Healthcare Strategy Class

HPM 231 is a healthcare organization and strategy class taught by the Chief Strategy Officer of Brigham and Women’s, Andy Shin. It’s cool that we have a real person in the industry talking to us about how to think through strategy. At the same time, I find these leadership and strategy class a lot like trying to learn how to ride a bike in a classroom with powerpoint slides.

We learned about company score card metris today and the difficulty of having strategy trickle down into execution and culture. For most students in the class who are medical students, they have never heard of the company score card. Questions like “Why can’t different departments collaborate on what to work on?” makes me think they haven’t really experienced the messiness of organizations. We learn the “best practices” on how to prioritize the “highest impact” projects, but these aren’t things I didn’t know before.

In a clean case study, it is easy to say this is good or this is bad. Just like in a classroom, it would be easy to say when you ride a bike, you pedal and try to not fall down. In theory, yes, that is correct and intellectually, you learn to recognize what good looks like, but the hardest part is doing this in practice. When strategic priorities are changing, when you have multiple important key stakeholders saying different things, when you don’t have buy in on which projects actually make the biggest difference to prioritize - and also every day there is a mountain of tasks and burning fires - how then do you lead a team toward the right path?

I am starting to suspect that the best way to learn these skills is not work for years then take a break and study for a stretch straight. I’ve seen great leaders protect their time during their work day to think and reflect, but that is still a solo activity. I think it might be every month or so, go find thought leaders and expand your thinking. How does strategy work in other places, can it work here?

This class and my previous experience has been that there are good strategists, typically consultant types, and then there are good operators. Rarely are there people who can do both really well. I’ve met a few and they are superb. Good strategists have the benefit of being divorced from day to day operations and have the luxury to have “thought leader” as their job, but the problem arises when the strategy cannot be executed because they don’t actually know the challenges of operations. The classic, it looks good on paper, but when we try to implement it, it didn’t work.

On the other hand, good operators are sucked in their day to day that they don’t have the capacity to dream about what can be changed. It requires creating a separate space where you think in years not days. They are great at making sure the trains are running, but not afforded the mental space to ponder about where they could go. I see good operators get stuck as operators and not be able to break through to the strategy layer of senior leadership.

I currently think the best people can do both. Although, commonly senior leaders can’t operate (do they lose this skill somewhere down the road? did they never have it in the first place?). But then again, is this like how one could think that a product manager and an engineering manager is best served in one person? In theory yes, but the time necessary to devote to doing both things well is more than one human can do.

Maybe that’s changing with AI. Individuals can do more and therefore we will see more strategic operators! Will keep tabs on this. Let me know if you have opinions on this.

Imposters in the Marketplace

In Health Economics we’re learning about target efficiency. Target efficiency basically means how do we tell who the imposters are from the people who actually need help from the government.

As the government, we don’t want to cover the people who can be covered by other means. Think young people < 26 who can be covered by their parents’ employer sponsored insurance and people who have jobs that offer health insurance. If you make the benefits too good and too easy to get, than what stops people who have other means of coverage from from trying to join to get discounted care? We frequently hear of these people as the 26 year old adult in their mom’s basement playing videogames all day rather than working. We don’t want to cover him because he should be working.

But also as the government, we don’t really have a way to force people into their respective buckets because people are free to choose what they want to do. It’s like imagine everyone is wearing a purple or orange shirt. As the government we set up a stand to give out balloons to only those with orange shirts. Naturally some purple shirt wearing person who wants a balloon will come to our stand and some orange shirt wearing person who should have a balloon will not come to our stand. Okay this is a very basic example and the real world the shirts are actually a gradient and it’s harder to tell if they should or should not get a balloon, but you get the idea.

Some solutions to stop the imposters from signing up:

Positive incentive:

• Offer generous benefits in specialties that a healthy person wouldn’t necessary need like long term care. Compare this to something like, we will give everyone who signs up $10k to use on their health as they choose fit. I would sign up for that! That feels like free money!

Negative incentives:

• Make it harder for people to sign up. We’re already doing this with the paper forms for Medicaid and now with the twice a year eligibility requirement rather than once a year. The point is that only people who really need it will take the time to fill this out and the people who don’t need it will find it cumbersome. But aren’t the people who need this coverage the same people who will find it difficult to fill out these forms?
• Higher cost sharing or make the plans less good so if people have better options, they won’t want it. But then wouldn’t that hurt the people who need it more?
• Work requirements so we get rid of people who are not working - the 26 year old in the basement. But then wouldn’t it be easier for people who are working and have employer sponsored insurance to fulfill this requirement than someone who is a caregiver?

