https://www.snomed.org/membersEffective nutrition care relies on the ability to share, receive, reuse, and interpret structured nutrition data seamlessly across healthcare settings. Structured nutrition data play a crucial role in supporting comprehensive care transitions, ensuring continuity and consistency in nutrition interventions as patients move through different levels of care.
The use cases provided on this page serve as examples of how SNOMED CT terms can be applied to document various aspects of nutrition care. These examples are illustrative rather than exhaustive, offering guidance on practical applications of SNOMED CT for capturing nutrition-related information within clinical workflows.
Standardizing the documentation of nutrition care using SNOMED CT allows for consistent recording of patient data across different EHR systems . Structured documentation facilitates interoperability between healthcare providers and enables seamless sharing of nutrition-related data during patient transitions between care settings (e.g. when a patient is discharged from a hospital to a long term care facility). Development of functional digital nutrition care templates in EHR systems, and subsequent seemless data acquisition and analysis are greatly facilitated by the SNOMED CT NCPT reference set and other available concept groupings such as value sets. Value sets are lists of codes and corresponding terms, such as SNOMED CT that define clinical concepts to support effective and interoperable health information exchange. In the United States, the Value Set Authority of the National Library of Medicine maintains value sets. The Academy stewards over 160 nutrition and dietetics related value sets that are available at the Value Set Authority (log in required).
A patient with newly diagnosed diabetes mellitus type 2 is referred to a credentialed dietitian for nutrition care. The patient has not seen a dietitian before.
During the initial assessment, the dietitian records the patient's dietary habits, anthropometric measurements, biochemical data (e.g. blood glucose concentration), and relevant medical history using standardized SNOMED CT concepts within the EHR. This structured documentation allows for seamless sharing of the patient's nutrition assessment data with other healthcare providers involved in their care, such as endocrinologists, primary care physicians, and nurses.
*Healthy Eating Index (HEI) 2015 scale is: from 1 point (lowest) to 100 points (highest diet quality), five categories:
**Health Related Quality of Life (HRQOL) scale is: excellent, very good, good, fair, or poor
Best practice tip: There is discussion in many countries like Sweden about minimizing double documentation. In the EHR, any professional can read information other professionals have documented. Thus, it should not be necessary for dietitians to re-document values like blood glucose concentration. The dietitians could refer to the related section in the EHR.
By leveraging SNOMED CT concepts for nutrition diagnosis within the SNOMED CT NCPT reference set (first release April 2024), healthcare professionals can accurately identify and categorize patients' nutrition problems. This standardized approach enhances the ability to apply evidence-based interventions and track outcomes effectively.
By applying standardized terminology, the healthcare team can accurately identify the patient's nutrition-related problems and prioritize interventions to address the patients' specific needs.
The NCPT includes a group of terms to record nutrition diagnosis status when recording a diagnostic statement. At present, there is one available mapping for one of the nutrition diagnosis status terms (mapping is from NCPT to SNOMED CT). An EHR build may have alternate built in options to record 'status' of nutrition diagnosis. Also, FHIR includes condition clinical status codes that may be considered.
In general, having the SNOMED CT NCPT reference set supports the ability to generate automated frequency reports and gather data for quality improvement. There can be huge benefits when data is aggregated on a group level. In the United States, a web based data aggregation platform has facilitated data collection of all types of cases (this registry is called the Dietary Outcomes Registry) where the prevalence of nutrition problems was determined, the percent of problem resolution was identified, and using machine learning analyses jointly with IBM Watson revealed types of nutrition care (phenotypes) where nutrition counseling was found to be the most effective intervention to achieve nutrition problem improvement.
Utilizing SNOMED CT for documenting nutrition interventions enables healthcare providers to select and implement appropriate dietary recommendations and therapies based on standardized terminology. This supports personalized care planning and ensures consistency in treatment strategies across different care settings.
The dietitian provides an intervention that supports carbohydrate control and portion sizes. The use of SNOMED CT ensures consistency in documenting dietary recommendations and facilitates communication between the dietitian, nurse, endocrinologist, and other members of the healthcare team involved in the patient's care.
Standardized documentation of nutrition monitoring and evaluation data using SNOMED CT facilitates ongoing assessment of patients' nutritional status and progress over time. This enables healthcare professionals to identify trends, adjust interventions as needed, and evaluate the effectiveness of nutrition care plans in achieving desired outcomes.
Transitional care models are practice systems that “follow patients across settings (e.g., from hospital to home), improve coordination among health care providers, and help individuals better understand their post-hospital care. When implementing SNOMED CT for nutrition care documentation, concurrent effective use of data standards (such as Health Level 7 FHIR) allows for data to follow the patient effectively. Standardized documentation of nutrition care using SNOMED CT is a critical pre-step to the use of data standards such as FHIR as the structured data is more readily exchanged.
For these types of use cases and many others, utilizing approved SNOMED concepts is very important to ascertain high quality documentation and to be able to leverage data standards that allow seamless data exchange between electronic health record systems. This best practice is essential for effective transitions of care.
