Enterprise AI Analysis
AI assisted triage of UK patients in mental health care services: a qualitative focus group study of patients' attitudes
The referral process between healthcare services can be complex, especially in psychiatry, leading to significant delays and 'hidden waiting lists'. Digital approaches may be helpful. The CHRONOSIG (CHRONOlogical SIGnature) project aims to improve the referral and triage process by applying machine learning (ML) technology to information in electronic health records. We used a focus group methodology to ascertain the views of patients and participants on using CHRONOSIG and similar digital approaches to support decision making in triaging referrals in difficult to treat depression, and the potential benefits and disadvantages of such an approach. Patient engagement is a key challenge for digital tools in mental health, but previous studies in digital decision support tools have focussed on clinician feedback. In this study we ascertained the views of lived experience participants in mental healthcare triage and referral in difficult to treat depression. Participants identified delays, errors and confusion in the referral process and expressed positive views on the ability of the CHRONOSIG tool to help to improve waiting times and time spent between services, particularly when used as an addition to a high-quality clinical consultation. In many countries there are shortfalls in mental health care provision with increasing waits in both recorded and unrecorded waiting lists. This study supports a potential route to improve these processes; by more accurately and efficiently identifying the needs of patients and matching these to suitable services and research opportunities.
Executive Impact & Key Metrics
Based on a thorough analysis, implementing AI solutions like CHRONOSIG can significantly improve operational efficiency and patient outcomes in mental healthcare services.
0
Participants in Focus Group
0
Median Age of Participants
0
White British Participants
0
Patients Waiting >12 Weeks
Deep Analysis & Enterprise Applications
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The Nuance of Mental Health Assessments
Participants emphasized the multifactorial nature of depression and the need for a holistic view, integrating background, childhood experiences, social, and physical factors alongside specific mental health considerations. The interlinked nature of mental and physical health was a recurring theme, complicating straightforward assessments.
Systemic Hurdles in Mental Healthcare
Key challenges identified included incomplete and fragmented medical notes, lack of continuity in healthcare provision (e.g., seeing different GPs), and insufficient collaboration between various teams. These issues contribute significantly to delays, referral bouncing, and the pervasive problem of 'hidden waiting lists' for specialist treatment, impacting patient wellbeing and increasing reliance on emergency services.
Navigating AI: Trust, Data, and Explainability
General concerns about AI-based tools revolved around the principle of "garbage in, garbage out"—highlighting the critical need for accurate and diverse training data. Participants questioned the 'explainability' of AI, fearing clinicians might not understand how a tool reached its conclusions. The potential for traumatizing patients with risk predictions was also raised, emphasizing the need for constructive and empathetic application.
AI's Role: Mental vs. Physical Health
A significant discussion point was the perceived difference in applying AI to mental versus physical health. While tools like QRISK for physical health are seen as more factual and easier to estimate, mental health prediction (like DTD) was viewed as more complex due to patient factors and narrative data. Despite differences, participants recognized the deep interlinkage between mental and physical health, suggesting a unified approach is often necessary.
Benefits and Harms of AI in Mental Health
While acknowledging potential harms like over-reliance on AI or incorrect data leading to false predictions, participants were generally positive. They saw AI as a valuable addition to clinical consultations, offering benefits such as improved prediction accuracy, reduced bias (especially for underrepresented communities), better identification of specialist needs, and ultimately, a wider range of treatment options and faster access to care.
Future Considerations: Inclusivity and Human Touch
For future AI implementation, participants stressed the importance of inclusivity and accessibility (considering language, ethnicity, neurodiversity). A unanimous sentiment was that AI tools should be an addition, not a replacement, for human clinical consultations. Empathy and effective communication remain paramount, with the human element of treatment and a strong therapeutic relationship being non-negotiable.
16
Participants in Focus Group
52.5
Median Age of Participants (Years)
87%
White British Participants
CHRONOSIG AI-Assisted Triage Process
Patient Referral Documents & Existing Medical Notes
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CHRONOSIG CDST Processing (NLP/ML)
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Suggested Triage Outcome Delivered
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SMH Services Assisted Decision Making
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Improved Patient Pathway & Care
23%
Patients Waiting >12 Weeks for Treatment
AI in Mental vs. Physical Health: A Comparison
| Feature | AI in Physical Health (e.g., QRISK) | AI in Mental Health (e.g., CHRONOSIG) |
|---|---|---|
| Complexity of Prediction | More factual, often easier to estimate with clear biological markers. | More based on patient factors, holistic context, and nuanced narratives, harder to predict. |
| Interrelatedness with Other Health | Physical conditions can influence or be influenced by mental health. | Mental health conditions often deeply interlinked with physical health and social determinants. |
| Data Type Focus | Primarily structured, quantitative data (e.g., lab results, vital signs). | Heavily reliant on narrative, unstructured free text data from EHRs and referrals. |
| Patient Acceptance & Concerns | Generally higher acceptance for clear-cut risk factors. | Concerns about loss of human element, 'explainability' of AI, bias in data, and holistic assessment. |
78%
Hidden Waiting List Patients Using Emergency Services
CHRONOSIG: Transforming DTD Patient Pathways
Introduction: The CHRONOSIG project directly addresses critical bottlenecks in mental healthcare by leveraging AI for improved referral and triage processes, particularly for difficult-to-treat depression (DTD).
The Challenge: Patients with Difficult-to-Treat Depression often face prolonged 'hidden waiting lists', repeated referrals, and a lack of timely access to appropriate specialist care due to the complexity of their needs and fragmented care pathways.
The Solution: CHRONOSIG uses natural language processing (NLP) and machine learning on electronic health records (EHRs) to generate a patient's 'longitudinal signature,' identifying DTD caseness more accurately and suggesting optimal triage outcomes.
The Result: This leads to improved accuracy in identifying specialist needs, reduced waiting times, and more personalized treatment options, allowing clinicians to make better-informed decisions and ensure patients receive timely, effective interventions, reducing reliance on emergency services.
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