ArticleBMC medical informatics and decision making2026
Evaluating the external validity of an artificial intelligence-based mobile support app for caregiving relatives by an online expert survey.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMobile Care Backup is a support app for family caregivers that provides textual information on topics personalized to their specific care situation. Personalization is performed by an artificial intelligence-based expert system. Here, we present the evaluation of the expert system’s validity with project external nursing experts. Furthermore, we discuss the general limitations of an online survey as an evaluation methodology for expert systems.
methodsThis study was conducted as an online survey in German and English. A total of nine experts, all of whom were female and had extensive (outpatient) care experience, were included. The participants were presented with descriptions of multiple fictitious family caregivers and the system’s personalized list of topics. They were then asked to rate the appropriateness on a five-item Likert scale and suggest additional topics. The collected data was analyzed descriptively to investigate whether MoCaB‘s topic recommendation strategy aligns with project external experts. For deviating topic sequences, the consensus of the experts was verified by pairwise rank correlation using Spearman’s Rho. Additional suggested topics were checked to see if they were part of the system but not provided (false negatives).
resultsIn the 495 submitted ratings, participants rated the suggested topics‘ appropriateness relatively high, with an average rating of 4.4 and a median of 5. This indicates that participants consider most of the recommended topics important for the fictitious family caregiver. The system‘s personalization performance was high (precision of 0.965 and recall of 0.986). Overall, the experts are unanimous. There is no unique alternative sequence regarding the rare cases of disagreement with the system in the ordering of topics.
conclusionsThe MoCaB system’s external validity is high, and isolated inconsistencies will be resolved in the project group. Using an online survey to evaluate the system’s validity with external experts is complex and time-consuming. Participants need a very high degree of competence, as they must infer from the title to the content. Nevertheless, it is an essential step in the evaluation process of expert systems and, if carried out correctly, can identify weak spots and further improve the expert system.
Indexed as
Identifiers
What OpenQuestion holds
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.