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Where Evidence & Value Meet

Where Evidence & Value Meet

Eleos is proven to deliver significantly higher results than the leading alternatives in clinical settings. By using Natural Language Processing (NLP) to extract clinical evidence-based insights, Eleos helps facilitates greater client improvement.

Eleos is a proven tool for success.

The Technology for Any Treatment Modality

Eleos identifies empirically-supported interventions and intervention strategies directly from the session

Cognitive Behavioral
Therapy (CBT)

Dialectical Behavioral
Therapy (DBT)

Acceptance and Commitment
Therapy (ACT)

Motivational
Interviewing (MI)

Zero Suicide Framework
(ZSF)

Supportive
Psychotherapy

Proven Results

Leveraging the Power of Nondisruptive Technologies to Optimize Mental Health Treatment.
Sadeh-Sharvit, S., & Hollon, S. D. (2020). Leveraging the Power of Nondisruptive Technologies to Optimize Mental Health Treatment: Case Study. JMIR mental health, 7(11), e20646.

Depressive Symptoms

Measured with industry assessment standard PHQ-9

48% Reduction
after full treatment
36% Reduction
after 4 weeks
Compared to real-life settings, where depression remission rates are 37%.

Depressive Symptoms

Measured with industry assessment standard PHQ-9

Anxiety Symptoms

Measured with industry assessment standard GAD-7

34% Reduction
after 4 weeks
55% Reduction
after full treatment
Compared to real-life settings, where depression remission rates are 37%.

Anxiety Symptoms

Measured with industry assessment standard GAD-7

Rigorous Clinical Studies

Collaborators Status Clinical Trial Project
Ozark Center, The Freeman Health Center, Missouri   
Published
Randomized controlled trial (RCT)
Effects of an Artificial Intelligence Platform for Behavioral Interventions on Depression and Anxiety Symptoms
Simon Rego, PsyD, Albert Einstein College of Medicine and Montefiore Medical Center   
Published
Retrospective Study
Use of Natural Language Processing to Identify Climate Distress in Therapy
C. Barr Taylor, MD, Stanford University, and Josef Ruzek, VA   
Published
Algorithm Development and Validation
Machine Learning Model to Predict Assignment of Therapy Homework in Behavioral Treatments
Katherine C. Kellogg, PhD Department of Work and Organization Studies, MIT Sloan School of Management   
Published
Review paper
Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians
Hanni Flaherty, PhD, Yeshiva University School of Social Work   
Published
Perspective
Using technology to enhance experiential learning through simulated role plays: A teaching note
Simon Rego, PsyD Albert Einstein College of Medicine and Montefiore Medical Center   
Published
Retrospective Study
A Comparison Between Clinical Guidelines and Real-World Treatment Data in Examining the Use of Session Summaries
Steve Hollon, PhD, Vanderbilt University   
Published
Case report
Leveraging the power of nondisruptive technologies to optimize mental health treatment
Donna Sheperis, PhD, Palo Alto University   
Published
Perspective
Using AI-Supported Supervision in a University Telemental Health Training Clinic
C. Barr Taylor, MD, and Joe Ruzek, PhD, Stanford University   
Published
Retrospective study
Action recommendations review in community-based therapy and depression and anxiety outcomes: a machine learning approach
Joe Ruzek, PhD, Stanford University; Eduardo Bunge, PhD, Palo Alto University; Ellen Fitzsimmons-Craft, PhD, Washington University at St. Louis   
Published
Perspective
Training the Psychologist of the Future in the Use of Digital Therapeutics
Donna Sheperis, PhD, Palo Alto University; Eduardo Bunge, PhD, Palo Alto University   
Submitted for publication
Program evaluation
Integrating Artificial Intelligence in a Telemental Health Clinic
Steve Hollon, PhD, Vanderbilt University   
Submitted for publication
Perspective
Revolutionizing Mental Healthcare Services through AI-Augmentation: A New Model
Eleos Health team   
Submitted for publication
Retrospective study
Artificial Intelligence to Capture Social Determinants of Health Discussions in Behavioral Health Sessions
Björn Schuller, PhD, Imperial College London   
Submitted for publication
New Methodology
The Sound of Mental Health: Audio Features as Indicators of Depression and Anxiety Symptoms in Behavioral Treatment
Ron Kessler, Harvard University Medical Center   
Data analysis
Case series
Do therapist factors and participant involvement mediate the effects of group therapy for youth with depression and anxiety?
Jonathan Huppert, PhD, The Department of Psychology, The Hebrew University   
Data analysis
Randomized controlled trial
A single-session CBT-based intervention for anxiety sensitivity
Paula Riggs, MD, University of Colorado Denver School of Medicine   
Data collection
Case series
Evaluation of Clinical Effectiveness, Cost, and Implementation Factors to Optimize Scalability of Treatment for Co-Occurring SUD and PTSD Among Teens
Ron Kessler, PhD, Harvard University Medical Center   
Data collection
Case series
Care consultant's adherence and competence in crisis hotlines for Health Plans
Trilogy Behavioral Health, Inc.   
Data collection
Randomized controlled trial
Evaluating the Effectiveness of an AI Platform in Outpatient Therapy
Orya Tishby, PhD, The Department of Psychology, The Hebrew University   
Data analysis
Case series
Therapist and client interactions in short-term psychodynamic treatment for depression

The Future of Behavioral Health

“ Eleos Health enables us to pull the admin
function out of the therapeutic relationship
and show clinicians their impact. ”

Carl Clark, MD President & CEO, WellPower

“ Knowledge without data is opinion. We need to have data-informed organization at every level. Eleos provides that to us. ”

Dr. Dale Klatzker CEO, Gaudenzia Home

“ AI helps us to drive revenue, but perhaps more importantly, it can help us recruit and train new staff ”

Hattie Tracy, President & CEO, Coleman Health Services

It's time to free your clinicians from administrative tasks.