The future is now: How can hospitals harness artificial intelligence (AI)?
But many still wonder just how AI technologies will be used to enhance healthcare decision-making and ultimately affect patient care. As several studies suggest, AI is poised to become an essential component in treating a variety of diseases and conditions.
Understanding AI devices
Companies develop AI systems under many names, but, in general, AI technologies used in healthcare can be divided into two separate categories:
- Machine learning systems: These systems are used to analyze structured information, including the results of genetic testing, imaging, and electrophysiological data. Clinicians use these types of systems to help identify trends and better predict disease outcomes.
- Natural language processing systems: Instead of analyzing structured data, natural language processing systems comb through unstructured information, such as medical journals or clinical notes, to pull out certain facts that supplement structured medical data, like imaging results.
In many cases, natural language processing systems can be used to first important data from unstructured sources. This data can then be applied to individual patient care through machine learning systems.
Current applications in healthcare
It isn’t possible for a patient to walk into a hospital and receive complete care from a machine quite yet, but AI systems are already helping clinicians make important decisions about treatment. In oncology, systems like IBM’s Watson have been used to screen millions of unique patient records for clues to better identify patients at higher risk for leukemia. Other systems already help providers analyze and interpret various medical images to better detect cancerous tumors and cardiovascular abnormalities.
Patients with neurological conditions are also benefitting from AI technologies. AI applications have shown to be effective in predicting stroke sooner by identifying early stroke symptoms many healthcare providers often overlook. Machine learning systems have been applied to neuroimaging data, including the results of MRI and CT scans, to identify abnormalities that could cause further complications. Depending on the algorithms used by AI systems, neural abnormalities are identified correctly between 65-74% of the time.
Implications for nurses
While AI systems will continue to be used to interpret genetic information and imaging results, future AI devices may be used by nurses to more accurately identify proper nursing diagnoses, enhance decision-making, and improve the overall quality of healthcare services delivered to patients.
Nursing care is delivered through a series of steps that make up the nursing process. This includes initial data collection, assignment of nursing diagnoses, creation of nursing care plans, and the implementation of those care plans. But some research indicates that persistent staff shortages, increasing patient loads, and a limited amount of time in which to care for patients causes chronic stress that leads many nurses to eventually leave the bedside.
In the future, AI systems may help nurses streamline their clinical practice by reducing workload and allowing for more direct, high-quality patient care. AI decision support systems may one day help acute care nurses by suggesting appropriate nursing diagnoses and treatment options based on information gathered at the bedside, such as specific patient symptoms or the results of diagnostic tests. In some clinical trials, AI systems have been used to create accurate clinical diagnoses based on patient information with a final accuracy rate of 84.8%.
Ultimately, AI systems may help boost nursing job satisfaction rates as these technologies help save time by analyzing tremendous amounts of data for clues about a patient’s condition. As job satisfaction increases among the healthcare workforce, patient care and safety measures will also benefit.