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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