The negative incentives feel easier to do - kick people out and make it hard for people to get in. As the government, that will save money because it is just less people we have to cover, but it also feels like a lazy way to do it. What would it look like to get creative? Just spit balling here, but what if you’re an employer who doesn’t offer health insurance, then maybe they should be required to enroll their workers into Medicaid? That’s kind of the same as work requirements, but the burden is on the employers. Or fine, let’s keep it hard to sign up, but then autoenroll everyone who we know are not meeting an income threshold who meet the other requirements?

The more I think about these problems, it feels like trying to think at the scale of the federal government is hard and an easy trap is to assume you only have blunt tools. The other trap I’ve seen is that everything gets lumped into “fraud and abuse” because it’s easy for everyone to agree that is bad. Nuanced spectrums get collapsed into good vs. bad, but this problem feels worth spending extra time to find creative solutions that cause minimal damage and cover the appropriate number of people, knowing that some purple shirt people will get balloons and some orange shirt people won’t. We should have some metric we’re striving for. Is 80% of orange shirt people getting balloons good enough?

Why are we learning this?

This semester, one of the required courses is Political Analysis and Strategy for US Health Policy. As someone who is concentrating on Health Policy, this should be the bread and butter. This should feel exciting, but it doesn’t and it doesn’t to my international classmates either.

The class is structured to understand how bills get passed through the different chambers, how lobbying works, what legislative staffers do. It’s the types of things we would want to know if we work for Congress after graduation. That’s the dream, right?

For me, the more I find out about how the government actually works, the more it makes me not want to work there. The rules are very complex and it’s all about who you know. People are rotating in and out all the time. But more than that, the rules of how things are done are (for better or for worse) being rewritten in the Trump administration. For example, the president now negotiates with Pharma companies directly for lowering drug prices, like a business deal. If that is happening, where does lobbying go? Also what happens to lobbying if one function they serve is to educate the congressional staff, but the congressional staff can just ask AI to get them up to speed? All of this feels very fluid right now and I wish we were learning about how things are changing rather than how things have been.

For my international friends, I’ve heard them say that all of this is very specific to the US and there isn’t much transferrable skills to take back to their respective governments. I wonder if this is because of the diminishing US hegemony. At some point, I would think that they would have wanted to come and work for the US government or at least learn how this nation’s government works. That there were at least some parts of it that is worth learning from. There’s been a strong desire to do comparative analysis of other governments, which I’m not sure existed as strongly before. But thinking about it logically, it does make sense. The US spends twice as much as other comparable OECD countries and our outcomes are still worse. Our government keeps shutting down because we can’t agree on who deserves healthcare. The system is so complex that no one really knows what’s going on and everyone agrees it is bad. So what’s the point of learning how this terrible system works if you don’t live here?

Group Think is NPS

In MPH 103 - Leadership and Communication, a mandatory course which I found mostly unhelpful, except for this one exercise. We were in groups of six students and within the group we had to stand for a view on climate change at a mock town hall. The six stances were: alarmed, concerned, cautious, disengaged, doubtful, and dismissive. Our group just assigned ourselves the roles based on our last names.

Before got into character, one of my classmates said “It’s probably going to be the alarmed and dismissive (the two extremes) being the loudest and everyone in between not saying much”. That’s what I expected too. The other thing I expected was that since it was a spectrum, it would be even. 3 negatives and 3 positives.

My role was disengaged. I said things like “Why do we think we can impact climate change at all? Why not focus on things we have more control over?” and “There isn’t much we can do in the US when places like China and India are bigger polluters.”

What ended up happening through was 2 vs. 4. Everyone in the center including myself sounded negative. It was NPS.

NPS stands for Net Promoter Score. It’s the classic “Rate your experience from 0-10” that you receive after you go or do anything. You would think that 5 and above is a positive experience, but actually only 9 and 10s are considered ‘promoters’. They are the ones to go out of their way to try to convince other people of a new product or service.

Being a comparable 5 in my assigned role of climate change, it was easy for me to question the ‘promoters’ of climate change. It seemed way harder to be a promoter because the more they became alarmist about it, the more I felt myself questioning whether everything they said was true. People on all sides threw out ‘facts’ and it actually got very confusing.

