Implementation of an electronic solution to improve malnutrition identification and support clinical best practice

Patient-Led, Technology-Assisted Malnutrition Risk Screening in Hospital: A Feasibility Study

Malnutrition risk screening is crucial to identify at-risk patients in hospitals; however, screening rates can be suboptimal. This study evaluated the feasibility, acceptability, and potential cost-effectiveness of patient-led, technology-assisted malnutrition risk screening. A prospective multi-methods study was conducted in a 750-bed public hospital in Australia. Patients were recruited from seven wards and asked to complete an electronic version of the Malnutrition Screening Tool (e-MST) on bedside computer screens. Data were collected on feasibility, acceptability, and cost. Feasibility data were compared to pre-determined criteria on recruitment (≥50% recruitment rate) and e-MST completion (≥75% completion rate). Quantitative acceptability (survey) data were analyzed descriptively. Patient interview data were analyzed thematically. The economic evaluation was from the perspective of the health service using a decision tree analytic model. Both feasibility criteria were met; the recruitment rate was 78% and all 121 participants (52% male, median age 59 [IQR 48-69] years) completed the e-MST. Patient acceptability was high. Patient-led e-MST was modeled to save $3.23 AUD per patient and yield 6.5 more true malnutrition cases (per 121 patients) with an incremental cost saving per additional malnutrition case of 0.50 AUD. Patient-led, technology-assisted malnutrition risk screening was found to be feasible, acceptable to patients, and cost-effective (higher malnutrition yield and less costly) compared to current practice at this hospital.

Using an interactive nutrition technology platform to predict malnutrition risk

AIMS

The Nutrition Dashboard is an interactive nutrition technology platform that displays food provision and intake data used to categorise the nutrition risk of hospitalised individuals. This study aimed to investigate the Nutrition Dashboard's ability to identify malnutrition compared to a validated malnutrition screening tool.

METHODS

A retrospective observational study at a 99-bed hospital was conducted using medical record and food intake data presented via the Nutrition Dashboard. Inter-Rater Reliability of food intake estimation between hospital catering staff and a dietitian reported good agreement across 912 food items (κ = 0.69, 95% CI 0.65-0.72, p < 0.001). Default nutritional adequacy thresholds of 4500kJ and 50g protein were applied for Nutrition Dashboard categorisation of supply and intake. Generalised estimating equation regression models explored the association between the Nutrition Dashboard risk categories and the Malnutrition Screening Tool, with and without controlling for patient demographic characteristics.

RESULTS

Analyses from 216 individuals (1783 hospital-stay days) found those in the highest risk Nutrition Dashboard Category were 1.93 times more likely to have a Malnutrition Screening Tool score indicating risk compared to the lowest Nutrition Dashboard Category (unadjusted odds ratio 1.93, 95% CI, 1.17-3.19, p<0.01). When patient weight was added to the model, lower weight became the only significant predictor of MST≥2 (p<0.01) CONCLUSIONS: This study indicates a role for nutrition intake technology in malnutrition screening. Further adaptions that address the complexities of applying this technology could improve the use of the Nutrition Dashboard to support identification of malnutrition. This article is protected by copyright. All rights reserved.