An implementation framework and a feasibility evaluation of a clinical decision support system for diabetes management in secondary mental healthcare using CogStack

Abstract

Background Improvements to the primary prevention of physical health illnesses like diabetes in the general population have not been mirrored to the same extent in people with serious mental illness (SMI). This work evaluates the technical feasibility of implementing an electronic clinical decision support system (eCDSS) for supporting the management of dysglycaemia and diabetes in patients with serious mental illness in a secondary mental healthcare setting. Methods A stepwise approach was taken as an overarching and guiding framework for this work. Participatory methods were employed to design and deploy a monitoring and alerting eCDSS. The eCDSS was evaluated for its technical feasibility. The initial part of the feasibility evaluation was conducted in an outpatient community mental health team. Thereafter, the evaluation of the eCDSS progressed to a more in-depth in silico validation. Results A digital health intervention that enables monitoring and alerting of at-risk patients based on an approved diabetes management guideline was developed. The eCDSS generated alerts according to expected standards and in line with clinical guideline recommendations. Conclusions It is feasible to design and deploy a functional monitoring and alerting eCDSS in secondary mental healthcare. Further work is required in order to fully evaluate the integration of the eCDSS into routine clinical workflows. By describing and sharing the steps that were and will be taken from concept to clinical testing, useful insights could be provided to teams that are interested in building similar digital health interventions.

Enhancing Mental Health Support through Artificial Intelligence: Advances in Speech and Text Analysis within Online Therapy Platforms

“…The fr_core_news_sm model, an efficient component of the spaCy library, was integral to our NLU pipeline. Its pre-trained embeddings were crucial for the linguistic analysis tailored to our project's needs, aligning with methodologies proven in health sector research [28][29][30][31][32]. The configuration of our NLP pipeline, optimized for our simulated dataset, is presented in Figure 5.…”

“…We transformed our textual data into tokens suitable for machine interpretation by employing the SpacyTokenizer, which uses the linguistic annotations from the "fr_core_news_sm" model [28][29][30][31][32]. Following tokenization, we utilized the SpacyFeaturizer to generate dense word embeddings, where a mean pooling strategy was applied to create aggregated phrase representations.…”

“…As our research progresses, our primary objective is to refine the NLP components. The aim is to seamlessly integrate them, producing a holistic conversational AI system (Figure 4) that stands as a testament to the potential of NLP in revolutionizing voice-assisted systems [28][29][30][31][32].…”

Implementation of an Electronic Clinical Decision Support System for the Early Recognition and Management of Dysglycemia in an Inpatient Mental Health Setting Using CogStack: Protocol for a Pilot Hybrid Type 3 Effectiveness-Implementation Randomized Controlled Cluster Trial

“…We have previously reported on the design and development of a novel eCDSS built using the information retrieval and extraction platform CogStack, deployed at the South London and Maudsley National Health Service (NHS) Foundation Trust, UK (CogStack@Maudsley), comprising a real-time computerized alerting and clinical decision support system for dysglycemia management that has been previously validated for use in secondary mental health care [23].…”

“…We previously developed an eCDSS for dysglycemia hosted within CogStack@Maudsley [23]. The eCDSS is designed to alert clinicians automatically when patients are admitted under their care regarding the need for screening for, or management of, dysglycemia.…”