AIDS Care Psychological and Socio-medical Aspects of AIDS/HIV
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Personal health records for people living with HIV: a review Kea Turner, Stacey L. Klaman & Christopher M. Shea To cite this article: Kea Turner, Stacey L. Klaman & Christopher M. Shea (2016): Personal health records for people living with HIV: a review, AIDS Care, DOI: 10.1080/09540121.2016.1153594 To link to this article: http://dx.doi.org/10.1080/09540121.2016.1153594
Published online: 26 Feb 2016.
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Personal health records for people living with HIV: a review Kea Turnera, Stacey L. Klamanb and Christopher M. Sheaa Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, USA; bDepartment of Maternal and Child Health, University of North Carolina at Chapel Hill, Chapel Hill, USA
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ABSTRACT
ARTICLE HISTORY
Personal health records have the potential to improve patient outcomes, but the state of the literature on personal health record usage by people living with the human immunodeficiency virus (HIV) is unclear. The purpose of this review is to examine the impact of personal health records on HIV-related health beliefs and behaviors. We used the Health Belief Model to guide a review of studies examining the impact of electronic personal health records on the health beliefs and behaviors among people living with HIV. The search yielded 434 results. Following abstract review, 19 papers were selected for full-text review, and 12 were included in the review. A limited number of studies in this review found a positive impact of personal health records on HIV-related beliefs and behaviors. Additional research is needed to identify which personal health record features are most influential in changing health behaviors and why adoption rates remain low, particularly for groups at greatest risk for poor HIV outcomes. Theory-informed interventions are needed to identify which patients are likely to benefit from using personal health records and how to reduce barriers to personal health record adoption for people living with HIV.
Received 22 October 2015 Accepted 7 February 2016
Introduction Medical advancements and improved access to antiretroviral therapy (ART) have helped extend the life expectancy of individuals diagnosed with the human immunodeficiency virus (HIV) in the US (CDC, 2011a; Cohen et al., 2013; Zolopa et al., 2009). ART not only reduces an individual’s risk for acquired immune deficiency syndrome, but it also reduces their infectiousness to others and, subsequently, the spread of HIV (Cohen et al., 2013; Iyidogan & Anderson, 2014; Zolopa et al., 2009). To benefit fully from therapy, however, people living with HIV (PLHIV) must consistently adhere to ART. Despite the availability of effective therapy, adherence to ART is suboptimal in the US— with estimates ranging from 10% to 50% among adults living with HIV (Cooke, Lee, & Xing, 2014; Heckman, Catz, Heckman, Miller, & Kalichman, 2004). Therefore, identifying methods for improving ART adherence is a priority for HIV prevention and care. Evidence suggests that ART adherence among adults is strongly influenced by an individual’s HIV-related health beliefs (Gao, Nau, Rosenbluth, Scott, & Woodward, 2000; Reynolds et al., 2004; Thompson et al., 2012). The Health Belief Model (HBM) (Janz & Becker, 1984), in particular, has proven useful for identifying CONTACT Kea Turner NC 27599, USA © 2016 Taylor & Francis
[email protected]
KEYWORDS
HIV; personal health records; medication adherence; health belief model; health behavior
which beliefs are most likely to influence ART adherence. The HBM suggests that an individual’s perceived risk for a given health problem and perceived benefits and barriers of taking action can explain their engagement or lack of engagement in a health behavior (Janz & Becker, 1984). The HBM has been modified to include the concept of self-efficacy due to increasing evidence of the role of self-efficacy in health behavior (Glanz & Bishop, 2010). Self-efficacy, or an individual’s confidence in their ability to adhere to their HIV medication regimen, was first identified by Bandura, and has been shown to predict ART adherence across several studies (Bandura, 1986; Barclay et al., 2007; Beer & Skarbinski, 2014; Gao et al., 2000; Reynolds et al., 2004). Some of these same studies have shown that perceived severity, or whether an individual believes that non-adherence will result in negative health outcomes, also is associated with improved ART adherence (Barclay et al., 2007; Beer & Skarbinski, 2014; Gao et al., 2000). Self-management interventions that target HIVrelated beliefs, such as reminder devices and text messaging, have proven effective in improving ART adherence (Horvath, Azman, Kennedy, & Rutherford, 2012; Lester et al., 2010; Levy et al., 2004; Pop-Eleches et al., 2011; Saberi & Johnson, 2011; Safren, Hendriksen, Desousa,
University of North Carolina at Chapel Hill, Gillings School of Global Public Health, 135 Dauer Drive, Chapel Hill,
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Boswell, & Mayer, 2003; Schneider, Kaplan, Greenfield, Li, & Wilson, 2004). Personal health records (PHRs) could expand the reach of self-management tools, as PHRs are becoming increasingly available to patients through providers who adopt meaningful use electronic health record (EHR) incentive programs (CMS, 2014). PHRs are defined by the Office of the National Coordinator (ONC) for Health Information Technology as “an electronic application used by patients to maintain and manage their health information in a private, secure, and confidential environment” (ONC, 2014). PHRs have the potential to improve HIV self-management by allowing patients to track their health information and communicate with their providers. However, the state of the literature specific to PHRs and ART adherence is unclear. As PLHIV increasingly gain access to PHRs, it is important to understand how, if at all, PHR usage influences ART adherence and whether individuals at greatest risk for non-adherence are benefiting from PHRs. This review has three aims: (1) to identify types of self-management tools available through PHRs for PLHIV; (2) to examine how PHR usage impacts HIVrelated health beliefs and ART adherence among PLHIV; and (3) to determine if there are differences in PHR usage based on socio-demographic variables that influence ART adherence.
