By: Milan Desai RIG Inc Intern Researcher

Data in Healthcare:

The Healthcare system in the United States is evolving into electronic based records that will increase the quantity of clinical data that is available in today’s system. The cost for medical insurance is much higher than it should be and we are in need of data-driven thinking in this area. There are unprecedented opportunities to use big data in order to reduce the costs and increase the quality of the health care system in the United States. Data gathering and management are getting bigger, and professionals need help in the matter.

Some advancements in the Healthcare system using data are given below:


  • Electronic Health Records (EHRs): The healthcare prices in the United States are at their highest, double the cost compared to other countries. This increase in trajectory needs to be improvised. The Electronic Health Records (EHRs) is a collection of patient data which is stored electronically in digital format. EHRs include different types of patient data such as demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, as well as personal statistics like age, weight, and billing information. EHRs have resulted in better quality care, cost, time, and prevention of human errors along with other benefits.


  • Reducing Overdoses in United States: Recent studies indicate that overdoses from misused opioids have caused more accidental deaths in the U.S. than the most common cause of accidental death, road accidents [1]. An application of data analytics addressed this issue. Data scientists at Blue Cross Blue Shield have started working with data at Fuzzy Logix to address this problem. Using years of insurance and pharmacy data, Fuzzy Logix analysts have been able to identify the risk factors that predict with a high degree of accuracy whether someone is at risk for abusing opioids. With this information clinicians can then reach out to high-risk patients which can help save lives and cost the system less money. [1]

  • Improving Supply Chain Management: With a disrupted supply chain, everything is likely to abide, from patient care and treatment to long-term finances. Institutions can save money by using big data in tracking the supply chain metrics and data-driven decisions concerning a company’s operations and spending. Both descriptive and predictive analytics models can be used to enhance decisions for negotiating pricing, reducing the variation in supplies, and optimizing the ordering process as a whole.


  • Managing patient’s data remotely: Big data in healthcare can be useful for tackling the risk of specific patients with chronic diseases. Holding the records for medication type, symptoms, and the frequency of medical visits can make it possible for the institutions to provide preventative care and there after reduce hospital admissions. This may also save cost to the in-house patients. Data in healthcare if used wisely can improve the quality of patient care while making the organization more economically secure in key areas.


  • Research: Different types of patient healthcare data can help in research centers as well. Researchers can analyze data of different patients and identify the consistency of abnormality occurrences. This analysis can help improve the quality of care, as well as save millions on research.



Data in Finance:

Financial data refers to the structured and unstructured data that can be used to anticipate customer behaviors and create strategies for banks and financial institutions. The financial industry creates plenty of data. Structured data is information managed within an organization in order to provide key decision-making insights. Increasing volumes of unstructured data is stored at multiple institutions and offers significant analytical opportunity. By utilizing this data, finance companies can make informed decisions with the ability to analyze diverse sets of data.


  • Stock Market Management: Data is revolutionizing stock markets across the world and the way investors are making their decisions. Machine learning is enabling accurate predictions and human-like decisions when fed data, executing trades at rapid speeds and frequencies. The business archetype monitors stock trends in real-time and incorporates the best possible prices. This allows analysts to make smart and rapid decisions and reduce manual errors that arise due to behavioral influences and biases. In conjunction with big data, algorithmic trading is resulting in highly optimized insights for traders to maximize their portfolio returns.

  • Fraud Management: Using predictive analytics, data can predict fraudulent activities. The system can analyze large amounts of data in real-time and detect fraudulent transactions. Banks are able to access real-time data, which can be potentially helpful in identifying fraudulent activities. This provides a forecast of the products and/or services customers may buy. These products can be explicitly promoted to the customer and proactive offers can be created.


  • Risk management:Finance experts can use data resources to help organizations anticipate and/or prevent and/or plan for risks to help protect the organization. For example, social media can effectively inform early warning systems of shifts in consumer sentiment or serious social and political risks. With different sets of data, financial analysts can help better identify and prevent the risks faced by their organizations.



Healthcare organizations can trust on data to provide better patient outcomes, save on costs, and build efficiency across all departments. Everyday data helps clinicians and hospitals provide more targeted healthcare and see improved results. Healthcare providers can rely on data to tackle issues like remission rates, high-risk patient care, staffing issues, dosage errors, costly healthcare systems etc.

Data is becoming crucial for finance organizations. As large companies continue to move forward to full adoption of data solutions. New technology will provide low-cost solutions that give both small and large companies access to innovation as well as new cost saving methods.







[1]. Sandra Durcevic in Business Intelligence, Oct 21st 2020 “18 Examples Of Big Data Analytics In Healthcare That Can Save People” in The datapine Blog.