Artificial intelligence (AI) has become a key technology with many applications across every industry. Healthcare has been adopting it increasingly as a tool to support clinical and operational outcomes. AI in healthcare has significant potential, with the power to evolve and reshape how the industry uses and analyzes data.
So, what are the key use cases that will make the most difference in healthcare?
Use Cases for AI in Healthcare
For AI to be a useful tool in healthcare, there is focus on its practicality and how it can leverage data and reduce manual work. In these applications, the big fear is that it will replace human resources and intelligence. However, that’s not the case. Rather, it serves as a way to augment it. AI processing and analyzing capacity is faster than what human brains are capable of. It can surface trends, outliers, and patterns that assist human decision-making. In other words, AI is a compliment to human labor.
AI in Diagnosis and Treatment
Machine learning is a type of AI that centers around data and algorithms to simulate how humans learn, improving accuracy as it ingests more information. This framework lends itself to supporting clinical diagnosis and treatment. It has already begun to demonstrate value across many specialties and diseases, including:
Oncology: It can detect tumors faster, which one study found it could do so accurately 88% of the time.
Pathology: With a shortage of these clinicians, machine learning can fill the gap by increasing the speed of profile scanning.
Strokes: Algorithms can differentiate between resting and stroke-related paralysis and predict the recovery curve of ischemic stroke patients.
Diabetes: Machine learning can assist with pre-diabetes screening, predicting treatable complications, and identifying genetic markers for the disease.
AI in Population Health Management
Other types of AI, such as Natural Language Processing (NLP), enables computers to interpret and use human language by utilizing statistical models to estimate the probability of a sequence of words. NLP particularly excels in assimilating extensive medical records to furnish diagnostic outcomes. Developers can imbue systems with the capability to review millions of EHR records to look for abbreviations, words, or phrases associated with a disease. Within minutes, these NLP-powered systems can identify patients with certain conditions. This can be helpful in the evolving world of population health management.
Another way that NLP supports healthcare diagnosis and treatment is by overcoming current limitations relating to procedure and diagnosis codes that lack specificity. Nuances in conditions aren’t reflective in these codes, but NLP can scrub unstructured data and discover, highlight, and bring attention to these nuances
AI in Healthcare Operation
Outside the clinical capabilities, AI offers many benefits on the business side of healthcare. The most widely used application is automation. When AI is part of software programs, it can eliminate the need for humans to perform mundane tasks, freeing up staff to focus on higher level work, which can improve overall productivity and efficiency.
AI can also improve internal workflows, so they are streamlined and less of a strain on resources. Examples include:
Eliminating manual data entry through automation
Providing medical coding support
Automating eligibility checks and prior authorizations
Managing and monitoring claims
Processing invoices
Migrating data between legacy and newer systems
In addition, AI is capable of in-depth data analysis. Hospitals have massive amounts of data related to all aspects of business operations. Deep evaluation of it can lead to many insights. Examples include:
Supply chain management: AI can process millions of data points relating to current inventory, past usage trends, and forecasted needs to deliver recommendations for reducing costs.
Patient flow and staffing: AI can combine and analyze patient trends as well as internal resources to provide more accurate staffing models.
Asset management: Hospitals have numerous assets to track and maintain, and by capturing and assessing data, it can provide better understanding of asset longevity and when replacement will be necessary.
AI in Healthcare Has Great Promise
As the use of AI increases in healthcare, its ability to deliver value multiplies. There is still much to discover, and challenges related to data privacy and security, training algorithms with richer data sets, and costs associated with having a sophisticated platform are still present. Those using it also have reservations about its capabilities, so AI still needs to “prove” itself to be reliable and consistent.
Looking forward, it will be a crucial part of the future of healthcare, enabling organizations to improve patient care and operational efficiency.