Human Oversight and Collaboration Strategies to Ensure Safe and Accurate Use of Artificial Intelligence in Clinical Settings

AI tools in healthcare offer many benefits. They can look at lots of clinical data quickly, help with diagnosis, and tailor treatment to patients. But even with AI’s help, human supervision is very important to make sure things are correct and safe. Experts say this clearly.

Crystal Clack, MS, RHIA, CCS, CDIP, says human oversight is needed to check AI-generated messages. This can catch biases, mistakes, or harmful content before it affects patients. Without human review, AI might suggest wrong or unsafe actions. For example, AI could make wrong diagnoses or bad treatment plans if the data it uses is biased or incomplete.

Nancy Robert, PhD, MBA/DSS, BSN, from Polaris Solutions, says healthcare groups should not rush to use AI everywhere. They should focus on a few specific uses and keep checking the AI often. This careful approach lowers risks and helps AI fit better in healthcare settings.

David Marc, PhD, CHDA, from The College of St. Scholastic, says being clear about AI is important too. Patients and doctors should always know when AI is involved, not a person. This builds trust and avoids confusion during care decisions.

Collaboration Strategies Between Humans and AI

Using AI well in healthcare is not just about setting up machines. It needs good teamwork between AI and healthcare workers, such as doctors, administrators, and IT staff. Here are some ways to do this:

• Clinician Involvement in AI Development and Deployment
  Only about 22% of AI healthcare studies included doctors during development. This lack of involvement can cause problems like hard-to-use tools, bias, and trouble putting AI into action. Having clinicians involved early helps make AI that fits real clinical work better. Doctors can point out safety and bias issues and check AI accuracy across many kinds of patients. Rules from NHS and FDA say including clinicians makes AI tools more useful over time.
• Clear Role Definitions and Transparency
  It is important to be clear about what AI does in healthcare workflows. Managers should make sure staff know which tasks AI handles and when humans need to check AI results. Clear talking can improve how well AI fits into work and builds trust. Also, training and documents should explain AI limits and when to step in. This helps avoid “algorithm aversion,” which happens when doctors stop trusting AI because its mistakes are not explained.
• Regular Monitoring and Validation
  AI tools need ongoing checks after use starts to find if their performance goes down or bias appears over time. A feedback loop with doctors, IT, and managers helps gather real data on AI safety and fairness. Outcomes-based contracting (OBC) links payments to patient outcomes, so vendors and healthcare groups work together to keep AI effective.
• Data Governance and Privacy Responsibility
  Health organizations and AI vendors must agree on who is responsible for data privacy and security. Since AI handles sensitive patient info, rules like HIPAA must be followed carefully. Nancy Robert from Polaris Solutions says these agreements should cover data sharing, audits, security rules, and how to handle incidents. This makes trust and keeps legal rules.

AI and Workflow Automation in Healthcare

Even though human oversight is important, AI can also help with automating office jobs. This support helps medical practices work better. Some companies focus on AI phone answering and front-office tasks to help patient communication. Here are some points to think about when adding AI to office work:

• Reducing Administrative Burdens
  AI can do repeated tasks like setting appointments, sending reminders, and answering calls. Automating these jobs frees up staff to focus more on patient care.
• Improving Patient Engagement
  AI health assistants can give personal reminders about medicines or visits and answer common questions. This helps patients follow care plans and be happier with their care.
• Efficient Call Management
  Automated phone answering cuts wait times and makes sure patients get quick replies. This means fewer missed calls and smoother appointment bookings for medical offices.
• Maintaining Privacy and Security
  Automation must keep data safe. Encryption, strict access rules, and HIPAA compliance are needed to protect patient information during calls or scheduling.
• Supporting Staff with AI Assistance
  AI should help office workers by handling simple calls and sending harder questions to trained staff. This teamwork helps office work run better and lowers mistakes.

Using AI carefully in office work can make healthcare operations better without losing safety or patient trust.

Addressing Challenges in AI Integration

Using AI in US healthcare faces some problems that administrators and IT managers must watch out for:

• Bias and Equity Concerns
  AI may work badly if it was trained on data that is not fair to all groups. Bias can cause wrong or unfair care, hurting some patients more. Checking for bias regularly and using different data can help fix this.
• Interoperability Issues
  AI systems may not work smoothly with Electronic Health Records (EHRs), making data sharing hard. Groups like the Office of the National Coordinator (ONC) are working on rules to fix this, based on models like TEFCA. This will help AI fit better across systems.
• Skill Erosion Among Clinicians
  Studies like the ACCEPT trial show that relying on AI might cause doctors to lose some skills when AI is not available. Doctors need to stay involved by interpreting AI results and keeping up their judgment, with ongoing training.
• Regulatory and Compliance Complexity
  US healthcare has many rules about patient safety, privacy, and data security. AI tools must follow laws like HIPAA and FDA rules, along with new federal guidelines about ethics and clear use.
• Vendor Selection and Management
  Healthcare groups must carefully choose AI vendors. They should ask about how the vendor tests AI, how clear the AI results are, how they handle data, and if they will keep supporting and monitoring the AI over time.

The Federal and Ethical Environment for AI in Healthcare

• The National Academy of Medicine (NAM) has made an AI Code of Conduct to guide ethical AI use in healthcare. It supports transparency, human oversight, fairness in data, and privacy.
• The FDA keeps approving AI medical devices, mostly in high-risk fields like radiology and heart care, showing focus on safety.
• Companies like Microsoft and others say AI should be designed with humans in mind, making sure patient safety and fairness come first.
• Laws require clear rules on who is responsible for AI results in clinical care.

Healthcare administrators should keep up with these rules to ensure their AI use meets ethical and legal standards.

Practical Recommendations for Medical Practice Admins and IT Managers

• Implement Structured Training: Make sure clinical and office staff know how to use AI tools, what they can and can’t do, and when to step in.
• Develop Monitoring Protocols: Create systems to keep checking AI performance, including bias audits and ways to report errors.
• Promote Team Collaboration: Encourage open talks among clinical teams, IT, and office staff about AI work and problems.
• Choose Vendors Carefully: Pick AI vendors who test their tools openly, have clear data policies, and stick to rules.
• Maintain Patient Communication: Let patients know when AI helps with their care and provide ways to reach human help.
• Balance Automation with Human Touch: Use AI to help, not replace, human decisions and personal care in both clinical and office work.

By focusing on human oversight and teamwork when using AI, US medical practices can benefit from technology while keeping patient care safe and trusted. AI can lower office workloads and help with clinical decisions, but it needs real effort from healthcare workers to be used well and safely.