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Evaluation of an automated pediatric malnutrition screen using anthropometric measurements in the electronic health record: a quality improvement initiative

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Supportive Care in Cancer Aims and scope Submit manuscript

Abstract

Purpose

Malnutrition related to undernutrition in pediatric oncology patients is associated with worse outcomes including increased morbidity and mortality. At a tertiary pediatric center, traditional malnutrition screening practices were ineffective at identifying cancer patients at risk for undernutrition and needing nutrition consultation.

Methods

To efficiently identify undernourished patients, an automated malnutrition screen using anthropometric data in the electronic health record (EHR) was implemented. The screen utilized pediatric malnutrition (undernutrition) indicators from the 2014 Consensus Statement of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition with corresponding structured EHR elements. The time periods before (January 2016–August 2017) and after (September 2017–August 2018) screen implementation were compared. Process metrics including nutrition consults, timeliness of nutrition assessments, and malnutrition diagnoses documentation were assessed using statistical process control charts. Outcome metrics including change in nutritional status at least 3 months after positive malnutrition screen were assessed with the Cochran-Armitage trend test.

Results

After automated malnutrition screen implementation, all process metrics demonstrated center line shifts indicating special cause variation. For patient admissions with a positive screen for malnutrition of any severity level, no significant improvement in status of malnutrition was observed after 3 months (P = .13). Sub-analysis of patient admissions with screen-identified severe malnutrition noted improvement in degree of malnutrition after 3 months (P = .02).

Conclusions

Select 2014 Consensus Statement indicators for pediatric malnutrition can be implemented as an automated screen using structured EHR data. The automated screen efficiently identifies oncology patients at risk of malnutrition and may improve clinical outcomes.

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Authors and Affiliations

  1. Division of Oncology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA

    Charles A. Phillips, Yimei Li, Anne Reilly & Jason Freedman

  2. Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA

    Charles A. Phillips, Yimei Li, Anne Reilly & Jason Freedman

  3. Department of Clinical Nutrition, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Judith Bailer & Elizabeth Smith

  4. Whiterabbit AI, Sunnyvale, CA, USA

    Emily Foster

  5. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Yimei Li

  6. Office of Clinical Quality Improvement, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Preston Dogan

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  1. Charles A. Phillips
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  2. Judith Bailer
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Correspondence to Charles A. Phillips.

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Conflict of interest

The authors have no conflict of interest disclosures to report. Quality improvement work presented in this paper was supported by the Children’s Hospital of Philadelphia. The Children’s Hospital of Philadelphia retains primary control of the data presented in this manuscript. Data may be made available for external review if permission is obtained from the Children’s Hospital of Philadelphia.

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Phillips, C.A., Bailer, J., Foster, E. et al. Evaluation of an automated pediatric malnutrition screen using anthropometric measurements in the electronic health record: a quality improvement initiative. Support Care Cancer 28, 1659–1666 (2020). https://doi.org/10.1007/s00520-019-04980-1

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