Measure language-based indicators of mental health, commorbidities, and the therapeutic alliance with Receptiviti's APIs.

Receptiviti is built on a foundation of decades of research into the unique relationship between language and mental health by renowned experts in language, psychology, machine learning, and data science. Our API is integrated into a growing number of digital health platforms.

Learn why traditional natural language processing isn't suited for digital health text analytics.
News: New research from Leiden and Utrecht Universities shows Receptiviti outperforms BERT, 95.2% vs. 60.7% in predicting mental disorders from languageRead more
 
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The language people use when they write, speak, and communicate with others, including their interactions with therapists and digital health platforms, can provide insight into their psychological state and their status along their journey toward healing.

Extensively validated science, used in thousands of mental health and therapy-related studies

Receptiviti identifies many of the language-based psychological indicators associated with mental health and distress such as depression, stress, and social anxiety. Our models have been constructed and extensively validated by panels of psychologists and have been cited in over 19,000 research studies, many of which focus on understanding the linguistic fingerprints of mental health, coping, disease, recovery, and treatment delivery.

Here's a small sample of the research:

Watch the TEDTalk
Receptiviti co-founder Dr. James Pennebaker discusses the fascinating relationship between stop words and human psychology.
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How is Receptiviti used by

digital health platforms?

Receptiviti helps digital health platforms enhance their diagnostic capabilities, reduce patient attrition, and improve outcomes. Our indicators can be used to measure the therapeutic alliance between provider and patient, monitor provider empathy, identify signals of comorbidities, and track treatment progression and success.

Increase the likelihood of successful outcomes identifying the psychological markers of progression and success through changes in functional language patterns. For example, markers of reduced anxiety include diminishing use of words associated with negative emotion, anxiety, causation, insight-related language, and increasing use of past-tense related language.

Improve patient outcomes by measuring indicators of the therapeutic alliance. According to Lambert and Barley (2001), 30% of patient outcomes in psychotherapy can be attributed to the therapeutic alliance and facilitative conditions, such as empathy, warmth, and congruence. Receptiviti measures empathy through analysis of provider language style, and through quantification of language synchrony between healthcare providers and patients.

 

Improve patient adherence by tailoring communications to each patient’s psychology and cognitive style. By analyzing patient language, Receptiviti uncovers how they think, make decisions, and interpret information, enabling providers to optimize communication style with patients and platform users to increase engagement, improve adherence, and the likelihood of treatment success.

Talk with one of our digital health API specialists:

Have questions, or want to learn more about how digital health providers typically use Receptiviti's APIs in their research and products?

 

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