A high-risk patient is someone more likely to have health problems or complications. This can happen because of long-term diseases, social issues, or not getting care regularly. For example, people with diabetes who live in poor areas might have trouble getting fresh food, paying for medicine, or traveling to appointments. On the other hand, people with more money often have harder medical needs but fewer social problems.
Tailored interventions are plans made just for certain patients. These plans are based on data that predicts what each patient needs. They can include extra reminders about appointments, help with transportation, or special care coordination that fits a patient’s social and medical situation. The goal is to close gaps in care and help patients get better health results.
Artificial intelligence, or AI, is becoming more useful in healthcare. AI can look at large amounts of patient information and predict risks better than old methods. For doctors and IT managers, AI helps find patients who might miss visits, need extra care, or require social help.
For example, a hospital in the United Kingdom created an AI system to lower missed appointments. The AI looks at things like how far patients travel, how poor their area is, and their past attendance at visits. This system helped reduce missed appointments by 40% during tests. Groups in the UK want to use this system in more hospitals.
The U.S. healthcare system is different but faces similar problems. When patients miss appointments, clinics lose money and patients often get worse health results. AI tools that predict who needs more attention can make care better and easier to manage.
Social factors like income, education, neighborhood safety, and food access play a big part in people’s health. The U.S. Department of Health and Human Services says these factors can make up half of a person’s health outcomes. Ignoring them can make health gaps worse.
For example, in Alabama, people with low income are seven times more likely to delay care because of costs. Diabetes is also more common in poor neighborhoods, causing differences in treatment and results. A study from 2019 showed that income-related gaps in diabetes care grew over ten years. Low-income patients face challenges like paying for medicine and finding healthy food while higher-income patients deal with more complicated medical needs.
Other problems include broken healthcare data systems, poor internet access, language barriers, and low health knowledge. To fix these problems, health data needs to be combined carefully, and AI should use both medical and social information to create good plans.
Data analysis and AI can mix clinical data with social factors to sort patients by risk. By joining different sources—like health records, patient info, location, and social needs—AI makes a clearer view of who is at risk.
This helps doctors give better care plans. For example, patients who have trouble getting rides may get vouchers for rideshares. Those who can’t afford medicine might get help from assistance programs. These tailored plans handle social problems as well as medical care.
Biomedical informatics plays an important role in these advances. Schools like George Washington University focus on AI tools that help find high-risk patients quickly. This helps doctors act early and use resources better.
Besides spotting risks, AI can automate work tasks to reduce busy work and make care easier to manage. This is useful for doctors, office managers, and IT teams.
Automation can include smart phone systems that remind patients of appointments, answer common questions, and help sort calls. A service called Simbo AI does this using language processing. This lowers no-shows and lets staff focus on harder problems.
Also, AI can give care workers tips on how to follow up with patients. For example, it can suggest the best way to contact someone likely to miss an appointment. AI tools can also support remote monitoring and telehealth to reach patients who can’t travel easily.
The Center for Insights to Outcomes (I2O) shows how data science and AI can time interventions well. Their work helps make sure the “right patient gets the right help at the right time.” This improves how efficiently clinics work and helps patients stay involved in their care. It also reduces stress on healthcare workers.
Even with benefits, AI use needs care. Leaders and IT staff must consider ethical questions, privacy, and possible bias in AI systems. AI models should be clear and understandable to avoid unfair results for certain patients.
Humans must still work with AI. The tools should help, not replace, doctors and nurses, especially where care needs kindness and careful judgment. Training is important so staff feel confident and understand how to use AI in care.
Rules about AI in healthcare are still changing. Clinics must follow laws like HIPAA to keep data safe. Patients need to know how their data is used to build trust and get their cooperation.
By using detailed patient data with social information, AI-based tailored care can reduce health inequalities. These plans reach groups who often get worse results.
For example, the Accelerate Health Equity (AHE) program uses data and evidence to promote fairness. It focuses on high-risk groups with complex medical and social needs.
Value-based care in the U.S. benefits from AI’s ability to find risks and target help. Providers using these tools can improve quality scores, make patients happier, and cut costs from missed visits, hospital stays, and poorly managed diseases.
AI keeps changing healthcare. It can improve care for individuals and help make the whole system fairer in the U.S. For clinic leaders and IT staff, using AI-supported tailored plans helps with missed appointments, managing chronic illness, and social problems that block care.
As healthcare moves toward precise medicine, value-based care, and managing groups of patients, AI tools that combine clinical and social data will be very important. Using these tools the right way, with focus on ethics and teamwork, can make care more efficient and fair for all patients.
By focusing on the specific needs of high-risk patients with AI and workflow automation, health systems and clinics can make real progress in closing care gaps, reducing health differences, and improving quality of life for their communities.
The AI system aims to reduce missed hospital appointments and address health inequalities, specifically within the NHS.
Around 7% of all outpatient appointments are missed each year at Royal Berkshire NHS Foundation Trust.
Each missed appointment costs the NHS approximately £100.
During the initial pilot, the tool achieved a 30% reduction in missed appointments among high-risk patients.
After improvements, a subsequent pilot achieved a 40% reduction in missed appointments among high-risk patient groups.
The tool considers factors such as travel distance, level of deprivation, and attendance history.
The tool presents tailored suggestions for interventions that encourage attendance among patients identified as high-risk.
The team was led by Dr. Weizi (Vicky) Li from the Informatics Research Centre at the University of Reading.
The project was invited by NHS England and NHS Improvement to present proposals for scaling up the application for use in other hospitals.
Reducing missed appointments improves clinical outcomes for patients and enhances operational efficiency for hospitals.
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