Methods PHRs come in many forms including paper-based or electronic and standalone or tethered. For this review, we are focusing on electronic PHRs, which have unique features different from paper-based PHRs, such as viewing provider notes or using secure messaging. We are including both standalone and tethered PHRs in our definition of electronic PHRs. Standalone refers to a PHR where a patient fills in health information from her or his own records, and stores the information on the Internet or a personal computer (ONC, 2014). Standalone PHRs, in some cases, can also accept data from health care providers and laboratories. A tethered PHR is linked to a health care organization’s EHR or health care plan’s information system, and allows patients to access their information through a secured portal (ONC, 2014). Search strategy After consulting with a librarian, a search strategy was developed based on key terms and MeSH headings for PHRs and PLHIV. Articles indexed from 1 January 2009 to 18 July 2015 in the following databases were
included: PubMed, CINAHL, Web of Science, Scopus, EMBASE, and PsycINFO. Search results were exported and duplicate articles were removed using the citation manager software (EndNote X7). Study selection Abstracts were included in this review if the study met the following criteria: (1) examined use of electronic PHRs among adult PLHIV; (2) reported original results; (3) was conducted in the US; and (4) was written in English. One investigator reviewed all abstracts and another investigator reviewed 40% of the abstracts to ensure that the inclusion and exclusion criteria were consistently applied. The two investigators did not have any disagreements on included articles. The full text of retained articles were then reviewed based on two criteria: (1) provided a description of the self-management tools available through the PHR intervention and (2) reported outcomes for HIV-related health beliefs, ART adherence, or the role of socio-demographic variables in PLHIV’s PHR usage. Two investigators reviewed the full text of selected abstracts, disagreed over one article to include, re-reviewed the inclusion criteria, and came to a consensus on the final set of articles to include in the study. Assessment of study quality The reporting quality of the studies was assessed (Tables 1 and 2). Criteria for these assessments were adapted from previous systematic reviews of HIV research (Mills et al., 2006). Data analysis We constructed an abstraction table in Excel, which included study objectives, methods, results organized by intervention characteristics and outcome measure, and study limitations. Findings from articles retained for full-text review were abstracted based on the following intervention characteristics and outcome measures. Personal health record features Each study was evaluated to assess the characteristics of the PHR intervention based on a framework of common PHR features and to determine whether the PHR was a standalone or tethered system. HIV-related health beliefs Studies were examined to assess how PHR usage impacts HIV-related health beliefs. HIV-related health beliefs
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Table 1. Quality criteria of qualitative studies. Study criteria Were the data transcribed verbatim?
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Was an interview guide created for in-depth interviews? Was the focus group facilitator trained for focus groups? Did the study include a health behavior theory? Was there a description of how the research themes were identified? Did more than one individual analyze the qualitative data? Did study participants review the analysis to verify the findings (i.e., member check)? Were the original data presented (i. e., quotations)?
