Rule based Semantics and Big Data Based Methods for Effective Clinical Decision Support CDS A Pediatric Severe Sepsis Case Study using ICU Data

The recognition diagnosis and management of sepsis remain among the greatest challenges in pediatric critical care medicine The diagnosis of pediatric severe sepsis is particularly challenging since it is time critical and frequently must rely on clinician interpretation of an equivocal non specific age dependent constellation of clinical signs and symptoms that occur in association with an infection or other inciting events Currently available EMR data screening tools designed to identify pediatric severe sepsis using consensus based guidelines criteria have poor sensitivity specificity and or positive predictive value that can result in poor perceived usability Recent studies show that in general sepsis CDS alert fatigue is common either in the form of false positives or false negatives e g alerting focused on the most severely ill with unequivocal signs of sepsis As a result such tools are at best modestly clinically effective in improving early detection of unrecognized pediatric sepsis and in infants can result in antibiotic administration to large numbers of uninfected newborns We believe significant improvement in CDS usability can only be achieved with a significant improvement in sensitivity specificity and positive predictive value PPV operating at a clinician specified level of risk This will require alerting algorithms that leverage the combined analytic power of computerized semantic models rules natural language processing embodied in consensus pediatric sepsis guidelines pediatric critical care clinician expertise and retrospective analytics over existing large repositories of clinical encounter data using machine learning algorithms Late last year we were awarded a SBIR Phase NIH research grant focused on the use of advanced ontological models combined with big data predictive analytics machine learning ML techniques over ICU data as a foundation for an adult sepsis CDS We demonstrated an AUC of and PPV in excess of for our sepsis detection tool for a patient sample of We will leverage these results to establish exceptionally sensitive and specific tool that monitors PICU data to accurately predict pediatric patients at risk for impending acute clinical deterioration due to severe sepsis septic shock Our goal is to commercially deploy highly useable technology that achieves high levels of clinician acceptance and demonstrably influences timely treatment and pediatric patient outcomes Our product development concept will employ human factors engineering to achieve highly synchronicity with clinical workflows combined with a clinician centered design of the interfaces between our CDS and the institutional pediatric sepsis protocols and EMR data sources Our product vision is an early sepsis detection CDS with actionable accuracy and usability compatible with any modern EMR in use at a client hospital that is effective in reducing pediatric sepsis mortality in both critical and non critical care settings Even with the advent of powerful computer system technologies and modern antibiotics antivirals used in hospitals one third of children who die in U S tertiary care pediatric intensive care units have severe sepsis The goal of this research is to develop and validate a real time pediatric severe sepsis surveillance software system that can be routinely used in conjunction with electronic medical record systems in hospitals to detect early stages of this syndrome Our approach uses modern artificial intelligence techniques including cognitive reasoning machine learning and natural language processing to dramatically reduce preventable mortality in hospitalized neonates and children