Surgical site infections are incredibly costly to hospitals. So when the University of Iowa managed to reduce patient infection rates by 74%, people paid attention. Through leveraging their data in combination with machine learning, surgeons were able to better assess patient risk and implement the appropriate treatment plan. This had a big payoff-- ultimately saving the hospital an estimated $1.2 Million.
How They Did It
The University of Iowa Hospital and their analytics consultant set out to unlock the historical data stored in their EHR and combine it with real-time patient vital monitoring information. The project, which began in 2012, combined patient data with machine-learning technology to create a risk score to assess the likelihood of surgical site infection at the point-of-care.
You’re in the OR and preparing to close the patient. The AI software is working silently in the background. It pulls patient data from the EHR such as the surgeon, case duration or estimated blood loss flows. This information is then combined with surgical data at critical junctures throughout the surgery. As you prepare to close up the patient, the software notifies you that the patient is at high risk for an infection. You are presented with the World Health Organization’s Surgical Safety Checklist and a list of possible interventions to minimize patient risk. Using this score, surgical staff can course correct and ensure that appropriate care and preventative steps are taken. Similarly, patients with low risk scores won’t receive unnecessary and costly treatment.
Through this process, surgical staff are able to standardize procedures at the point-of-care. Reducing surgical variation means adherence to best practices. With the sheer volume of data being collected now, it is simply unrealistic for surgeons to assess, process, and act on all the information available to them; augmenting their skill sets with data analytics models means that doctors can provide patients with a higher level of customized care, tailored to their specific needs or risk profiles.
AI- Enabling the Future
This is just one example of how AI and machine learning are changing the hospital. Predictive medicine that draws on AI is being used to treat a whole host of diseases ranging from pancreatic cancer to depression. And with value-based care now using outcomes to determine payment, there has never been a greater push to improve diagnostic tools and patient care. The University of Iowa’s million-dollar idea is an early indicator of a much larger trend -- healthcare professionals estimate that by 2030, the industry will be almost entirely digitized.