Healthcare Consumerism and the Use of Personal Health Records (PHR)

Healthcare consumerism may sound new but it has already existed for over a decade, since patient-centered services became the focus. It is defined as the consumption of healthcare services by patients also termed as consumers from health providers and institutions (Cooper, 2012). Nowadays, the trend for healthcare consumerism is increasing exponentially with provider-patient partnership, online resources and technological advancements on healthcare (Dell EMC, 2017). Innovations, such as mobile medical apps, smartphones and health information technology (HIT), have empowered consumers over their health in the last ten years. Indeed, there are several aspects of healthcare consumerism but this paper will focus on the use of personal health records (PHR), as studied by Jilka et al. (2016).
Introduction
PHRs are HIT applications that allow digital access, management and sharing of health data among the consumers themselves with assurance of privacy and security (Abramson et al., 2014). Past studies showed that PHRs promote active participation and effective communication between patients and healthcare providers such as in prescribing medications and scheduling appointments. These benefits are in accordance to the Stage 2 requirements for the meaningful use (MU) incentive program in the adoption of electronic health records (EHR). However, the utilization of PHR remains low since it has started last 2001. Jilka et al. (2016) identified healthcare vendors’ and providers’ unwillingness to serve the patient fully and current policies as barriers in its widespread utilization. If these are addressed effectively, the study forecasted that the PHR products are more likely to surpass the Stage 3 requirements which feature secure messaging, access patient education materials online and access to their entire record (Jilka et al., 2016).
Methodology
The study utilized survey data from the Health Information National Trends Survey (HINTS) of US citizens conducted on years 2008, 2011 and 2013. Furthermore, PHR adoption rates were calculated based on consumers’ utilization of both features of PHR: 1) electronic storage of health information and 2) as a means of communication with healthcare providers (Jilka et al., 2016).
The study also used Bass modeling, which predicts the number of customers and the time to which they will adopt a novel product. Bass model named two broad factors that affect the prediction: 1) external influence coeeficient (p) and 2) internal influence coefficient (q). The former includes the influence of innovation and advertising while the latter is from the impact of word-of-mouth and diffusion across society. The study then conducted a sensitivity analysis to test these coefficients. The study used years 2001 and 2007 as PHR innovation start dates based on the timeline of milestones of PH and year 2004 as midpoint (Jilka et al., 2016).
Statistical analyses and forecasts were done using Microsoft Excel for Mac 2011 (Microsoft Corporation). The study utilized ...