The health problems faced by older adults are often complicated. They need medical knowledge combined with new technology to find solutions. The Johns Hopkins Artificial Intelligence and Technology Collaboratory (AITC) shows how experts from different fields working together can help make progress.
The AITC is funded by the National Institute on Aging. Its goal is to create AI tools that improve health for older adults, especially those with brain diseases like Alzheimer’s. The center includes doctors, engineers, data scientists, and business leaders. They work as a team to develop technology that can be used in real clinics. For example, some AI tools are designed to work with electronic health records (EHR), which helps doctors use them easier.
The center also focuses on helping underserved older adults in rural and city areas. This means they make and test the technology with feedback from many different users.
At UTHealth Houston, the AI-AGING team also brings people from different jobs together. They work on improving geriatric care using AI and computer science. One project makes AI systems to find bad drug reactions and confusion (delirium) in older hospital patients. These problems often go unnoticed but can be serious. By linking medical knowledge and data science, they create tools that read EHRs to catch these issues quickly.
Teams like those at UTHealth Houston and Johns Hopkins show that crossing the line between healthcare and technology can bring better ideas to help older adults.
AI in healthcare is changing fast. It is moving from simple computer learning to deep learning. Deep learning uses a lot of data to find patterns that humans might miss. This helps doctors diagnose problems and make better choices.
One way AI helps older patients is by studying electronic health records. These records have much information but are hard to read quickly. AI programs can review them faster and catch signs of bad drug reactions, delirium, and other age-related problems.
AI also helps make treatments fit each patient better. Big data allows doctors to plan care based on a person’s health history, genes, and lifestyle. This personalized care can work better and keep patients safer.
The Core for Precision Resource Utilization (CPRU) at Penn Medicine uses AI, machine learning, and prediction tools to improve how healthcare is given. CPRU focuses on getting patients involved, using resources smartly, and making clinical work smoother.
Using AI with electronic health systems helps healthcare workers give care faster, avoid repeated hospital stays, and improve health results for older adults.
AI systems do more than help with diagnosis and data. They also change how doctors and staff work every day. Automation can lower paperwork, improve communication, and speed patient care.
For example, AI solutions that help with phone calls ease the work for staff caring for older patients. Simbo AI, a US company, makes phone systems that answer calls, schedule appointments, handle patient questions, refill prescriptions, and guide patients with basic health issues.
This kind of automation lets clinics spend more time on patients, not on paperwork. It also helps patients get quick replies, shorter waits, and steady communication.
With more healthcare data being made, AI tools linked with electronic health records and clinical decision guides help doctors see important patient facts during visits. The AI-AGING group at UTHealth Houston works on AI alerts to catch bad drug reactions and early confusion signs in time.
When clinical experts and data scientists work together, they make sure technology fits what doctors need without making their work harder. IT managers and healthcare leaders pick, set up, and keep these systems working well with current routines.
These groups show national efforts to join AI research and geriatric care improvements.
AI automation is helpful for handling the growing tasks in geriatric care. For managers of clinics and hospitals, using AI to improve work routines is becoming more important to keep care good.
Front-office tasks like scheduling, registration, and communication take much staff time. AI phone systems like those from Simbo AI use language processing and machine learning to handle these jobs well. They automate appointment reminders, confirmations, and basic patient questions. This cuts down missed visits and lets clinics use resources better.
Inside clinics, AI connected with electronic health records helps doctors spend more time with patients and less on paperwork. AI can alert care teams about possible bad drug events, preventive care reminders, and unusual lab results.
AI prediction models also help find older patients at higher risk of hospital stays or complications. This allows earlier help and better resource planning.
Working well with AI needs teams of IT specialists and clinical leaders. They must make sure interfaces are user-friendly, data is kept private, and staff receive training. Simbo AI’s focus on front-office automation fits these needs, helping improve communication and lower staff workload while caring for older patients.
Even with progress, many challenges remain in using AI well for elder care. Data quality is a problem, especially since older adults see many doctors and have complex health histories.
AI models trained in one group may not work well in others. So, continuous testing and updating of AI systems are needed to keep them accurate and useful.
Including diverse groups, especially underserved older adults in rural and city areas, is hard but necessary. Projects at Johns Hopkins AITC and UTHealth Houston work on involving these groups during research and testing.
Teams from geriatrics, data science, engineering, and healthcare administration must keep working closely. Strong support from academic and healthcare leaders is needed for funding, rules, and infrastructure to bring AI tools into real elder care.
Bringing AI and data science into elder medicine in the United States depends on teamwork across many fields. Centers like Johns Hopkins AITC, UTHealth Houston’s AI-AGING team, and Penn Medicine’s CPRU show how doctors, engineers, and data experts can work together to improve care for older adults.
For clinic managers, owners, and IT staff, knowing about these efforts helps pick and use AI technology that really meets elder care needs. Tools that help with workflow automation, communication, and clinical decisions will be more important as the number of older adults grows.
Using AI in elder care means focusing on technology and effective teamwork among different professionals. This helps create systems that are practical, fair, and patient-centered.
The main goal of the AITC is to promote the development and implementation of novel artificial intelligence (AI) and technology approaches to improve the health and well-being of older adults.
The AITC is funded by the National Institute on Aging, under Grant P30AG073104.
The AITC provides pilot funds for AI/tech development, access to relevant stakeholder groups, tech and AI design expertise, and human subjects research infrastructure.
The leadership includes Peter M. Abadir, MD, Rama Chellappa, PhD, Jeremy D. Walston, MD, and Alexis Battle, PhD.
The AITC supports AI and tech development by facilitating national competitions for pilot awards and leveraging expertise across different disciplines.
The AITC focuses on several aging-related conditions, including Alzheimer’s Disease and other neurodegenerative disorders.
The AITC aims to provide access to underserved elderly populations in rural and urban areas for feedback and testing of new technologies.
The AITC leverages resources from Johns Hopkins University in fields like clinical research, engineering, and data science for developing AI technologies.
Technology, especially AI, is crucial for developing adaptive products that can enhance the health, independence, and well-being of older adults.
The AITC organizes scientific sessions and webinars to disseminate knowledge and foster collaboration in the field of aging research.
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