Clinical Workflow Automation: Where AI Is Making Real Inroads in Healthcare

Clinical Decision Support

Radiology has long been the leader in adopting AI-powered tools. Nearly 80% of FDA-approved AI devices are for medical imaging purposes, and researchers say the tools have contributed to earlier disease detection and improved patient outcomes.

Dr. David Kirk, an intensive care unit physician and the chief medical officer at Regard, is a proponent of using AI as a diagnostic assistant to ensure diagnoses aren’t missed, especially in emergency situations. “The EHR is like a huge novel for some patients,” Kirk says. “The AI can quickly condense all that data and help me figure out which page is the most important for a patient’s care.”

Prescription Renewal Automation

In addition to enabling prescription renewals to process more quickly, AI-powered tools are improving price transparency by helping clinicians identify lower-cost options for their patients. “You’re starting to see pricing integrated at the healthcare provider level in the EHRs,” says Joseph Kleiman, president of Buzz Health: “When there’s a check for prior authorization, the pricing options are already there.”

Utah has also launched a pilot program with autonomous health platform Doctronic, allowing an “AI doctor” to approve renewals on medications that have already been prescribed by a licensed provider. (It’s worth noting that the Utah Medical Licensing Board has questioned the program’s safety.)

EXPLORE: Health systems can improve clinical workflows with Microsoft Dragon Copilot today.

Revenue Cycle Automation and AI Coding

Billing inefficiencies cost hospitals 3% to 5% of net revenue each year. AI can help prevent those losses by reducing coding and billing errors, analyzing insurance denials and drafting appeals, improving clean claims rates and providing revenue forecasts.

Cameron says an ongoing worker shortage in revenue cycle management makes automation a “no-brainer. I don’t know how else you effectively run a hospital without automation in this space because there are just not enough people.” 

Patient Communication Automation

Automation has been transforming patient communication for years. Automated text reminders about upcoming appointments can reduce no-show rates, and remote patient monitoring devices share patient data directly to the EHR. 

A study at the UC San Diego School of Medicine found that using generative AI to help draft nonemergency messages, which physicians would then edit and sign, decreased cognitive load. 

And AI chatbots, such as Penny at the University of Pennsylvania Abramson Cancer Center, can assist with follow-up care. Penny texts chemotherapy patients about their daily medication schedule and asks how they’re feeling. If there is a concern, the AI is designed to alert the provider. 

Click the banner below to sign up for HealthTech’s weekly newsletter.

What To Assess Before Deploying Clinical Workflow Automation Tools

Before deploying an AI-powered tool, and after determining that an existing process is actually worth automating, experts say IT and clinical teams should thoroughly assess:

• Clinical validation metrics
• Compatibility with the EHR
• Cybersecurity risks
• Data ownership
• Data reliability 
• Drift rate
• Memory retention structure 
• Traceability

“You need to have really in-depth conversations about how and why the tool works,” says Cameron. He adds that when his hospital parters with third-party vendors, “we hold the developers accountable to the metrics they say the solution will achieve. If it doesn’t, we ask for an early out of that contract.” 

Measuring Automation ROI: The Metrics Clinical and Finance Leaders Care About

To measure tangible value from automation and AI, healthcare leaders may look at metrics such as:

• Clinician burnout scores
• Clean claims rates
• Documentation time per encounter
• No-show rates
• Reduced denial rates
• Staff turnover
• Quality star ratings

However, the most important metrics may be the ones that indicate whether staff is actually adopting available tools, such as process automation rates, login frequency and error reductions. A report from the Massachusetts Institute of Technology found that in 2025, 95% of generative AI pilots failed to generate tangible financial returns. The researchers concluded that across industries, organizations weren’t properly implementing the technology into workflows.

To ensure workflow automation solutions have buy-in from clinicians, Kirk emphasizes that providers should be included when deciding which technologies might best serve the organization. “Physicians are often the last ones brought into the discussions,” Kirk says. “When that happens, you may end up with a tool that IT and information systems people love, but the physicians don’t find it helpful and end up not using it.”