AI for Providers, Life Sciences, and Payers in Healthcare

Between 2019 and 2022 alone, healthcare organizations saw a 22% increase in per-patient costs. As the demands and costs of healthcare ramp up, leaders on the forefront of change are meeting the challenges through artificial intelligence. AI can be everywhere at once, increasing productivity, documenting visits, managing claims, training, imaging, and even designing medications.

Despite the promise, AI is not a panacea. With lives (and quality of life) at stake, healthcare demands uniquely rigorous controls for data privacy, ethics, and diversity. Medical organizations face singular requirements to ensure robust encryption and security, conformity with HIPAA guidelines, and data leak prevention.

How Healthcare Organizations Are Already Seeing Returns from AI

AI has already shown impressive proficiency and efficiency at multiple levels of the healthcare industry, from imaging and reimbursement to searching records and drug development. Here are a few ways that AI is already in place:

Imaging Enhancement
AI excels at identifying complex patterns, making it particularly useful at spotting deviations in medical images that may not be visible to the human eye. Vision Transformer (ViT) architecture has already proven effective at identifying diseases such as Alzheimer's, and clinicians at Lahey Hospital and Medical Center in Massachusetts are using an innovative AI system to diagnose stroke and other conditions.

Electronic Health Record (EHR) Search
AI can sift through massive data sets in the blink of an eye, delivering more relevant results than a typical semantic search engine that only looks for keywords. The Mayo Clinic has partnered with Google to implement a HIPAA-ready AI search app that sifts through patient EHRs to help clinicians diagnose disease and recommend treatment more quickly and effectively.

Reimbursement-related Communications
Reimbursement is one of the more time-consuming workflows in the healthcare sector. Communications centering on reimbursement are packed with "toil"-non value-added work that clogs processes and hampers productivity. Since many communications between payers and providers are repetitive, AI can add significant efficiencies. For instance, Schneck Medical Center in Indiana uses AI to reduce claim denials, eliminating time-consuming, repetitive renegotiation and communication.

Drug Development
The pharmaceutical industry is a front-runner in AI development. This sector has been digitizing their data at a breakneck pace for several years, and using AI to solve complex clinical problems from bench to bedside. AI is already being used to predict molecular structures, determine drug activity, design new molecules, predict reaction yields, toxicity, and bioactivity-and perform dozens of other time-intensive tasks. Models such as MoIGPT and MOLBERT have been developed to synthesize specific compounds and predict drug-target interactions.

Data Analytics
The healthcare business is drowning in data, with approximately 2,300 exabytes created every year. Multiple healthcare providers are using AI tools to manage and analyze their data troves, helping clinicians classify medical findings, make patient health predictions, and recommend treatments. Letting AI analyze patient data can take the burden off human healthcare teams, so they can devote more time to patient care.

Training Support
As healthcare organizations continue to upskill, AI is stepping in to help. GE Healthcare partnered with Microsoft in 2022 to develop an AI-assisted mixed reality (MR) tool that can train technicians to operate and maintain their MRI machines. AI can also recommend certifications and trainings to ensure that healthcare workers follow a career development path that best serves their needs and their employer's.

Real-time Monitoring
Remote patient monitoring technology has been in place for more than a decade in the form of implantable cardiac monitors, glucose monitors, and other patient health tracking devices. However, valuable patient information often hides in the data glut produced by these healthcare IoT devices. Providers are now using AI to monitor and analyze this data to diagnose and flag patient conditions.

Special Considerations for Healthcare Organizations Implementing AI

Despite AI's power to drive new productivity, a growing body of advanced research is urging caution. The healthcare industry faces unique requirements for data privacy, security, and compliance, along with special considerations surrounding ethics and diversity.

Data Privacy and Security
The average cost of a healthcare data breach was $4.76 million in 2023, and there were 725 breaches that year, for a staggering total cost of $3.45 billion. Fines, penalties, and lawsuit settlements for HIPAA violations snowball, underscoring the importance of security and data privacy. The U.S. Government Accountability Office (GAO) released a 2022 report to guide organizations in the implementation of AI in healthcare.

Ethics and Diversity
While AI excels at identifying and using patterns, those same patterns can get its users into trouble when they touch on sensitive patient information. A comprehensive review of generative AI in healthcare published by Cornell University advocated caution to prevent discrimination against patient populations

Compliance
The healthcare sector requires rigorous clinical validation to assure safety and reliability. Healthcare organizations can build compliance guidelines into emerging AI tools and processes to ensure adherence to current regulatory standards.

How Users in Healthcare Can Safeguard Data and Compliance for AI Applications

Healthcare organizations can protect data privacy and ensure compliance by implementing AI systems with strong encryption and compliance built-in, and by training employees on how to use AI tools ethically.

• More robust encryption and security: Healthcare organizations may need to devote substantial resources to reduce the risk of patient data breaches.
• Built-in HIPAA integration and ethics: Dedicated AI tools can be developed for specific healthcare use cases, with HIPAA readiness, ethical guidelines, and security designed into the tools.
• AI training for employees: Healthcare staff can be trained in how to ethically use AI tools and the information they generate.
• Regular audits: Leadership can schedule periodic assessments of AI systems to verify compliance with changing regulatory standards.
• Ensuring transparency: Organizations can design transparent AI decision-making processes to build trust among clinicians and patients.

What's Next for AI in Healthcare

No matter where AI takes us, one thing is clear: the healthcare industry has only breached the seal on what AI can do. From documenting patient visits, to triage, diagnosis, and supply-chain monitoring, AI is poised to realize a sea change in the medical world.

Need help deploying AI at scale in today's rapidly evolving healthcare ecosystem? Learn more about AI through Connection or our services and solutions today!

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