Advancing precision medicine for personalized care

If you were receiving care for a health condition today, would you want a treatment with average effectiveness for most people — or one based on the specific physical, behavioral, and social drivers of your own health?

Fully realizing the potential for precise, personalized care will require advanced analytics operating on massive amounts of data while preserving the privacy of protected health information at scale. It will also require a smarter and better-connected healthcare system that continuously evolves to care for the whole person. By combining powerful technologies, artificial intelligence (AI), and data, it is becoming more possible than ever to deliver personalized health insights and prevention and treatment options that most effectively meet the health needs of each person.

 

Precision medicine in practice

 

A precision medicine approach accounts for an individual’s genes, health behaviors, and environment to provide the right care at the right time.

Doctors know which treatments and preventions will work for most people as they navigate their health conditions. Finding the best one for a specific person, however, often requires a trial-and-error period with many viable treatment options. Precision medicine could help identify the most effective treatment much more quickly, which is why it is in use for conditions like autism, cancer, and stroke.

Carelon recently analyzed data on 5.5 million people with Type 2 diabetes and found more than 385 different treatment paths. Using precision medicine, analysts could predict the safest and most effective treatment choices for each person. By leveraging these personalized insights, doctors could then work with their patients to choose from top treatment options instead of from hundreds of potential paths that may not as specifically address their healthcare needs.

Using precision medicine, a care provider can show how certain treatment options have worked in someone just like the person they are talking to. This personalized approach would help the person understand why their care provider recommends one option specifically for them out of all the possible treatment options.

 

Expanding data

 

To predict what will work for a specific person, doctors and health plans need quality and appropriately shared information from a variety of people experiencing similar health conditions. The information also needs to represent the full range of people throughout the population.

In order to predict what will work well for a specific person, doctors and health plans need information on what has worked well for highly similar people. The first step to doing this is research, and the National Institutes of Health (NIH)’s All of Us Research Program is helping with this goal of broad, representative data. It aims to build a large database that accurately captures the diversity of the U.S. population for research purposes. All of Us is a program from the federal Precision Medicine Initiative, an effort to transform how we improve health by personalizing healthcare. The next step will be operationalizing those research findings at scale at the point of care.

 

Analyzing data

 

In addition to having the appropriate data, the health system needs safe and effective ways to analyze and share data and insights.

Patient registries — observational data on people with a particular disease or condition — help organize data into a usable form. In addition to tracking interventions and outcomes, they can standardize data collection. This is particularly important when many different data collection methods are possible, such as with genetic testing.

However, having a large and diverse data set is just the starting point. Going from observational data to insights about what intervention will work best for each person requires cutting edge analytics and ways to securely share the insights. This task is made even harder because real-world data is not perfect, as demonstrated in registries that frequently have missing data.

 

Appropriately sharing data

 

Variations in the way data is collected and stored need to be accounted for and eliminated where possible. The health system has made progress in standardizing and appropriately sharing some health data, but challenges remain.

For clinical health data, electronic health records (EHRs) represent a major opportunity. EHRs have a wealth of data on millions of people over time for every condition and disease. The challenges again include data gaps — especially in behavioral and social drivers of health, historical mis- or under-representation of certain groups, and lack of standardization — particularly in unstructured data like doctor’s notes.

An additional challenge with EHRs is interoperability. A 2020 interoperability rule from the Office of the National Coordinator for Health Information Technology (ONC) moved forward data and data transmission standards through secure, standardized, Application Programming Interfaces (APIs). Additional rulemaking and activities are anticipated, which will further promote interoperability between healthcare tools and stakeholders.

At Carelon, we are working to make connected care possible through advanced technologies and digital solutions that help tackle barriers to data sharing and address the disconnected services and data silos that hinder effective and efficient care delivery. With these barriers resolved, we can transform data into insights and more seamlessly deliver personalized treatment across the spectrum of care settings — whether digital, virtual, or in-person. While further progress is needed to fully realize the potential of precision medicine, the building blocks are in place to begin personalizing healthcare decisions based on each person’s unique factors. When this more personalized approach to care is in place, people will be able to get the best treatment more quickly, with fewer doctor visits, lower costs, and less frustration.

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