I have been talking about the applications of AI on Healthcare IT problems. Here is a great case study.
Sramana Mitra: Let’s start by having you introduce yourself as well as PeriGen to our audience.
Matthew Sappern: I’m the CEO of of PeriGen. PeriGen is a software developer. We make software as a medical device. We’re FDA-cleared. Our primary goal is to build software that helps clinicians prevent adverse outcomes in childbirth, which is a pretty important task.
Sramana Mitra: Double-click down on that and explain what exactly are we talking about. Give us a use case and talk us through how this works.
Matthew Sappern: I’ll give you a couple of statistics why this is an urgent need. As you look out across both maternal and fetal outcomes in childbirth, more than 50% of adverse events, whether it’s a mom who hemorrhages and suffers some form of maternal morbidity post-childbirth or a baby who suffers brain injury because of hypoxia, are avoidable.
The vast majority of those avoidable cases are attributable to what is officially called failure to recognize and treat clinical warning signs. In my mind, that is just a failure of medical device developers to build technology that helps nurses and other clinicians deal with what has become a fairly complex patient population.
Sramana Mitra: How do you treat that? What interventions would deal with that from a software point of view?
Matthew Sappern: Here’s what we do. The issue with the trend of failure to recognize and treat clinical warning signs is that nurses are doing a million different things. The clinical setting is very complex. They are documenting. They are talking to patients and their families. They’re helping colleagues.
The reality is the current fetal monitoring solutions are looking at only 10 minutes at a time or showing 10 minutes of that fetal heartrate at a time. When things start to go bad, it’s much more about trends. It’s much more about, “Is there an abnormality and what has occurred to that abnormality?” These are things that a nurse who is running around doing a bunch of different things is unfairly charged with trying to monitor on a 24/7 basis.
The issue is they often don’t have the requisite information at hand into how the baby and the mother are tolerating labor. Oftentimes, they’ll start to recognize that things are starting to go wrong too late in the game. A secondary issue, which is also solved by AI-oriented software is that every nurse looks at these fetal scripts differently.
While there is a number of studies and trainings that occur over time about how to manage specific instances, you’ve got to get to a point where every nurse is looking at these strips the same way. A series of fetal heartrate deceleration to Nurse Mary looks exactly the same way as a series of fetal heartrate decelerations to Nurse Jane in California.
That is the real struggle because every nurse is different. They’ve got a different set of experiences and biases that they may bring to that delivery room as well as a different set of training of time served. It’s very difficult to create standardized care if you build all of these protocols on a very sandy and unstable foundation, which is interpreting what’s going on at any given time in the same way.