Studies that met criteria Dhanireddy et al. (2014); McInnes, Solomon, et al. (2013); Odlum et al. (2012) McInnes, Solomon, et al. (2013); Odlum et al. (2012) None
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Gordon et al. (2012); Odlum et al. (2012) McInnes, Solomon, et al. (2013); Odlum et al. (2012) None Dhanireddy et al. (2014); McInnes, Solomon, et al. (2013); Odlum et al. (2012)
HIV awareness and knowledge: awareness of HIVrelated health outcomes including CD4 count and viral load, and knowledge of healthy ranges for CD4 count and viral load. Perceived severity: belief that non-adherence of ART will result in a negative health outcome, such as changes in CD4 count or viral load. Self-efficacy: an individual’s confidence in their ability to adhere to ART, such as taking the proper dosage of medication at the correct times.
Table 2. Quality criteria of quasi-experimental and crosssectional studies. Study criteria Were participants randomly selected? Was the recruitment strategy described in sufficient detail? Was the subject/comparison group selection sufficiently described (i.e., participant demographics)? Did the study include a theory of change or conceptual model? Did the study include a comparison or control group? Did the study report a response rate greater than 50% for surveys? Were survey instruments tested for validity? Were relevant confounding variables controlled for? Were the results reported in sufficient detail (i.e., consistent reporting of significance level and estimates of variance)? Did the study include objective outcome measures (i.e., non-selfreported measures)?
.
None
were categorized based on knowledge of HIV and HBM constructs, which are defined below. .
.
Studies that met criteria None Crouch et al. (2015); Kahn et al. (2010); McInnes, Shimada, et al. (2013); Wicks et al. (2010) Crouch et al. (2015); Gordon et al. (2012); McInnes, Shimada, et al. (2013); Oster et al. (2015); Ralston et al. (2013); Wicks et al. (2010) Crouch et al. (2015) Crouch et al. (2015); Oster et al. (2015) McInnes, Shimada, et al. (2013) Crouch et al. (2015); McInnes, Shimada, et al. (2013) Crouch et al. (2015); McInnes, Shimada, et al. (2013); Oster et al. (2015); Ralston et al. (2013) Crouch et al. (2015); McInnes, Shimada, et al. (2013); Oster et al. (2015); Ralston et al. (2013) McInnes, Shimada, et al. (2013); Ralston et al. (2013)
.
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Perceived benefit: belief that engaging in self-management behaviors, such as viewing laboratory results within PHRs, will reduce the likelihood of negative HIV health outcomes. Perceived barrier: belief that the negative aspects of self-management behaviors, such as privacy concerns when viewing health information within the PHR, serve as impediments to engaging in self-management behaviors. Cues to action: strategies that prepare an individual to engage in self-management behavior, such as communication with a health care provider.
Changes in ART adherence Information about the impact of PHRs on changes in ART adherence was abstracted for each study. Role of socio-demographic variables known to predict ART adherence Information about PHR usage and the role of sociodemographic variables known to predict ART adherence were summarized for each study. Research has shown that Black race, younger age, female gender, lower socioeconomic status, substance use, and mental health conditions are associated with lower ART adherence (Beer & Skarbinski, 2014; Golin et al., 2002). Therefore, demographic variables such as race, age, gender, socioeconomic status (including education and income), history of substance use, and mental health conditions were included.
Results The search resulted in 1123 citations (Figure 1). After duplicate studies were removed, 434 abstracts and 19 full-text articles were reviewed and irrelevant articles were excluded, leaving 12 studies to be included in this review. The study designs included two qualitative (Dhanireddy et al., 2014; Odlum et al., 2012), two mixed-methods (Gordon et al., 2012; McInnes, Solomon, et al., 2013), eight cross-sectional (Crouch, Rose, Johnson, & Janson, 2015; Kahn et al., 2010; Luque et al., 2013; McInnes, Shimada, et al., 2013; Ralston et al., 2013; Saberi et al., 2015; Wicks et al., 2010), and one quasi-experimental (Oster et al., 2015). The assessment criteria are summarized in Tables 1 and 2. Personal health record features Across the studies, PHRs were similar in design but varied in features. Nearly all PHRs, for example, were
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The same study found that 41% of participants believed that they had reduced HIV-related risk behaviors as a result of using the PHR. ART self-efficacy One cross-sectional study (n = 30) assessed how PHR usage affects ART self-efficacy, and found that self-efficacy scores significantly increased from pre- to postintervention (Luque et al., 2013).
