Spencer Dorn

I am a gastroenterologist, physician leader, clinical administrator, informatics physician, academic, and advisor.

Committed to the greater good, I write here and at Forbes to connect the dots, spark thoughtful conversations, and learn from and connect with others who share a passion for improving healthcare.

Sitting at the intersection of clinical practice, operations, and technology, I enjoy explaining how healthcare works and imagining how it can work better. 

I am fascinated by how artificial intelligence and other digital technologies are reshaping medicine. I am also particularly interested in redesigning specialty care.

I aim to integrate mind and body in both clinical practice and life. 

Outside of work, I enjoy spending time with family and friends, music, sports, reading, and swimming, most of all in the ocean.

Professor of Medicine

I care for adults with acute and chronic GI conditions – particularly those with disorders of gut-brain interaction (e.g., IBS) and motility (e.g., gastroparesis) – in inpatient, outpatient, and procedural settings. 

I've served as site-PI for 20+ clinical trials and co-authored national constipation practice guidelines. I write and present on topics related to care delivery.

Founder at Out-Of-Pocket: outofpocket.health

I'm trying to make healthcare more accessible by using memes, shitposts, and humor to explain it. Check it out at outofpocket.health 

- a newsletter that will teach you how healthcare works AND talk about interesting trends at the edges of it
- Courses and workshops to get your people up to speed on skills (e.g. AI tool, claims data analysis, etc)
- Healthcare events for people that ACTUALLY want to learn
- Healthcare coworking spaces to work alongside other builders

If you go through the site and don't laugh once I'll send you an apology card and/or a copy of my children's book: https://www.amazon.com/dp/B08CWM8QZW?ref_=pe_3052080_397514860#

Nikhil Krishnan

It's been 6 years since I started Out-Of-Pocket, few random reflections

- There really is no point where you can coast. I thought eventually everything would be easier as the audience/brand grew and I could chill a bit, but things just keep changing and you will always effectively be in a sprint.

- Everyone talks about how you should bootstrap a business to own your destiny/have a better financial outcome. Those people don't talk about the existential stress of dipping into your savings for years while you figure it out + how much more acutely the pain feels when payroll is run and the credit card bill needs to get paid. It's also what makes it harder to play the long game mentally vs. cashing in chips sooner.

- There is a shortage of novelty in the world, and that's where we believe we can fill the void. Novel ideas, novel events, novel spaces, novel ways to make fun of things. It's both the hardest and most defensible thing we do.

- There will be a temptation to use outsourced sales from people that have a better rolodex than you. I have basically never seen or heard of outsourced sales working.

- Principles that can make your business standout can also be what holds the business back at a certain stage. For example, we lean very heavily on curation of everything we do which also makes it difficult to grow.

-  The natural instinct when you're running a company is to look at all the fires burning you need to deal with. But it's always good to have some regular cadence of reflections to see how far you've come. 

- The best part about starting something is other people cheering you on and wanting you to win. I really don't take that for granted. 

6 years down, hopefully more than 6 years to go. Thanks to everyone that's helped us get this far. If you wanna hang, you should see what we’re up to: www.outofpocket.health

Founder/Thinkboi at Out-Of-Pocket: outofpocket.health

I had to update a classic

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(P.S. - if you actually want to learn how RCM works, you should sign up for our healthcare 101 crash course where I'll explain it. Starts 3/23 AND we'll teach you claude code basics for fun: https://lnkd.in/eKycDuTV)

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New post: it's all about the healthcare agents.

What makes agents different than previous types of automation? How do we train agents and monitor them? We talk about the shift from robotic process automation to autonomous agents, and how new challenges emerge as you try to train and deploy these automations

We then go under the hood of Amigo (our sponsoring partner) to walk through how simulation and testing actually works for their agents

If you're interested in the role of clinical agents in healthcare, you'll learn a lot from this one. Full post is here: https://lnkd.in/eFhNGxJR

one of my hopes is that open-sourcing this Medicaid claims dataset exposes regular people to how weird the actual underlying claims data is

- The chaos of trying to sort out NPIs from intermediaries vs. people delivering care

- how modifiers get used in billing data that make it extremely difficult to parse out what services were actually delivered

- how not having the actual records data from an encounter makes it nearly impossible to figure out what things happened vs. what's simply being documented for insurance purposes

- How wacky the billing becomes when you change the site of are itself (e.g. inpatient vs. outpatient)

There are a lot of bad analyses on here that don't understand the context around how billing works today or assume it's similar to something like payments data. 

