Interaction Analytics for Automatic Assessment of Communication Quality in Primary Care

The goal of this PhD project is to investigate the interaction and communication of between doctors and patients.

Studies have demonstrated the effect of doctors' interpersonal skills on concrete medical outcomes, both while interacting with patients and within teams. Recent work in the domain of argumentation and negotiation modelling has shown that natural language processing (NLP) and machine learning can be used to monitor and assess surface and cognitive communication skills. In addition, the same techniques have started to be deployed in the medical domain, e.g. tests in mental health conditions screening for assessment and diagnosis of Alzheihmer's disease and dementia.

In the scope of this PhD, two scenarios are being investigated for their complementarity. The first scenario features one-to-one communication, i.e. doctor-patient during medical interview in a GP's office setting. In this scenario, the focus is to monitor the different aspects of the consultation, based on state of the art practices and guidelines. In addition to the monitoring of the practitioner, it involves the analysis of the patient's discourse and behaviour. The reference for this scenario are the phases of the Cambridge guide to the medical interview: gathering information, providing structure, relationship building, explanation and planning, tracking information and closing the session.

The second scenario features many-to-many communication in medical emergency settings, e.g. intensive care units. The patient, being in critical condition, will not be directly tracked but the contribution of indirect data acquisition in the form of vital signs will be explored. Doctors and nurses interventions in critical and stressful situations involve an additional, different set of skills. In this context, the focus is set on crisis resource management (CRM), a concept emerging from, and in use in the aviation industry. This is centred around non-technical skills (NTS), a set of general cognitive and interpersonal skills. NTS complements domain skills and is crucial to allow safe and efficient interventions.

The core analysis in these two scenarios share similar attributes centred around planning, communication, assertiveness and utilisation of information. New models of multimodal interpretation will be developed based on the different inputs modalities and their combination. A research platform will be developed in parallel to include and test the models.
The first part Video and 3D recognition will be used for posture, gesture and facial expressions interpretation. The second part of the interpretation will use a complete Natural Language Understanding (NLU) pipeline, including prosodic features analysis and semantic processing based on automatic speech recognition and speaker diarisation. Discourse modelling, i.e. high level interpretation of contextual information, will then be developed based on the fused low level interpretations for the exploration of more complex patterns and structures, grounded in conclusions and directions of studies in communication performance.

The current work is set on the definition of the requirements and the specification of the recording setups based on the analysis of data collections previously performed in the domain. The objective is to offer guidelines for the acquisition of high quality anonymised dataset that would benefit both manual and automatic analysis and would be suitable for advanced processing.