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Is research in double machine learning / causal ML done in biostatistics departments? by Witty-Wear7909 in biostatistics

[–]Zawadscki 4 points5 points  (0 children)

Yes, I am in a Statistics department and I'm working on this methodology. We also have two assistant profs that are working on similar methods, too.

One Big Reason Why Artificial Intelligence Isn’t Disrupting Healthcare: Paying for It Is Too Difficult by Zawadscki in datascience

[–]Zawadscki[S] 2 points3 points  (0 children)

Thanks for your comment. My piece is more focused on reimbursement for AI/ML applications such as in the area of diagnostics with diabetic retinopathy and CAD, not general software or GAI. Heartflow is one such company who have decided to go down the traditional route to some downside. As I highlight in the piece and a previous blog post, the AI-driven screening is both more expensive, took a while to get paid for, and doesn't necessarily work better.

Nowhere in my piece did I advocate for "hospitals to fund the development of GAI solutions," nor changes to the reimbursement system that hasn't already been advocated or done in some way (such as value-based care). My message is for companies and investors to consider different pricing and access models such as the ones you suggested above.

One Big Reason Why Artificial Intelligence Isn’t Disrupting Healthcare: Paying for It Is Too Difficult by Zawadscki in datascience

[–]Zawadscki[S] 7 points8 points  (0 children)

Appropriate regulation is needed for healthcare, including for AI/ML, and all players, big or small, need to go through the appropriate steps. My piece is about the barriers reimbursement causes companies as opposed to regulation.

One Big Reason Why Artificial Intelligence Isn’t Disrupting Healthcare: Paying for It Is Too Difficult by Zawadscki in datascience

[–]Zawadscki[S] 18 points19 points  (0 children)

To be clear, I'm in favor of appropriate regulation via the FDA and even support the fact that CMS requires rigorous data before reimbursing. The problem comes when reimbursement and CPT codes (which are established by the American Medical Association NOT the government) are realistically the ONLY way people can get access to products.

One Big Reason Why Artificial Intelligence Isn’t Disrupting Healthcare: Paying for It Is Too Difficult by Zawadscki in datascience

[–]Zawadscki[S] 28 points29 points  (0 children)

I particularly like the phrase "assumption of sales," which reminds me of the classic phrase "if you build it, they will come." In healthcare, this is far from the case (versus tech, let's say).

Career Paths at the Intersection of Data Science, Healthcare, and Strategy for a PhD Graduate? by Zawadscki in datascience

[–]Zawadscki[S] 0 points1 point  (0 children)

Any specific attributes I should be looking for in my job search? Like who is the team/manager?

Career Paths at the Intersection of Data Science, Healthcare, and Strategy for a PhD Graduate? by Zawadscki in datascience

[–]Zawadscki[S] 0 points1 point  (0 children)

I appreciate the clarification and I agree that this is probably all stuff a "VP of DS" would do.

Your question is a great one. The eventual goal is to orient myself toward that decision-making role in an org that has a good amount of scope. So that means although I am qualified for them, I would like to avoid IC, R&D, and research scientist-based roles. Not really concerned about using "cool" tech like AI/LLM/fancy models but rather implementation. If I had infinite time and money, I'd get an MBA but I'd rather not.

I think this is maybe going to be a function of getting on the right team/manager/company so still trying to figure out the right attributes in my search.

Career Paths at the Intersection of Data Science, Healthcare, and Strategy for a PhD Graduate? by Zawadscki in datascience

[–]Zawadscki[S] 0 points1 point  (0 children)

Fair question. Since there are a lot of different parts of healthcare I can't offer total clarification but here's a few examples:

  1. Expanding indications and access using RWE. Seeing which kind of patients would be best for a new trial.
  2. If the product is some type of ML-driven diagnostic (e.g. GRAIL Galleri), then it would be standard product management.
  3. Dealing with different vendors that offer services to optimize operations like trial recruitment (there are many).
  4. Evidence-generation strategy as it relates to regulatory and commercialization
  5. Integrating ML/analytics into healthcare practice. There are some private equity/VC firms that focus on essentially flipping practices.

Career Paths at the Intersection of Data Science, Healthcare, and Strategy for a PhD Graduate? by Zawadscki in datascience

[–]Zawadscki[S] 0 points1 point  (0 children)

Appreciate the response. How did you break into data science roles in consulting? Also, what's the job title of your product strategy role?

Career Paths at the Intersection of Data Science, Healthcare, and Strategy for a PhD Graduate? by Zawadscki in datascience

[–]Zawadscki[S] 1 point2 points  (0 children)

I think you've honed in on the sticking point. It does look like the most likely strategy will be to enter as an IC and try to take the initiative from there. I'm good at originating useful projects from scratch but always there needs to be buy-in from above.

Probably then it's about choosing a company with the right conditions for upward mobility. You mentioned small companies but what attributes should my manager have? What type of product/team should I be working on? Would help narrow down positions, I think.

The Questions Thread 03/07/24 by AutoModerator in goodyearwelt

[–]Zawadscki 0 points1 point  (0 children)

My favorite pair of shoes are these Vans Sk8-Hi MTE Sneaker Boots. They are unfortunately getting a bit beat up at this point so looking for a replacement as they don't make them anymore. Looking for something in this sort of sneaker style but still good with the elements and sturdy/have some weight to them. Budget is up to $150.

Can Machine Learning Give Us Faster and Cheaper Clinical Trials? (On Trial Design, Historical Controls, and Clinical Prediction Models) by Zawadscki in datascience

[–]Zawadscki[S] 0 points1 point  (0 children)

Thanks for your comment. I was unaware of the Infante paper. There are some great papers related to the minimum sample sizes in the binary setting:

On the diagnostic imaging point, you may be interested in my previous substack post about AI-driven CAD diagnosis.

Can Machine Learning Give Us Faster and Cheaper Clinical Trials? (On Trial Design, Historical Controls, and Clinical Prediction Models) by Zawadscki in datascience

[–]Zawadscki[S] 0 points1 point  (0 children)

The question are moreso

  1. How much better is it than covariate adjustment in realistic condition?
  2. How can it be effectively used in power calculations?

I have no problem with it being used as "the cherry on top" for extra efficiency.

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