Background/ Purpose: Electronic personal health records (ePHRs) are commonly viewed as potential tools to improve clinical outcomes through increasing patients’ ability to self-manage healthcare. Older cancer survivors could be benefited from using ePHRs for their supportive care. This study aims to examine factors associated with ePHRs use among older cancer survivors.
Methods: This study analyzed data collected from the 2018 iteration of the Health Information National Trends Survey (HINTS 5, Cycle 2). The present study includes only respondents ages 65 and older who were diagnosed with cancer and were offered the use of ePHRs (N=207). Applying Anderson’s model, weighted binary logistic regression was used to examine factors predictive of ePHRs use.
Results: Around half of older cancer survivors reported accessing ePHRs (51.97%). Participants with more social support experienced lower odds of moderate/high ePHR use (OR=0.88). Confidence in ePHRs safeguards (OR=6.23) and health-related Internet use (OR=4.48) were found to be positively related to moderate/high ePHR use.
Conclusion and Implications: Despite various benefits of ePHRs, older adults are more likely to be left behind in terms of digital healthcare. Addressing the factors impacting the utilization of ePHRs is important to decreasing healthcare disparity in digital age for older cancer survivors who lack social support. More support regarding ePHRs use is needed due to their higher needs of ePHRs. Moreover, after offering access to ePHRs, healthcare providers should provide education programs guiding this particular population of older cancer survivors on the use of ePHRs and addressing their concerns about ePHRs’ security.
Around 17 million people in the United States were identified as cancer survivors, 63% of whom were over 60 years old (Miller et al., 2019). Due to the risks associated with aging, older cancer survivors not only require specialized supportive healthcare and surveillance from professional medical teams, but also require daily self-management. In the US, most patients showed positive attitudes toward accessible self-management, and patients were also satisfied with their records being recorded completely and accurately via online health information (Hassol et al., 2004; Jansen et al., 2015). Older cancer survivors are willing to manage their health by improving the accessibility of supportive healthcare, especially for healthy lifestyle programs (Jansen et al. 2015). Moody et al. (2015) revealed that online self-management for cancer survivors demonstrated the potential of providing supportive healthcare with different levels (Moody et al. 2015). Supportive care in self-management through online health records and information may enhance health outcomes and address future needs for older cancer survivors (Bluethmann et al. 2018).
The idea of personal health records (PHRs) emerged four decades ago to improve patient self-management and healthcare engagement (Bouayad et al., 2017). With the advent of the Internet era, updated versions of traditional personal health records created patient-centered platforms and aimed to assist patient self-management and improve healthcare outcomes (Archer et al., 2011; Bouayad et al., 2017). Electronic personal health records (ePHRs) in the present study refer to patients’ accessibility of their electronic health data by web pages or patient portals from healthcare providers (Halamka et al., 2008).
As a digital healthcare service, ePHRs were specifically designed to assist cancer patients with managing their own health and showed potential toward promoting patient–provider collaborations and improving healthcare outcomes (Baudendistel et al., 2015; Bouayad et al., 2017; Greenberg et al., 2017; Tarver et al., 2019). By providing various credible data, health information and knowledge to patients, ePHRs empowered patients to track their health status in collaboration with healthcare providers, thereby promoting early interventions (Tang et al., 2006). Additionally, ePHRs provided a path to achieve a better quality of shared information (Ancker et al., 2015). In sum, the ePHRs usage improved patient–provider communication and patient self-management, especially for those with cancers (Tang et al., 2006; Halamka et al., 2008). Despite addressing the functionalities of ePHRs, more studies are needed to elucidate the effectiveness of ePHRs in self-management associated with health behavioral-related factors to cancer patients and survivors, in order to reduce health disparities for older cancer patients and survivors.
