Hospitalized patients often have trouble staying involved in their care. This happens because their health problems can be hard to understand. On top of that, pain, stress, medication side effects, and mental difficulties make it harder for them to follow what is going on. Research by Maher Khelifi at the University of Washington shows that patients in hospitals find it hard to understand health information, manage safety issues, and take part in medical decisions.
Modern hospital care is very complex, and patients get a lot of information quickly, mostly through talking with doctors and nurses. If patients do not understand, they may feel cut off from their treatment. This can hurt how well they follow their care plans and how they recover. The harder the health problems, the bigger the gap becomes. This shows that tools are needed to make communication easier and help patients handle their medical care better.
Artificial intelligence agents have been used to help patients in hospitals. These AI programs take care of some of the tasks involved in sharing and explaining information. One example is an AI called NURI, created by Maher Khelifi. NURI helps patients understand talks with doctors by turning complicated medical language into simple notes and explanations that patients and caregivers can review later.
These AI agents do more than just translate information. They also help patients pay attention and feel less worried. The tools show medical details in ways that fit patients’ mental and physical condition. They give clear explanations about diagnoses, treatments, and procedures. This kind of help may reduce hospital readmissions and improve patient safety. It also encourages patients to take part in their own care.
Patients, caregivers, and clinicians who have used tools like NURI generally like them. They see that the AI helps but does not replace talking with doctors. The technology supports understanding in places where talking alone might not be enough.
Good communication between patients and doctors is very important in hospitals. Talking during visits gives patients important facts. But these talks can be complicated and fast. Most patients learn about their health and treatment by listening to doctors. How well they understand depends on how the doctor explains things and how well the patient can follow along.
Using a method called “Muse cards,” researchers asked patients and family members how they think technology can help hospital communication. This way of working includes patients in making tools that actually meet their needs. It shows that talking with doctors should be supported by AI that gives explanations suited to how well patients understand.
By focusing on talking and using AI to take notes and highlight key points, hospitals can give patients clear and easy-to-understand summaries of medical talks. This lowers the chance of mistakes and keeps family members informed about care decisions.
Adding AI to hospital care also means fitting it into how hospitals currently work. People who run medical offices and IT teams need to check that AI tools are easy to use and work well with other systems.
Some AI tools, like those made by Simbo AI, help with front-office work such as answering phones and scheduling appointments. These tools handle common questions so staff can focus on more difficult patient needs. This helps shorten wait times and makes it easier for patients to get information quickly.
In hospital care, AI tools like NURI can be set up so notes and explanations are made automatically after doctors talk with patients. This saves time for hospital workers and gives better patient support without disturbing regular work.
AI that organizes and explains large amounts of medical conversations can turn raw talks into helpful information that patients and caregivers can see anytime. This helps hospitals follow rules about consent and paperwork, while also teaching patients about their care.
While AI tools have benefits, using them in U.S. hospitals comes with some challenges. One main issue is making sure AI helps but does not replace human care. Feelings and kindness from doctors cannot be copied by AI, so these tools must only support the real people providing care.
Hospitals must think about all kinds of patients, who have different levels of health knowledge, speak different languages, and have different cultures. AI programs need to adjust to these differences. Also, hospitals should train staff to use AI tools well and to include them in patient talks.
Data privacy laws like HIPAA in the U.S. require that patient information handled by AI is kept safe. IT teams must work closely with AI developers to follow these rules during design and use.
Before using AI widely, hospitals should test how well patients, families, and clinicians accept and use the tools, like what has been done with NURI. Getting feedback helps find problems and ways to improve the technology, making sure it fits real needs.
AI tools for hospital patients also help with personal health informatics. This field tries to make health information easy for patients to use. Changing patient-doctor talks into easy notes is one important step. Giving patients clear summaries about their health and treatment makes hospital care easier to understand.
This helps patients feel more involved in managing their health. Clear communication can lower mistakes caused by confusion. It also helps patients take medicine properly and speak openly about their worries or choices.
Research by Maher Khelifi provides advice on how to design and use these AI agents in hospitals. These suggestions focus on making tools that fit patients’ needs and protect privacy.
For healthcare leaders in the U.S., checking how AI tools help hospitalized patients is an important step to improve care. Studies show these tools can help patients understand medical information better. When added to hospital work routines, AI can lighten the communication load on staff while helping patients stay involved and safe.
Hospitals face many pressures like following rules and treating harder health problems. AI can help by giving patients steady and easy-to-get information. Investing in AI that shares medical talks clearly helps close the gap in patient engagement, supporting a more patient-focused healthcare system.
Hospitalized patients struggle with engagement due to the complexity of their care, cognitive and physical impairments from pain, stress, and medications, and difficulty comprehending abundant and complex health information.
AI agents, by automating information processing and communication, can provide patients with better understanding and insights about their illness and treatments, helping bridge the engagement gap caused by complex health problems.
‘Muse cards’ is a user-centered research method designed to inspire patients and family caregivers to envision hospital technologies that better support evolving patient needs and roles in hospital settings.
Patient-clinician conversations are a core source of information, and their verbal communication impacts patients’ understanding, influenced by both patient and clinician perspectives.
NURI is an AI agent developed to help patients understand medical conversations with clinicians, enhancing patient comprehension and engagement during hospital stays.
The acceptance of NURI was assessed through testing with patients, caregivers, and clinicians, focusing on their perceptions of its usefulness and role in supporting care.
It contributes a toolkit enabling patients to reimagine and co-design hospital technologies and improves understanding of automated agents’ roles in assisting patients and families in clinical settings.
It offers an annotation framework that converts patient-clinician conversations into patient-facing notes, enhancing patient understanding and engagement with their health information.
The research provides design and implementation guidelines for AI agents, advancing knowledge on their application to support hospitalized patients effectively.
Patients often lack complete understanding due to complex illnesses, cognitive overload, physical impairments, and the steep learning curve required to process detailed health data.
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