Patient and clinician opinions on internet search data use in therapy: Insights, considerations, and guidelines for integrating a new digital phenotyping measure

Abstract

Objective: The integration of technology in mental health care has proliferated to address accessibility and symptom monitoring through smartphones and apps. While the analysis of internet search data has proved fruitful in predicting various outcomes like youth suicidality and psychosis relapse, there has been scant research assessing opinions of patients and clinicians on adopting it in outpatient care, and no work on assessing the perceived utility of visualized data. We sought to address this gap by evaluating (1) the comfort, feasibility, and utility of sharing internet search data, and (2) various data visualizations from a novel pipeline to inform how patients and clinicians might start its potential integration immediately.

Methods: Patients (n = 21) and clinicians (n = 9) were interviewed on their opinions about integrating internet search data in therapy. Thematic analysis was used to derive common themes from interviews.

Results: We revealed three common themes: varying comfort with internet search data in therapy, benefits of internet search data in therapy, and risks of internet search data in therapy. Patients and clinicians were generally comfortable integrating internet search data into therapy, but expressed reservations regarding data privacy and security, liability and complete patient autonomy. More granular search data in visualizations was considered more useful when compared to vague search frequencies.

Conclusion: Our results reveal a spectrum of comfort and utility surrounding integrating internet search data in therapy. We introduce a simple data processing pipeline for dynamic visualizations of search data at a variety of comfort levels, laying the immediate groundwork for its personalized implementation in outpatient care.

Keywords: Internet search data; digital phenotyping; mental health; psychiatry; thematic analysis.

Figure 1.

Mock internet search data visualizations.

Figure 2.

Average visualization ratings.

Figure 3.

Example use case visualizations.