Voice Technology - an Opportunity to Broaden Participation in Patient Reported Outcomes Research

Author(s)

Demiya S1, Neumann C2, Chu C3, Chand K1, Yu E1
1IQVIA Solutions Japan, Tokyo, Japan, 2easyDialog GK, Tokyo, Japan, 3IQVIA Solutions Japan, Tokyo, 13, Japan

OBJECTIVES : Paper surveys tend to exclude out-of-the box answers and require cognitive/literate skills. In this study, participants were interviewed by a voice bot understanding natural language and compared with a paper survey containing the same questions.

METHODS : Based on the Japanese version of EQ-5D-5L, we designed a dialog flow that was implemented as an Amazon Alexa skill deployed on Echo Dot devices. The dialog featured a short-spoken tutorial, 8 demographic, 5 EQ-5D-5L health questions, and 1 question soliciting survey feedback. In order to capture most of the expected linguistic and stylistic variations, the focus was on training a robust ing state-of-the-art NLP (Natural Language Processing) techniques, and on designing a user-friendly voice dialog by 5 robust questions. After completing the voice survey participants were asked to give the same answers in a corresponding conventional EQ-5D-5L paper survey. Results of both survey methods were semi-automatically correlated and then curated by hand. A pilot study was undertaken prior to a final convenience sample of 100 participants to be performed.

RESULTS : From the pilot 22 participants completed both methods of interview. Differing responses were observed in 31.8% of the EQ-5D-5L answers given by natural language answers comparing to fixed wordings used by the EQ-5D-5L categories. With the paper survey as the baseline, 93.6% of the voice survey answers were classified correctly and 6.4% of all answers did not match any of the 5 levels of difficulty.

CONCLUSIONS : Voice technology can make surveys accessible to currently excluded target groups broadening participation, whilst accommodating their desire to express nuances and out-of-box answers that do not fit the rigid standard categories of a paper survey, more participants to supply additional new thoughts and dimensions that were not foreseen by the creators of the survey whilst responding to questions.

Conference/Value in Health Info

2020-09, ISPOR Asia Pacific 2020, Seoul, South Korea

Code

PNS2

Topic

Medical Technologies, Methodological & Statistical Research, Patient-Centered Research

Topic Subcategory

Artificial Intelligence, Machine Learning, Predictive Analytics, Digital Health, Patient-reported Outcomes & Quality of Life Outcomes, Survey Methods

Disease

No Specific Disease

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