Factors Associated with ePHRs Use
In the past decade, multiple studies have investigated the factors associated with improved patient–provider collaborations and patient self-management through the use of ePHRs. Tarver et al. (2019) investigated the perceived usefulness, ease of use, and satisfaction associated with ePHRs use among older colorectal cancer survivors. Colorectal cancer survivors highly rated the usefulness of ePHRs for summarizing cancer treatment and reviewing follow-up schedules, as well as the ability to search and identify necessary information from ePHRs (Tarver et al., 2019). In a study of patients with multiple chronic conditions, Greenberg et al. (2017) revealed a significant relationship between ePHRs usage and possession of health insurance/a regular healthcare provider. Factors related to patients utilizing ePHRs to add information, filter information, and communication electronically were explored. Greenberg and colleagues (2017) found that one of the central needs of cancer patients was to filter and categorize information. Archer and colleagues (2011) reviewed five sets of factors associated with ePHRs usage among patients, including system attributes, purpose, adoption and acceptance, barriers, and clinical outcomes and process changes. This scoping review demonstrated that the elderly with chronic conditions were more likely to adopt ePHRs with self-management and disease prevention. Furthermore, previous studies investigated demographic characteristics associated with ePHRs usage, including gender (Baudendistel et al., 2015; Gerber et al., 2014; Greenberg et al., 2017; Hassol et al., 2004; Tarver et al., 2019), age (Baudendistel et al., 2015; Gerber et al., 2014; Greenberg et al., 2017; Hassol et al., 2004; Tarver et al., 2019), race/ethnicity (Gerber et al., 2014; Greenberg et al., 2017; Hassol et al., 2004), marital status (Tarver et al., 2019), educational attainment (Hassol et al., 2004; Tarver et al., 2019), and household income (Greenberg et al., 2017; Tarver et al., 2019).
After reviewing existing literature, it became evident that most studies of ePHRs primarily focused on 1) exploring usefulness and functionalities of ePHRs, 2) assessing needs for ePHRs, and 3) evaluating satisfaction of cancer survivors and patients using ePHRs. Very limited research has examined behavioral health-related factors associated with ePHRs usage among older cancer survivors. The present study seeks to address this gap between functionalities of and healthcare practice with ePHRs by investigating the associations between behavioral health-related factors and ePHRs usage among older cancer survivors.
Recognizing the importance of assessing the behavioral health-related factors, the present study draws upon Anderson’s Behavioral Model of Health Services Use (ABM) to assess utilization of ePHRs. ABM included three main components that affected health care utilization directly or indirectly: social determinants, health services system, and individual determinants (Andersen & Newman, 1973). According to Anderson & Newman (1973), individual determinants of health care utilization (which are the focus of the current study) are dependent upon three factors: (1) predisposing factors—indicating the predisposition of the individual to use services, including demographic, social structural, and attitudinal-belief variables; (2) enabling factors—indicating the individual’s ability to secure services, including income, health care access and sources, health insurance, rural/urban environment, etc.; and (3) need factors—indicating the individual’s illness level, including self-perceived illness and evaluated illness.
The Present Study
By applying Anderson’s Behavioral Model of Health Services Use (1973), the purpose of the present study is to examine the associations between behavioral health-related factors (e.g. race/ethnicity, social support, and frequency of visiting healthcare provider) and ePHRs usage among older cancer survivors. The specific aims of this study are: (1) to describe ePHRs use level among participants, and (2) applying ABM, to examine predisposing, enabling, and need factors associated with ePHRs use among participants.
Study Population and Data Collection
This study analyzed data collected from the 2018 iteration of the Health Information National Trends Survey (HINTS 5, Cycle 2), which was collected through a self-administrated mailed survey between January 2018 and May 2018. Two-stage sampling strategy was applied for the data collection. The final data included 3,504 respondents, resulting in a response rate of 32.39%. As only individual-level data were available through HINTS, only individual determinants within the ABM model are examined in the current study. Given the focus of the study, participant records were included only if individuals were older than 65 years old, were diagnosed with cancer and were offered access to ePHRs. The final analytic sample within this study was 207. A public dataset was used, therefore approval from the Institutional Review Board was not needed. According to The Office for Research Compliance at The University of Alabama, Health Information National Trends Survey (HINTS) is listed as a dataset approved for use by investigators without seeking Institutional Review Board (IRB) approval.
