The NICU Dietitian Perspective: Better Data for Better Insights
Nutrition is a critical component of patient care in the NICU because of its role in both physical and neurodevelopmental growth and development. However, nutrition is rarely taught in medical school. Neonatologists often defer to registered dietitians (RDs) to support proper nutrition for the preterm infants in their care. To properly assess those nutritional needs, RDs rely heavily on data and data analysis. RDs tend to be the enforcers of the clinical practice guidelines for feeding, and they regularly monitor and track nutritional metrics and milestones like time to full enteral feeds to days to regain birth weight to growth velocity. However, there are often many obstacles to collecting this data, conducting a comprehensive analysis, and evaluating adherence to protocols.
The Impact of Optimized Nutrition
Addressing the challenges of data collection and analysis of feeding and nutrition management in the NICU can help to optimize nutrition for preterm infants. Custom nutrition-specific analytics can offer essential insights for everyone caring for the preterm infant. RDs need to analyze and provide benchmarking data to colleagues and other healthcare professionals to empower better nutrition-related decisions and track patient progress.
Research has demonstrated that optimizing nutrition in preterm infants, primarily during the phase of weaning off parenteral nutrition, can prevent growth failure by avoiding energy and protein deficits and improve weight-for-age z-scores. Improved data collection and analysis allows change to occur earlier, helping dietitians avoid growth restriction and a cumulative growth deficit. Being impactful early during an infant’s NICU stay can positively affect his or her health trajectory.
Difficulty with Data Collection
In order to properly assess the nutritional needs of their patients, RDs often create their own data collection and analysis tools, but one of the primary challenges of these tools is the lack of integration with the electronic medical record (EMR). They need to hunt through each patient’s medical record to find the data they need to populate their own tools. These challenges become particularly acute when it comes to time management and accuracy of data. Discrepancies occur when using EMR data hand-copied on paper or typing data into another program, impacting the reliability of data collected.
Another challenge RDs face is rooted in the adherence to the established clinical practice guidelines. There is significant variability in how clinicians administer care. This variability can apply to the initiation of enteral feeds, the progression of feeds, and fortification timing. Clinical practice groups have developed consensus-based enteral feeding protocols to provide evidence-based care with less variability, however monitoring compliance is necessary. Minimizing practice variations has been shown to reduce the length of stay, decrease costs, and improve the preterm infant’s growth and development. Studies have shown that preterm infants can achieve oral feedings sooner by following standardized feeding protocols. However, dietitians are often ill-equipped with the data required to track protocol compliance.
Real-Time Data Analysis
In addition to issues dietitians face with data collection, timely and comprehensive data analysis also presents challenges. It is often difficult to assess the data for a particular patient or situation in real time, which is essential to optimize nutrition while the infant is still in the NICU.
Ideally, having patient data right at your fingertips enables more informed analysis and improved decisions. The ability to intervene in real-time, rather than after the fact, is vital. During rounds, when conversations are moving quickly, having access to data to determine all the calculations needed to assess various aspects of nutrition accurately is imperative. Custom data and reports can provide the RD with information about an individual patient’s nutritional requirements based on data specific to that baby. It also allows dietitians to communicate more clearly with other healthcare team members, which can enhance workflow as well as nutritional outcomes.
The Solution: NICUtrition®
What if RDs could easily access and understand all the data they need to make well-informed decisions? NICUtrition® is an easy and intuitive clinical decision support tool that provides feeding data with real-time analysis of metrics and milestones.
NICUtrition® extracts feeding and nutritional data directly from a hospital’s EMR to provide valuable insights and analytics through a configurable dashboard display. The platform digitizes a hospital’s enteral feeding protocol, tracks protocol adherence, and includes outcomes and associated compliance statistics. NICUtrition® enables clinical teams to track improvements in feeding milestones, test quality improvement outcomes over time, and quantify the correlated patient outcomes — for an individual baby or the entire NICU.
NICUtrition® specifically supports a dietitian’s work, making nutrition management more efficient and more easily understood by visual numeric and graphic presentations. Having all the information in one place, like daily intake, days to full feeds, days to regain birth weight, z-score data, and deficiency monitoring, provides timely, valuable, and patient-specific information to the dietitian.
Retrospective data is also available through the NICUtrition® dashboard to analyze growth throughout the length of stay in the NICU, including grouping patients by gestational age or birth weight, and all this data can be easily exported for research or quality improvement purposes.
Real-time Equitable Care Intelligence
NICUtrition® also includes an Equitable Care Intelligence (ECI) platform that enables unit-wide tracking of key care performance metrics by race and ethnicity. This provides real-time data to assist clinicians in assessing the provision of equitable care practices across the unit through comparison against a set of predetermined comprehensive standards.
The ECI platform includes multiple dashboards allowing the clinician to identify which patient populations require additional attention during their stay. Individual patient report cards generate a snapshot of the infant’s health during his or her stay relative to peers.
Looking to the Future: NICUbiome™
Based on the largest dataset of preterm infant microbiome profiles and corresponding clinical data, NICUbiome™ is a digital platform that leverages machine learning to provide quantification of and insights to preterm infant gut health. Drawing on Astarte Medical’s proprietary and extensive dataset, NICUbiome™ enables better decision-making through benchmarking and personalized care through risk stratification. NICUbiome™ will accelerate the development, adoption, and tailored use of new nutritional or microbial interventions.
Data Improves Clinical Outcomes
NICUtrition® provides real-time, clinical decision support that helps clinicians make nutrition management smarter, personalized, and data driven. When fully developed, NICUbiome™ will utilize machine learning on clinical and microbiome data to identify infants at risk of growth failure and life-threatening comorbidities before they occur. The result will be proactive nutritional care, and microbial interventions. Paired together, these two programs will address some of the most challenging issues in the NICU.
Although medical science has improved the survival rate of preterm infants, the quality of survival is important. Astarte Medical has created a platform to improve quality indicators in the NICU. Being able to intervene early and provide optimal nutrition is made possible through enhanced nutrition analytics and insights. RDs have the greatest opportunity to improve neonatal growth outcomes. The ability to use data to show a relationship between variations in practice and clinical outcomes provides the NICU clinical care team with valuable information that can help establish protocols and standards of care essential to positive health outcomes.