A Friday Chat About Working in the Data/Economics Side of Healthcare

STINA: Hi, can you see me?

RAVENCLAWED: Hi, yes I can!

STINA: So maybe start with what we originally majored in as undergrads and why/if it changed? Unless you were inspired even earlier? 

RAVENCLAWED: Sounds good! I went to a small liberal arts school and double-majored in International Relations and Economics, with much more interest originally geared towards the IR major — thought I was going to work for the State Dept or some exciting job abroad. Economics was something I became interested in after a few Global Econ courses for the IR major. I liked the quantitative aspect and being able to organize data in a framework. What about you?

STINA: I sort of stumbled eventually into my career. I had started as a social work major and ended up getting a social work/sociology major pretty much just because so many of the requirements overlapped. It wasn’t until I did the internship part of the social work training (I was shadowing a community worker with seniors living in the community) that I realized “If anything bad ever happens to one of my people, whether directly related to something I did or not, I am going to lose it.” So I was trying to find classes for my last semester of undergrad, I had really liked the required social policy course, and lo and behold I found a whole graduate school devoted to studying policy in the class directory.  

RAVENCLAWED: That’s really interesting how oftentimes an internship will be the first direct experience into your subject matter and often changes how you view something (becoming either more or less interested in it!). I had a similar experience in that my internships in college had a significant influence in my career path. I only applied for PAID internships, as I had to make money for any spending needs the following school year outside of tuition/room/board, and unsurprisingly, the ones that paid well were mostly in econ/data science type work. My initial entry into the healthcare world was working at the National Center for Health Statistics, doing the grunt work of cleaning and coding large datasets. Not exactly glamorous, but I learned how the basics of SAS, as well as survey design and methodology. And got paid! What was the work experience component of your grad program?

STINA: Paid internships, always sweet. I had a summer internship at the State of Wisconsin Employee Benefits, in their health insurance division. I helped create their dual-choice employee handbook. Were you trying to work in health or did you end up in it and found you liked it?

RAVENCLAWED: It was definitely by accident. My first job out of college was supporting PhD economists in anti-trust litigation, and about ~50 percent of the cases were related to healthcare — hospital mergers & acquisitions, physician group consolidation, payer rate setting and reimbursement, and patent disputes between the branded and generic manufacturers. Eventually, I became really interested in healthcare, mostly because it’s such a large ecosystem with so many different players and angles. It’s 20 percent of the country’s GDP! That’s huge! And affects every single one of us at some point in our lives, regardless of whether we want it to or not. I’m fascinated to know more about your experience in the insurance side of things — from both the state and private payer, correct?

STINA: Yes, once again going along with my history of stumbling into opportunities, I definitely started grad school with the intent to do healthcare policy because in my social work so much of elderly people’s lives are so wrapped up in and influenced by Medicare and Medicaid. So out of grad school my first job was with a center that studied nursing home and long term care quality, which is where I learned to work with large survey datasets and Medicare data. Unfortunately the grant I was funded under got terminated due to a political squabble in D.C. so I needed to work and a local private health insurance company was hiring. It was such a useful experience because I worked for the Medical Directors who came up with my projects which could look at any area of medicine, so I did projects like “Are we using the proper anesthesia for colonoscopies?” (Yes.) “Will using this private vendor to approve radiology procedures save us any money? (No.) “How did this year’s flu vaccine seem to compare to last year’s?” etc. etc. But I really missed doing the bigger work, as it were, of academic and non-profit work so I went back to it and now I do more work both for epidemiology-related studies and healthcare policy work, a lot of it evaluating Medicaid programs in states.   

RAVENCLAWED: I have so many questions! First, for anyone who might be intimidated into learning how to analyze large datasets, how long did it take before you felt comfortable doing the work? Second, at the private health insurance company, how did your company make decisions about care and cost? Was it always cost-driven? Did it actually overlap pretty well (i.e., maybe this expensive procedure actually saves money over the long run, so we will cover it)?

STINA: And I’ll fire some back at you in a second but to answer yours first. My sociology major got me to first work with data and it was with programs like SAS and others where you had to write like run;Format mmddyy10. Blah blah and I hated it at first! I thought it was the slowest, putziest thing ever and was glad I was “actually” doing social work instead. When I was in the state internship we just had Access and Excel and I found myself thinking “this would be so much easier with SAS.” So when I get frustrated with trying to make the computer do what I want it to do I remind myself BECAUSE computers are still stupid is the reason they still need me. I work for several professors and they are the main brain in the project, they design the study with my input for econometrics and surveys and I am the brawn in getting everything set up. It’s actually pretty satisfying, I’ve found I really like digging deep into data and both figuring out how to use existing data to find things and discovering new things.  

As to working for a private insurer, I actually got off pretty easy in that I worked for an insurer owned by the doctors that practiced in the clinics. If anyone was turned down for a procedure the doctors would have to see them face-to-face, so it definitely was geared more towards working in the interests of the patients. I started to go to the “big payments” meeting and I was thinking “Okay here we are, I’m going to see the underbelly now” but it pretty much was just double-checking that there wasn’t another insurer that should be billed as well and that was it. However, I personally am glad that the ACA eliminated lifetime caps because, while I didn’t see anyone kicked off during my time, I did see us scrambling to try and safely keep people under their limit.  

