Big data is changing most industries in the world, and the medical industry is no exception. Through the analysis of medical data, human beings can not only predict the outbreak trend of epidemic diseases, avoid infection and reduce medical costs, but also enable patients to enjoy more convenient services.

Doctors often hope to collect as much patient information as possible and find the disease as soon as possible. For patients, it can not only reduce the risk of damage to their health, but also reduce medical expenses.

Let’s take a look at five specific cases of the application of data analysis in the medical industry.

1. Electronic medical record

By far, the most powerful application of big data is the collection of electronic medical records. Each patient has its own electronic records, including personal history, family history, allergies and all medical test results.

These records are shared among different medical institutions through a secure information system (whether it is safe or not is debatable). Every doctor can add or change records in the system without time-consuming paper work. These records can also help patients master their medication, and they are also important data references for medical research.

Network security risks

Potential safety hazards of the data collector in data storage, transmission and use (leakage, damage, tampering, etc.);

The third-party medical institutions that obtain data sharing have potential safety hazards in the storage, transmission and use of the data.

2. Real time health alarm

Another innovation in the medical industry is the application of wearable devices, which can report the health status of patients in real time.

Similar to the software for analyzing medical data inside the hospital, these new analysis devices have the same functions, but can be used outside the medical institutions, reducing medical costs. Patients can know their health status at home and get treatment suggestions provided by intelligent devices.

These wearable devices continuously collect health data and store it in the cloud.

In addition to providing real-time information for individual patients, the collection of this information can also be used to analyze the health status of a group, and can be used for medical research according to geographical location, population or socio-economic level. Finally, on the basis of these previous studies, formulate and adjust the disease prevention and treatment plan.

The asthma inhaler equipped with GPS positioning is a typical example. It not only observes the asthma of a single patient, but also finds a better treatment scheme suitable for the region from the asthma law of multiple patients in the same region.

Another example is the blood pressure tracker. Once the blood pressure reaches the warning value, the sphygmomanometer will send an alarm to the doctor. After receiving the alarm, the doctor immediately reminded the patient of timely treatment.

Wearable devices can be seen everywhere in our daily life. Pedometers, weight trackers, sleep monitors and home sphygmomanometers all provide key data for medical databases.

Network security risks

Wearable devices are a small part of the Internet of things. In addition to personal information such as name, ID card and telephone number, our physical health should also be “on the cloud” and monitored.

Although the collection of health data is of great significance for the timely detection of diseases, if it is not protected, once the data is obtained by criminals, there will be negative effects such as telephone harassment of promoting medical products, telecommunications fraud related to physical health, mastering the physical location of wearable device users and so on.

3. Arrange the “lineup” of medical staff according to the prediction of patients’ needs

The on-demand deployment of medical resources can greatly reduce medical costs, so this work is of great significance to the global medical industry.

It seems like an impossible task, but big data has helped some “pilot” units realize this idea. In Paris, France, four hospitals predict the number of patients per day and per hour in each hospital through data from multiple sources.

They used a technique called “time series analysis” to analyze patient admission records over the past 10 years. This study can help researchers find the rules of admission and use machine learning to find algorithms that can predict the rules of admission in the future.

This data will eventually be provided to hospital managers to help them predict the “lineup” of medical staff needed in the next 15 days, provide more “counterpart” services for patients, shorten their waiting time, and help arrange the workload for medical staff as reasonably as possible.

Network security risks

Once the data is tampered with, the shift management of medical staff will fall into chaos, affect the normal operation of the hospital, and even delay the timely treatment of patients.

4. Big data and artificial intelligence

Another application of big data in the medical industry is attributed to the rise of AI.

In short, artificial intelligence technology analyzes complex medical data through algorithms and software to approximate human cognition. Therefore, AI makes it possible for computer algorithms to predict conclusions without direct human input.

For example:

Brain computer interfaces supported by AI can help restore basic human experiences, such as speech and communication lost due to nervous system diseases and nervous system trauma.

Creating a direct interface between the human brain and the computer without using a keyboard, monitor or mouse will significantly improve the quality of life of patients with amyotrophic lateral sclerosis or stroke injury.

AI is an important part of a new generation of radiation tools. It helps analyze the whole tumor through “virtual biopsy”, rather than through a small invasive biopsy sample. The application of AI in the field of radiation medicine can use image-based algorithm to represent the characteristics of tumor.

Especially in developing countries, medical staff proficient in radiology, ultrasound and other fields are very scarce. AI can complete the diagnostic behavior that needs human participation to a certain extent. For example, AI imaging tools can screen X-rays and reduce the need for a professional radiologist in practice.

AI can also improve the efficiency of electronic medical record entry. The electronic input of patient information needs a lot of time and energy.

At present, it is feasible to record every patient’s visit in the form of video. AI and machine learning obtain more valuable information by retrieving the information in the video.

In addition, virtual assistants like Amazon Alexa can enter real-time information at the patient’s bedside, or help medical staff deal with the patient’s routine requests, such as drug addition or notification of test results.

In conclusion, AI can greatly reduce the workload of medical workers in management.

Network security risks

Since machines can be used by good people to benefit mankind, they can also be controlled by evil people to undermine social stability. The security risks in artificial intelligence are no longer limited to data. What we are worried about is that these machines that imitate human beings are controlled by malicious hackers and act against morality and ethics.

5. Application of big data in medical imaging

Medical images include X-ray, nuclear magnetic resonance imaging and ultrasound, which are the key links in the medical process.

Radiologists often need to view each examination result separately, which not only produces a huge workload, but also may delay the best treatment time of patients. But big data can completely change the way they analyze.

For example, hundreds of thousands of images can build an algorithm to recognize the model in the image. These models can form a numbering system to help doctors make diagnosis. The number of images that the algorithm can study far exceeds that of the human brain. No radiologist can compete with the speed and intensity of the machine in his whole life.

Network security risks

If the sample data in the information system is stolen and tampered with, the doctor will make a wrong diagnosis according to the wrong analysis results, endangering the patient’s life.

Editor in charge: CC

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