Real World Evidence (RWE) has become increasingly important in understanding and treating mental health conditions, particularly those caused by trauma. This is an important factor on the rehabilitation pathway, which often focuses on the physical challenges that can be addressed by medication; or with a standardised approach to the detriment of the individual patient and the insights that RWE might give us.
As we seek to understand the complexities of trauma-related mental health issues, artificial intelligence (AI) offers tools to transform the insights we can get from RWE reporting. This article looks in detail as to how AI can be used to gather, analyse, and report data on trauma-induced mental health cases, as well as providing a case study of how it has already been used by one organisation – PTSD Resolution – to understand the impact of their work on different groups of patients
Understanding Trauma-Related Mental Health and RWE
Trauma can lead to various mental health conditions, including post-traumatic stress disorder (PTSD), anxiety, depression, complex trauma disorders and moral injury. These conditions often present unique challenges in treatment and long-term management.
RWE can include data from a much wider range of sources than is typically analysed in a clinical trial. These can include electronic health records, patient-reported outcomes, wearable devices, and social media; all of which can provides crucial insights into how these conditions manifest and respond to treatment in real-world settings.
How AI Enhances RWE Reporting for Trauma-Related Mental Health
One of the greatest strengths of AI is in aggregating and integrating diverse data sources. For trauma-related mental health cases, this means combining clinical data with patient-reported experiences, physiological data from wearables, and even social media activity. This holistic approach provides a more complete picture of a patient's mental state and how it evolves over time.
It is able to do this through it Natural Language Processing (NLP) capability. Trauma narratives are often complex and nuanced. NLP algorithms can analyse unstructured data from clinical notes, patient interviews, and online forums to identify patterns, triggers, and treatment responses specific to trauma-induced mental health conditions. This capability allows for a more nuanced understanding of patient experiences and treatment efficacy.
Building on this base AI can open up the potential of predictive analytics. By analysing large datasets of trauma survivors, AI can predict potential outcomes and identify early warning signs of deterioration or relapse. This predictive capability is particularly valuable in managing conditions like PTSD, where timely interventions can significantly impact patient outcomes.
Every individual’s experience of trauma is different and effective support requires a tailored approach to support. That support needs to be personalised, adaptive and dynamic. , something that is not offered in the way healthcare services are normally structured. AI can help tailor treatments to individual patients based on their specific trauma experiences, symptoms, and response patterns. This personalization is crucial in trauma-related mental health, where one-size-fits-all approaches are often ineffective. This needs to be backed up by Real-Time Monitoring and Reporting. Once again AI-powered systems can continuously monitor patient data, enabling real-time reporting of changes in mental health status. For trauma survivors, this immediate feedback can be crucial in managing triggers and preventing acute episodes.
For organisations delivering trauma support, AI can help with standardization and Quality Control to improve adherence to standardized reporting protocols, reducing variability in how trauma-related mental health data is collected and reported. This standardization enhances the reliability and comparability of RWE across different studies and clinical settings.
In dealing with sensitive information related to trauma, AI can assist in maintaining patient privacy through advanced anonymization techniques, ensuring ethical use of data while still allowing for comprehensive analysis.
AI can enhance the capability of individual therapists by enabling them to augment their experience with insights from a much larger number of patients. AI can help identify subtle patterns in complex trauma cases that might be overlooked by human observers. This capability is particularly valuable in understanding the long-term effects of repeated or prolonged trauma exposure.
Challenges and Considerations
While AI offers significant potential in improving RWE reporting for trauma-related mental health, several challenges need to be addressed. Data privacy and security to ensure the confidentiality of sensitive trauma-related information is paramount. AI systems must be carefully designed to avoid perpetuating biases in trauma assessment and treatment.
Seamless integration with current healthcare IT infrastructure is crucial for widespread adoption. This means it is often easier for small organisations to become early adopters if they have less of a legacy system to link in to. Finally building trust in AI systems among mental health professionals and patients is essential for successful implementation. There will be fears among both professionals and patients about the approach and also how to challenge insights coming from a ‘black box’. AI can in fact help to explain its own logic – so long as there are practitioners ready to challenge it. This potential iteration can improve the system and also help to build the understanding amongst practitioners as to how it can augment their capacity rather than substitute it.
