Public perspectives on the use of different data types for prediction in healthcare.

ABSTRACT:

Objective

Understand public comfort with the use of different data types for predictive models.

Materials and methods

We analyzed data from a national survey of US adults (n = 1436) fielded from November to December 2021. For three categories of data (identified using factor analysis), we use descriptive statistics to capture comfort level.

Results

Public comfort with data use for prediction is low. For 13 of 15 data types, most respondents were uncomfortable with that data being used for prediction. In factor analysis, 15 types of data grouped into three categories based on public comfort: (1) personal characteristic data, (2) health-related data, and (3) sensitive data. Mean comfort was highest for health-related data (2.45, SD 0.84, range 1-4), followed by personal

SUBMITTER: Nong P 

PROVIDER: S-EPMC10990535 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature