Nutrition Interventions Deliver Value in Healthcare: Real-World Evidence
Bibliographic record
Abstract
Suela Sulo,1 Leah Gramlich,2 Jyoti Benjamin,3 Sharon McCauley,4 Jan Powers,5 Krishnan Sriram,6 Kristi Mitchell7 1Global Health Economics & Outcomes Research, Abbott, Chicago, IL, USA; 2Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada; 3Nutrition, Kaiser Permanente, Bellevue, WA, USA; 4Quality Management, Academy of Nutrition and Dietetics, Chicago, IL, USA; 5Nursing Research & Professional Practice, Parkview Health, Indianapolis, IN, USA; 6eICU, Advocate Health Care, Downers Grove, IL, USA; 7Health Policy, Avalere Health, Washington, DC, USACorrespondence: Suela SuloAbbott Nutrition, Abbott Park, Chicago, IL 60064, USATel +1 224-668-1377Fax +1 224-668-8355Email suela.sulo@abbott.comAbstract: Value is a key guiding principle in healthcare, yet value is defined in varying ways by different stakeholders. In this paper, we review evidence of the health and financial tolls of malnutrition or poor nutrition, report positive results from recent nutrition-focused quality improvement programs in hospitals, and discuss clinical and policy implications of realizing best-practice nutrition care. Hospitalized patients with malnutrition diagnoses have up to two-fold greater hospital costs for care compared to inpatient stays for adequately nourished patients. By contrast, implementation of nutrition care programs for hospitalized adults (nutrition status screening, assessment and diagnosis of malnutrition, oral nutritional supplements provided when indicated) is associated with substantial per-patient, per-episode healthcare savings approaching $4,000. Improved nutrition care has also been associated with fewer complications and faster recovery (shortened lengths of stay, lower readmission rates). Nutrition care thus delivers value, which is evidenced by better patient outcomes at cost savings to healthcare systems.Keywords: nutrition, value, healthcare, real-world evidence, quality improvement programs
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.321 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".