By using a combination of hardware, software, and communication technologies, Real-Time Location Systems (RTLS) enable the real-time tracking and management of people and assets within a designated area. In a hospital setting, RTLS can be used to locate patients, staff, and equipment, which can help improve efficiency and patient care. In this blog post, we will explore how an RTLS built to locate patients can feed artificial intelligence (AI) and suggest some use cases.
AI has the potential to revolutionize healthcare in many ways, from drug discovery to patient diagnosis and treatment. Here are some use cases where AI can be used in healthcare:
- Predictive analytics: AI algorithms can analyze large amounts of data to identify patterns and predict outcomes. This can be useful for predicting patient outcomes, identifying high-risk patients, and improving patient safety.
- Clinical decision support: AI can be used to provide decision support for clinicians, helping them to make more accurate diagnoses and treatment decisions.
- Personalized medicine: AI can be used to analyze patient data and develop personalized treatment plans based on individual patient characteristics.
- Medical imaging analysis: AI algorithms can analyze medical images to identify abnormalities and assist in diagnosis.
- Robotics and automation: AI-powered robots can be used to perform tasks such as surgery, drug dispensing, and patient monitoring.
Connecting an RTLS to AI involves integrating the data collected by the RTLS system into an AI platform. Once the RTLS system is up and running, the data collected needs to be extracted and transformed into a format that can be used by the AI platform. This may involve using middleware software to extract the data and transform it into a format that is compatible with the AI platform. Once the data is transformed into a usable format, it can be analyzed using AI algorithms to identify patterns and trends in patient movements. This data can then be used to optimize patient flow, reduce wait times, and improve patient outcomes in specific hospital departments.
By having the RTLS system up and running for a while before connecting it with AI, hospitals can ensure that the data collected is accurate and reliable, which is essential for making informed decisions and improving patient care. Additionally, having a well-established RTLS system in place can make the process of connecting with AI smoother and more efficient, as the data collection and transformation process will already be established.
MYSPHERA’s RTLS system is an ideal source of data to feed AI algorithms due to its ability to accurately track patient movements in real-time and provide valuable insights into patient flow throughout the hospital.. Here are some examples:
- Emergency department: In the emergency department, MYSPHERA’s RTLS can be used to track the time that patients spend waiting for treatment and their overall length of stay. This data can then be analyzed using AI to optimize patient flow and ensure that the most critically ill patients receive the appropriate care first.
- Surgical block: In the surgical block, MYSPHERA’s RTLS can be used to track patients’ movements from the preoperative area to the operating room and postoperative care. AI can then be utilized to analyze patient outcomes and determine which surgical protocols are the most effective.
- Patient safety: MYSPHERA’s RTLS can be used to track patients’ movements throughout the hospital and ensure that they do not wander into restricted areas or leave the facility without authorization. If a patient is at risk of wandering, AI can be utilized to alert staff to the potential danger and take appropriate action.
- Infection control: MYSPHERA’s RTLS can be used to track patients’ movements and the duration of time that they spend in various areas of the hospital. AI can be utilized to identify high-risk areas for infection transmission and implement preventative measures, such as increased cleaning or enhanced personal protective equipment.
- Patient experience: MYSPHERA’s RTLS can be used to track patients’ movements and provide them with information about their wait times and treatment schedules. AI can be utilized to analyze patient feedback and identify areas for improvement in the patient experience.
In conclusion, an RTLS built to locate patients like MYPHERA’s can feed AI algorithms with real-time data to improve patient care. MYSPHERA’s RTLS is an excellent example of how RTLS can be used to improve patient flow, reduce wait times, and improve patient safety. By combining RTLS with AI, hospitals can optimize patient care, improve staff efficiency, and reduce costs. The potential benefits of this technology are significant, and we can expect to see more hospitals adopting RTLS and AI in the coming years.
