Healthcare Data and Policy Making

Introduction

The Christmas to New Year's break offered some time to reflect on 2023 and due to the nature of my work, an area of reflection was on the healthcare conversations I've had. Pouring through my notes, I felt some of the most interesting notations were focused on how healthcare data can impact public policy. Rather it be over lunch, Zoom, or a happy hour drink, I've come to seriously enjoy asking our clients how they'll utilize the cloud environments, data platforms, and analytics work we provide to scale their impactful work and bold dreams.

Inevitably the conversation moves from data and opportunity to a system level challenge that requires a change and a recognition that serious change can occur when potential treatments, therapies, policies, and ultimately healthcare outcome improvements are proven through data.

We now live in a data-driven world and the world of modern healthcare is better for it, but the use of analytics in policy making must catch up (from this outsider's perspective). There is an opportunity to not only reshape existing policies but also revolutionize the approach to public health challenges.

The Role of Data in Healthcare Policy

Data-driven decision making in healthcare is now the norm rather than the exception. As patients, we may not always see this shift but from pre-trial research to providing care this shift has been gaining momentum for decades and is maturing rapidly thanks to better cloud data platform technologies, robust machine learning libraries, and the growth of deep learning libraries (and of course an honorable mention to GPTs).

The land of policy making and policymakers is increasingly divisive - I don't intend to delve into my thoughts on that. However, I do believe it is safe to say that future policy makers at the city, county, state, and federal levels will need to have an increasingly strong skillset in leveraging data to better represent their constituents. Healthcare data should be recognized as a key source to identify trends, allocate resources, and develop responsive healthcare strategies at every level.

Sources and Integration of Healthcare Data

A key aspect of data-driven policy making is the integration of various data sources. Healthcare systems, cities, counties, and states made significant investments in their data ecosystems and the way they work together during the pandemic. Many researchers and provider leaders are recognizing the value of this new data foundation. The section below highlights a few of my most memorable conversations on healthcare data moving these foundations forward and driving potential policy changes.

Case Study Areas & Conversations

1. Social Media Data for Mental Health Interventions: Suicide rates have increased significantly in recent decades. From 2007 - 2021 suicide rates for Americans aged 10-24 increased by 62%, with the sharpest rises occurring in the teen girl population (CDC). Numerous studies have highlighted the role of social media in suicide rates. A few bold thought leaders in the mental health space are asking the question if a user's social media data can be used to identify mental health trends and needs, enabling near real-time interventions. While they may be late to regulation on social media platforms, policymakers have an opportunity to learn and act from research in this space. Thank you to both Dr. Patricia Cavazos-Rehg and Dr. Jeffrey Thompson for these conversations.
2. Food Subsidy Program Utilization and Health Outcomes: I was lucky enough to attend Dr. Ruopeng An's 2023 presentation on leveraging machine learning libraries to ask challenging research questions on existing subsidy programs. He and his team's efforts, and sheer technical prowess, certainly made an impression. Serious budgets are allocated to subsidy programs with the purpose of helping those in need better their outcomes. Dr. An's (and others findings) found thoroughly interesting incentive relationships from health food discounts and total cost of care. Public health improvements can likely be driven in a meaningful scale if food subsidy program effectiveness increased.
3. EMR Integration & Care Access: One of the more interesting individuals I get the privilege of chatting with is Dr. Randi Foraker (I'm hopeful she will appear in a future Q&A). Our last conversation left me full of questions - specifically about those underrepresented individuals that seek care at Federally Qualified Health Centers. These centers typically lack the same EMR systems (namely EPIC and Cerner) of their neighboring hospitals. This gap leaves holes in knowledge when those hospital systems provide care. When you expand the lens outside of FQHCs there is a daunting amount of individuals that are not represented (at least holistically) in care data - the blank spaces they leave take no part in precision medicine and population health efforts which beg the question of how effectively and inclusively can we move healthcare forward?

Conclusion:

There are real challenges in leveraging healthcare data at regional or national levels to improve public policy decision making, but, I don't see the leaders I've spoken with backing down. In fact, it feels to me that healthcare data is likely to take a leading role in the next decade transforming public policy - not just in the three areas above but likely across a wide spectrum including transportation, utilities, and public services.

It's unclear to me if the challenges around data ownership, governance, security, and privacy are best solved through de-identification, modern regulation, synthesized data improvements, or a combination of those things. In the end, they all feel very "figure-out-able". The biggest hurdles, the hardest questions and considerations will be how quickly will public policy makers recognize the value in healthcare data, how will they adapt to utilization, and where will they turn when the data says system level changes are necessary?