers?
 
Jack:    That’s an excellent question. 

The individual that says statistical programmers do not need to have clinical knowledge probably is coming from a heavily siloed environment.  I am trying to not be negative when I say that.  In that environment, statistical programmers are head down, and generate data sets and TFLs.  That’s what they do and if you spend more than 15 minutes generating that one listing then you’re off task.  The programmers are almost an automaton here.  They’re executing the instructions.  They are executing the specifications.

There is something to be said for that.  However, I don’t really like that because when programmers work in that fashion, they’re disengaged from what’s really going on.  And by that, I mean if statistical programmers don’t have therapeutic area knowledge, then they will not be able to properly comprehend a protocol, read a statistical analysis plan, or understand what the key endpoints are, what the therapeutic intervention is, how does this intervention act on patients, and what are the safety concerns.  In contrast, if programmers do understand those things, then in their work whether they’re deriving variables or generating output, they can look at it and understand better what they’re seeing because they understand clinically what’s going on.  
    
Kelly:    Sort of like understanding the big picture, eh? 

Jack:    Exactly.  And that’s what’s valuable.  I can’t count the number of times that statistical programmers have caught problems in the data, or problems in the analysis.   If a statistical programmer can churn out a listing or table in 10, 15 minutes but he doesn’t really understand what he is churning out, it could be garbage.  

It’s really the lead statistician and the lead statistical programmer that know the study better than anybody else.  And if that lead statistical programmer is just executing, and he doesn’t understand what he is looking at, you’re going to have a problem. 

So, it’s a balance.  Speed is important.  So are attention to details, and understanding clinically what’s going on.  

Kelly:    Another common complaint from statistical programming managers is that statistical programmers’ resumes often look alike.  It is difficult to tell who is a better candidate for the job.  This may have to do with the standard execution part of a statistical programmer’s job.  How do you tackle this issue?
 
Jack:    An excellent question again.  Unfortunately, the solution to that problem is to pick up the phone.  You have to talk to people because, guess what, a lot of the time the resume is not necessarily reflective of what the person knows.  So you can get on the phone fairly easily and assess with a few questions what they actually know and what they don’t.
  
Kelly:    So, what are some of your favorite questions to ask in a phone screen?

Jack:    One blatant thing is whether what the person is telling me they know is reflective of what’s on their resume.  If you seemingly only know two or three of the twelve things on your resume, I already have a trust issue.  It is not going to go very far.  It’s better to be honest on your resume.  Put the things on there that you know. 

Another thing is frankness in conversation, honesty in conversation.  If I feel like somebody is not being honest, if I feel like they are stretching their abilities that is a big turn off.  Eagerness, desire to do the work, positive attitude go a really long way as well.

Kelly:    I see.  How about technical competency?  Do you assess that in a phone screen?  

Jack:    Yes, but I tend to stay higher level.  For example if I’m hiring a SAS Programming position, I’m going to hit the higher level areas.  What have you done?  Do you know specific areas of SAS?  How are you with say ODS Graphics, Macro language, Data Step, SQL?  What have you done with the language?  But it’s not going to get super deep.  Usually a phone screen runs for me about half an hour to an hour. 

Then after that, if I want to bring that person in and have them meet the team, then we will administer a don’t-lie-to-me quiz.  It’s a basic competency quiz.   By bringing somebody in and doing the in-person interview you also get the sense of personal fit with the team.

Kelly:    Talking about fit with the team, statistical programmers can be quiet by nature.  Many prefer to focus on the technical issues at hand and minimize interactions with others.  However, if you do want to be a valuable contributor to the team, the ability to work and communicate effectively with others such as teammates, statisticians, clinicians, data managers, and regulatory folks becomes increasingly critical.  Do you have some advice for the quieter type of programmers on how to communicate better?  
    
Jack:    Yes, that’s an excellent question as well.  Many people go into mathematics and statistics are—how do we put this—not the most outgoing, gregarious, communicative people.  They can be fairly quiet, heads down working. Unfortunately, that can be career limiting.  What we see happening is the software and the tools that we use are getting better and better.  So the ability to sling code isn’t going to be as valuable as the ability to speak to a clinician and a statistician and wield software to produce what you need to do.

So there’s going to be a shift from being technically brilliant to being interpersonally brilliant.  The need to interact, the need to be able to take requirements from somebody who’s not technology savvy and make software do what it needs to be done is going to become more and more important. 

And along with that, the ability to write is critical as well.  We live in a world of twitter.  People are forgetting how to write.  It is dangerous because if you can’t express your thoughts coherently to somebody, it doesn’t matter how technically brilliant you are.  You can’t get your message through.

Kelly:    Last but not least, Jack, I want to talk about your book, SAS Programming in the Pharmaceutical Industry.  This is the first and one of the best books I’ve read on the topic.  I know the updated edition will be out in March 2014.  So what can people expect from this book and the March release? 
 
Jack:    The book is designed for beginner to intermediate SAS programmers in the pharmaceutical industry.  It defines the context for the work and it gives you background on the industry including all the people you will work with in the industry.  Then it takes you through the types of programming that you will need to do to be successful. So, pulling data, massaging it, building your analysis data set, generating tables, figures, listings and all the challenges involved in doing that.  It ties into regulatory issues as well.  Things you need to know for regulatory submissions.

But it doesn’t go super deep into the statistics. If you want to do that, there is another book I co-authored with Glenn Walker that covers the statistics and that is Common Statistical Methods for Clinical Research with SAS Examples, Third Edition.  There’s another book that I did with Chris Holland on implementing CDISC that came out last year, and that is called Implementing CDISC Using SAS: An End-to-End Guide.

I wrote the first edition of SAS Programming in the Pharmaceutical Industry over 10-years ago.  So it needed some updating.  The upcoming March release has undergone a complete overhaul to make it more current with SAS and industry standards.  For instance, every example in the book now is based on CDISC data models, so it uses SDTM and ADaM data slices.  All of the old SAS GRAPH technology has been retired in the book, and it’s all based on ODS graphics now.  Then, some updating on the coding information, but the framework of the book is similar to the way it was. 

Kelly:    That’s a great overview, Jack.  Good luck with the new release and thank you for taking the time today to do the interview. 

Jack:    Thank you very much, Kelly. I really appreciate your time.

About Jack Shostak
Jack Shostak, Associate Director of Statistics, manages a group of statistical programmers at the Duke Clinical Research Institute. A SAS® user since 1985, he is the author of SAS® Programming in the Pharmaceutical Industry, and coauthor of Common Statistical Methods for Clinical Research with SAS® Examples, Third Edition, as well as Implementing CDISC Using SAS®: An End-to-End Guide. Shostak has published papers for the Pharmaceutical SAS Users Group (PharmaSUG) and the NorthEast SAS Users Group (NESUG), and he contributed a chapter, "Reporting and SAS Tool Selection," in the book Reporting from the Field. He is active in the Clinical Data Interchange Standards Consortium (CDISC) community, contributing to the development of Analysis Data Model (ADaM), and he serves as an ADaM trainer for CDISC. Shostak received an MBA from James Madison University and a BS in Statistics from Virginia Polytechnic Institute and State University.

About bizi LLC
bizi is a niche resource partner for life sciences manufacturers specializing in commercial planning, market access and HEOR via high end independent consultant support and executive search.