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Data Innovation Challenge in Healthcare

One advice that’s given to young engineers these days is to focus on machine learning. The next advice is to focus on life sciences. The field of healthcare will see a lot of transformation with the injection of better technology over the next decade.

Companies are investing in data innovation, trying to make the best use of the gluttony of data that’s created through digital patient interactions. I’ve talked about this a lot before, but let me summarize it again here.

Data is generated when a patient contacts a medical office and requests an appointment. The medication they are prescribed and how quickly they return for their refill, along with the lab values which are generated from that patient, all of that is data.

We have no problem accumulating most of this data but there is no good way, yet, to take all of this information and put it to good use. We have an oversupply of data and an undersupply of ideas and methods of taking this data and improving healthcare.

 

Data Innovation Challenge

The main reason for this post is to announce that Bluecross and Blueshield are putting on a Data Innovation Challenge which anyone can enter. The prize is access to a lot of resources from BSBC and their HIMSS partner. Their goals is to achieve:

  • Better care for Individuals
  • Better Health for Populations
  • Lower Per Capita Cost

They list 3 specific areas where they are hoping to make a difference with data innovation. It’s all related to healthcare which makes it the ideal scene for a healthcare professional to make a difference.

Whether you have an idea yourself and need a team to to work with you on it, or if you want to act as the clinical mentor for another data science team, there are a lot of ways of entering this data innovation challenge.

Here are the areas of interest:

1. Patient Engagement

BCBS wants to identify and predict which individuals are going to experience a better health outcome if they engage with the healthcare system in a new manner or earlier. Chronic disease prediction is a very good example of this; figure out which patient has a silent chronic disease or is at high risk for a chronic disease and figure out how to get them engaged in healthcare.

We can utilize information we have about a particular patient group by scouring all of their previous EHR data. Or figure out a way to flag the patient based on the method in which they engage with various health systems.

Think of elderly switching over to Medicare. Or someone with a cancer diagnosis who stops engaging with their PCP and only sees their oncologist.

2. Reducing Barriers of Care

The best healthcare system in the world can’t help a patient who is unable to or unwilling to interact with that system. There is a lot which falls on the patient’s shoulders such as having to make the appointment, having to pick up their medications, having to arrange babysitting to get to an appointment, etc.

BCBS’s data innovation challenge is interested to learn about ways these barriers can be reduced so that patients can have easier access, And we need to measure if the easier access truly makes a difference in the health outcome of that patient population.

  • patients who are embarrassed by their disease
  • youth who can’t get adequate STI care
  • rural households who are isolated
  • unwilling or unable to navigate the complex healthcare system
  • addressing the elderly who might have dementia or inadequate social support

3. Member Journey Through the Healthcare System

The US healthcare system is so large and complex that it’s easy for the patient’s actual health to be ignored. By the time all the data is collected and analyzed, all the tests are done and a diagnosis is reached, the patient may have fallen by the wayside. This can lead to them feeling less engaged, less empowered.

  • How can we make better decisions for patients?
  • How can we prevent the patient from falling through the cracks?
  • How can we ensure patient adherence to lifestyle changes?
  • How can we recognize and predict when and who will likely drop out of the system?

Tactics for Healthcare Professionals

If you have any interest in this space, I could see a few ways in which you could put together a proposal for entry into this data challenge contest. Partnering up with others who can complement your skillset will probably make the most sense.

Interest Groups

You can reach out to a local AI or machine learning interest group in your area. Look for colleges and universities. Also, you can search scholar.google.com for articles on healthcare data and AI or machine learning. Next, you can reach out to these individuals and see if they have something worth presenting and which you can be a part of.

Simply by applying to this innovation challenge, you’ll likely make some interesting connections with others interested in this field. Perhaps you won’t win but you’ll have new ideas which you can pursue in the future. It’s also a great way to make a name for yourself in the healthcare consulting space.

Linkedin/Twitter

Consider reaching out on LinkedIn or Twitter, looking for physicians or data science engineers who post about artificial intelligence or machine learning relating to healthcare. It doesn’t have to be healthcare but they might be more engaging with a physician.

Healthcare Startups

There are thousands of healthcare startups all over the world. Consider perusing aggregation websites such as Startup Health to find a good candidate who is active in this space. Maybe they are working on EHR integration or data processing based on NLP.

Employers

You might be working for a medical group or healthcare provider who would be interested in supporting you on this journey. If you work for a large medical group then you likely have a strong informatics team whom you can petition for help.

 

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