What are the applications of big data in the medical field?

The application of big data in the medical field: 1. Electronic medical records; 2. Real-time health status alarms; 3. Arrangement of a "lineup" of medical staff based on patient demand predictions; 4. Big data and artificial intelligence can Complex medical data; 5. Analyze medical images to help doctors make diagnosis.

The operating environment of this tutorial: Windows 10 system, Dell G3 computer.

Big data is changing most industries around the world, and the medical industry is no exception. Through the analysis of medical data, humans can not only predict the outbreak trend of epidemic diseases, avoid infections, reduce medical costs, etc., but also provide patients with more convenient services.

Doctors often hope to collect as much patient information as possible and detect diseases as early as possible. For patients, this not only reduces the risk of physical health damage, but also reduces medical expenses.

Let’s take a look at 5 specific cases of data analysis applied in the medical industry.

1. Electronic Medical Records

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

These records are shared between different medical institutions through secure information systems (whether secure is debatable). Each doctor can add or change records in the system without having to go through time-consuming paper work. These records can also help patients understand their medication status and are also important data references for medical research.

Network security risks

The data collector’s security risks in data storage, transmission, and use (leakage, damage, tampering, etc.);

Potential security risks in the storage, transmission, and use of data by third-party medical institutions that obtain data sharing.

2. Real-time health status alerts

Another innovation in the medical industry is the application of wearable devices that can provide real-time reporting to patients health status.

Similar to the software that analyzes medical data within hospitals, these new analysis devices have the same functions, but can be used outside medical institutions, reducing medical costs and allowing patients to learn about their health at home. conditions, and also receive treatment suggestions provided by smart devices.

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

In addition to providing real-time information to individual patients, the collection of this information can also be used to analyze the health status of a group and be used for medical research based on geographical location, population or socioeconomic level. Finally, disease prevention and treatment plans are formulated and adjusted based on these preliminary studies.

The asthma inhaler equipped with GPS positioning is a typical example. It not only observes the asthma of a single patient, but also can find a better fit for the area from the asthma patterns of multiple patients in the same area. treatment plan.

Another example is a blood pressure tracker. Once the blood pressure reaches the warning value, the blood pressure monitor will alert the doctor. After receiving the alarm, the doctor immediately reminds the patient to receive timely treatment.

Wearable devices are everywhere in our daily lives. Pedometers, weight trackers, sleep monitors, home blood pressure monitors, etc. all provide key data for medical databases.

Network Security Hazards

Wearable devices are a small component of the Internet of Things. In addition to personal information such as name, ID card, and phone number, our physical health status also needs to be "clouded" 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, phone harassment to promote medical products, telecommunications fraud related to physical health, and information that can be obtained Negative impacts such as the physical location of wearable device users will also follow.

3. Arrange a “lineup” of medical staff based on patient demand forecasts

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

It may seem like an impossible task, but big data has helped some “pilot” units realize this idea. In Paris, France, four hospitals used data from multiple sources to predict the number of patients per hospital per day and hourly.

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

This data will eventually be provided to hospital managers to help them predict the "line-up" of medical staff needed in the next 15 days, provide patients with more "targeted" services, and shorten their waiting time. , and it is also conducive to arranging the workload as reasonably as possible for medical staff.

Network security risks

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

4. Big data and artificial intelligence

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

Simply put, artificial intelligence technology uses algorithms and software to analyze complex medical data to achieve the purpose of approximating human cognition. AI therefore makes it possible for computer algorithms to predict conclusions without direct human input.

For example:

01

Brain-computer interfaces powered by AI could help restore basic human experiences, such as speech and speech lost due to neurological disease and neurological trauma. communication function.

Creating a direct interface between the human brain and a computer without the use of a keyboard, monitor or mouse would dramatically improve the quality of life for people with ALS or stroke injuries.

02

AI is an important part of the new generation of radiation tools, helping to analyze the entire tumor through "virtual biopsy" instead of a small invasive biopsy sample. The application of AI in the field of radiation medicine can use image-based algorithms to express the characteristics of tumors.

Especially in developing countries, there is a shortage of medical staff who are proficient in fields such as radiology and ultrasound. AI can complete diagnostic behaviors that originally require human participation to a certain extent. For example, AI imaging tools can screen X-rays, reducing the need for a dedicated radiologist in practice.

03

AI can also improve the efficiency of electronic medical record entry. Electronic entry of patient information requires a lot of time and effort.

It is currently feasible to record every patient’s medical visit in the form of video. AI and machine learning can 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 handle routine patient requests, such as medication refills or notification of test results.

In short, AI can significantly reduce the management workload of medical workers.

Network Security Hazards

Since machines can be used by good people to benefit mankind, they can also be controlled by evil people and used 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 humans are controlled by malicious hackers and make actions that violate ethics.

5. Application of big data in medical imaging

Medical imaging includes X-ray, MRI, ultrasound, etc., these are A key link in the medical process.

Radiologists often need to review each examination result individually, which not only creates a huge workload, but may also delay the optimal treatment time for patients. But big data can completely change the way they analyze it.

For example, hundreds of thousands of images can be used to build an algorithm that recognizes patterns in images. These models can in turn form a numbering system to help doctors make diagnoses. The number of images an algorithm can study far exceeds that of the human brain, and no radiologist could possibly match the speed and power of the machine in his lifetime.

Network security risks

If the sample data in the information system is stolen or tampered with, doctors will make wrong diagnoses based on wrong analysis results, endangering patients' lives.

Written at the end

The above five application practices profoundly demonstrate the unshakable status of big data in the medical industry.

Big data has greatly improved the medical experience of patients around the world, and also optimized the diagnosis and treatment efficiency and accuracy of medical institutions to a great extent.

It’s just a blessing and a curse. Big data is also unavoidable, and there is a scourge lurking in it—network security. Without defense and restraint, this beast will wake up sooner or later.

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