But maybe those analyses will also make it clear that many of the suspected frauds pointed out are because billing is so convoluted, and we should ACTUALLY fix how billing is done

Writing a post about medical malpractice insurance - came across a pretty interesting stat that 1% of physicians basically account for a third of claims paid.

It raises a question of whether there a small number of bad actors or a small number of physicians that take on complex cases where something is more likely to go wrong/have a suit

—

If you’re interested, the post is going to be about how medical malpractice is going to have to change with AI + a fracturing standard of care where everyone wants to be monitored all the time

Also some basics on how malpractice works today - sign up here to get it: outofpocket.health

Very excited to announce the first wave of sponsors for our upcoming Hardware Hackathon in SF…Samsung Healthcare , Open Wearables, and Medplum!!

We're really excited to see what people build with these tools - we think there's a huge opportunity to connect the immense amount of data that wearables create and healthcare workflows. 

Grateful to our sponsors who see that same vision - wave 2 to be announced soon 👀

Healthcare companies seem to be struggling to hire engineers proficient in AI tools. This is a 3 part problem

1) AI tools have given good engineers a gigantic jump in productivity, so they can do way more themselves (including starting their own companies) This means the opportunity cost of joining a company just got much higher.

---This might mean that we need to rethink compensation, vesting schedules, or even doing micro joint ventures for 10x employees that have ownership over a specific business line

2) Every engineer SAYS they know how to use AI tools, but then you get into the interview and they aren't as good as they say. Vetting becomes difficult!

---Back channels, seeing projects they've build, work trials will become mouch more important parts of the process

3) Convincing an engineer that healthcare is a worthwhile place for them to bring their skills vs. productivity and dev tools that are making beaucoup bucks way faster

--- This requires way more selling of healthcare and probably attracts a different kind of engineer that actually wants to be in the field, see their impact, and is attracted to the mission of the company more than speed of getting ARR

Btw if you're hiring, this is exactly why we do our hackathons. It gives you a chance to talk to AI-native people (engineers and others), it's a self-selecting group of people that like healthcare, and many of them are exploring other things to work on.

Healthcare relies heavily on measurement tools (eg. measuring diseases). But many of those tools that are currently in use today are very old, were validated in a different time, and frequently rely on the subjectivity of the person administering the test.

Could open source make existing measurements better and less static? Some examples I think are interesting

Brooklyn Health's* OpenWillis is an open source Python library for digital phenotyping. It has software packages that can quantify things like facial expressivity, voice characteristics, and motor functioning as objective markers of mental health. This feeds into their commercial eCOA platform for pharma companies running trials that want to add these digital measurements into their trials.

Kintsugi built an AI that could detect depression and anxiety from 20 seconds of voice. They recently announced they were shutting down, and open sourced 
their models and methodology.

Pfizer's Scikit Digital Health is an open source Python package for processing wearable sensor data. If you strap an accelerometer on a patient in a clinical trial, the raw data you get back is basically meaningless noise until you process it. Scikit Digital Health has algorithms that turn that raw data into actual clinical metrics. Things like gait speed, physical activity levels, sleep patterns, sit-to-stand transitions, etc. which might be digital endpoints you’d want to capture during a trial.

The obvious question is what validation looks like for a new measure and who maintains that validation over time. A traditional scale gets validated once and sits in a PDF forever. An open source digital measurement tool needs ongoing maintenance. If nobody's maintaining the repo or things keep getting added, how do we know it’s still a good measure? Will people continue to use it if they aren’t sure it’s still good?

There are a few different ways you could legally game risk adjustment. One example is vague code fishing.

Basically there were lots of unspecific codes and broad "catch-all" diagnoses to generate higher risk adjustment scores without a ton of clinical evidence. They were vague enough that you could say like...yeah, Nikhil probably has that.

Some examples:

E44.1 (Mild protein-calorie malnutrition) - nothing a Starbucks Matcha with protein foam can't fix? Malnutrition was one of the most gamed codes and the OIG flagged it repeatedly

N17.9 (Acute kidney failure, unspecified) - who needs specificity?

Z89.41 (Acquired absence of great toe) - honestly just weird phrasing for this one

The new coding system V28  closes this by dropping many of these non-specific codes entirely and requiring higher clinical specificity to map to an HCC. Fewer codes qualify, and the ones that do need real documentation.

I built a Claude artifact you can play around to see for yourself some of the other games

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Spencer Dorn

Nikhil Krishnan

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The Battle

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Nikhil Krishnan

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Nikhil Krishnan's LinkedIn Performance

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