Evidence map›Paper›PMID 39306725›Full record

ArticleNutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition2025

Transforming the electronic health record from a documentation application to an automated diet program for personalizing neonatal nutrition and improving feeding administration safety through process improvement.

Alisha Harmeson Owen, Ameena Husain, David ElHalta, Suzie A Chen, Jennifer Spackman, Jennifer Murphy, Belinda Chan

Abstract read
In one paragraph

Article in Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Alisha Harmeson OwenClinical Nutrition, University of Utah, Salt Lake City, Utah, USA.
Ameena HusainDivision of Neonatology, Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA.ORCID http://orcid.org/0000-0003-1274-7235
David ElHaltaClinical Informatics, University of Utah, Salt Lake City, Utah, USA.
Suzie A ChenClinical Informatics, University of Utah, Salt Lake City, Utah, USA.
Jennifer SpackmanClinical Informatics, University of Utah, Salt Lake City, Utah, USA.
Jennifer MurphyNeonatology, St. Mark's Hospital, Salt Lake City, Utah, USA.
Belinda ChanDivision of Neonatology, Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Delivering adequate nutrition to preterm and sick neonates is critical for growth. Infants in the neonatal intensive care unit (NICU) require additional calories to supplement feedings for higher metabolic demands. Traditionally, clinicians enter free-text diet orders for a milk technician to formulate recipes, and dietitians manually calculate nutrition components to monitor growth. This daily process is complex and labor intensive with potential for error. Our goal was to develop an electronic health record (EHR)-integrated solution for entering feeding orders with automated nutrition calculations and mixing instructions. The EHR-integrated automated diet program (ADP) was created and implemented at a 52-bed level III academic NICU. The configuration of the parenteral nutrition orderable item within the EHR was adapted to generate personalized milk mixing recipes. Caloric, macronutrient, and micronutrient constituents were automatically calculated and displayed. To enhance administration safety, handwritten milk bottle patient labels were substituted with electronically generated and scannable patient labels. The program was further enhanced by calculating fortifier powder displacement factors to improve mixing precision. Order entry was optimized to allow for more complex mixing recipes and include a preference list of frequently ordered feeds. The EHR-ADP's safeguarded features allowed for catching multiple near-missed feeding administration errors. The NICU preterm neonate cohort had an average of 6-day decrease (P = 0.01) in the length of stay after implementation while maintaining the same weight gain velocity. The EHR-ADP may improve safety and efficiency; further improvements and wider utilization are needed to demonstrate the growth benefits of personalized nutrition.

Indexed as

Electronic Health RecordsInfant Nutritional Physiological PhenomenaInfant, PrematureIntensive Care Units, NeonatalDietDocumentationEnergy IntakeFemaleHumansInfant, NewbornMaleParenteral NutritionPrecision Medicineautomated diet programelectronic health recordfeeding recipesgrowth failureneonatenutritionnutritional value calculatorpretermsafety labels

Identifiers

PMID39306725
PMCPMC11713221

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.