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Cues to action for seeking out HIV-related information
Figure 1. Review of articles for inclusion in review.
tethered to EHRs (n = 11 studies). Most PHRs allowed PLHIV to view their medication lists (81.8%), laboratory results (72.7%), and provider notes (63.6%). Many PHRs had features, such as viewing upcoming medical appointments and online prescription refill (45.4%). Very few PHRs allowed patients to view problem lists (27.2%) or schedule appointments online (18.1%). HIV awareness and knowledge Four studies evaluated how PHR usage impacts HIV awareness and knowledge among PLHIV (Crouch et al., 2015; Dhanireddy et al., 2014; McInnes, Solomon, et al., 2013; Wicks et al., 2010). Researchers found that PHR usage improved awareness of HIV-related health outcomes, including CD4 count and viral load (Dhanireddy et al., 2014; McInnes, Solomon, et al., 2013; Wicks et al., 2010). Researchers also found that PHR users were significantly more likely to correctly estimate their CD4 count and viral load compared to non-users (80% vs. 68%, p = .003) (Crouch et al., 2015). Additionally, participants found that using PHRs helped to reinforce HIV-related information shared by a provider during a recent visit. Perceived severity of non-adherence One study (n = 177) evaluated how PHR usage impacts beliefs about the severity of non-adherence to ART, and found that 63% of participants agreed that PHR usage helped them to better understand the consequences of non-adherence to ART (Wicks et al., 2010).
In four studies, researchers examined how PHR usage served as a cue to action for PLHIV to engage in HIV self-management (Dhanireddy et al., 2014; Gordon et al., 2012; McInnes, Solomon, et al., 2013; Oster et al., 2015). Two studies discussed how viewing health information within the PHR prompted PLHIV to seek out additional HIV-related health information on the Internet to answer questions, particularly regarding their laboratory values (Gordon et al., 2012; McInnes, Solomon, et al., 2013). Two studies found that PLHIV used health information from their PHR to initiate conversations with their providers (Dhanireddy et al., 2014; Oster et al., 2015). Perceived benefits of personal health records for HIV self-management Five studies assessed the perceived benefits of PHRs for HIV self-management (Kahn et al., 2010; Luque et al., 2013; McInnes, Solomon, et al., 2013; Odlum et al., 2012; Oster et al., 2015). Perceived benefits included increased transparency of health information and ability to share information with other providers and caregivers. Additionally, PLHIV described how access to their own health information within the PHR gave them a greater sense of control over their health. Perceived barriers of using personal health records for HIV self-management Across the studies, similar barriers were reported, including lack of home Internet access, concerns about confidentiality, and lack of computer literacy (Dhanireddy et al., 2014; Gordon et al., 2012; Kahn et al., 2010; Odlum et al., 2012; Oster et al., 2015). Researchers found that PLHIV were more likely to access the Internet through public library computers than other patients (22% vs. 4%, p = .0001) (Oster et al., 2015). PLHIV explained that lack of home
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Internet access served as a barrier to PHR usage because it prevented them from being able to access their PHR in a private location (Dhanireddy et al., 2014; Odlum et al., 2012). Patients were specifically concerned that sharing HIV data with additional health care workers could lead to accidental or purposeful disclosure of an individual’s HIV status (Odlum et al., 2012). Additionally, PLHIV were concerned about the security of PHR data and whether health insurers would try to obtain health information within the PHR to deny medical claims (Dhanireddy et al., 2014). Another barrier to PHR usage was inadequate computer literacy, as computer literacy was found to be associated with higher PHR usage (Gordon et al., 2012). PHR-associated changes in ART adherence Four studies evaluated the impact of PHRs on ART adherence (Crouch et al., 2015; McInnes, Shimada, et al., 2013; Oster et al., 2015; Wicks et al., 2010). Two studies examined the impact of PHRs on intentions to take ART as recommended and found that PHRs had a significant effect on patients’ intentions (Oster et al., 2015; Wicks et al., 2010). Two studies examined the impact of PHRs on ART adherence (Crouch et al., 2015; McInnes, Shimada, et al., 2013). After controlling for socio-demographic characteristics and health status, one study reported that PHR users were significantly more likely to adhere to ART (OR = 1.80; CI = 1.35– 2.38). The second study did not find that PHR usage impacted adherence but reported that baseline levels of adherence were high among the study population (Crouch et al., 2015). Role of socio-demographic variables known to predict ART adherence Studies consistently reported that PLHIV who identified as Black, individuals with lower socioeconomic status, and individuals with substance use disorders were less likely to adopt PHRs (McInnes, Shimada, et al., 2013; Ralston et al., 2013). One the other hand, PLHIV with depression were more likely to use PHRs than PLHIV without depression (RR = 1.24, p = .0001) (Ralston et al., 2013). There were inconsistent findings on the impact of age and gender on PHR adoption. Two studies found that younger patients were more likely to adopt PHRs, whereas one study found that older patients were more likely to adopt PHRs (McInnes, Shimada, et al., 2013; Ralston et al., 2013). Additionally, one study reported that women were less likely to use PHRs, while another study reported that men were less
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likely to use PHRs (McInnes, Shimada, et al., 2013; Ralston et al., 2013).
