Improve treatment outcomes by analyzing language to uncover actionable insights into patient psychology and the quality of the therapeutic alliance.

Receptiviti APIs enable digital health platforms to improve and predict outcomes by enhancing detection, measurement and understanding of a wide range of linguistic markers of patient psychology, the therapeutic alliance, signals of comorbidities, and indictors of treatment progression that are associated with successful treatment outcomes.

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, Receptiviti APIs are integrated into a growing number of digital health platforms.

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.

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?

Increasing 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.

Improving 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.


Improving 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.

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

Receptiviti's 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:

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