Healthcare organizations, no matter the size, need ways to maximize their resources to deliver high-quality care to their patients. Fortunately, generative artificial intelligence (AI) and other technologies can provide that support.
In this article, we’ll discuss the potential and applications of generative AI in healthcare, exploring how this technology tackles some of the industry’s most pressing challenges. We’ll also touch on insights from Episode 4 of the series Unlocking Big Data, which explores the importance of delivering trusted data to healthcare decision-makers.
Generative AI is a revolutionary tool that uses advanced algorithms to create new data and generate insights for enhanced decision-making and optimized workflows. While healthcare organizations can use this tool to enhance operations and efficiencies, AI requires accurate data to perform well.
By leveraging vast amounts of healthcare data, this technology can ultimately transform patient care for the better. Jake Hochberg, VP of Analytics and Chief Analytics Officer at Arcadia, explains the importance of high-quality data for generative AI in the following clip from Episode 4 of Unlocking Big Data:As explained in the video, a data analytics platform can deliver a high-quality data asset to AI models in two crucial ways. First, storing your data in an analytics platform and regularly analyzing it in one place ensures it’s cleaner. Second, when data is in one centralized place, it’s easier to transfer it to models from external sources.
There are several generative AI models, but the two most popular within the context of healthcare are:
However, a study by HIMSS Market Insights revealed that more than three in five leaders of healthcare organizations with fewer than 2,500 employees say that their organizations are not currently building or running AI models on their systems. Generative AI is not yet accepted and adopted across the healthcare sector, and understanding the utility of this technology is critical to widening its impact.
Studies show that clinicians spend roughly 33% of their workday on activities outside of patient care, such as administrative requirements. AI can handle many of these essential administrative tasks, such as patient scheduling and comprehensive data management.
LLMs, in particular, can automate the process of summarizing patient data by reading through electronic health records (EHRs) to understand patients’ medical histories and identify trends or diagnoses. Also, integrating AI into EHRs could reduce medical errors — such as a misdiagnosis or prescription mistakes — by ensuring important information isn’t missed.
Smaller organizations in particular have a significant opportunity to enhance their resources and use AI to fill administrative gaps. Watch how Hochberg explains it in Episode 4 of Unlocking Big Data:
The result? Generative AI has the potential to optimize workflows and efficiency in healthcare organizations.
Generative AI’s ability to analyze and glean insights from vast amounts of healthcare data can uncover hidden patterns and correlations not immediately apparent to human researchers. These insights can provide new understanding and breakthroughs, driving medical research through:
Plus, this technology can significantly speed up research by automating data analysis and generating hypotheses.
Generative AI, and chatbots in particular, provide effective ways for smaller organizations to reach more patients in ways they feel most comfortable. Ultimately, they can communicate with and serve more patients without adding more burden to current staff.
AI can help us with chatbots starting to reach out to patients more directly. Email is archaic; they don’t use it anymore. Calling people; they don’t answer the phone. But chatting they feel really good about.
This is especially helpful for sharing personalized educational content or interactive learning experiences. For example, AI can generate information about managing blood sugar levels to share with a patient diagnosed with diabetes. Or, the AI can generate responses to the patient’s questions about their diagnosis, helping them to better understand their condition.
It’s no mystery that data analysis can inform decisions on patient care. AI optimizes this process by analyzing a patient’s genetic makeup and medical history to create personalized treatment plans. In fact, providers can use generative AI in healthcare for the following processes:
In addition, analyzing patient data using AI can help these organizations uncover social determinants of health and resolve issues with transportation or financial problems that prevent them from getting care.
To adopt generative AI in healthcare, organizations must first prepare their data to ensure they equip their tool with accurate information. This process involves:
The large volume of data required by generative AI in healthcare necessitates the use of a data storage and management system. Arcadia’s innovative data platform does more than store healthcare data — it transforms your organization’s data into actionable insights so you can deliver the highest quality of care.
As healthcare organizations confront financial challenges, provider burnout, workforce shortages, and health equity concerns, generative AI and data analytics platforms offer powerful solutions to enhance resource efficiency and expand patient care capabilities.