So what areas do you work in now, how about your experiences in data?  

RAVENCLAWED: Yes, cheers to computers who haven’t made us totally irrelevant (yet). And glad to hear about the decision-making process that seems relatively “good!” I currently work in biotech, on the commercial analytics and strategy side. I focus mostly our oncology portfolio and have had a variety of roles related to forecasting, market research, data science, pricing/contracting, etc. My company is fantastic to work for — they pay really well, there’s a healthy work-life balance (rarely work more than 45 hours a week), good perks/benefits, and a big emphasis on career development. I’ve had four different roles in the roughly five years I’ve worked there, so I feel like I’ve gained a ton of experience in all different areas of cancer drugs — from launch products, to stable in-line brands, to figuring out how to deal with biosimilar competition (ie, when a product loses patent protection). It’s probably about 30 percent heavy data science, but honestly, there’s a lot more focus on the “soft” skills — telling a compelling story, convincing a Sr VP why she should invest in a particular strategy, etc.

STINA: How much go you get to see of the patient/development/result side of the business? Have you had any adverse experience being in big data? I have been doing things like attending a “women in big data” group and I try to mentor other women and people from minority groups who are coming up. I haven’t experienced any overt problems other than maybe societal/self sabotage. I thought maybe I wasn’t smart enough to do this because my mathematical skills are only average for the college-educated but it turns out my analytical skills test perfect or near perfect.  

RAVENCLAWED: On the patient side, I’ve seen a good bit — I’ve managed the analytics for our patient support programs, and seen some pretty cool stuff like the CEO’s decision to expand our free drug program to anyone who makes less than $150K a year. I’m significantly less involved on the actual R&D side — we really only start to get involved with clinical assets when they are in Phase 3 clinical trials. We will do some competitive intelligence about other biotech’s pipeline, but again, mostly Phase 3. As for results, I own the sales forecast and actuals reporting, so I see results on a daily basis and am on an insider trading blackout list that prevents me from selling stock until quarterly earnings are released.

As for adverse experiences, I am happy to report that it’s a very supportive environment for women. Our corporate goals (which are tied to everyone’s year end bonus) have included stuff like “Increase the percentage of women in leadership roles by xx percent.” My specific group is 50/50 men and women (I ran the numbers myself), both at the individual contributor level as well as leadership. It’s majority-minority as well, so I never look around a room during a meeting and see all white older men, which definitely was the case at my first job out of college. It’s also pretty family-friendly (which shouldn’t be just a women issue, but ya know) — subsidized childcare onsite, no pressure if you need to leave early for kid pickup, etc. I actually just got off a conference call with someone who has a toddler who was babbling in the background and no one even flinched. Do you feel that your workplace is well represented?

STINA: I’m glad to hear about your workplace. We need to do better for racial and ethnic minority representation definitely. But women in my area at least are well represented. Though I once told one of the professors that I work with who got tenure as soon as it was possible, has Bloomberg calling her for quotes, won all these prestigious economics awards yadda yadda, well I  privately asked that she stop saying “This may be a stupid question” before her questions because they are always reasonable and she is one of the people I know that are so smart that she has redefined the upper limits of what I thought was possible in smartness.

Any advice for someone thinking of working in our field generally?   

RAVENCLAWED: Hmmm. I think I’ve actually been surprised by what’s gotten me promoted and new opportunities — it’s not necessarily the hardcore programming skills. Deep technical expertise is required, but it’s not enough on its own. It’s been the ability to communicate and influence — building a coalition of support, bringing others along with you, etc. So, I guess I would say don’t let the technical component deter you. That’s actually probably the easier thing to learn (versus more stereotypically “female” soft skills). What about you?

STINA: On the technical side at least if you learn one analytical software program (SAS, R, STATA) is a good resume builder because people that interview you that know what they are talking about will know that learning how to work with data is important and once you know that leaping to another system/language is easy. So when you all out there see “Needs 5 years experience with R” and you know SAS or whatever, ignore that and apply anyway. SQL and Python are useful too. But it’s more about being persistent and double-checking everything before you send it out, if you were just supposed to study men double check that all are actually men! Need 65+? Double-check that age variable!  It’s the simple things that will trip you up. Decent writing skills and knowing how to make a nice-looking chart help as well. But I feel gratified now that I’m talking with Ravenclawed, international woman of biotech and kick ass project manager.

RAVENCLAWED: Haha, you’re too kind! And likewise. I think that’s great advice about being confident enough that your skills will transfer to a different language, b/c they definitely will! Last question — what’s next for you?

STINA: I really like the type of work that I do and hope to just continue in it. After attending a conference a few years ago I am hoping to continue to encourage people and myself to make disparities a priority inclusion in their research. So many papers say “I’ve controlled for race in my results” which is a nerdy-scientifically important thing but not enough work is done in how race or economic status affect people differentially in healthcare research.  

RAVENCLAWED: Very cool! Great chatting with you!

STINA: You as well!

If you would like to be part of a future Billfold chat, or if you would like to set up a chat with another Billfolder, email nicole@thebillfold.com.

Photo by Markus Spiske on Unsplash.

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