AI (Artificial Intelligence) has become an ubiquitous part of our lives. We may not even realize how often we use it. For example, millions of people use face recognition to unlock their phones without knowing that this is possible only because we have AI. The number of contexts in which AI is being applied is growing daily. Today, not only IT companies, but also banks, government units, businesses, and even healthcare systems have become active users of AI technologies. Let’s talk about ways AI helps healthcare workers save patients' lives.
The variety of AI-based technologies used in healthcare is impressive, and new AI technologies are constantly being implemented. Here are some examples of AI implementation for medical purposes.
- Analyzing medical imaging analysis
Much research suggests that AI can perform as well as, sometimes better than, humans at diagnosing disease. These results are confirmed by the fact that at present, AI is already outperforming radiologists at spotting malignant tumors.
- Decreasing the cost to develop medicines
Based on a database of molecular structures, AI can predict which potential medicines would and would not be effective for various diseases.
- Analyzing unstructured data
It is not easy to manage the enormous amount of data that accumulates for every patient during the process of medical treatment. AI can collect, store, analyze, and categorize data regardless of its format, making it easy for medical staff to work with records.
- Predicting chronic diseases
AI analyzes a patient's entire medical history, connecting it with symptoms, lifestyle, habits, and illnesses that affect other family members. Based on this data, AI makes a predictive analysis that allows it to detect illness at its beginning stages, making it possible to treat disease before it becomes life-threatening.
Artificial intelligence is not one technology but a field which combines different aspects of computer science such as machine learning and deep learning. The following are specific AI technologies that are related to healthcare.
- Machine learning, neural networks, and deep learning
- Natural language processing
- Rule-based expert systems
- Physical robots
- Robotic process automation
- Behavior monitoring
The behavior monitoring technique utilizes a combination of big data analytics and artificial intelligence to identify patterns, trends, anomalies, and other useful information. It is used in various industries from e-commerce to baking, cybersecurity to healthcare. The exact definition of the term can vary depending on industry, but the core idea of analyzing data to identify anomalies and trends in behavior is common to all uses.
AI-based behavior monitoring is a video analytics technique based on face recognition and person detection technology. Behavioral monitoring allows the analysis of live streaming data, individual identification, and real-time location identification.
Behavior monitoring can help in preventing and detecting one of the biggest problems among elderly and other at-risk patients: slipping and falling. About 700,000 to 1 million patient falls occur yearly in U.S. hospitals alone. As a result, around 250,000 patients sustain injuries of various degrees of severity, and 11,000 patients die. Therefore, we see that behavior monitoring is a much-needed feature in physical security systems. Patient behavior monitoring ensures that medical or security personnel can respond to an evolving situation as quickly as possible, providing patients with immediate assistance. In the long run, it can indicate places where slips and falls occur repeatedly, leading to identification, analysis, and improvement.
Traditional remote patient monitoring requires setting up a camera and streaming video over a network for staff to monitor. The biggest shortcoming of traditional patient monitoring is that it requires 24/7 oversight by a human, which can lead to human error and necessitate large investments in human resources.
AI-based patient monitoring helps to overcome those disadvantages. AI-based behavior monitoring technology is more effective and provides more valuable insights, such as whether a patient is in or out of bed, where he or she falls, and whether he or she has visitors.
Remote patient monitoring is automated and requires minimum human intervention. It also allows expanded monitoring, such as the ability to watch several patients at once and the option to evaluate patient conditions from a remote location.
Unfortunately, today healthcare workers more frequently than ever before are prone to becoming victims of physical violence. Almost 70 percent of emergency nurses and about 50 percent of emergency physicians report being kicked, hit, or physically assaulted in other ways in the workplace. The fact that many incidents of physical violence remain unreported makes the problem even more serious. Healthcare workers tend to consider injuries caused by patients unintentional, accepting them as an unavoidable part of their jobs.
CCTV cameras may be helpful in preventing violence to some extent, but ultimately their effect is quite insignificant. It is simply beyond human capabilities to monitor huge streams of video data and detect all violent acts. As a result, the majority of incidents are discovered only after they occur. AI-based behavior monitoring helps increase situational awareness of security staff and prevent conflict escalation between staff, patients, and their guardians.
Every hospital has sensitive assets such as medical equipment, drugs, and patients’ personal data that must be strictly secured. Given the size and complexity of hospitals, this is not an easy task. AI video analytics and behavior monitoring can effectively protect the perimeter, reporting abnormal behavior in real time. Implemented together with an AI-based door access control system, AI-based video analytics and behavior monitoring are the most effective way to provide overall hospital security.
Behavior monitoring systems are also effective in reducing crime adjacent to a hospital, in places such as parking areas, for example. Parking areas tend to be high-crime locations at many public facilities. They present a risk of attacks, especially on women, along with car break-ins and thefts.
In addition, behavior monitoring in parking areas can help with capture and analysis of slips and falls during bad weather conditions.
AIIR Scout is an AI-based scalable video analytics solution by ALCHERA that provides a perfect behavior monitoring solution.
AIIR Scout uses several algorithms to provide precise analysis: face recognition, person detection, and tracking and behavioral monitoring. Our solution automatically analyzes data in real time and notifies the end user of unusual events. One of the biggest advantages of AIIR Scout is that it doesn't require any special hardware so can be used with existing CCTV cameras. Therefore, ALCHERA’s solution is affordable and easy to implement.
If you are seeking the best solution for behavior monitoring, contact our sales department to receive a consultation.