AI in Healthcare: Breaking Down Barriers of Risk

The Problem with AI in Healthcare

For hospital CFOs, AI is a natural fit. You're tasked with improving margins and efficiency, and AI offers a way to do just that. The challenge? It’s not the tech…it’s the regulatory maze. With compliance and risk in the mix, not every AI tool is built for critical hospital operations.

A recent study by MGMA uncovered that 53% of medical groups had not added or expanded the use of AI at their practices, so it's clear there is hesitation in the air. Let’s dive into how to break down barriers with strategies that are cost-effective, data-safe, and built to deliver results.

The healthcare industry operates under some of the strictest data regulations out there, and for good reason. So, it’s no surprise there’s been some hesitation around adopting AI tools that could handle sensitive information. AI is making waves across industries, with new tools launching left and right. But many of these lack the decades of proven security healthcare systems count on.

AI can absolutely help optimize hospital operations, but it’s all about starting smart. Focus on use cases that balance safety with efficiency. Your sweet spot is low risk, high reward. A simple framework can help you weigh the risks and rewards of each potential use case and guide you toward the right AI tool for the job.

A Framework for Assessing AI Applications (The Four Quadrants)

The Four Quadrants risk-reward framework can help you determine which use cases in your healthcare operations are the best candidates for AI implementation. By categorizing AI opportunities into four risk levels (internal vs. external, PHI vs. no PHI), CFOs can make calculated, informed decisions that balance risk with reward.

Each quadrant introduces varying risk levels, making it easier to determine where to implement AI safely.

Internal
Operations that occur inside your hospital. You have full control over the environment and data.

External
Operations that occur outside the internal systems, such as marketing and patient portals

PHI
Involves private health information that requires strict protection

No PHI
No private health information is handled

Breaking Down the Four Quadrants

The Four Quadrants approach to use-case classification is ideal for helping CFOs determine the best AI implementation for each hospital and healthcare facility. Hospital AI use cases require careful consideration of security, data control, and the potential for beneficial results. Each available AI toolkit will also be ideal for different hospital use cases.

1. Internal Facing, No PHI (Least Risk)

The first quadrant is internal facing and does not involve PHI, representing the lowest risk. Internal operations give you complete control over how data and processes are handled, and because no private health information is involved, they’re safer to use.

Each of these use cases is low risk and high reward for cost-saving measures. Example use cases include:

Supply Chain Management
AI-powered supply chain management can simplify the routine and often time-consuming process of working with vendors and suppliers to keep your hospital stocked with essentials. AI can handle inventory tracking, supply request forms, invoices, and routine supplier communications.

Provider Onboarding
When you bring new staff onto your healthcare provider team, AI provider onboarding can simplify the necessary paperwork, handbooks, and policies so that your providers can step more quickly and efficiently into their roles with all the digital resources they need.

Revenue Cycle Management
AI finance tools excel at revenue cycle management, handling medical billing, patient payment plans, overdue notices, collections procedures, and invoices from suppliers.

2. External Facing, No PHI

The second most approachable quadrant for applying AI in hospitals is external-facing with no PHI. External-facing operations primarily involve engaging with patients for non-clinical purposes. In this quadrant, use cases focus on interactions that do not require personal health information. AI can personalize and enhance these interactions to improve patient experiences when engaging with your hospital through digital channels. Example use cases include:

Patient Acquisition (Marketing)
AI patient acquisition can implement marketing tools to boost your hospital's visibility and reach new patients looking for healthcare providers and solutions.

Patient Support (Non-Clinical)
On-site chat AI engagement can give patients non-clinical answers about the facility, such as hours, services, and locations. This ensures patients can get quick answers without your staff spending time on the phone.

Provider Relationship Management
AI-assisted provider relationship management takes the guesswork out of maintaining strong referral networks. It tracks referral activity and alerts physician liaisons to any dips in volume, ensuring they can step in at the right time. With intuitive dashboards and advanced analytics, liaisons can quickly pinpoint the root causes of referral declines. Plus, AI handles busy work like updating accounts and drafting follow-up communications so liaisons can focus on building relationships and driving results.

3. Internal Facing, PHI

In the middle of the risk/reward chart are internal-facing use cases that handle patients' private health information. These applications of AI in healthcare require tools that conform to HIPAA security requirements, with advanced security measures and fine-tuned control access. Fortunately, several AI tools designed for healthcare applications meet these requirements, opening the way for automation in some of the most critical areas of hospital efficiency.

This quadrant represents high ROI opportunities despite moderate risk. Proper implementation using compliance-ready AI tools minimizes risk and maximizes potential benefits. Example use cases include:

Intelligent Data Processing
One of the things AI tools do best is large-scale data analysis and intelligent data processing. AI can extract and qualify clinical data, provide smart record retrieval using intuitive search features, track health trends, and triage urgent care more efficiently than your human team, freeing up your providers to focus more on high-quality care.

Clinical Trial Matching
AI helps providers find eligible participants faster by analyzing and connecting data from every corner of the health tech stack, including EHRs, clinical systems, marketing tools, and more. We’re talking about detailed health histories, demographics, and social determinants of health. By taking this all-in approach, AI ensures participants get the care they deserve while saving providers time, effort, and resources.

4. External Facing, PHI (Most Risk)

The highest-risk corner of the Four Quadrants model is external-facing use cases that handle PHI. AI has tremendous potential, but only with advanced risk and compliance considerations. The requirements make this quadrant the most complex to implement, but it still represents significant opportunities for cost savings and improvement in patient experience. Example use cases include:

Advanced Therapy Management
AI can provide guidance to patients undergoing outpatient recovery and medical therapy. Advanced therapy management will guide patients through the routines they must complete, including reminders, motivations, prescription refills, and scheduled check-ups with each patient's care team at the appropriate times. For safety, physicians should make any final clinical decisions.

Patient Intake
AI can also assist with patient intake, scheduling both appointments and treatments, helping patients fill out their paperwork with accurate data, and simplifying the intake process each time a patient must arrive in person for care.

Patient Portals
Patient portals provide a convenient way for individuals to take charge of their healthcare by logging into their own health accounts. These platforms often include features powered by conversational AI, enabling users to easily manage tasks such as scheduling appointments, accessing test results, and even communicating with their healthcare providers. AI-driven patient portals help save time and make healthcare more accessible.

Real ROI Opportunity with AI in Healthcare

When a hospital CFO explores AI tools to capture cost savings and enhance efficiency, the first step is focusing on low-risk, high-reward use cases, yielding quicker ROI with limited risk. As you progress from the lowest-risk to higher-risk applications, it becomes critical to tailor AI solutions to healthcare-specific needs, focusing on compliance to achieve ROI while maintaining necessary data security.

Penrod can help hospital CFOs implement AI solutions tailored to each facility's unique challenges and needs. Schedule a consultation to begin building your custom AI solution and unlock the potential for cost savings, automated workflows, and better experiences for staff and patients alike.