Discussion Our findings suggest that PHRs could be an effective intervention for influencing HIV-related health beliefs and improving ART adherence. However, additional research is needed to understand how to maximize the effectiveness of PHR interventions for PLHIV. In particular, future studies should evaluate whether specific PHR features are more effective than others at changing HIV-related health beliefs and adherence outcomes. Health behavior theories, such as the HBM, could be useful for informing this future research. Consistent with past research (Archer, Fevrier-Thomas, Lokker, McKibbon, & Straus, 2011), the majority of studies included in this review reported low PHR adoption rates. To improve PHR adoption rates, researchers should test interventions that minimize adoption-related barriers for PLHIV, such as lack of home Internet access. Additionally, our review found that PLHIV were concerned that PHRs might increase risk of HIV status disclosure. Future studies could identify the informational needs of PLHIV regarding PHR privacy and security. Furthermore, studies have shown that Black patients are less likely to adopt PHRs than White patients (Roblin, Houston, Allison, Joski, & Becker, 2009; Yamin et al., 2011). Further research is needed to understand how race impacts PHR adoption since HIV disproportionately impacts Black Americans (CDC, 2011b), for example, by building upon previous research indicating that racial differences in patient–provider trust contribute to disparities in ART non-adherence (Saha, Jacobs, Moore, & Beach, 2010). Future studies could examine whether the relationship between patient–provider trust and PHR adoption differs between Black and White PLHIV. Findings from this review indicated that few studies have used a theory-informed approach to maximize the effectiveness of PHR interventions on ART adherence. The HBM is a particularly useful theory since its constructs have been shown to predict ART adherence. Future studies could use HBM constructs to guide PHR interventions. For example, studies in this review found that patients who experience a negative change in their HIV-related health status are more likely to activate and view information in their PHR. This finding is consistent with the HBM, which suggests that a cue to action, such as a change in health status, is often needed to prepare patients to engage in self-management (Janz & Becker, 1984). Future studies could examine the
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optimal time for prompting PLHIV to use a PHR to increase the likelihood of PHRs influencing adherence behaviors. Our review has some limitations. First, relatively few studies have been conducted that examine PHR usage among PLHIV, which may limit the generalizability of the review. However, the review highlights specific issues that require further investigation to strengthen the evidence base. Second, the review did not examine provider perspectives on the benefits and barriers of PHRs, such as changes in provider workload and efficiency. Although these topics were beyond the scope of this review, they are important for researchers to consider when designing programs to promote PHR adoption.
Conclusion Prior to the implementation of the Patient Protection and Affordable Care Act, limited access to health care coverage prevented many PLHIV from maintaining consistent access to ART (Kates, Garfield, & Young, 2014). As coverage is expanded, and access barriers are reduced, there is a window of opportunity to better engage individuals in their own care and ensure that all PLHIV are achieving the maximum benefit of ART. Initial evidence suggests that PHRs offer promising tools for improving ART adherence among PLHIV; however, more research is needed to determine how to optimize and effectively implement these tools.
Disclosure statement No potential conflict of interest was reported by the authors.
