5 Benefits of Using AI in Healthcare
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From diagnosis and risk assessment, to choice of treatment procedures, there are many opportunities for healthcare organizations to deploy AI and deliver more impactful, efficient and precise interventions to their patients.
As the volume of healthcare data continues to increase, AI is poised to drive innovations and improvements across the care continuum. This is predicated on the ability of AI tools and machine learning (ML) algorithms to deliver proactive, intelligent and often hidden insights that inform diagnostic and treatment decision-making.
AI can be extremely beneficial to both patients and providers when applied in the following areas: improving care, chronic disease management, early risk identification, and workflow automation and optimization. To help providers best understand how to take advantage AI within their ecosystem, let's take a look at the top five benefits of using AI in healthcare.
Healthcare organizations can use AI to aggregate and analyze patient health data to proactively identify and prevent risk, close preventive care gaps, and better understand how clinical, genetic, behavioral and environmental factors affect the population. Combining diagnostic data, exam findings and unstructured narrative data provides a holistic view of patients' health and reveals actionable insights that prevent disease and promote wellness. AI-driven tools can help collate, analyze and compare a constellation of such data points against population-level patterns to help reveal early disease risks.
Predictive analytics can be gleaned as these data points are compiled to provide a view into the populace. These insights can then be used for risk stratification of populations based on genetic and phenotypic factors as well as behavioral drivers and social determinants. Armed with these insights, healthcare organizations can provide more personalized, data-driven care while optimizing resource allocation and utilization, and ultimately driving better patient outcomes.
Applying artificial intelligence in certain healthcare processes can reduce the time and resources needed to examine and diagnose patients. With this, medical personnel can save more lives by acting faster. Machine learning (ML) algorithms can identify risk exponentially faster and with much more accuracy than traditional workflows. Done correctly, these algorithms can automate inefficient, manual processes thus speeding up diagnosis and reducing diagnostic errors — which remains the single largest cause of medical malpractice claims.
What's more — AI-enabled solutions can compile and comb through large reams of clinical data to provide clinicians with a more holistic view of the health status of patient populations. These solutions give the care team access to real-time or near-real-time actionable information at the right time and place to drive significantly better care outcomes. Automating the aggregation and interpretation of the terabytes of data flowing within the hospital walls allows the entire care team to work top of license.
One of the most innovative AI use cases in healthcare is in surgical robotics applications. The maturity of AI robotics has led to the development of AI surgical systems that can accurately execute the tiniest movements with perfect precision. These systems can perform complex surgical operations, thus reducing the average wait period for procedures, as well as the risk, blood loss, complications and possible side effects of said procedures.
Machine learning also has a role to play in enabling surgical operations. It can provide healthcare professionals and surgeons with access to real-time information and intelligent insights about a patient's current condition. This AI-backed information enables them to make prompt, intelligent decisions before, during and after procedures to ensure the best outcomes.
Studies show significant gaps in average life expectancy between developed and underdeveloped nations as a result of limited or zero healthcare accessibility. Developing nations lag behind their counterparts in deploying and leveraging innovative medical technologies that can deliver appropriate care to the population. Also, a shortage of qualified healthcare professionals (including surgeons, radiologists and ultrasound technicians) and properly equipped healthcare centers impact care delivery in such regions. AI can enable a digital infrastructure that facilitates faster diagnosis of symptoms and triage patients to the right level and modality of care to foster a more efficient healthcare ecosystem.
Relatedly, AI in healthcare can help mitigate the shortage of professionals in remote, low-resource areas by taking over certain diagnostic duties. For instance, leveraging ML for imaging allows for rapid interpretation of diagnostic studies such as X-rays, CT scans and MRIs. Additionally, teaching institutions are increasingly leveraging these tools to enhance training for students, residents and fellows while decreasing diagnostic errors and risk to patients.
Modern healthcare operations are a complex combination of deeply interconnected systems and processes. This makes it quite difficult to optimize cost while maximizing asset utilization and ensuring low wait times for patients.
Health systems are increasingly using artificial intelligence to sift through the volumes of big data within their digital ecosystem to gain insights that can help improve processes, drive productivity and optimize performance. For instance, AI and ML can (1) improve throughput and effective and efficient use of facilities by prioritizing services based on patient acuity and resource availability, (2) improve revenue cycle performance by optimizing workflows, such as prior authorizations claims and denials, and (3) automate routine, repeatable tasks to better deploy human resources when and where they are most needed.
Used strategically, AI and ML can provide administrators and clinical leaders with the wisdom to improve the quality and speed of hundreds of decisions they need to make each day, thus facilitating the smooth transition of patients through various clinical services.
The rapidly increasing volume of patient data both within and outside hospital walls shows no signs of slowing down. Stretched by continued financial challenges, operational inefficiencies, a global deficit of health workers and rising costs, healthcare organizations need technology solutions that drive process improvement and better care delivery while hitting crucial operational and clinical metrics.
The potential of AI in healthcare could improve the quality and efficiency of the delivery system by analyzing and extracting intelligent insights from the huge reams of healthcare data is limitless and well-documented.