The use of ePHRs was the main outcome of this study, which was measured by asking participants “How many times did you access your online medical record in the last 12 months?”. While responses were divided into 5 categories including “none”, “1 to 2 time”, “3 to 5 times”, “6 to 9 times”, and “10 or more time”, ePHRs use was analyzed as a dichotomous variable, where 0 represented “do not access at all” (none in the past 12 months) and 1 represented “access ePHRs at least one time” (more than 1 times in the past 12 months).
According to Anderson’s Behavioral Model of Health Services Use (1973), predisposing factors (gender, social support, confidence in ePHRs safeguard), enabling factors (frequency of visiting healthcare providers, annual household income, health-related internet use, health-related social media use, and general social media use) and need factors (perceived health status and evaluated health status) were included in the analyses.
Of the predisposing factors, gender was treated as dichotomous variables. Social support was measured continuously by 6-items about support from family or friends with a 5-point response scale ranging from “Never” to “Always” (0 to 4) in HINTS: (1) Is there anyone you can count on to provide you with emotional support when you need it?-such as talking over problems or helping you make difficult decisions?; (2) Do you have friends or family members that you talk about your health?; (3) Do you have someone to prepare your meals if you are unable to do it yourself?; (4) Do you have someone to take you to the doctor if you need it?; (5) Do you have someone to help with your daily chores if you are sick?; (6) Do you have someone to run errands if you are sick?. A total social support score was obtained by summing the score from each item, ranging from 0 to 24. In terms of confidence in ePHRs safeguards, participants were asked, “How confident are you that safeguards (including the use of technology) are in place to protect your medical records from being seen by people who aren’t permitted to see them?”, with a response ranging from “Not confident” to “Very confident” (0 to 2). Confidence in ePHRs safeguards was analyzed as a continuous variable ranging from 0 to 2.
With regards to enabling factors, frequency of visiting healthcare providers in the past 12 months was also dichotomized (0=below 5 times, 1=5 times and above). Health-related internet use and two variables regarding social media use were measured as continuous variables. For health-related internet use, respondents were asked in HINTS if they have used a computer, smartphone, or other electronic means to do any of the following (0=no, 1=yes): (1) looked for health or medical information for yourself; (2) looked for health or medical information for someone else; (3) bought medicine or vitamins online; (4) used e-mail or the Internet to communicate with a doctor or a doctor’s office; (5) tracked health care charges and costs; and (6) looked up medical rest results. A total score of health-related technology use was summed from each question (range from 0 to 6). In HINTS, social media was defined as using the Internet to connect with other people online through social networks like Facebook or Twitter. To measure social media use, respondents were asked if they have used the internet for any of the following reasons (0=no, 1=yes): (1) to visit a social networking site, such as Facebook or LinkedIn; (2) to share health information on social networking sites, such as Facebook or Twitter; (3) to write in an online diary or blog (i.e., Web log); (4) to participate in an online forum or support groups for people with a similar health or medical issue; (5) to watch a health-related video on YouTube. A total score of health-related social media use was summed from question (2), (4), and (5), ranging from 0 to 3. A total score of general social media use was summed from question (1) and (3), ranging from 0 to 2.
Perceived health status and evaluated health status were included in need factors. For perceived health status, Participants were asked, “whether a doctor or other health professional ever told them that they had any medical conditions (i.e., diabetes or high blood sugar, high blood pressure or hypertension, heart condition, chronic lung disease, arthritis or rheumatism, depression or anxiety disorder). To measure evaluated health status, the number of medical conditions diagnosed by health professionals was added up and analyzed as a continuous variable (range from 0 to 6).
In order to take complex sampling process into account, all data analyses were conducted by Stata/SE 15.1. First, descriptive statistics were conducted to examine the descriptive information of participants and weighted percentage under Anderson’s model among all participants. Secondly, bivariate analyses with chi-square tests were used to show the association of each factor with accessing to ePHRs. Factors correlated with utilization of ePHRs among older cancer survivors were then examined by binary logistic regression to estimate the effect size (odds ratios) of identified relationships. Jackknife replicate weights were applied for variance estimation in the analyses.
