Reducing Hospital Readmissions with AI

At Xyonix, we build custom AI models that can analyze your hospital discharge and readmission data to reduce the rate of readmissions.

The costs of readmitting a recent patient into a hospital are significant. For example one study found 20% of Medicare patients discharged from a hospital were readmitted within 30 days with a potentially preventable cost of 15 to 20 billion dollars per year [1,2]. Many variables may prove helpful in predicting the chances of readmission for a given patient including things like the patient’s health condition [3], insurance type [4], and demographic information [5].  Using our Mayetrix machine learning platform, we can help you predict and prevent hospital readmissions leading to more efficient use of scarce hospital resources while improving the overall quality of care that patients receive.

Check out some of our recent projects to get a taste of our capabilities
NLP for Public Health: Vaccine Hesitant Persona Mapper, A Case Study

Vaccine Hesitant Persona Mapper

We were asked by Columbia University to help build a map of vaccine hesitancy to assist public health officials in increasing vaccination rates. We built a corpus of millions of social media messages from platforms like Twitter, Reddit and Youtube comments. We are now analyzing this data by manually annotating thousands of training examples using a multi-parent taxonomy and iteratively (active learning driven) training a multi-label machine learning and NLP powered parser. If we are successful, we hope to save lives by convincing the vaccine hesitant to protect themselves and their communities.

Virtual Patient Intake Specialist for Online Health Services, A Case Study

Virtual Intake Specialist

We helped a company build a powerful virtual intake specialist that automatically communicates with patients to route them to the best care for their condition. Our AI system listens to patients and asks them just the right follow up questions, much like a traditional intake nurse might, to help schedule the best care option.

AI Med Spa Assistant

AI Med Spa Assistant

Xyonix teamed up with an early-stage startup to launch an AI iPhone app based assistant to help Med Spa nurse practitioners. The AI technology adeptly categorized facial features, aiding in the assessment of treatments and spotting issues like temporal visual changes, ineffective product batches or injector performance. Integrated with iOS for precise patient driven photo alignment, the system enabled consistent treatment tracking. We played a pivotal role in technology development and strategic product planning; our involvement was key in fusing medical accuracy with technological innovation in product development.

Interested in using AI to reduce hospital readmissions? Contact us and lets dig in together.

 
Featured
Sherri Engvall

“Xyonix’s most impressive characteristic is their passion for helping us solve our problems. They truly believe in what we’re trying to achieve — they’re excited about it. Their members see the value of our work and understand our vision.”

Sherri Engvall, IT Manager @ Delta Dental of Washington [more]

Ian Kalin

“Xyonix proved instrumental in helping us formulate our AI and machine learning strategy. They asked great questions and provided deep analysis which helped us better understand our customers, business and data.”

— Ian Kalin, CTO @ eHealth, Inc. [more]

References

  1. Rehospitalizations among patients in the Medicare fee-for-service program. - Jencks SF, Williams MV, Coleman, N Engl J Med. 2009;360(14):1418.

  2. Medicare & Medicaid Statistical Supplement. Baltimore: Centers for Medicare & Medicaid Services, 2007. Available at: www.cms.hhs.gov/MedicareMedicaidStatSupp/downloads/2007Table5.1b.pdf

  3. Agency for Healthcare Research and Quality (AHRQ) Study on readmissions from 2011

  4. Risk factors for 30-day hospital readmission in patients ≥65 years of age, Silverstein et al, Proc (Bayl Univ Med Cent). 2008 Oct; 21(4): 363–372.

  5. Patient Factors Predictive of Hospital Readmissions Within 30 Days - Kroch, Eugene; Duan, Michael; Martin, John; Bankowitz, Richard A., Journal For Healthcare Quality: March/April 2016 - Volume 38 - Issue 2 - p 106–115

  6. Photo by icathing / CC BY