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Digital technology has been a rapidly emerging field in clinical care over the past decade (Steinhubl, Muse, & Topol, 2015). Previous work had shown the utility of electronic applications for various purposes such as collecting informed consent, gathering patient reported outcomes, assessing quality of life, providing medical education, delivering diagnostics, and filling out questionnaires (Abernethy et al., 2008). The advantages of data collection via electronic application include reduced costs in the long term, easier data storage, improved data quality, and guaranteed data consistency (Barentsz et al., 2014; Kumar et al., 2013; Lofland, Schaffer, & Goldfarb, 2000). However, the emerging health care technologies may exacerbate the digital divide in certain patient populations (Arcaya & Figueroa, 2017).
Underserved populations (Silow-Carroll, Alteras, & Stepnick, 2006) who are with limited health literacy, low in-income, elderly, racial and ethnic minorities are usually excluded from new health technology studies because they are unable to complete the study requirements, or lack interests in the studies (Chaudry, Connelly, Siek, & Welch, 2012; M. S. Goel et al., 2011; Mita Sanghavi Goel et al., 2011; Hahn et al., 2004). Few previous studies have evaluated the use of digital health and electronic devices in such populations (Aiello et al., 2006; Bravo, O’Donoghue, Kaplan, Luce, & Ozanne, 2014; Levy, Janke, & Langa, 2015; Lin, Neafsey, & Strickler, 2009; Sarkar et al., 2010; Vargas, Robles, Harris, & Radford, 2010). Moreover, these populations may have a harder time learning to use the electronic devices and less likely to access the technology (Levy et al., 2015; Sarkar et al., 2010).
Without health technology, accurate and detailed data collection totally relies on paper forms. Paper based data collection requires significant additional time resources for manual data entry, which can be complicated and impractical for primary care providers in poorly-resourced health care systems (Qureshi et al., 2007). Studies showed that the time limitation of the office visit caused by comprehensive family and personal history collection led to low preventive service delivery rate, although US Preventive Services Task Force (USPSTF) and many national agencies provide guidelines to prevent chronic diseases and cancer (Yarnall, Pollak, Østbye, Krause, & Michener, 2003).
Given the importance of family and personal history as predictors of cancer, efficient data collection is essential especially when the amount of provider-patient time is more limited. As breast cancer is the leading cancer diagnosis in Hispanic women (“Cancer Facts & Figures for Hispanics/Latinos 2015-2017,” 2017), comprehensive personal and family risk factor information must be collected in order to stratify breast cancer risk (Murff, Byrne, Haas, Puopolo, & Brennan, 2005; Rich et al., 2004). Therefore, it is critical to develop and test new information collection strategies to increase data collection efficiency, decrease physicians’ workload in the busy clinics, and while ensuring patient satisfaction especially in underserved populations.