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As Covid-19 has stretched healthcare resources thin, digital health technology has sped up innovation to offer more efficient mechanisms to deliver care. Defined as the tools used to empower individuals to improve their health and wellness, digital health technology has the potential to significantly improve the healthcare system. While investment in the industry continues to grow, divisions exist between who actually uses the technology. My thesis seeks to understand how the adoption of digital health has evolved over time, how it varies across demographic features and to identify what similarities different groups of digital health customers share. Using national survey data from Rock Health, I find overall digital health adoption rates are increasing over time but dropped slightly in 2020. More specifically, individuals are searching for in-person services and receiving care electronically (with live video being an exception) less but are owning wearables and tracking digital health metrics more. Age, income, education, gender and health rating all significantly impact whether an individual chooses to adopt digital health. Using K-Means clustering, I find three distinct customer segments with their own defining features: group one is defined as young, healthy & wealthy and has the highest adoption rates, group two is middle-aged, moderate income & fair health, and group three is old, lower-income & unhealthy with the lowest adoption rates. I conclude with what needs to be done to increase the engagement of individuals in group three.

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digital health, customer segmentation, trend analysis

Trends in the Adoption of Digital Health Technology