In June the HCn3D Team submitted their application to the CMS Artificial Intelligence (AI) Health Outcomes Challenge. Robert Ripley, MD and his team have been working towards healthcare value solutions using AI for the past few years. The goals of Healthcare in 3 Dimensions align with those of the AI Health Outcomes Challenge. Participating affords both the opportunity to contribute, and apply concepts and technologies that will reduce cost and increase value.

The Challenge has two primary objectives:

1. 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).
2. 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.

For the last few years the HCn3D team has been working zealously towards objective #2, but understanding that Data Science (objective #1) alone will not solve the value crisis in healthcare. Predicting outcomes is not enough, behavior must change. An impediment to that change is physician burnout. It is estimated that 50% of physicians either have, or are at risk for burnout. Adding complexity in the form of new systems and/or programs will only exacerbate the problem unless they deliver tangible value that serves to emancipate. Perhaps no one understands this cycle of engagement, estrangement, and emancipation better than the HCn3D team led by Robert Ripley, MD a 42 year veteran of cardiology practice. HCn3D Blog: "A pathway for burnout: From physician engagement to Emancipation or Estrangement"

Coupled with the data science of Counterfactual analysis, the HCn3D Team has devised a unique and untried approach towards identifying and classifying causal relationships using information beyond that of the EHR and beneficiary transactions, objective #1 of the Challenge.

HCn3D’s objective of developing methodologies and tools to address the value crisis in healthcare did not begin with and will not end with the CMS Challenge. The interest in Artificial Intelligence as applied to health care in the private and public sectors is growing, and very promising, as long as the science of data does not distract. Simply identifying potential solutions is not enough; it must be effectively applied to change behavior.