# Mount Sinai's dietitians use a deployed machine-learning score at five hospitals to decide which inpatients to assess first for malnutrition

Page: https://flyvolo.ai/en/changes/ev-20200101-dietitian-12
Date: 2020-01-01 · Stage: Deployment
Can this record move a task judgement? yes
Occupation: https://flyvolo.ai/en/careers/dietitian

## Scope

New York, United States. The health system's own researchers say it deployed a machine-learning model (MUST-Plus) to detect malnutrition on hospital admission, that the model has been deployed at five of its hospitals since 2020, and that the registered dietitian team uses its prediction to prioritise which inpatients to evaluate on daily rounds, after which the dietitian documents a malnutrition diagnosis against clinical criteria. The paper also finds the model was miscalibrated in some periods and groups. One health system; the model ranks patients for dietitians, it does not assess or diagnose them, and no change in dietitian time is reported.

## Source

- Mount Sinai Health System authors — 'Assessing calibration and bias of a deployed machine learning malnutrition prediction model within a large healthcare system', npj Digital Medicine 7:149 (6 June 2024) — https://pmc.ncbi.nlm.nih.gov/articles/PMC11156633/ (primary source)

## What this means

In real hospitals, software now decides the order in which dietitians see patients, while the assessment stays with the dietitian.

## What it does not show yet

One health system, a model that ranks rather than assesses, and no measure of dietitians' time.

## How to verify it yourself

Open the npj Digital Medicine paper (PMC11156633) and find "used by the RD team to prioritize which inpatients to evaluate".


More: https://flyvolo.ai/llms.txt