“The CMS Artificial Intelligence (AI) Health Outcomes Challenge is an opportunity for innovators to demonstrate how AI tools – such as deep learning and neural networks – can be used to predict unplanned hospital and skilled nursing facility admissions and adverse events.
Partnering with the American Academy of Family Physicians and Arnold Ventures, the CMS AI Health Outcomes Challenge will engage with innovators from all sectors – not just from healthcare – to harness AI solutions to predict health outcomes for potential use in CMS Innovation Center innovative payment and service delivery models.
Challenge Objectives
- Use AI/deep learning methodologies to predict unplanned hospital and SNF admissions and adverse events within 30 days for Medicare beneficiaries, based on a data set of Medicare administrative claims data, including Medicare Part A (hospital) and Medicare Part B (professional services).
- Develop innovative strategies and methodologies to: explain the AI-derived predictions to front-line clinicians and patients to aid in providing appropriate clinical resources to model participants; and increase use of AI-enhanced data feedback for quality improvement activities among model participants.” Learn more about the challenge here.
Participant: Accenture Federal Services
Proposed Solution: Accenture Federal Services AI Challenge
Participant: Ann Arbor Algorithms Inc.
Proposed Solution: Generalizing Time-to-event Algorithms to Deep Learning-based Prediction for CMS Data
Participant: Booz Allen Hamilton
Proposed Solution: Booz Allen Launch Stage Submission
Participant: ClosedLoop.ai
Proposed Solution: Healthcare’s Data Science Platform
Participant: Columbia University Department of Biomedical Informatics
Proposed Solution: The CLinically Explainable Actionable Risk (CLEAR) Model from Columbia University Department of Biomedical Informatics
Participant: CORMAC
Proposed Solution: CORMAC Response to Challenge Questions
Participant: Deloitte Consulting LLP
Proposed Solution: Further, Faster: The Deloitte Team’s Approach to Harnessing the Power of AI to Improve Health Outcomes
Participant: Geisinger
Proposed Solution: Reducing Adverse Events and Avoidable Hospital Readmissions by Empowering Clinicians and Patients
Participant: Health Data Analytics Institute
Proposed Solution: HDAI’s Analytic Platform Technology for Healthcare Improvement
Participant: HealthEC, LLC Proposed Solution: Leveraging Artificial Intelligence to Predict and Improve Health Outcomes, Maximize Quality Improvement, and Reduce Costs
Participant: Hospital of the University of Pennsylvania
Proposed Solution: The Intelligent Risk Project
Participant: IBM Corporation
Proposed Solution: AI for Explainable Adverse Event Prediction: Empowering Beneficiaries and Providers to Improve Health Outcomes
Participant: Innovative Decisions Inc. (IDI)
Proposed Solution: Multi-Modeling with Augmented Datasets for Positive Health Outcomes (MADPHO)
Participant: Jefferson Health
Proposed Solution: Using AI to Improve Medicare Population Health, Optimize Ambulatory Scheduling, and Reduce Adverse Events at Hospitals
Participant: KenSci Inc.
Proposed Solution: Assistive Intelligence for Unplanned Admissions and Adverse Events Prediction
Participant: Lightbeam Health Solutions, LLC
Proposed Solution: AI Risk Predictions- preventing hospital, ER and SNF admissions
Participant: Mathematica Policy Research, Inc.
Proposed Solution: The CPC+ AI Model by Mathematica
Participant: Mayo Clinic
Proposed Solution: Claims-based Learning Framework (CBLF)
Participant: Mederrata
Proposed Solution: Boosting medical error and readmission prediction by leveraging Deep Learning, Topological Data Analysis, and Bayesian modeling
Participant: Merck & Co., Inc.
Proposed Solution: Actionable AI to Prevent Unplanned Admissions and Adverse Events
Participant: North Carolina State University (NCSU)
Proposed Solution: Multi-Layered Feature Selection and Dynamic Personalized Scoring
Participant: Northrop Grumman Systems Corporation (NGSC)
Proposed Solution: Reducing Patient Risk through Actionable Artificial Intelligence: AI Risk Avoidance System (ARAS)
Participant: Northwestern Medicine
Proposed Solution: A human-machine solution to enhance delivery of relationship-oriented care
Participant: Observational Health Data Sciences and Informatics (OHDSI)
Proposed Solution: OHDSI Submission
Participant: University of Virginia Health System
Proposed Solution: Actionable AI