Table 1 shows descriptive information of ePHRs use, predisposing, enabling, and need factors among all participants. Around half of older cancer survivors reported accessed ePHRs (51.97%). Approximately, half of participants were female (50.8%) and married or living as married (53.5%). Participants had high social support (M=20.21, Range=0-24). Slightly less than half of participants visited health care providers more than five times in the past 12 months (40.12%). More than half of respondents had annual household income between $20,000 and $74,999 (52.98%) and one third of respondents had high household income, exceeding $75,000 (35.38%). The average levels of health-related internet use (M=2.773, Range=0-6), health-related social media use (M=0.36, Range=0-3), and general social media use (M=0.54, Range=0-2) were low. On average, participants reported two medical conditions (M=1.94, Range=0-6) and around 75.14% of participants reported good/very good/excellent health status. Additionally, Table 1 shows that health-related technology use was significantly associated with using ePHRs among older cancer survivors (p<0.001) without controlling other variables.
Table 1: Description of ePHRs Utilization, Predisposing, Enabling, and Need Factors (n=201)
Associated factors with using ePHRs among all participants are shown in Table 2. With respect to predisposing factors, a negative relationship was observed between social support on ePHR use, where participants with more social support experienced lower odds of ePHR use (OR=0.85, CI=0.73, 0.98, p<0.05). In addition, confidence in ePHRs safeguards was positively associated with using ePHRs (OR=6.23, CI=1.53, 25.28, p<0.05), indicating that participants with higher confidence level in ePHRs safeguards showed higher odds of using ePHRs. As for enabling factors, health-related internet use was also found to be positively related to ePHR use (OR=2.68, CI=1.84, 3.92, p<0.001). There was no need factors found to be linked to using ePHRs.
Table 2: Weighted Binary Logistic Regression examining associated factors with using ePHRs among Older Cancer Survivors
The present study examined the associations between behavioral health-related factors and ePHRs usage among older cancer survivors (applying Anderson’s Behavioral Model of Health Services Use) by addressing two specific aims: (1) describe the levels of ePHRs use among participants, (2) examine behavioral health-related factors associated with ePHRs use among participants.
Addressing the first aim, half of participants reported accessing ePHRs. Relevant literature on the utilization of ePHRs among older cancer survivors was limited. Nonetheless, Johnson and colleagues (2020) used data combined from HINTS 5 cycle 1 and cycle 2 and reported that 51% of cancer survivors accessed their ePHRs more than one time in the past 12 months, among which more than half were older adults above 65 years old. Although the rates of utilization of ePHRs has been increasing (Hong et al., 2020), older adults tended to have lower rates of use compared to their younger counterparts (Greenberg et al. 2017).
Regarding the second aim, results indicated that older cancer survivors who reported less social support tended to use ePHRs, which might be explained by certain aspects of social support. In this study, social support involved emotional support, friends or family to talk about health, help from others with meals, chores, errands when participants are not able to do it themselves, and having someone to accompany participants to doctors when it is needed. Participants who do not have friends or family to talk with about healthcare might be in higher need of health communication with healthcare providers or information to inform decisions around treatment of illness, which can be facilitated through ePHRs. Moreover, utilization of ePHRs enable remote refill requests for medications or remote completion of paperwork related to health care, which might be more frequently adopted by participants who do not have anyone to take them to hospitals. While the impact of social support on use of ePHRs among older cancer survivors has not been previously documented, one related study found that social support (including marital status, household composition, and care partners) improved odds of ePHRs among low income older adults (above 55 years old) (Arcury et al., 2017). This finding of positive associations between social support and ePHR use conflicts with the findings reported in the current study. Future research is necessary to explore the differential influence of social support on ePHR utilizations among older cancer survivors.
