Have you ever wondered that by the end of 2025 there will be more than 200 Zettabytes of data available in global cloud storage?
This ever-increasing data is either available in an unstructured or semi-structured form. The health insurance sector is one of the major contributors to this global data.
The rapid digital transformation of the insurance sector is powered by artificial intelligence, machine learning and predictive analysis. Big data in the field of health insurance has started playing a crucial role.
In order to transform the unstructured data into a structured one, organizations need detailed algorithms. Trained professionals from the field of data analytics can build and apply these algorithms in a strategic way to make the best use of the data.
There are no two ways that data analytics is transforming the insurance sector at a much faster pace, yet the unique nature of the health insurance market poses many challenges to meeting the requirements. If you are looking to make your career as a data analyst in the health insurance sector, you should first understand some major data-related challenges existing in the health insurance sector.
In order to facilitate flawless services, two major challenges faced by the health insurance sector are Regulatory compliance and data integrity.
Regulatory Compliance
Most of the challenges in any process which is governed by rules and regulations majorly set by the state are the matter of regulatory compliance. Even the slight shift in the set of the state and the federal regulators may result in a major shift in terms of execution and thus always having a close eye on the latest developments has become the need of the hour.
One such regulatory Act in the health insurance sector is the Health Insurance Probability and Accountability Act (HIPAA). Despite the understanding of HIPPA’s privacy policies, very few insurers are aware of its data security and protection.
For example, e-PHI contains the electronic records of personal health information as guided by HIPAA’s security rule book. These guidelines ensure the insurer will maintain the confidentiality of the data they receive through e-PHI.
In order to safeguard crucial and confidential data, insurers need to identify and protect the data from potential threats and need to ensure that the entire workforce during execution follows all the compliance.
Data Integrity
Data integrity is not a very new challenge, many solutions to it exist, but the lower standards in terms of quality can cause major issues.
The main challenges related to data integrity lie in the health reports of patients. To deal with these challenges, special data understanding is required. In addition to this, the nature and scope of the patient-provider relationship lie in precisely capturing the events such as illness, diagnosis, prescription, claims, etc.
The problem lies in identifying the policyholders who are not in active engagement with the insurers. Another related problem lies in identifying the policyholders who stop filing prescription-related claims.
What would a Data Analyst do to overcome these challenges?
If you are looking for some data analytics courses in India, to build your career as a data analyst in the health insurance sector, you can contribute at every stage, right from data mining to data architecting to statistics.
Data analysts design the required infrastructure that suits the organizational requirement of data integrity and compliance dynamics. Data analysts play a crucial role in designing independent systems which help them analyze the data, engineer the data and eventually get the best out of the data.
To get a clear sense of what data analysts do, we should see data analysts as data architects, data scientists, data engineers, and statisticians at different phases of the project.
If all this information regarding big data in health insurance has piqued your interest, you must research more about the data analytics courses in India which would provide you with the next steps to get that much closer to becoming a full-fledged data analyst yourself.
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