Ignite Creation Date:
2024-05-06 @ 8:03 PM
Last Modification Date:
2024-10-26 @ 3:19 PM
Study NCT ID:
NCT06233968
Status:
RECRUITING
Last Update Posted:
2024-02-14
First Post:
2024-01-23
Brief Title:
Using Data Science To Center Patient Perspectives in Mechanism Discovery
Sponsor:
Cambridge Health Alliance
Organization:
Cambridge Health Alliance
Study Overview
Official Title:
Using Data Science To Center Patient Perspectives In Psychological Mechanism Discovery and Intervention Development
Status:
RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
CPP
Brief Summary:
Including patient perspectives when developing new therapy interventions is crucial because it can help to understand response heterogeneity and promote engagement Yet analyzing patient interview data is difficult and time-consuming This study aims to explore the potential for natural language processing and deep learning to analyze patient interviews and identify potential ways in which therapy leads to psychological change This study will recruit participants from an existing clinical service that offers a 16-week online group therapy model and adjunct individual therapy sessions called Program for Alleviating and Resolving Trauma and Stress PARTS based on a therapy called Internal Family Systems IFS The investigators will use a mixed methods approach applying natural language processing and deep learning to develop models that identify potential mechanisms of change These models will be based on patient perspectives of psychological change as expressed in interviews and be compared to models based on clinical measures
Detailed Description:
Demand for cost-effective novel scalable trauma-focused interventions is high Prevalence rates of posttraumatic stress disorder PTSD and Complex PTSD in US community mental health clinics are estimated to be as high as 50 Yet response heterogeneity to PTSD interventions remains high with non-response rates reaching 50-60 and dropout rates for traditional interventions ie cognitive behavioral exposure therapies at 30-40 Moreover research populations in typical stepwise efficacy-driven clinical research trials are often characterized by strict exclusion criteria and low representation from underrepresented communities The homogeneous nature of efficacy-based research populations creates an incomplete picture especially in public sector and community-based mental health facilities Studies have suggested not only does this homogeneity limit effectiveness across diverse populations but may contribute to exacerbating health disparities
Engaging patient perspectives is crucial to research because it can provide insight into response heterogeneity and engagement ultimately leading to an understanding of mechanisms and creating more patient-centered interventions One way to center the patients voice and increase the potential of identifying unique mechanisms of change for a novel therapy is to use qualitative interviews because it directly accesses the lived experience and its context Despite the potential benefits of utilizing qualitative data in stepwise randomized control trials several obstacles persist including resource constraints the inability to quantify interactive elements and concerns regarding the practical value of the gathered information Innovative methods that reliably and rapidly extract value-laden relevant themes and discern non-verbal conversational elements may facilitate the integration of patient experience and inclusion of their perspectives in clinical intervention trials
This single-arm study aims to evaluate the feasibility of using natural language processing NLP and deep learning to identify potential mechanisms of PTSD symptom change from patient interviews The study will utilize ongoing cohorts from a clinical service that offers a 16-week live-online group therapy model and adjunct individual therapy sessions called Program For Alleviating And Resolving Trauma and Stress PARTS that uses the IFS model The investigators will use a convergent mixed methods approach applying machine learning and natural language processing to develop models that identify potential mechanisms of change
Analysis The investigators will use several different methods to develop our models including Latent Dirichlet Allocation pre-trained language models transfer learning recurrent neural networks generative adversarial network and penalized regression-based models These models will use data derived from patient perspectives of psychological change as expressed in interviews and will be compared to models derived from clinical measures The study will use standard performance metrics and cross-validation scores to evaluate comparative performance of the models As an exploratory aim the study will evaluate the feasibility of using features derived from language processing models and clinical measures to predict individual therapy visits post-intervention The exploratory data will also include structured clinical data social determinants of health and therapy-based utilization dates provider type length
Anticipated results The development of two validated models one derived from patient interview data and the other based on clinical measures to comprehensively identify mechanisms of change from group-based therapy models of IFS for PTSD
Engaging patient perspectives is crucial to research because it can provide insight into response heterogeneity and engagement ultimately leading to an understanding of mechanisms and creating more patient-centered interventions One way to center the patients voice and increase the potential of identifying unique mechanisms of change for a novel therapy is to use qualitative interviews because it directly accesses the lived experience and its context Despite the potential benefits of utilizing qualitative data in stepwise randomized control trials several obstacles persist including resource constraints the inability to quantify interactive elements and concerns regarding the practical value of the gathered information Innovative methods that reliably and rapidly extract value-laden relevant themes and discern non-verbal conversational elements may facilitate the integration of patient experience and inclusion of their perspectives in clinical intervention trials
This single-arm study aims to evaluate the feasibility of using natural language processing NLP and deep learning to identify potential mechanisms of PTSD symptom change from patient interviews The study will utilize ongoing cohorts from a clinical service that offers a 16-week live-online group therapy model and adjunct individual therapy sessions called Program For Alleviating And Resolving Trauma and Stress PARTS that uses the IFS model The investigators will use a convergent mixed methods approach applying machine learning and natural language processing to develop models that identify potential mechanisms of change
Analysis The investigators will use several different methods to develop our models including Latent Dirichlet Allocation pre-trained language models transfer learning recurrent neural networks generative adversarial network and penalized regression-based models These models will use data derived from patient perspectives of psychological change as expressed in interviews and will be compared to models derived from clinical measures The study will use standard performance metrics and cross-validation scores to evaluate comparative performance of the models As an exploratory aim the study will evaluate the feasibility of using features derived from language processing models and clinical measures to predict individual therapy visits post-intervention The exploratory data will also include structured clinical data social determinants of health and therapy-based utilization dates provider type length
Anticipated results The development of two validated models one derived from patient interview data and the other based on clinical measures to comprehensively identify mechanisms of change from group-based therapy models of IFS for PTSD
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None