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Ralston, J. D., Silverberg, M. J., Grothaus, L., Leyden, W. A., Ross, T., Stewart, C., … Catz, S. L. (2013). Use of webbased shared medical records among patients with HIV. American Journal of Managed Care, 19(4), e114–124. Reynolds, N. R., Testa, M. A., Marc, L. G., Chesney, M. A., Neidig, J. L., Smith, S. R., … Vella, S. (2004). Factors influencing medication adherence beliefs and self-efficacy in persons naive to antiretroviral therapy: A multicenter, crosssectional study. AIDS and Behavior, 8(2), 141–150. doi:10. 1023/B:AIBE.0000030245.52406.bb Roblin, D. W., Houston, T. K., Allison, J. J., Joski, P. J., & Becker, E. R. (2009). Disparities in use of a personal health record in a managed care organization. Journal of the American Medical Informatics Association, 16(5), 683–689. doi:10.1197/jamia.M3169 Saberi, P., Catz, S. L., Leyden, W. A., Stewart, C., Ralston, J. D., Horberg, M. A., … Silverberg, M. J. (2015). Antiretroviral therapy adherence and use of an electronic shared medical record among people living with HIV. AIDS and Behavior, 19(Suppl. 2), 177–185. doi:10.1007/s10461-014-0982-x Saberi, P., & Johnson, M. O. (2011). Technology-based selfcare methods of improving antiretroviral adherence: A systematic review. PLoS One, 6(11), e27533. doi:10.1371/ journal.pone.0027533 Safren, S. A., Hendriksen, E. S., Desousa, N., Boswell, S. L., & Mayer, K. H. (2003). Use of an on-line pager system to increase adherence to antiretroviral medications. AIDS Care, 15(6), 787–793. doi:10.1080/09540120310001618630 Saha, S., Jacobs, E. A., Moore, R. D., & Beach, M. C. (2010). Trust in physicians and racial disparities in HIV care. AIDS Patient Care and STDs, 24(7), 415–420. doi:10.1089/ apc.2009.0288 Schneider, J., Kaplan, S. H., Greenfield, S., Li, W., & Wilson, I. B. (2004). Better physician–patient relationships are associated with higher reported adherence to antiretroviral therapy in patients with HIV infection. Journal of General Internal Medicine, 19(11), 1096–1103. doi:10.1111/j.15251497.2004.30418.x Thompson, M. A., Mugavero, M. J., Amico, K. R., Cargill, V. A., Chang, L. W., Gross, R., … Nachega, J. B. (2012). Guidelines for improving entry into and retention in care and antiretroviral adherence for persons with HIV: Evidence-based recommendations from an International Association of Physicians in AIDS Care panel. Annals of Internal Medicine, 156(11), 817–833, W-284, W-285, W-286, W287, W-288, W-289, W-290, W-291, W-292, W-293, W294. doi:10.7326/0003-4819-156-11-201206050-00419 Wicks, P., Massagli, M., Frost, J., Brownstein, C., Okun, S., Vaughan, T., … Heywood, J. (2010). Sharing health data for better outcomes on PatientsLikeMe. Journal of Medical Internet Research, 12(2), e19. doi:10.2196/jmir.1549 Yamin, C. K., Emani, S., Williams, D. H., Lipsitz, S. R., Karson, A. S., Wald, J. S., & Bates, D. W. (2011). The digital divide in adoption and use of a personal health record. Archives of Internal Medicine, 171(6), 568–574. doi:10.1001/ archinternmed.2011.34 Zolopa, A., Andersen, J., Powderly, W., Sanchez, A., Sanne, I., Suckow, C., … Komarow, L. (2009). Early antiretroviral therapy reduces AIDS progression/death in individuals with acute opportunistic infections: A multicenter randomized strategy trial. PLoS One, 4(5), e5575. doi:10.1371/ journal.pone.0005575
ISSN: 0954-0121 (Print) 1360-0451 (Online) Journal homepage: http://www.tandfonline.com/loi/caic20
Personal health records for people living with HIV: a review Kea Turner, Stacey L. Klaman & Christopher M. Shea To cite this article: Kea Turner, Stacey L. Klaman & Christopher M. Shea (2016): Personal health records for people living with HIV: a review, AIDS Care, DOI: 10.1080/09540121.2016.1153594 To link to this article: http://dx.doi.org/10.1080/09540121.2016.1153594
Published online: 26 Feb 2016.
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AIDS CARE, 2016 http://dx.doi.org/10.1080/09540121.2016.1153594
Personal health records for people living with HIV: a review Kea Turnera, Stacey L. Klamanb and Christopher M. Sheaa Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, USA; bDepartment of Maternal and Child Health, University of North Carolina at Chapel Hill, Chapel Hill, USA
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ABSTRACT
ARTICLE HISTORY
Personal health records have the potential to improve patient outcomes, but the state of the literature on personal health record usage by people living with the human immunodeficiency virus (HIV) is unclear. The purpose of this review is to examine the impact of personal health records on HIV-related health beliefs and behaviors. We used the Health Belief Model to guide a review of studies examining the impact of electronic personal health records on the health beliefs and behaviors among people living with HIV. The search yielded 434 results. Following abstract review, 19 papers were selected for full-text review, and 12 were included in the review. A limited number of studies in this review found a positive impact of personal health records on HIV-related beliefs and behaviors. Additional research is needed to identify which personal health record features are most influential in changing health behaviors and why adoption rates remain low, particularly for groups at greatest risk for poor HIV outcomes. Theory-informed interventions are needed to identify which patients are likely to benefit from using personal health records and how to reduce barriers to personal health record adoption for people living with HIV.
