In medical settings, a large amount of data is created every day—from electronic health records (EHR), lab results, wearable devices, patient histories, and clinical trials. It can be hard to understand all this information. But when health data is shown as clear pictures or charts, doctors and staff can quickly see patient risks and health results.
For example, tools use line charts to show changes in vital signs over time, heat maps to show where diseases are common in certain areas, and dashboards to monitor hospital performance in real time. These visual tools help find patients at high risk early. This lets healthcare teams act before diseases get worse.
Groups like the Geisinger Health System use AI-based data visualization to lower hospital readmissions. By spotting patients at high risk, they use their resources better, which improves patient care and saves money. Saint Joseph Mercy Health System also raised compliance rates by almost 79% after using interactive dashboards that show real-time care process info.
It is important to know not only who is at risk but also when they are at risk. Chronic diseases often develop slowly with small changes that may not set off immediate alarms but can affect health down the road. Seeing how risk factors change over a person’s life helps target care better.
Researchers at the University of Utah made an AI toolkit called RiskPath. It predicts chronic diseases years before symptoms show with an accuracy of 85% to 99%. This tool goes beyond simple risk scores by showing how different factors—like screen time for kids getting older—can lead to conditions like ADHD at important times.
RiskPath shows easy-to-understand visuals that point out when in a patient’s life care is most useful. For healthcare managers, this helps make better prevention plans that save resources while giving the best care.
This timeline view is different from old risk models that only show yes-or-no risk or offer fixed risk levels without explanation. This dynamic, time-based view helps care teams work earlier and with more focus. It can lower hospital visits and slow disease progress.
Getting patients involved is key to good healthcare. A study tested a “Digital Health Avatar” system. It changes real-time data from devices like blood pressure monitors into a 3D image showing the patient’s current health.
Patients can watch health changes in their avatar. This makes complicated medical info easier to understand. In a survey of 61 people, most said that using digital avatars in their health checks helped them feel less worried and made them want to make healthier choices.
Medical managers in the U.S. might add this kind of tech to patient portals or engagement tools. Helping patients understand their data better can lead to better follow-through with treatments and prevention.
The healthcare analytics market in the United States is growing fast. Worth $35.3 billion in 2022, it is expected to reach $167 billion by 2023, growing at a rate of 21.4% each year. More healthcare groups are investing in advanced analytics and visualization tools.
Good visualization needs accurate and diverse data from sources like EHRs, clinical trials, wearable devices, and outside databases. This data must be checked and combined into easy-to-use platforms that keep patient privacy safe.
Popular tools include Tableau, Power BI, QlikView, and Looker. These tools show data in many forms, such as maps highlighting high-risk places or charts showing how diseases change over time.
The Centers for Disease Control and Prevention (CDC) uses data visualization in its National Syndromic Surveillance Program to watch emergency visits and outbreaks almost in real time. Aetna uses visualization to find members who might need costly services soon, so they can give preventive care and lower spending.
For healthcare IT managers and administrators, having fast, clear, and useful data is very important to improve care and run the organization better.
Managing high-risk patients depends not only on clinical tools but also on how well the office works. Simbo AI offers AI-powered phone automation and answering services made for medical offices.
The front office handles many routine tasks like scheduling, answering questions, checking insurance, and reminding about follow-ups. Doing these tasks poorly can cause missed appointments, delays, and more work for staff.
Simbo AI uses conversational AI to automate many simple calls. This lets staff focus on harder patient needs. It makes calls faster and more accurate, and cuts wait times. Clinics say patients are happier because communication runs smoother.
When AI phone systems work with health data visualization tools, the workflow is better. For example, after a risk check shows a patient needs follow-up, Simbo AI can make calls to schedule visits or remind patients about tests. This helps healthcare move from reacting to problems to stopping them early.
By cutting down paperwork and improving patient contact, AI automation helps healthcare groups respond quickly to changing needs while saving money.
The growing use of AI tools and health data visualization is changing how U.S. medical practices handle chronic disease. By using these tools to find risks early and guide timely care, healthcare administrators and doctors can improve patient results and help lower the overall impact of chronic illnesses on the healthcare system.
The research focuses on developing RiskPath, an open-source AI toolkit that predicts diseases before symptoms appear, enhancing preventive healthcare.
XAI refers to artificial intelligence systems that provide understandable explanations for complex decisions, helping users comprehend the reasoning behind predictions.
RiskPath can predict eight different conditions, including depression, anxiety, ADHD, hypertension, and metabolic syndrome.
RiskPath achieves an unprecedented accuracy of 85-99% in identifying at-risk individuals.
RiskPath uses advanced time-series AI algorithms that make predictions explainable, allowing for better understanding of risk factor interactions.
Prevention is emphasized as crucial, enabling targeted strategies for individuals identified as high-risk before symptoms arise.
It provides intuitive visualizations that show how different life periods contribute to disease risk, helping to identify optimal intervention times.
The team aims to integrate RiskPath into clinical decision support systems and expand research to include additional diseases and diverse populations.
The research was led by Nina de Lacy, MD, alongside Michael Ramshaw and Wai Yin Lam from the University of Utah’s Department of Psychiatry.
The institute combines research expertise with integrated mental health care, leveraging its resources to tackle complex mental health issues with innovative approaches.
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