Exploring the benefits of health technology in the USA

I had a lot of insightful conversations over there. Some highlights included:

Inequalities in health-technology

I was fortunate enough to attend the launch of the Ichan School of Medicine’s (Mount Sinai) Diversity Innovation Hub launch. This hub is “a new initiative that will partner with the local community, health care and technology experts to address disparities in health and health care”. Despite the room being incredibly diverse, the whole discussion was centred around helping those most socio-economically disadvantaged to benefit from health innovation. Mount Sinai is very close to Harlem, a deprived area of New York (as was referenced at the event “only 40% of Harlem is connected to the internet”).

This fact formed the basis of a most of the discussion, epitomised by a call to action in the need for Harlem to “invest in equity infrastructure”. The health system in the US ranks amongst the most inequitable in the world, compared to the UK which is one of the most equitable. After hearing the panellists’ thoughts, our single-payer system never felt so good.

Social Determinants of Health

The structure of the US health system means that there is a tighter feedback loop between health outcomes and interventions within the community that address the fundamental social, structural and environmental factors. Cityblock Health and Roster Health were just two companies I met with, which stimulated lots of thought-provoking discussions on this.

Prevention is so often included as a potential of Big Data/artificial intelligence - yet in the UK, simultaneously as huge investment goes into AI, funding to public health, social care and education is being slashed. Maybe the US is where we will see robust evidence, at scale, of tech-enabled interventions into social determinants of health for better health outcomes?

The start-up grind

I met with many individuals working in health-tech start-ups. There is a whole separate blog to be written on all the interesting problems being solved, innovative solutions and complex commercial models. However, one thing that particularly struck me was the intensity of the hustle. This is not unique to health-tech, and is particularly pervasive in New York, but the breaking-point of individuals working in New York start-ups felt too close to the surface for comfort.

I heard countless stories of aggressive poaching across start-ups, eye-watering rates of staff turnovers, perverse incentives and toxic cultures. I struggle to see the extent to which individuals can be mission-driven to solve the right challenges in healthcare, when the environment wears its employees down quite so much.

Perception of health data scientists

We see this issue emerge in the UK too. There is a peculiar divide between the health-data scientists (technologists working with fancy methods in prestigious institutions) and the health analysts (often seen as administrators, working in a basement in a far-off hospital, but whose work is significantly more directly impactful). New York and Boston are both dominated by highly prestigious institutions such as New York University, the Mount Sinai Health System and Harvard University. Yet this status this does not always map onto the scale of healthcare providers.

Those less prestigious providers often had a far larger number of patients and hospitals in their groups. I learnt so much from meeting those at either end of the data-analyser spectrum, enabling me to understand how the profession is seen and respected, which also acts as a proxy for the strategic priority with which providers are viewing data science.

Operational health research

This is an area where we can learn a lot from the Americans. In the NHS, we are not too good at making use of data to inform how health providers run efficiently. Data is sparse and unconnected, there are limited teams and funds to support airport control-like operations. Furthermore, the balance between the researcher and the provider, the ability to data-share and capacity are not always aligned when looking to apply the latest data science to operational efficiency.

A real highlight was going to visit a group at NYU Langone Health. This group is co-funded by New York University and the health provider and conducts research into hospital operations. It has an in-house engineering team to build tools and products that improve hospital efficiencies. However, it also acts as a filter for incoming companies wanting to sell the hospital the latest AI tool at inflated prices.

I went into the Fellowship wanting to learn about collaboration structures in health-technology, start-up culture in this space, and how different initiatives are addressing the skills and capacity shortage in health-data science. But I have found myself learning a lot more by being less focussed.

As an academic, it is fairly easy to search for initiatives, find evidence and look at evaluations of programmes. But I soon found that tacit knowledge and being sensitive to tone, culture and attitudes were a lot more rewarding. In addition, not speaking to the most senior person in the room, but the person willing to be most honest, is where my best insights have come from.

For me, this Fellowship has come at an important time. I am wrapping up a PhD at University College London and the Alan Turing Institute, looking at whether we can use medical records from primary care to find early signs for dementia. It has been a truly fun yet difficult few years. Whilst the PhD milestone has not gone from my attention quite yet, I am definitely looking up and out for the first time in years and thinking about what the next steps for me are.

I say this because different Churchill Fellows carry out their Fellowship in different ways. Some visit one or two locations and do a deep dive with a small group of people. Others interview tens of individuals and draw up thorough and impressive guidance. The focus and intensity of the last few years has meant that I have truly missed sitting back and listening to individuals. Allowing organic conversation, not having an agenda, and simply listening are difficult to justify. Particularly in London where everyone is busy, meetings get made months in advance and actions are clear and results-oriented. But I learnt the most on my first leg when I was trying the least.

One woman in a meeting said the dire state of the US health system felt like a fast-flowing river no one could stop. She said she felt that everyone was just finding the eddies and exploiting them to make as much money as possible - and that those innovations rarely resulted in making the river flow any better. I’m going to steal that metaphor. It’s too good.