Enhancing Palliative Care Efficiency: The Role of GPT-Based AI Tools in Streamlining Clinical Documentation and Decision Support Processes

Palliative care teams take care of patients with many health needs. These needs include managing pain, controlling symptoms, handling medications, and working with different specialists and caregivers. One big problem for clinicians is the amount of documentation they must do. Writing daily notes, preparing discharge papers, and keeping track of patients’ changing conditions take a lot of time. This time could be used for direct patient care instead.

According to the 2022 U.S. Surgeon General’s advisory, too much paperwork leads to burnout for healthcare workers, especially nurses and doctors. Spending a lot of time on paperwork can make clinical communication worse, delay important actions, and increase the chance of mistakes. In palliative care, this problem is bigger because it requires careful symptom tracking and teamwork among many professionals.

Good documentation and decision support help improve patient care and reduce clinician stress. AI tools made for these tasks have been gaining interest. GPT-based language models show promise in helping with this work.

GPT-Based AI Tools in Palliative Care: Recent Research Findings

A study done in April 2025 looked at a GPT-based AI tool used in a hospital palliative care unit with 25 patients. The AI helped create draft notes, discharge summaries, and drug monitoring reports from data doctors entered using a text system. Clinicians checked and finished these documents to make sure they were correct.

• Impact on Documentation Time:
  Clinicians used to spend about 20.4 ± 5.6 minutes making discharge summaries. With the AI tool, this dropped to 6.1 ± 1.8 minutes. This saved time allows care units to move patients faster.
• Clinical Trend Recognition:
  The AI found important health changes in eight patients, like rising C-reactive protein (CRP) levels. Spotting these trends early helped doctors check patients sooner. The AI also gave medication suggestions in six cases. Experts reviewed all to keep patients safe.
• Physician Cognitive Load:
  Doctors said their mental effort decreased while using the AI for documentation. Less mental fatigue helps them focus better and make fewer mistakes.
• Improved Communication:
  The AI also created patient education summaries to help explain care plans to families. Clear summaries help families understand what is happening, which is important in palliative care.

AI and Workflow Integration in Clinical Settings

Using AI tools well means connecting them to current clinical workflows. Having AI alone is not enough. It needs to work with electronic health records (EHR) and existing clinical steps.

• Streamlining Administrative Tasks:
  Tasks like charge capture, authorizations, and appointment scheduling take much staff time. AI automation can manage these tasks, reducing manual work. This speeds up patient care access and lets staff focus on harder jobs.
• Clinical Documentation via Voice Recognition and Generative AI:
  Voice recognition AI turns spoken words into clinical notes fast. For example, BayCare Health System uses voice assistants in patient rooms to help patients control their environment and reduce calls for nurses. These tools help nurses spend more time with patients. Generative AI writes summaries and reports from patient data, cutting down documentation time while keeping notes clear and complete.
• AI-Powered Staffing and Task Automation:
  AI can predict staffing needs based on past patient numbers, severity, and clinician experience. This helps schedule nurses better and reduce burnout. Robots, called cobots, at places like ChristianaCare help deliver supplies and medicines. They connect with EHRs to know what tasks to do, supporting nurses’ work.
• Supporting Clinical Decision Making:
  AI-powered decision support helps detect patient problems early. The CONCERN tool uses nurse notes to predict patient decline up to 42 hours earlier than regular methods. It does this without adding more documentation. Early prediction helps care teams act sooner and protect patients.

Clinical Support Systems and AI: Evidence from Leading Organizations

UpToDate by Wolters Kluwer is a popular clinical decision support system used by over 3 million healthcare workers worldwide. It gives updated, evidence-based clinical and drug information at the point of care. It works through EHRs, phones, and remote devices.

The platform uses Clinical Generative AI to help doctors quickly bring together guidelines, patient data, and treatment choices. This aids fast decision-making, especially in complex areas like palliative care. Dr. Eduardo de Oliveira from Brazil praises UpToDate as a key tool in good clinical practice.

Using AI responsibly, with tools like UpToDate, is getting more attention. Some groups, like Frost & Sullivan, give awards for AI innovation in healthcare. Clinicians feel positive about generative AI when it comes with expert-reviewed content to make sure it is safe and accurate.

Ethical and Practical Considerations in AI Adoption

Introducing AI in healthcare needs careful attention to ethics, safety, and operations. This is very important in sensitive fields like palliative care. AI-made documents are just drafts. Doctors must review and approve them so their judgment stays central.

Hospitals and clinics in the U.S. should involve nurses, doctors, and IT staff early and throughout AI development and use. Nurses have a special duty to know AI’s limits to keep care fair and correct. Taking part in tests and supervision helps avoid unexpected problems and makes sure AI fits clinical needs.

AI-Enhanced Workflow Automation: Transforming Palliative Care Operations

• Automating Routine Documentation:
  AI can write progress notes, discharge papers, and medication checklists. This cuts down paperwork for doctors and nurses. They then have more time with patients. Frequent updates in palliative care make this automation helpful.
• Integrating Clinical and Administrative Systems:
  IT managers should connect AI tools with electronic health records, pharmacy systems, and scheduling apps. This lets AI use patient data, suggest treatments, and update care plans smoothly without disturbing clinicians’ work.
• Real-Time Clinical Alerts and Monitoring:
  AI watches patient data all the time for important changes. For example, high CRP levels may signal infection. AI alerts the care team early so they can act before problems grow.
• Optimizing Staff Workloads:
  Balancing staff work helps prevent burnout, which affects patient care. AI studies patient needs and predicts nurse needs. This helps schedule the right number of skilled staff when care demand is high. Better schedules reduce staff quitting and improve satisfaction.
• Improving Patient and Family Communication:
  AI creates easy-to-understand summaries explaining patient conditions and plans. Families get clearer information, which helps reduce confusion. Good communication is important in palliative care for emotional support.

Relevance for U.S. Healthcare Practices and Administrators

In the U.S., healthcare faces many problems like rising costs, not enough workers, and more complex patients. Hospital leaders, medical owners, and IT managers working in palliative care can use GPT-based AI tools to improve work efficiency and patient results.

Less time spent on documentation but with good accuracy raises provider productivity. Early warning of patient health problems keeps patients safer and lowers expensive hospital readmissions. AI-driven staffing helps lower burnout and staff turnover, which recent CDC and Surgeon General reports show as ongoing problems.

Using AI to improve communication with patients and families also supports patient-centered care. This is key in quality and payment models common in U.S. healthcare.