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• W3204127050 abstract "Current assessment methods have enabled many advancements in the fields of psychology and psychiatry by standardizing diagnoses for treatment, prognosis and research. They have created a “common language” for both clinicians and researchers. Such assessments, like the Patient Health Questionnaire and the Generalized Anxiety Disorder Questionnaire, have demonstrated high sensitivity and specificity for detecting categorical diagnoses. With these advantages, however, come some limitations, which are important to recognize and which motivate an understanding of the role technology might have in advancing the field. Some of the limitations of self report measures and structured clinical interviews include: reliance on subjective self report; subjectivity to individual recall bias; lacking in temporal precision, and lacking in capacity to account for naturalistic contextual factors. In addition, such assessments require effort and some degree of psychological, behavioral, and physiologic insight on the part of the patient or client. Technology has the potential to complement, and thereby improve traditional assessment methods by addressing their shortcomings. Advances in the use of data from electronic health records, personal mobile sensors, social media platforms, and search engine databases paired with machine learning technologies provide opportunities for improving mental health on an individual and population level. Pairing such data with predictive models can modernize clinical assessment, making assessments accessible, continuous, low cost, scalable, and better able to account for the pathophysiologic complexities of mental health disorders." @default.
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• W3204127050 date "2022-01-01" @default.
• W3204127050 modified "2023-09-26" @default.
• W3204127050 title "Technological Advances in Clinical Assessment" @default.
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• W3204127050 doi "https://doi.org/10.1016/b978-0-12-818697-8.00171-0" @default.
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