Harnessing Generative AI in Healthcare: Applications from Information Security to Operational Efficiency

In 2024, many industries, including healthcare, have increased their investments in generative AI. Research shows that 80% of organizations spent more on generative AI than the year before. None reported spending less. This shows that more people believe AI can help improve business results. Healthcare providers in the U.S. are part of this change.

Big healthcare groups like hospitals and health systems are leading the way. About 46% of these hospitals say they use AI in important areas like revenue-cycle management (RCM). Also, 74% use some kind of automation, like robotic process automation (RPA) and AI. Using generative AI in healthcare has helped both patient care and running the office. It also helps with managing data and keeping it safe.

Enhancing Information Security through AI

One important area where generative AI helps is information security. Healthcare providers have a lot of sensitive patient data. Laws like HIPAA protect this data. Practice administrators and IT managers must keep this data safe and private.

Generative AI helps by watching network activity and spotting strange behavior that could mean a security problem. These AI systems learn quickly from new data and can adjust to new threats. AI security tools lower the chance of human errors that might cause data leaks. They provide constant protection for digital health information.

For example, IBM works with healthcare groups to create safe AI platforms for healthcare IT systems. IBM’s watsonx Assistant AI chatbots support clinical and patient services while also helping keep healthcare data and business processes secure. This makes these AI tools very useful for healthcare IT teams that must balance quality service with security rules.

Improving Patient Service with AI Automation

Patient experience is important to healthcare providers. AI helps by improving how patients interact through automation in front-office tasks like answering phones and giving information.

Simbo AI is a company that uses AI for phone automation and answering services. This shows how AI can improve patient communication. The system automates appointment scheduling, answers common questions, and sends urgent calls to human staff. This reduces the work for front-desk workers and cuts down on wait times for patients on the phone.

IBM research shows hospitals that use AI chatbots and automated answering systems have fewer mistakes in patient communication. They also provide 24/7 service, which is important for emergencies or calls after hours. This continuous help can make patients happier by making sure their calls are always answered quickly.

Generative AI and Revenue-Cycle Management (RCM)

Revenue-cycle management is a complicated part of healthcare administration. It includes steps like verifying insurance, billing, and collecting payments. AI has helped improve this process a lot.

Auburn Community Hospital in New York uses AI and robotic process automation. They cut their cases of discharged patients who aren’t finally billed by 50%. They also increased coder productivity by more than 40%. This speeds up how money comes into the hospital and reduces errors and costs in administration.

Banner Health uses AI bots to find insurance coverage and manage information requests. These bots also write appeal letters for claims. This helps reduce denied payments and improves collections.

A health network in Fresno lowered prior-authorization denials by 22% and service denial rates by 18% with AI tools for claim reviews. The AI saved up to 35 staff hours per week that used to be spent on manual follow-up and managing denials.

The improvements AI brings to revenue cycle processes include:

• More accurate coding and billing using natural language processing (NLP) tools.
• Using data analysis to predict denials and forecast revenue.
• Custom patient payment plans optimized by AI.
• Less need for staff in verifying eligibility and handling prior authorizations because these tasks are automated.

Overall, these changes improve productivity in medical call centers by 15% to 30%, says McKinsey & Company research from 2023. AI is already changing how many U.S. hospitals handle money and billing, and this is expected to grow.

AI and Workflow Automation: Redefining Healthcare Operations

AI-driven workflow automation is a big change for healthcare practices. It automates simple, repetitive tasks. This lets healthcare staff, including doctors and administrators, focus more on patients and important decisions instead of paperwork.

Healthcare IT departments also gain from more AI use. Generative AI adoption in IT grew from 4% in 2023 to 27% in 2024. AI is automating tasks like managing information systems, entering patient data, scheduling appointments, and coding.

Generative AI chatbots, like those from IBM used at University Hospitals Coventry and Warwickshire NHS Trust, help doctors by answering clinical questions, studying health trends, and supporting real-time patient monitoring. This example from the UK shows AI’s possible benefits for U.S. health systems too. The hospital could serve 700 more patients each week because of this.

Besides clinical work, AI also helps with administrative work. AI agents are used more often for tasks like:

• Writing routine emails.
• Doing billing coding.
• Sending patient follow-ups or reminders.
• Managing eligibility and prior authorization tasks.

Research predicts that within one to three years, 82% of organizations plan to add these AI agents to daily work. This shows that AI agents are expected to play a big role in handling daily tasks while keeping clear and responsible actions.

AI use in the front office also improves patient relationship management. Companies like Simbo AI offer AI answering systems that change healthcare customer service from just completing tasks to building better patient connections. This lets staff spend more time on individual patient needs instead of routine questions.

Investment and Adoption Trends in the United States Healthcare Sector

Big healthcare organizations see generative AI as needed not only to work better but also to stay competitive. Among groups making over $20 billion a year, 49% use generative AI in many areas. For smaller groups earning $1 to $5 billion, only 10% have adopted it.

Bigger companies spend more on generative AI. Those with over $20 billion in revenue spend around $158 million each year on AI solutions. Those making $1 to $5 billion spend about $85 million. This money goes to buying and making AI systems. It includes advanced language models, computing power, and AI engineers.

For example, Accenture and Google Cloud work together to give healthcare companies access to over 130 large language models through the Model Garden on Vertex AI. This helps health systems choose and adjust AI models that fit their needs. Google’s Gemini model, known for strong reasoning, is already being tested by large companies and will get better soon.

Accenture also says many companies have moved from just trying AI to running full projects that bring real business value.

Generative AI Supporting Compliance and Ethical Use

As AI use grows, healthcare leaders worry about risks like biased AI results, data privacy, and mistakes from automation. It is very important for medical practice leaders and IT managers to use strong rules and controls.

Good strategies include:

• Setting clear rules for managing data.
• Checking AI results with human review.
• Training staff on proper AI use.
• Using safe systems to protect patient data.
• Being open about how AI makes decisions.

These controls help make sure AI tools treat everyone fairly, follow laws, and keep data secure.

Summary for Healthcare Leaders in the U.S.

Generative AI is becoming an important technology that helps many areas in U.S. healthcare. It protects patient data, automates front-office communication, improves revenue management, and supports clinical work. AI offers useful choices for healthcare organizations.

Medical practice administrators, owners, and IT managers have both chances and challenges when using AI. Investing in AI systems, following strict rules, and fitting AI into current workflows are key steps to using this technology well. Many leading healthcare groups already show benefits like better productivity, fewer denied claims, improved patient service, and secure data handling.

As AI use grows, healthcare providers in the U.S. should get ready to include AI tools more in daily work. Using generative AI well can help improve healthcare delivery and make both clinical and administrative work more efficient.