The role of artificial intelligence in nursing ( second part ) بالانجليزية
Patient monitoring: Artificial intelligence (AI) is being used in nursing for remote patient monitoring (RPM), which connects sensors and equipment to patients. The key stages of RPM are to gather patient data and the data collection, transport, and storage in the cloud for analysis. This technology is used to manage chronic diseases, fall detection , and mental health disorders. As well as to monitor cardiac arrhythmias, hemodynamic, vital signs, and offer guidance. For patients with cardiac illness, wearable sensors can track their heart rate, ECG, and breathing rate. For the elderly, wearable and ambient sensor-based fall detection devices are essential. Mental health systems advise patients to take their medications and to monitor adherence, while diabetes monitoring systems track blood glucose levels, food intake, and blood pressure. These systems have also been applied to patients with Alzheimer's and bipolar disorder. All these systems can detect even slight changes in a patient's condition, allowing nurses to address potential issues before they become more serious.
Electronic health records (EHR) and predictive analytics: AI and EHR can improve patient care by reducing data complexity and enabling nurses to make informed decisions. AI can analyse patients' medical histories, family histories, and genetic predispositions, enabling healthcare providers to better understand medical data. AI systems like ChatGPT can analyse free text entries in EHRs to produce real-time summaries of patient care. On the other hand, predictive analytics can identify trends and risk factors, allowing nurses to anticipate patient needs and customize interventions. For instance, AI has been effectively applied in cardiology to improve risk assessment for patients suspected of having coronary artery disease, predict the onset of heart failure. Also, it predict adverse events like infections or deteriorating conditions. This enables nurses to proactively intervene, providing timely care and preventing complications.
Clinical decision support (CDS): It is a tool often found in electronic health record systems that gives nurses extra information to help them make informed decisions, alert them to medication interactions, decide on treatment options, and support nurses in providing appropriate patient care.
Prediction of fall risk: AI technology may better predict fall risk and provide automatic alerts, reducing the need for manual calculations.
Medication management: AI and machine learning techniques have many benefits for medication management, from clinical decision support to drug safety. It can help by verifying prescriptions, checking for potential drug interactions, and automating medication administration. This reduces errors and improves patient safety. AI is also being used to create new tools that support patients in following their treatment regimens in order to improve their overall health. AI-powered smartphone applications have shown to be successful in tracking and encouraging drug adherence. These applications can remind patients to take their medication, provide instructions on how to do so, and help detect any instances of non-adherence. Additionally, patients have been empowered and supported in taking their medications by using reminder methods like SMS alerts. Patients eventually experience better clinical outcomes when there is an improvement in drug adherence. Since some hospitals already use robots to distribute medication, artificial intelligence may also be able to handle pharmaceutical administration.
Patient engagement: To ensure patient acceptance, it's critical to plan new health technology with their participation in mind. Patients often use web portals, social networks, or mobile apps to manage their illness and communicate with healthcare providers. Patient education, treatment regimen reminders, health data collection, and assistance are all accomplished through patient engagement platforms like chatbots and smartphone applications. Virtual assistants and AI chatbots are also being utilised more frequently to interact with patients and enhance their overall experience receiving care.
Diagnosis: IBM's Watson for Health is utilizing medical data from journals, case studies, and a database of symptoms and treatments to help nurses in providing patient care.
Treatment: Nurses are able to utilize AI systems to obtain information that is necessary for creating a comprehensive care plan for their patients.
End-of-life care: It involves robots equipped with artificial intelligence engaging with older individuals to address feelings of loneliness and social disconnection. These robots can assist with tasks, communicate, and provide emotional support.
Following healthy living: Health applications promote a healthier lifestyle and help individuals better manage their health conditions.
Patient education: AI-powered chatbots have the ability to interact with patients, offering them information regarding their health condition, medications, and post-hospitalization care. These chatbots can also be utilized by nurses to respond to inquiries and monitor patients' well-being from a distance.
Robotic technology: With its ability to improve patient care, robotics is revolutionising the healthcare industry. Hospitals and assisted living institutions are using telepresence robots, voice assistants, autonomous robots, and emotionally sensitive robots as caregiving tools. Not only can these robots help nurses with patient data collection and home care, but they can also lower suicide rates and perform tasks like meal and supply delivery. Essential nursing procedures like patient paperwork, wound care, IV insertion and removal, patient transport, and education can also be aided by artificial intelligence and robot helpers. Robots could also automate some jobs and assist with nursing care in hazardous hospital environments. This include assisting with catheter removal safely, managing urinary tract infections, and identifying patients at risk for complications after surgery.
Resource allocation: The utilization of new technologies plays a vital role in helping healthcare industry reduce expenses through optimized resource allocation and cost management. As healthcare settings have problems related to nurse staffing and resource allocation. In order to optimise staffing levels, AI can analyse patient data, hospital census data, and nurse schedules. This ensures that there are enough qualified nurses on hand to deliver top-notch care. These technologies also offer the potential to streamline candidate management, reduce hiring time, and lower recruitment costs. As well, digital technologies provide a lot of data and automate jobs, which streamlines the recruitment process. Additionally, AI systems simplify salary and benefits planning, allowing for a focus on strategic initiatives. Despite the high initial costs, digitalization and AI enhancements lead to improved economic outcomes for organizations by boosting productivity, retention, satisfaction, and task efficiency.
Administration and time management: AI technology aids in scheduling appointments, managing prescriptions, and organizing discharge paperwork. It enables nurses to increase focus on patient care.
Cancer detection: Machine learning could potentially utilize algorithms to create a model for detecting cancer by using patient nomograms and web calculators, which would assist nurses in accessing cancer status and lowering mortality rates through early detection.
Triage patients: A prime illustration is the Emergency Triage Systems software, which helps nurses accurately prioritize patients in emergency departments.
Emergency interventions: AI has the ability to utilize an algorithm that can analyse real-time data from electronic health records to identify patients' health conditions and identify emergencies. Detecting emergencies faster can lead to improved outcomes for patients.
Improved healthcare: AI has the potential to enhance and broaden patient access to high-quality healthcare through personalized treatments and monitoring, leading to fewer errors and reduced costs. Also, AI has already streamlined healthcare by reducing time-consuming tasks that don't necessarily need a nurse's attention, allowing nurses to focus more on direct patient care.
Preparation: Artificial intelligence can create realistic simulations to help nurses get ready for various situations.
Role of artificial intelligence in the future of nursing: robots and computerized triage nurses may assist with tasks such as taking blood samples, scheduling shifts, and monitoring patient conditions. Additionally, wearable technology like intelligent shirts may be able to detect early signs of illnesses. Sensors monitor changes in how the body uses oxygen. This information is then used to foresee the development of Type 2 diabetes and heart or lung diseases. Advanced analytics, known as machine learning, can be utilized to forecast the probability of a patient experiencing a stroke, coronary artery disease, or kidney failure. By utilizing machine learning and advanced analytics, nurses can create personalized healthcare plans for individual patients based on various factors such as medical history and demographics.
https://doi.org/10.1007/s12178-020-09638-8
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