CHRONOSIG: Digital Triage for Secondary Mental Healthcare using Natural Language Processing - Rationale and Protocol
Joyce, D. W.; Kormilitzin, A.; Hamer-Hunt, J.; James, A.; Nevado-Holgado, A.; Cipriani, A.
Show abstract
BackgroundAccessing specialist secondary mental health care in the NHS in England requires a referral, usually from primary or acute care. Community mental health teams triage these referrals deciding on the most appropriate team to meet patients needs. Referrals require resource-intensive review by clinicians and often, collation and review of the patients history with services captured in their electronic health records (EHR). Triage processes are, however, opaque and often result in patients not receiving appropriate and timely access to care that is a particular concern for some minority and under-represented groups. Our project, funded by the National Institute of Health Research (NIHR) will develop a clinical decision support tool (CDST) to deliver accurate, explainable and justified triage recommendations to assist clinicians and expedite access to secondary mental health care. MethodsOur proposed CDST will be trained on narrative free-text data combining referral documentation and historical EHR records for patients in the UK-CRIS database. This high-volume data set will enable training of end-to-end neural network natural language processing (NLP) to extract signatures of patients who were (historically) triaged to different treatment teams. The resulting algorithm will be externally validated using data from different NHS trusts (Nottinghamshire Healthcare, Southern Health, West London and Oxford Health). We will use an explicit algorithmic fairness framework to mitigate risk of unintended harm evident in some artificial intelligence (AI) healthcare applications. Consequently, the performance of the CDST will be explicitly evaluated in simulated triage team scenarios where the tool augments clinicians decision making, in contrast to traditional "human versus AI" performance metrics. DiscussionThe proposed CDST represents an important test-case for AI applied to real-world process improvement in mental health. The project leverages recent advances in NLP while emphasizing the risks and benefits for patients of AI-augmented clinical decision making. The projects ambition is to deliver a CDST that is scalable and can be deployed to any mental health trust in England to assist with digital triage.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Listening to mental health crisis needs at scale: using Natural Language Processing to understand and evaluate a mental health crisis text messaging service 94%
- Large Language Models in Real-World Clinical Workflows: A Systematic Review of Applications and Implementation 91%
- Development and Validation of a Machine Learning Model Integrated with the Clinical Workflow for Inpatient Discharge Date Prediction 90%
Similar papers in this journal
- Synthetic Data Generation in Healthcare: A Scoping Review of reviews on domains, motivations, and future applications 91%
- A Deep Learning Method to Detect Opioid Prescription and Opioid Use Disorder from Electronic Health Records 91%
- Development and Evaluation of MADDIE: Method to Acquire Delivery Date Information from Electronic Health Records 90%
Similar papers in this journal
- Co-development of a best practice checklist for mental health data science: A Delphi study 91%
- Passive sensing data predicts stress in university students: A supervised machine learning method for digital phenotyping 90%
- Deep Multimodal Representations and Classification of First-Episode Psychosis via Live Face Processing 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.