Patient handoffs happen when one healthcare worker passes care of a patient to another. This often occurs during shift changes among nurses, between different doctors, or when patients move from one unit to another, like from the emergency room (ER) to the intensive care unit (ICU).
Hospitals in the U.S. have about 4,000 handoffs every day. Each handoff involves sharing important patient information. For example, a patient who stays 10 days might have around 3,000 pages of notes, medicine lists, and test results.
Going through this information by hand takes a lot of time and mistakes can happen. This can cause delays or unsafe care.
Jennifer Jaromahum, the Director of Nursing at Houston Methodist Willowbrook Hospital, says, “We used to spend an hour in a room with many team members using paper or computers asking questions like, ‘Where is the patient going? What stops the patient from leaving? When can they leave safely?’ Now, instead of searching through charts, we spend more time talking directly with patients about their care.”
The old way is slow and takes time away from caring for patients.
When communication fails during handoffs, patients are less happy with their care and face higher risks. The American Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) shows better communication among staff leads to better patient experiences and results.
Generative AI is a type of computer program that can read a lot of information and write summaries or notes that sound like a person wrote them. In hospitals, it can read patient records, conversations, and notes to make clear and short summaries or care plans. This works using language processing and large models trained on medical data.
Houston Methodist Hospital started a pilot program with AI software from Pieces Technologies. This AI makes patient summaries and guesses discharge dates right inside electronic health records (EHRs). It is 95% accurate and needs little editing.
This AI helps by:
Jaromahum says it’s important to get feedback from staff to make the AI better. This helps the AI match real work situations and stay accurate.
HCA Healthcare is also testing AI from Google Cloud’s Med-PaLM 2 to help with notes and nurse handoffs. This AI listens to doctor and patient talks and writes draft notes quickly. Nurses use it to share vital signs, lab results, and concerns without writing everything by hand. The goal is to improve quality, make work consistent, and keep patients safe.
Generative AI helps more than just notes. It also speeds up how teams work together and talk.
Meetings that once took hours to share information now go faster with AI summaries. Teams can spend more time on tough care decisions instead of gathering facts.
In intensive care units (ICUs), good communication on rounds is very important. A study from South Africa found doctors lead most talks about infection care, but nurses and pharmacists have less chance to share because of team hierarchy and ward layout.
When talks are unclear, 77% of tasks were not written down.
AI that makes clear summaries can help include all team members’ information and reduce missing notes.
AI tools that clearly explain infection care were helpful in 93% of talks. This shows AI can support better team communication.
Also, AI systems and virtual helpers help staff stay in contact with patients all day.
A study showed 79% of healthcare workers liked AI-generated answers better than those from other workers for medical questions. This means AI helps make answers faster and more reliable.
These AI helpers work 24/7 and can help schedule appointments, remind patients about health tasks, and decide who needs urgent care. This makes healthcare more available and keeps patients more involved.
AI in healthcare does more than write notes. It can also automate many steps, especially in communication and paperwork.
Doctors and nurses spend much time writing notes about patient care and shift changes. AI can listen to talks or use data to create first drafts of these documents.
Staff then check these drafts and finish them. This saves a lot of time and allows more focus on patients.
HCA Healthcare’s tests with AI that writes drafts from conversations show faster note-making without losing accuracy.
AI helps make patient information sharing during shift changes the same every time. This reduces mistakes caused by missing or wrong information.
Using AI-made templates keeps care steady, safe, and clear.
Many hospital tasks include handling lots of files, like billing, insurance forms, and claims.
AI-powered document processing uses machine learning to sort, pull data, and handle these files automatically.
This technology cuts delays in paperwork and lets staff spend more time with patients.
For example, the Swiss company SANITAS processes over 3.3 million documents a year using this AI method. This could also help U.S. hospitals and insurers reduce paperwork.
AI tools are being built into EHR systems like MEDITECH Expanse. These help doctors get full patient histories, clinical guidelines, and combined results quickly.
This fast access to useful summaries helps doctors give better care faster.
Prior authorization is a common delay where doctors approve care before it happens. This often uses up much doctor time.
AI tools help approve simple cases automatically and filter medium cases to support doctor decisions with important info.
This speeds up approvals, lessens workloads, and avoids care delays.
As AI grows in healthcare, hospitals and clinics in the U.S. should think about how it can help patient handoffs and communication.
Early users like Houston Methodist and HCA Healthcare show that AI can:
Hospitals need to balance using AI with keeping patient privacy, security, and ethics. Systems like Med-PaLM 2 follow rules like HIPAA to keep data safe.
They also need to train staff, get feedback from workers, and update AI tools regularly to make sure the tools fit clinical work and help safe care decisions.
For hospital managers and healthcare leaders, learning about AI’s role in patient handoffs and team communication can help improve how work is done and how patients are cared for.
Handling large amounts of patient data and busy schedules is hard. Using AI and automated systems is becoming a practical way to help with these challenges.
Making sure AI tools fit the needs of staff, keep patients safe, and support teamwork is very important to make the best use of AI in healthcare.
Hospital employees struggle to efficiently relay essential patient information during handoffs, often sifting through extensive documentation generated by busy medical environments.
U.S. hospitals average about 4,000 handoffs per day, highlighting the complexity of patient information transfer.
Houston Methodist has initiated a pilot program utilizing generative AI to produce real-time patient summaries and predicted discharge dates in their electronic health record.
Early results show reduced lengths of stay and readmission rates, enhancing overall hospital efficiency.
It provides structured, easy-to-read patient summaries, allowing nurses to spend more time with patients instead of searching through charts.
They utilize software from Pieces Technologies that employs natural language processing to extract insights from clinical notes.
An early analysis indicated that patient summaries required edits less than 5% of the time, demonstrating high accuracy.
AI has reduced the need for lengthy meetings where teams discuss patient discharge logistics, as summaries are readily available.
The focus is on reducing physicians’ administrative burdens, providing more time for direct patient care.
The AI-generated summaries facilitate better communication and handoffs between healthcare providers, enhancing care coordination.
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