Data Science can help address issues with healthcare data interoperability and information sharing in three key ways: 1) By analyzing claims data and other information using machine learning to understand patient populations and where/how to target services. 2) By using tools like reinforcement learning to evaluate which product features patients are most interested in and determining how effective solutions are clinically. 3) By developing more personalized healthcare solutions, similar to how technology persuades customers, but tailored to an individual's specific needs and context.

1. Data Science
Session 1
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ROLE OF MACHINE LEARNING IN FRAUD
DETECTION
Have you ever experienced any of the following scenarios?
· The alarm of the infusion-pump keeps beeping, but the nurse station can’t
recognize it. When you finally call the nurse, she says that there is an issue.
· The oximeter alarm goes off, and the nurse comes just to say that you don’t have
to worry because this happens a lot.
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You had an ECG lab test last week, but the reports aren’t available in the clinic’s
data because they are unable to access fax-machine data in the electronic health
record.
According to Healthcare IT News published back in 2013, 62% of hospitals shared
information electronically. Now, imagine, how much this percentage may have
increased today.
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However, while it seems easier to talk about how we can electronically share
information with patients, organizations, and providers through interoperability, its
effective execution still requires controlled standards and proper interfaces. We
can collect huge amounts of data but using this data anytime, anywhere in a
meaningful manner is the issue.
Here’s how interoperability issue can be solved:
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Data Science for Data Interoperability
The Chief Product Officer of Livongo, Amar Kendale, talks about breaking down
interoperability through data science.
Firstly, the data problem is divided into a few sections, and one of it is who are
they helping. They evaluate questions such as who should they focus on, what are
the risks, and in which sector/community/group there is motivation. Then, the
focus shifts to how the services offered can be positioned in this scenario so that
the users know it is the right choice for them.
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We all know in all these activities a great level of data science is utilize to come to
fruitful outcomes. Using data science, they analyze claims data and other data to
understand the user market – where the person is from, what affects them, etc.
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Secondly, the company evaluates the features of the program or product that the
user would like to get involved in. Here, tools and technologies such as data
science and machine learning again come in play — for instance, reinforcement
learning, in which many things are offered to the population. Then, the solutions
selected by them are learned. Later, it is also analyzed how effective these
solutions are clinically.
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It is simply like the technology used for persuading customers to buy more
products. In healthcare, a more personalized and advanced solution can be
offered to users.
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Alternative Method to solve
interoperability
Alternatively, these steps can be utilized to reduced problems related to
interoperability:
Making a standardized method to identify every patient uniquely. One of the best
ways is to assigning a unique patient identifier and sharing information effectively
between various health institutions.
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· While, today, every organization interprets standards differently, a global
standardization is needed. It should be understood that healthcare is global and it
is not just happening at one hospital or clinic. Hence, standardization is necessary.
· A consistent method for measuring interoperability across the industry is required
to improve measuring standards.
· More effective policies should be implemented to curb information blocking
habits across the country.
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Conclusion
Interoperability is essential for many sectors as it allows sharing of information.
However, there are various issues that we face daily while implementing
interoperability effectively. Hence, to ensure smooth transfer and sharing of
information, standardization of some policies is required. From there, a number of
things can be implemented to reduce the issue.
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