Like all Neonatal Intensive Care Units (NICUs) across the world, Cohen’s Children Medical Center in Long Island, New York is inundated with data about the critical patients under their care. These data come from two main sources: monitoring devices regularly worn by the baby (e.g., a pulse oximeter which monitors heart rate and blood oxygenation) as well as a myriad of tests run by physicians (e.g., ultrasounds and echocardiograms). It is estimated that a typical NICU in the United States generates approximately one terabyte of data per bed per year. Cohen’s has ~80 beds for NICU patients and is therefore generating ~80 TB of data per year. Properly interpreting the massive amounts of data is particularly crucial for infants, as they cannot proactively communicate indicators of underlying issues.
Despite the stakes and the tremendous amounts of data, neonatologists struggle with how to appropriately attack some of the most pressing issues facing premature infants. For instance, neonatologists disagree on the optimal rate to increase the number of calories being fed to extremely premature infants, despite this choice having a meaningful impact on long-term patient outcomes including cognitive development, growth and resistance to infection. Highlighting the importance of standardizing the approach to neonatal nutrition, a study in the Journal of Pediatric Gastroenterology, Hepatology and Nutrition underscores, “we need to know the nutrients and their rate of administration for preterm infants that would match preterm neonatal metabolism and growth.” In addition to the obvious tremendous risks to patient outcomes, this issue is a business problem as well. According to the non-profit organization the March of Dimes, the average cost of a patient per day in the NICU is $2,500-$3,000. Mistakes in patient care can result in complications, like the development of infections, which can result in months of additional hospitalization and hundreds of thousands of dollars of avoidable cost.
After being born at just 26 weeks gestational age, my daughter spent 85 days in the neonatal intensive care unit at Cohen’s. When I asked pointed questions about the logic of medical decisions, often doctors would rely on personal experience when statistical modeling could have helped inform the decision. It is clear that there was both an opportunity to (1) synthesize and utilize the stream of data being produced by infants under their care and (2) gain better access to data being produced by hospitals across the United States. The United States alone has ~20,000 Neonatal ICU beds, implying annual production of ~20,000 TB of data on the health of premature infants. Unfortunately, even if the doctors had access to the data being produced by their own and other hospitals, there is a limit to a human beings ability to critically absorb and analyze new pieces of information and ~20,000 terabytes per year is far beyond the scope of human capability.
In the near term, it appears the investment in improving the usage of data will continue to be incremental. Doctors at Cohen’s have emphasized experience shares and group huddles to draw from the expertise across the staff. In addition, new papers based on manually conducted research are regularly being published; when merited, the recommendations of these papers can be promptly reflected in updated patient care plans (e.g., the rapid adoption of surfactant to treat bronchopulmonary dysplasia). However, in the longer term, machine learning has the capacity to dramatically alter the way neonatal medicine is practiced. Professor Geraldine Boylan is the director at INFANT Research Centre at University College Cork and is working to adapt machine learning to improve NICU patient outcomes. Boylan, a neurophysiologist, is working to use AI to “objectively, consistently” look for patterns in patient data. For instance, she has helped develop a “smart system” called NEUROPROBE which uses historical data to better understand the link between electrical brain activity and blood pressure. The program will hopefully identify infants which require treatment for brain injury faster and more accurately than current methods. Over time, systems like NEUROPROBE can be used across the spectrum of neonatal care decisions in conjunction with an attending physician to proactively identify patients who need treatment earlier to avoid unnecessary patient suffering and undue financial burden on families. In the next 10 years, NICUs like Cohen’s should seek to partner with research organizations such as INFANT to develop treatment plans based on statistical models rather than less reliable human experience.
As in any medical context, the implementation of machine learning raises ethical concerns. Doctors will need to grapple with whether counter-intuitive results represent a response like Watson’s “Toronto????” guess in Final Jeopardy, an answer obviously wrong on its face, or presents an opportunity for groundbreaking new advances in the field. Is it ethical to test the plan on a person in order to find out? (Word count: 789)
 Khazaei, Hamzeh, “Health Informatics for Neontala Intensive Care Units: An Analytical Modeling Perspective,” in IEEE Journal of Translational Engineering in Health and Medicine, October 2015: https://www.researchgate.net/publication/282427778_Health_Informatics_for_Neonatal_Intensive_Care_Units_An_Analytical_Modeling_Perspective
 UCSF Children’s Hospital at UCSF Medical Center: Intensive Care Nursery Staff Manual. 2004. https://www.ucsfbenioffchildrens.org/pdf/manuals/15_FeedingPretermInfants.pdf
 Hay, William, “Nutritional Support Strategies for Preterm Infant in the Neonatal Intensive Care Unit,” in Journal of Pediatric Gastoenterology, Heptology and Nutrition. October 2018: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6182475/
 “AI Shows Success in Reducing Premature Births,” in Modern Healthcare, November 6, 2018: https://www.modernhealthcare.com/article/20181106/NEWS/181109949
 “Crical Care Statistics,” in Society of Critical Care Medicine, 2016: http://www.sccm.org/Communications/Critical-Care-Statistics
 Halliday, Henry, “History of Surfactant Since 1980,” in Karger Journal, May 2005: https://www.karger.com/Article/Pdf/84879
 Godsil, Jillian, “AI is Needed for Medical Health,” in Irish Technology News, November 9, 2018: https://irishtechnews.ie/a-i-is-needed-for-medical-health/
 Infant Center Research Site: http://www.infantcentre.ie/our-research/newborn-health (Company website)