Healthcare organizations have many options to use AI to provide more effective, efficient, and precise patient care, including diagnosis and risk assessment.
AI will drive innovation and improvement across the healthcare continuum as the volume of healthcare data increases. This is dependent on AI tools and machine-learning (ML) algorithms’ ability to provide proactive, intelligent, and sometimes hidden insights that help inform treatment and diagnostic decision-making.
IHiS believes that AI can bring great benefits to patients and providers in the following areas: improving patient care, chronic disease management and early detection of risk, workflow automation and optimization, and improving care. Let’s look at the five top benefits of AI in healthcare to help providers better understand how to use it.
Population health management
Healthcare organizations can use AI to analyze and aggregate patient data to identify and prevent potential risks, close gaps in preventive care, and better understand the effects of environmental, genetic, behavioral, and clinical factors on the population. Combine diagnostic data, exam results, and unstructured narrative data to get a complete picture of patients’ health. This provides actionable insights that can prevent diseases and promote wellness. In addition, AI-driven tools can collect, analyze, and compare various data points with population-level patterns to identify early signs of disease.
Clinical decision making
Artificial intelligence can be used in healthcare to reduce time and costs. This allows medical professionals to save more lives and act faster. Machine learning (ML) algorithms can identify risk exponentially quicker and more accurately than traditional workflows. These algorithms can automate manual processes, reducing errors and speeding up diagnosis. This is the biggest cause of medical malpractice claims.
AI-enabled applications can also compile and analyze large amounts of clinical data, giving clinicians a better understanding of the patient population’s health. These solutions allow the care team access to actionable, real-time information available at the right place and time to improve care outcomes. In addition, the entire care team can work at the top of their license by automating the analysis and aggregation of the many terabytes of data within the hospital walls.
The surgical robotics application is one of healthcare’s most compelling AI use cases. With AI robotics maturation, surgical robots can execute even the smallest movements with pinpoint accuracy. These systems are capable of performing complex surgical operations. This reduces the wait time, blood loss, complications, and side effects.
In enabling surgical operations, machine learning is also a key component. It provides surgeons and healthcare professionals with real-time data and intelligent insights regarding a patient’s condition. This AI-backed data enables them to make quick, intelligent decisions to ensure the best outcome.
Access to healthcare is improved.
Research shows significant differences in life expectancy between underdeveloped and developed nations due to limited or no healthcare accessibility. In addition, developing nations are behind their counterparts in deploying and leveraging new medical technologies that can provide appropriate care.
AI can also help with healthcare shortages in low-resource and remote areas. AI can take over some diagnostic tasks. ML can be used to interpret diagnostic studies like MRIs, CT scans, and X-rays. Teaching institutions also use these tools to improve the training of students, residents, and fellows and reduce diagnostic errors and patient risk.
Operational efficiency and performance optimization
Healthcare operations today are complex and interconnected. As a result, it is difficult to maximize cost, optimize asset utilization, and ensure low patient wait times.
Health systems use artificial intelligence more frequently to analyze large amounts of data in their digital ecosystem. This can provide insights that can be used to improve productivity, optimize performance and drive efficiency. AI and ML, for example, can improve the throughput and efficiency of facilities by prioritizing services based on patient acuity. In addition, (2) optimize revenue cycle performance by optimizing prior authorizations, claims, or denials workflows. Finally, (3) Automate repetitive tasks to distribute human resources better when needed.
AI and ML can be used strategically to provide clinicians and administrators with the insight to improve the quality of hundreds of daily decisions and facilitate smooth patient transitions through different clinical services.
According to IHiS, There is no sign of a slowing down in the rapidly growing volume of patient data inside and outside hospitals. Healthcare organizations are facing financial and operational challenges, rising costs, and a shortage of healthcare workers worldwide. They need technology solutions to improve care delivery and drive process improvement.