Unplanned hospital admissions cause many problems in healthcare. They often happen because of sudden worsening of long-term illnesses like heart failure, chronic obstructive pulmonary disease (COPD), or pneumonia. Bad patient events like infections caught in the hospital, sepsis, and breathing failure can make treatment harder, keep patients in the hospital longer, and increase the chance of death or serious illness.
Doctors and hospital managers have wanted better ways to predict which patients might have these problems. If they could do this, they could use resources better, act sooner, and possibly stop patients from getting worse and needing hospital or intensive care unit (ICU) stays.
One important step to improve prediction in healthcare came from the Artificial Intelligence Health Outcomes Challenge. This contest was started by the Centers for Medicare & Medicaid Services (CMS) Innovation Center in 2019. It was supported by groups like the American Academy of Family Physicians and Arnold Ventures. Over 300 groups sent in AI solutions designed to forecast unplanned hospital and skilled nursing facility (SNF) admissions, and bad patient events in 30 days.
They used Medicare data from hospital services (Part A) and doctor services (Part B). The challenge used AI methods like deep learning and neural networks to make very accurate prediction models.
The contest had stages. In Stage 1, 25 groups moved forward by October 2019. In Stage 2, seven finalists were chosen by October 2020. Each got $60,000 and competed for a grand prize of $1 million and a runner-up prize of $230,000. The total money was about $1.65 million.
The AI models in the CMS challenge mainly focused on predicting unplanned hospital admissions because of serious illnesses like heart failure, pneumonia, and COPD. They also predicted problems like infections caught in hospitals, sepsis, and breathing failure. These are important because acting early can change how the patient’s health goes.
In Stage 2, participants also worked on predicting who might die within 12 months. This helped show risks over a longer time for Medicare patients.
The CMS challenge also made sure AI models did not treat some patient groups unfairly. It was important so AI would not increase health differences between different populations in the U.S.
The finalists came from groups like Ann Arbor Algorithms, Deloitte Consulting, Geisinger, Jefferson Health, Mathematica Policy Research, the University of Virginia Health System, and ClosedLoop.ai, which won the grand prize. They worked with doctors from the American Academy of Family Physicians to make sure AI results were clear and useful for health workers.
CMS Administrator Seema Verma said, “AI is the future of health care, and a powerful resource helping providers make predictions and engage in more meaningful health conversations with patients.” This shows a plan where AI tools help doctors use big sets of data to make fast, accurate guesses.
For medical practice managers and healthcare IT workers, using AI tools can:
Alongside the CMS challenge, a study in an Australian hospital tested a real-time AI early warning system to predict major bad events. This system used a Deterioration Index (DI), mixing patient details, vital signs, and lab data with a math method called logistic regression. It looked for risks like death, ICU visits, or emergency team calls.
The DI was put into the hospital’s electronic medical records system. It sent alerts to senior nurses’ phones for quick action. Compared to the past year using normal warning scores, patients with this system had:
This was only one hospital study, and more testing is needed, but it showed real benefits from using AI in daily care work to improve safety and flow.
AI can also help make work easier by automating tasks. This can reduce paperwork and make hospitals and clinics run better. Practice managers and IT staff can use AI tools to handle front-office jobs like phone calls and scheduling.
AI can do tasks such as:
This frees up staff to care for patients and make tough decisions. When used with AI that predicts health risks, automation can:
In places where resources are tight, using AI to predict risks and automate work can improve both how hospitals run and how patients do.
Using AI models and automation tools can help healthcare groups in the U.S. Managers and owners can improve patient care by lowering avoidable hospital visits and reducing bad events. The Medicare data used in the CMS challenge offers useful information to build AI tools fit for U.S. healthcare.
It is also important that doctors trust AI tools. They need to understand how AI makes predictions before they use them with patients. CMS requires AI explanations to help with this.
At the same time, using automation like phone services can improve patient communication and office work. IT teams should look for tools that work well with current electronic medical records so data flows smoothly and alerts come on time.
Artificial intelligence is becoming more common in healthcare. It helps predict unplanned hospital admissions and patient problems. The CMS AI Health Outcomes Challenge and Australian study show how AI is moving from research to actual use. For healthcare leaders in the U.S., using AI can help improve patient care, cut risks, and support better health plans.
AI also brings operational improvements by automating tasks. This helps healthcare workers manage more patients and complex needs effectively.
This article gives a clear view of new AI tools and uses that support better healthcare in the U.S., with examples useful to healthcare managers and technology staff.
The CMS AI Health Outcomes Challenge is a multi-stage competition launched by the Centers for Medicare & Medicaid Services to encourage the development of AI solutions that predict patient health outcomes, aiming to transform healthcare by improving prediction of unplanned admissions and adverse events.
Seven finalists were selected to advance to the final round, each receiving $60,000 in prize money and competing for a Grand Prize and Runner-Up awards.
The AI models aim to forecast unplanned admissions related to heart failure, pneumonia, COPD, and other high-risk conditions, as well as adverse events like hospital-acquired infections, sepsis, and respiratory failure.
Submissions were evaluated based on model accuracy, the ability to visually demonstrate clinical utility to improve patient care, and compliance with application requirements, including addressing algorithmic biases impacting health disparities.
Clinicians from the American Academy of Family Physicians reviewed and evaluated the visual displays of the AI models, ensuring clinical relevance and usability.
Addressing algorithmic bias is crucial to reduce health disparities and ensure AI solutions provide equitable healthcare predictions and recommendations across diverse patient populations.
The challenge was launched by the CMS Innovation Center in collaboration with the American Academy of Family Physicians and Arnold Ventures.
CMS sees AI as a key technology to manage complex healthcare data, enabling providers to predict outcomes and engage patients more meaningfully, thereby improving healthcare delivery and patient health.
Finalists include Ann Arbor Algorithms, ClosedLoop.ai, Deloitte Consulting LLP, Geisinger, Jefferson Health, Mathematica Policy Research Inc., and the University of Virginia Health System.
The Grand Prize winner may receive up to $1 million, while the Runner-Up can receive up to $230,000 in prize money by the end of April 2021.
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