A case study: PTSD Resolution
PTSD Resolution is a UK-based charity that specializes in providing trauma-focused therapy to veterans, reservists, and their families.
Their exclusive access to some 300 Human Givens Therapists centres on brief, targeted interventions designed to address post-traumatic stress disorder (PTSD) and related mental health issues. Their Human Givens Therapy (HGT) typically involves an average of 6-7 sessions, making it a relatively short-term intervention compared to many traditional PTSD treatments.
One of the key strengths of PTSD Resolution's approach is its flexibility and adaptability to individual needs, using a range of interventions, including psychoeducation, guided imagery, breathing techniques, and the "rewind" technique for processing traumatic memories. This tailored approach allows therapists to address the specific needs of each client, whether they're dealing with anxiety, depression, sleep disturbances, or moral injury. To date they have insights from more than 4000 patients providing Real World Evidence (RWE) on the impact of their approach. RWE insights provide a more accurate picture of how the therapy works in actual practice, outside the controlled conditions of clinical trials. This is particularly important when dealing with complex populations like veterans or incarcerated individuals, whose experiences may not be fully captured in traditional research settings.
Secondly, RWE allows for the evaluation of outcomes that matter most to patients in their day-to-day lives. The report shows improvements not just in clinical measures like the GAD-7 or PHQ-9, but also in practical areas like sleep quality, family relationships, and coping skills.
Thirdly, RWE can help identify unexpected benefits or challenges of the therapy. For instance, the report revealed that the treatment was particularly effective for prison clients, a finding that might not have been apparent without real-world data.
Like PTSD Resolution many organizations in the mental health field are beginning to leverage artificial intelligence to enhance their services and research capabilities. AI could potentially be used by PTSD Resolution to analyse large datasets of treatment outcomes, identify patterns in symptom presentation or recovery, or even assist in tailoring treatment plans to individual clients.
PTSD Resolution's commitment to collecting and analysing real-world data is evident in their comprehensive reporting. They use standardized measures like the PCL-5, GAD-7, and PHQ-9 to track client progress, and supplement this quantitative data with qualitative insights from therapy sessions. This mixed-methods approach provides a rich understanding of the therapy's impact.
The organization's focus on both military and civilian populations, including those in the criminal justice system, demonstrates their commitment to addressing trauma across diverse contexts. Their success in treating complex cases, including those involving moral injury, highlights the potential of their approach to address some of the most challenging aspects of PTSD.
Reflecting on PTSD Resolution's innovative, flexible, and evidence-based approach to trauma therapy, combined with their commitment to real-world evaluation, positions them as a pioneer in the field of veteran mental health. Their work not only provides crucial support to individuals affected by trauma but also contributes valuable insights to the broader understanding of effective PTSD treatment, and how other organisations might be able to build on their approach to RWE, and potentially AI.
Future Directions
As AI technology continues to evolve, we can expect even more sophisticated applications in trauma-related mental health reporting. Future developments may include advanced emotion recognition tools to better assess trauma responses, AI-assisted therapy sessions that provide real-time insights to therapists; and predictive models that can identify individuals at high risk of developing trauma-related disorders before symptoms manifest.
Conclusion
AI has the potential to significantly enhance the quality of reporting Real World Evidence in trauma-related mental health cases. By providing more comprehensive, nuanced, and timely insights, AI can help clinicians better understand and treat these complex conditions. As we continue to refine these technologies, the integration of AI in mental health care promises to improve outcomes for trauma survivors and advance our understanding of trauma's impact on mental health.
Credits
Special thanks to the team at PTSD Resolution for sharing their research and approach to using RWE and AI, as well as the Douglas Bader Foundation
for their ongoing support for this series of briefing papers on the future of amputee support – current challenges and emerging opportunities.
So relevant and insightful