How to utilize big data to improve the quality of healthcare services?

In recent years, big data has continued to penetrate the world's industries, affecting our food, clothing, housing and transportation. For example, when shopping online, we often find e-commerce portals recommending products to us, and often such products are what we need recently. This is because the data related to the trajectory of user online behavior are collected and recorded, and through big data analysis, the recommendation system is used to recommend the items that the user may need, so as to achieve the purpose of precision marketing. Below is a brief introduction to several big data application scenarios.

Application of big data in the medical industry

Big data makes it easier to get medical treatment. In the past, most of the treatment programs for patients were carried out through the experience of physicians. While good physicians can certainly provide good treatment programs for patients, it is difficult to ensure that patients can all receive the best treatment programs because of the different levels of physicians.

With the deep integration of big data in the medical industry, the big data platform has accumulated a huge amount of cases, case reports, cure programs, drug reports and other information resources. All common cases, past cases, etc. are recorded, and doctors are able to give patients high-quality and reasonable treatment programs through effective and continuous diagnosis and treatment records. This not only improves the efficiency of doctors, but also reduces the rate of misdiagnosis, so that patients can receive the best treatment in the shortest possible time. The following is a list of applications of big data in the medical industry, as follows.

(1) Optimize medical solutions to provide the best treatment.

In the face of a large number and variety of germs, viruses, and tumor cells, it is also difficult to confirm the diagnosis of disease and determine the treatment plan. With the help of a big data platform, it is possible to collect the disease characteristics, cases and treatment plans of different patients, thus establishing a patient classification database for the medical industry. If the development of genetic technology is mature in the future, patients can be categorized according to their genetic sequence characteristics, so as to establish a patient classification database for the medical industry. When doctors diagnose patients, they can refer to the patient's disease characteristics, laboratory reports and test reports, and refer to the disease database to quickly diagnose the patient and clearly locate the disease. When formulating a treatment plan, doctors can refer to the patient's genetic characteristics and retrieve effective treatment plans with similar genes, age, ethnicity, and physical condition, so as to formulate a suitable treatment plan for the patient, and help more people to undergo treatment in a timely manner. At the same time, these data also facilitate the pharmaceutical industry to develop more effective drugs and medical devices.

(2) Effective prevention of predicted diseases.

The easiest way to solve patients' diseases is to prevent them from happening in the first place. Through big data for the masses of human body data monitoring, the respective health data, vital signs indicators are assembled in the database and health records. Through the application of big data analysis, we can promote the integrated health service covering the whole life cycle of prevention, treatment, rehabilitation and health management, which is the new trend of health service management in the future. Of course, this not only requires medical institutions to speed up the construction of big data, but also requires the public to go for regular checkups and update their data in a timely manner, so that big data can be used to prevent and predict the occurrence of diseases, and to achieve early treatment and early recovery. Of course, with the continuous development of big data and its application in various fields, some large-scale influenza can also be predicted through big data.

The application of big data in the financial industry

With the application of big data technology, more and more financial enterprises have begun to devote themselves to the practice of big data application. A study by McKinsey shows that the financial industry ranks first in the Big Data Value Potential Index. Below is a list of several typical applications of big data in the financial industry, as follows.

(1) Precision marketing.

Banks in the impact of the Internet, the urgent need to grasp more user information, and then build a 360 stereo image of the user, you can segmented customers for precision marketing, real-time marketing, and other personalized intelligent marketing.

(2) Risk control.

The application of big data platform can unify the management of internal multi-source heterogeneous data and external credit data of financial enterprises to better improve the risk control system. The internal data integrity and security can be guaranteed, and the external user risk can be controlled.

(3) Decision support.

Improve operational decision-making through big data analytics, provide management with reliable data support, and thus make operational decisions more efficient, agile, and accurate.

(4) Service Innovation.

Through the application of big data, we can improve the interaction with customers, increase user stickiness, and provide value-added services for individuals and governments to enhance the core competitiveness of financial enterprises.

(5) Product innovation.

Through high-end data analysis and comprehensive data sharing, it effectively connects all kinds of financial products such as banks, insurance, trusts, funds, etc., so that financial enterprises can learn from other fields and create new financial products.

Application of Big Data in the Retail Industry

There was once a legend in the US retail industry that a certain store sold diapers and beer side by side, and as a result, the sales of diapers and beer both increased! Why can two seemingly unrelated commodities be paired together to achieve such amazing results? Later analysis found that most of these buyers are married men, these men in the child to buy diapers at the same time, will also buy some beer for themselves. After discovering this secret, Walmart supermarkets will boldly place beer in the diaper next to so that customers buy more convenient, sales will naturally rise dramatically.

The reason why we talk about the example of "Beer-Diapers" is to tell you that tapping into the potential value of big data is the core competitiveness of retail competition, and the following is a list of some of the innovative applications of big data in the retail industry, as follows.

(1) Accurately positioning the retail industry market.

Enterprises wanting to enter or develop a regional retail market should first conduct project evaluation and feasibility analysis, and only through project evaluation and feasibility analysis can they finally decide whether it is suitable to enter or develop this market. Usually, it is necessary to analyze how much mobile population is in the region? What is the consumption level? What are the consumption habits of customers? How is the market awareness of the product? The current market supply and demand situation and so on, these issues behind the massive amount of information contained in the retail industry market research constitutes the big data, the analysis of these big data is the market positioning process.

(2) Support industry revenue management.

The advent of the big data era provides a broader space for the development of enterprise revenue management work. Demand forecasting, market segmentation and sensitivity analysis have a great demand for data, and most of the traditional data analysis collects the enterprise's own historical data for forecasting and analysis, easily ignoring the entire retail industry information data, so it is inevitable that the forecast results are biased. In the process of implementing revenue management, if enterprises can collect more retail industry data on the basis of their own data and rely on some automated information collection software to understand more market information of the retail industry, it will help them to formulate accurate revenue strategies and win higher revenues.

(3) Tapping into new demands in the retail industry.

As a retail industry enterprise, if we can collect the review data of online retail industry and establish a large database of online reviews, then we can use segmentation, clustering, and sentiment analysis to understand consumers' consumption behavior, value orientation, new consumer demand reflected in the reviews, and product quality of the enterprise, so that we can improve and innovate our products, quantify the value of our products, set a reasonable price, and improve the quality of our services, and get more revenue from them. The company's goal is to improve and innovate its products, quantify their value, set reasonable prices, and improve service quality in order to generate greater revenue.