For example, the dismissive person said “Remember when the government told us that recycling was real and then we found out we just ship it to some other country? How do we know climate change is not that?” and the real me for a second was like “yeah! how do we know?!”

What I learned from this experience is to have a stance that I believe in, while being open to new facts. Because being unsure but still in the conversation is actually putting your voice towards the negative.

K-Nearest Neighbors

Have you heard the phrase “You are the average of your 5 closest friends?” Yes? Okay, then you know the machine learning model K-Nearest Neighbors (KNN). It might be my favorite of the ML models so far because it makes me laugh. Besides linear regression, which if you hear anyone say they use AI, changes are, they are just using linear regression. Side bar real quick, linear regression is you have a bunch of data and you’re trying to figure out how a parameter relates to the output. For example, miles run vs. amount of suffering. Then you just draw a line that best fits the data. Then if someone gives you a number of miles, the model predicts how much suffering based on the line you just drew. My data would just look like up and to the right - more miles = more suffering. *Note: This would only work if you’re like me and you don’t enjoy running. If your data points actually show that you suffer first but then are very happy the more miles you run, a line would not accurately represent the relationship and you’d need a non-linear model.

Back to KNN. So, KNN is like, you have a new datapoint - you meet a new person. Let’s say they are a potential partner because that’s fun. On this date, you ask them to bring their 5 closest friends. Then you have to guess for that datapoint, what they are like. So let’s say we’re trying to figure out is this person an asshole. They bring their 5 friends. 4 out of 5 are assholes. Then you predict that your date, is an asshole. On the otherhand, only 1 out of 5 is an asshole. Then most likely they are not an asshole.

Okay but what if they can only bring their closest friend. Just one. High stakes. Then it gets way harder because now, just because that one friend is an asshole, doesn’t mean that they are necessarily an asshole, but you (the model) would decide that they are an asshole. Thus, overfitting the data. On the otherhand, let’s say you tell them to bring 50 people who they consider close. First off, that would be very impressive because who has 50 people that are readily available to go on this date with them. But then it gets harder to tell because you have people from their best friend to their cousin once removed. Finding the right K (number of people to bring) is kind of an art.

The other point is that when you ask them to bring people, not everyone should be weighted the same, right? Their closest friend probably says more about them than that cousin once removed. The weighting of the datapoints by distance, where the farther away you are, the less you count, is called “kernel smoothing”. Now you know everything I know about KNN!

Fighting at the Margins

The One Big Beautiful Bill (OBBB) introduces work requirements for those seeking Medicaid enrollment. Medicaid since it was introduced has kept costing more and more. I’ve heard that Medicaid has huge fraud and waste. Specifically, that “single men living in their mom’s basement playing video games all day are getting tax credits to get healthcare” and the OBBB is targeting to remove those people who should work and are not from getting tax-payer dollars for their own healthcare. I’ve also heard that it is common for people on Medicaid to not have consistent jobs that can provide 80 hours of work per month to quality and without the premium tax credits, paying for health coverage out of pockets becomes impossible. So which is it? I suspect it is a little of both.

In my Machine Learning and Big Data Analytics class at the Kennedy school, they showed this K-Nearest Neighbor (KNN) model. The US is vast and the people are so diverse, yet when you are making policies like Medicaid and Medicare, it is similar to drawing a line to determine who gets what. It ends up being crude. Like this dark line that separates the people the government says should get healthcare aid, let’s say it’s the orange, and the people they say won’t get aid, let’s say is blue, there are some on the “wrong” side. Some people who should get aid are not getting aid and some people who shouldn’t get aid (the single men playing video games in their mom’s basement) who are. I would think the reality of the misclassification is quite small compared to all of the orange dots representing people who get aid, who otherwise would not have received anything. Yet, I think the news outlets, social media, and most of the public discourse is focused on the few misclassifications. Can we do better? Sure, but creating a brand new line as the OBBB is doing with harsher new work requirements feels rash when mistake in classification means real people will suffer.

I wonder if this is something that ML can help with. Developing a model that can predict what parameters are the best to indicate someone who needs help with healthcare coverage. But also, something tells me that it may not be a data issue, but a political one. I’ve found that logic can only counter logical arguments and when they are emotions based, no amount of data will help. One can hope though, right?