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Ideas, concerns, expectations, and effects on life (ICEE) in GP consultations: an observational study using video-recorded UK consultations

BACKGROUND

Eliciting patients' ideas, concerns, expectations, and whether a problem has an 'effect' on their life (ICEE), is a widely recommended communication technique. However, it is not known how frequently ICEE components are raised in UK GP consultations.

AIM

To assess the frequency of ICEE in routine GP consultations with adult patients and explore variables associated with ICEE.

DESIGN & SETTING

An observational study was undertaken. It involved secondary analysis of a pre-existing archive of video-recorded, face-to-face GP consultations in the UK.

METHOD

Observational coding of 92 consultations took place. Associations were assessed using binomial and ordered logistic regression.

RESULTS

Most consultations included at least one ICEE component (90.2%). The most common ICEE component per consultation was patient ideas (79.3%), followed by concerns (55.4%), expectations (51.1%), and then effects on life (42.4%). For all ICEE components patients more commonly initiated the ICEE dialogue, and in only three consultations (3.3%) did GPs directly ask patients about their expectations. Problems that were acute (odds ratio [OR] 2.98, 95% confidence interval [CI] = 1.36 to 6.53, P = 0.007) or assessed by GPs aged ≥50 years (OR 2.10, 95% CI = 1.07 to 4.13, P = 0.030) were associated with more ICEE components. Problems assessed later in the consultation (OR 0.60 per problem order increase, 95% CI = 0.41 to 0.87, P = 0.007) by patients aged ≥75 years (OR 0.40, 95% CI = 0.16 to 0.98, P = 0.046) and from the most deprived cohort (OR 0.39, 95% CI = 0.17 to 0.92, P = 0.032) were associated with fewer ICEE components. Patient ideas were associated with more patients being 'very satisfied' post-consultation (OR 10.74, 95% CI = 1.60 to 72.0, P = 0.014) and the opposite was true of concerns (OR 0.14, 95% CI = 0.02 to 0.86, P = 0.034).

CONCLUSION

ICEE components were associated with patient satisfaction and demographic variables. Further research is required to assess if the way ICEE are communicated affects these associations and other potential confounders.

BPI-MVQA: a bi-branch model for medical visual question answering

BACKGROUND

Visual question answering in medical domain (VQA-Med) exhibits great potential for enhancing confidence in diagnosing diseases and helping patients better understand their medical conditions. One of the challenges in VQA-Med is how to better understand and combine the semantic features of medical images (e.g., X-rays, Magnetic Resonance Imaging(MRI)) and answer the corresponding questions accurately in unlabeled medical datasets.

METHOD

We propose a novel Bi-branched model based on Parallel networks and Image retrieval for Medical Visual Question Answering (BPI-MVQA). The first branch of BPI-MVQA is a transformer structure based on a parallel network to achieve complementary advantages in image sequence feature and spatial feature extraction, and multi-modal features are implicitly fused by using the multi-head self-attention mechanism. The second branch is retrieving the similarity of image features generated by the VGG16 network to obtain similar text descriptions as labels.

RESULT

The BPI-MVQA model achieves state-of-the-art results on three VQA-Med datasets, and the main metric scores exceed the best results so far by 0.2[Formula: see text], 1.4[Formula: see text], and 1.1[Formula: see text].

CONCLUSION

The evaluation results support the effectiveness of the BPI-MVQA model in VQA-Med. The design of the bi-branch structure helps the model answer different types of visual questions. The parallel network allows for multi-angle image feature extraction, a unique feature extraction method that helps the model better understand the semantic information of the image and achieve greater accuracy in the multi-classification of VQA-Med. In addition, image retrieval helps the model answer irregular, open-ended type questions from the perspective of understanding the information provided by images. The comparison of our method with state-of-the-art methods on three datasets also shows that our method can bring substantial improvement to the VQA-Med system.