A standardized digital referral using SNOMED CT codes and a FHIR API is able to transfer nutrition related data of patients with malnutrition between dietitians (from hospital to a community-based meal provision organization) and this communication of care improves health outcomes post discharge.
By implementing SNOMED CT for nutrition care documentation, EHR systems can support clinical decision support tools that utilize standardized data to provide tailored recommendations for nutrition management. Furthermore, the consistent use of SNOMED CT enables aggregation of data for research purposes, allowing for the analysis of nutrition care practices, outcomes, and their impact on patient health.
A healthcare system implements a clinical decision support tool embedded within its EHR system to assist providers in managing patients with chronic kidney disease. The tool utilizes SNOMED CT-coded nutrition data to generate tailored recommendations for dietary modifications, fluid restriction, and electrolyte management based on the patient's stage of disease and comorbid conditions. Additionally, aggregated SNOMED CT-coded nutrition data from EHRs across the healthcare system are utilized for research purposes to analyze trends in nutrition-related outcomes among patients with chronic kidney disease and evaluate the impact of various interventions on disease progression and quality of life.
Users who would like to submit new use cases they have developed may do so at any time, and will be considered for inclusion. Please submit content, comments, and questions at: info@snomed.org |
---|
Chui TK, Proaño GV, Raynor HA, Papoutsakis C. A Nutrition Care Process Audit of the National Quality Improvement Dataset: Supporting the Improvement of Data Quality Using the ANDHII Platform. J Acad Nutr Diet. Jul 2020;120(7):1238-1248.e1. doi:10.1016/j.jand.2019.08.174
Colin C, Arikawa A, Lewis S, et al. Documentation of the evidence-diagnosis link predicts nutrition diagnosis resolution in the Academy of Nutrition and Dietetics' diabetes mellitus registry study: A secondary analysis of Nutrition Care Process outcomes. Front Nutr. 2023;10:1011958. doi:10.3389/fnut.2023.1011958
Kight CE, Bouche JM, Curry A, et al. Consensus Recommendations for Optimizing Electronic Health Records for Nutrition Care. Nutr Clin Pract. Feb 2020;35(1):12-23. doi:10.1002/ncp.10433
Krebs-Smith SM, Pannucci TE, Subar AF, et al. Update of the Healthy Eating Index: HEI-2015. J Acad Nutr Diet. Sep 2018;118(9):1591-1602. doi:10.1016/j.jand.2018.05.021
Lewis SL, Miranda LS, Kurtz J, Larison LM, Brewer WJ, Papoutsakis C. Nutrition Care Process Quality Evaluation and Standardization Tool: The Next Frontier in Quality Evaluation of Documentation. J Acad Nutr Diet. Mar 2022;122(3):650-660. doi:10.1016/j.jand.2021.07.004
Lewis SL, Wright L, Arikawa AY, Papoutsakis C. Etiology Intervention Link Predicts Resolution of Nutrition Diagnosis: A Nutrition Care Process Outcomes Study from a Veterans' Health Care Facility. J Acad Nutr Diet. Sep 2021;121(9):1831-1840. doi:10.1016/j.jand.2020.04.015
Lloyd L, Swan WI, Jent S, Vivanti A, Pertel DG. Worldwide Release of SNOMED CT Nutrition Care Process Terminology Problem List. J Acad Nutr Diet. 2024 Apr;124(4):531-534.
Long JM, Yoder A, Woodcock L, Papoutsakis C. Impact of a Registered Dietitian Nutritionist-Led Food as Medicine Program in the Food Retail Setting: A Feasibility Study. (2212-2672 (Print))
Maduri C, Sabrina Hsueh PY, Li Z, Chen CH, Papoutsakis C. Applying Contemporary Machine Learning Approaches to Nutrition Care Real-World Evidence: Findings From the National Quality Improvement Data Set. J Acad Nutr Diet. Dec 2021;121(12):2549-2559.e1. doi:10.1016/j.jand.2021.02.003
Moriarty DG, Zack MM, Kobau R. The Centers for Disease Control and Prevention’s Healthy Days Measures - Population tracking of perceived physical and mental health over time. Health Qual Life Outcomes. 2003;1:37. https://doi.org/10.1186/1477-7525-1-37
Proaño GV, Papoutsakis C, Lamers-Johnson E, et al. Evaluating the Implementation of Evidence-based Kidney Nutrition Practice Guidelines: The AUGmeNt Study Protocol. J Ren Nutr. Sep 2022;32(5):613-625. doi:10.1053/j.jrn.2021.09.006
Vergili JM, Proaño GV, Jimenez EY, Moloney L, Papoutsakis C, Steiber A. Academy of Nutrition and Dietetics Commentary on the Phosphorus Recommendation in the KDOQI Clinical Practice Guidelines for Nutrition in CKD: 2020 Update. J Ren Nutr. May 2024;34(3):192-199. doi:10.1053/j.jrn.2023.11.001