TY - JOUR
T1 - Malnutrition risk assessment using a machine learning-based screening tool
T2 - A multicentre retrospective cohort
AU - Parchure, Prathamesh
AU - Besculides, Melanie
AU - Zhan, Serena
AU - Cheng, Fu yuan
AU - Timsina, Prem
AU - Cheertirala, Satya Narayana
AU - Kersch, Ilana
AU - Wilson, Sara
AU - Freeman, Robert
AU - Reich, David
AU - Mazumdar, Madhu
AU - Kia, Arash
N1 - Publisher Copyright:
© 2024 The British Dietetic Association Ltd.
PY - 2024/6
Y1 - 2024/6
N2 - Background: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST-Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. Methods: This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID-19 and had a length of stay of ≤ 30 days. Results: Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST-Plus-assisted RD evaluations. The lag between admission and diagnosis improved with MUST-Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre-/post-implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. Conclusion: MUST-Plus, a machine learning-based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning-based processes to improve malnutrition screening and facilitate timely intervention.
AB - Background: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST-Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. Methods: This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID-19 and had a length of stay of ≤ 30 days. Results: Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST-Plus-assisted RD evaluations. The lag between admission and diagnosis improved with MUST-Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre-/post-implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. Conclusion: MUST-Plus, a machine learning-based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning-based processes to improve malnutrition screening and facilitate timely intervention.
KW - AI
KW - evaluation
KW - implementation
KW - machine learning
KW - malnutrition
KW - usability/acceptance
UR - http://www.scopus.com/inward/record.url?scp=85185487998&partnerID=8YFLogxK
U2 - 10.1111/jhn.13286
DO - 10.1111/jhn.13286
M3 - Article
AN - SCOPUS:85185487998
SN - 0952-3871
VL - 37
SP - 622
EP - 632
JO - Journal of Human Nutrition and Dietetics
JF - Journal of Human Nutrition and Dietetics
IS - 3
ER -