The healthcare sector generates a colossal amount of data daily but struggles to turn that data into actionable insights that help improve operational efficiencies and patient outcomes. Converting this structured and unstructured data into meaningful information requires advanced data analytics techniques. Data analytics is the process of interpreting and analyzing quantitative data through various methods to bring out qualitative insights, answer questions, and identify trends.
Healthcare data analytics collects and data analyzes healthcare information to derive insights and prompt decision-making. At micro and macro levels, operational efficiency can be boosted, processes can be simplified, overall costs can be curtailed, and patient care can be enhanced. This gives us a holistic view of each patient. The global RPA healthcare analytics market was valued at USD 23.6 billion in 2020 and is expected to expand at a compound annual growth rate (CAGR) of 23.8% from 2021 to 2028 (source).
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The impact COVID-19 pandemic has had on the healthcare sector is evident worldwide. Analysis of medical data has resulted in a better understanding of responding and treating patients. Today, healthcare providers and researchers are turning to data analytics to allocate resources, predict surges, improve patient care, and employ preventive measures. As a result of this pandemic, a mammoth rise of health data has been recorded and manipulated, permitting for bigger and better applications for data analytics in insurance.
Almost every industry depends on data to acquire dynamic insights into business activities and expand its operations. Although health data analytics automation still has a long way to go, it has played a vital role in resolving various persistent problems. This process unleashes numerous possibilities that can transform the healthcare sector if properly executed.
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