Participants with higher confidence in ePHRs safeguards were more likely to access their ePHRs. Although relevant studies among older adults or older cancer survivors are not found, privacy and security concerns were documented as a factor negatively associated with the intention to use ePHRs and the use of ePHRs. Previous studies have reported that concerns about security of ePHRs was perceived as a barrier to use ePHRs by older adults (Dontje et al., 2014; Mishuris et al., 2015; Tieu et al., 2015; Turner et al., 2015). Ancker and colleagues’ study (2015) suggested that concern for privacy of ePHRs was negatively associated with the use of ePHRs, which is in line with the current study. Focusing on adult participants with multiple medical conditions at all ages, Greenberg and colleagues (2017) did not find a significant relationship between confidence in ePHRs safeguards and accessing ePHRs. Future studies are needed to explore whether age modifies the relationship between confidence in ePHRs safeguards and ePHRs use.
Health-related Internet use was the enabling factor that was associated with moderate/high utilization of ePHRs. Participants reporting higher levels of health-related technology use may have more interest in their own health and more intention to apply health information technology, including ePHRs. While studies examining the link between health-related technology use and ePHRs utilization are rare, Taha and colleagues (2013) suggested that older adults with lower technology experience had more difficulty in using ePHRs. Previous studies also reported a positive relationship between internet use and ePHRs utilization (Ancker, et al., 2015; Arcury et al., 2017). More specifically, Kahn and colleagues (2009) indicated that broad adoption of ePHRs required consumers to have computer competency, including ability to navigate the Web, search information and send e-mails. Another study also found tailored and accessible training and support were necessary to help vulnerable patients with ePHRs utilization (Tieu et al., 2017). The positive relationship between health-related technology use and the utilization of ePHRs highlights the role of the digital divide in perpetuating health disparities.
This study had several limitations. First of all, as a cross-sectional study, the current study cannot examine the causality between the factors (predisposing, enabling, and need) and ePHRs utilization. Secondly, while ABM included three main components (social determinants, health services system, and individual determinants) that affected health care utilization directly or indirectly (Andersen and Newman 1973), the current study was only able to examine individual determinants of ePHR utilization due to the limited availability of systematic-level variables in HINTS 5, cycle 2. Lastly, use of ePHRs in this study was measured with a question regarding the frequency of accessing ePHRs, which might differ from actual ePHR use for healthcare needs.
Despite various benefits of ePHRs, older adults are more likely to be left behind in terms of digital healthcare (Greenberg et al., 2017). Addressing the factors impacting the utilization of ePHRs is important to decreasing healthcare disparity in digital age (Taha et al., 2013). The current study indicated that less social support, confidence in ePHRs safeguards, and higher use of health-related Internet were associated with using ePHRs among older cancer survivors — findings which suggest several implications for oncology practice for older adults. First, more support regarding ePHRs use is needed for older cancer survivors who have less social support due to their higher needs for ePHRs; for example, older cancer survivors who live alone. Social support was shown to be beneficial in supporting self-management of health among older adults (Martin et al., 2012). For older cancer survivors who lack social support, effective use of ePHRs might help better meet their healthcare needs. Second, after offering access to ePHRs, healthcare providers should provide education programs guiding this particular population of older cancer survivors on the use of ePHRs. Specifically, these programs should include three domains: (1) technical aspects covering how to use ePHRs, such as log in, navigate, search, etc.; (2) functional aspects covering what patients can do with ePHRs, such as how to send a message, share information, request medication refills, set up appointments, etc.; and (3) addressing the benefits and concerns of using ePHRs. Moreover, healthcare providers can provide contact information of consultants who are able to offer patient technical support for the use of ePHRs. While ePHRs provide the promise of improved healthcare access and outcomes for older cancer survivors, targeted supports addressing patients’ personalized aptitude toward adopting this technology — including individual behavioral-health factors — will ensure that potential benefits of ePHRs are realized and maximized for this vulnerable population.
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Yan Luo is a PhD Student in the School of Social Work at University of Alabama, Tuscaloosa, Alabama.
Qingyi Li is a post-doctoral research associate in the Department of Human Development at Cornell University, Ithaca, New York.
Leah Cheatham is an Assistant professor in School of Social Work at University of Alabama, Tuscaloosa, Alabama.
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