Evidence map›Paper›PMID 29351894›Full record

ArticleJMIR medical informatics2018

The Use of Technology in Identifying Hospital Malnutrition: Scoping Review.

Dino Trtovac, Joon Lee

Registry-linked trialOpen access · goldAbstract readScoping Review
In one paragraph

Article in JMIR medical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07183449 (Using a Novel, User Friendly Plate Imaging System to Quantify Food Consumption in Hospitals), which is not on this map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.9field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07183449 completednot on this mapstarted 2025, after this paper: background citation

Using a Novel, User Friendly Plate Imaging System to Quantify Food Consumption in Hospitals

TypeobservationalSponsorUniversity of SouthamptonRan2025 to 2026Enrolled50ConditionsStroke
3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 27 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Dino Trtovac *Health Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.ORCID http://orcid.org/0000-0002-4456-9122
Joon LeeHealth Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.ORCID http://orcid.org/0000-0001-8593-9321
University of Waterloo · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMalnutrition is a condition most commonly arising from the inadequate consumption of nutrients necessary to maintain physiological health and is associated with the development of cardiovascular disease, osteoporosis, and sarcopenia. Malnutrition occurring in the hospital setting is caused by insufficient monitoring, identification, and assessment efforts. Furthermore, the ability of health care workers to identify and recognize malnourished patients is suboptimal. Therefore, interventions focusing on the identification and treatment of malnutrition are valuable, as they reduce the risks and rates of malnutrition within hospitals. Technology may be a particularly useful ally in identifying malnutrition due to scalability, timeliness, and effectiveness. In an effort to explore the issue, this scoping review synthesized the availability of technological tools to detect and identify hospital malnutrition.

objectiveOur objective was to conduct a scoping review of the different forms of technology used in addressing malnutrition among adults admitted to hospital to (1) identify the extent of the published literature on this topic, (2) describe key findings, and (3) identify outcomes.

methodsWe designed and implemented a search strategy in 3 databases (PubMed, Scopus, and CINAHL). We completed a descriptive numerical summary and analyzed study characteristics. One reviewer independently extracted data from the databases.

resultsWe retrieved and reviewed a total of 21 articles. We categorized articles by the computerized tool or app type: malnutrition assessment (n=15), food intake monitoring (n=5), or both (n=1). Within those categories, we subcategorized the different technologies as either hardware (n=4), software (n=13), or both (n=4). An additional subcategory under software was cloud-based apps (n=1). Malnutrition in the acute hospital setting was largely an unrecognized problem, owing to insufficient monitoring, identification, and initial assessments of identifying both patients who are already malnourished and those who are at risk of malnourishment. Studies went on to examine the effectiveness of health care workers (nurses and doctors) with a knowledge base focused on clinical care and their ability to accurately and consistently identify malnourished geriatric patients within that setting.

conclusionsMost articles reported effectiveness in accurately increasing malnutrition detection and awareness. Computerized tools and apps may also help reduce health care workers' workload and time spent assessing patients for malnutrition. Hospitals may also benefit from implementing malnutrition technology through observing decreased length of stay, along with decreased foregone costs related to missing malnutrition diagnoses. It is beneficial to study the impact of these technologies to examine possible areas of improvement. A future systematic review would further contribute to the evidence and effectiveness of the use of technologies in assessing and monitoring hospital malnutrition.

Indexed as

automated datafood-intake monitoringhospital malnutritionmalnutritionmalnutrition assessmentmalnutrition detectionnutritional statusnutrition assessmentnutrition diagnosistechnology-driven health care

Identifiers

PMID29351894
PMCPMC5797288
OpenAlexW2784325276

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.