Received 22 October 2015 Accepted 7 February 2016
Introduction Medical advancements and improved access to antiretroviral therapy (ART) have helped extend the life expectancy of individuals diagnosed with the human immunodeficiency virus (HIV) in the US (CDC, 2011a; Cohen et al., 2013; Zolopa et al., 2009). ART not only reduces an individual’s risk for acquired immune deficiency syndrome, but it also reduces their infectiousness to others and, subsequently, the spread of HIV (Cohen et al., 2013; Iyidogan & Anderson, 2014; Zolopa et al., 2009). To benefit fully from therapy, however, people living with HIV (PLHIV) must consistently adhere to ART. Despite the availability of effective therapy, adherence to ART is suboptimal in the US— with estimates ranging from 10% to 50% among adults living with HIV (Cooke, Lee, & Xing, 2014; Heckman, Catz, Heckman, Miller, & Kalichman, 2004). Therefore, identifying methods for improving ART adherence is a priority for HIV prevention and care. Evidence suggests that ART adherence among adults is strongly influenced by an individual’s HIV-related health beliefs (Gao, Nau, Rosenbluth, Scott, & Woodward, 2000; Reynolds et al., 2004; Thompson et al., 2012). The Health Belief Model (HBM) (Janz & Becker, 1984), in particular, has proven useful for identifying CONTACT Kea Turner NC 27599, USA © 2016 Taylor & Francis
[email protected]
KEYWORDS
HIV; personal health records; medication adherence; health belief model; health behavior
which beliefs are most likely to influence ART adherence. The HBM suggests that an individual’s perceived risk for a given health problem and perceived benefits and barriers of taking action can explain their engagement or lack of engagement in a health behavior (Janz & Becker, 1984). The HBM has been modified to include the concept of self-efficacy due to increasing evidence of the role of self-efficacy in health behavior (Glanz & Bishop, 2010). Self-efficacy, or an individual’s confidence in their ability to adhere to their HIV medication regimen, was first identified by Bandura, and has been shown to predict ART adherence across several studies (Bandura, 1986; Barclay et al., 2007; Beer & Skarbinski, 2014; Gao et al., 2000; Reynolds et al., 2004). Some of these same studies have shown that perceived severity, or whether an individual believes that non-adherence will result in negative health outcomes, also is associated with improved ART adherence (Barclay et al., 2007; Beer & Skarbinski, 2014; Gao et al., 2000). Self-management interventions that target HIVrelated beliefs, such as reminder devices and text messaging, have proven effective in improving ART adherence (Horvath, Azman, Kennedy, & Rutherford, 2012; Lester et al., 2010; Levy et al., 2004; Pop-Eleches et al., 2011; Saberi & Johnson, 2011; Safren, Hendriksen, Desousa,
University of North Carolina at Chapel Hill, Gillings School of Global Public Health, 135 Dauer Drive, Chapel Hill,
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Boswell, & Mayer, 2003; Schneider, Kaplan, Greenfield, Li, & Wilson, 2004). Personal health records (PHRs) could expand the reach of self-management tools, as PHRs are becoming increasingly available to patients through providers who adopt meaningful use electronic health record (EHR) incentive programs (CMS, 2014). PHRs are defined by the Office of the National Coordinator (ONC) for Health Information Technology as “an electronic application used by patients to maintain and manage their health information in a private, secure, and confidential environment” (ONC, 2014). PHRs have the potential to improve HIV self-management by allowing patients to track their health information and communicate with their providers. However, the state of the literature specific to PHRs and ART adherence is unclear. As PLHIV increasingly gain access to PHRs, it is important to understand how, if at all, PHR usage influences ART adherence and whether individuals at greatest risk for non-adherence are benefiting from PHRs. This review has three aims: (1) to identify types of self-management tools available through PHRs for PLHIV; (2) to examine how PHR usage impacts HIVrelated health beliefs and ART adherence among PLHIV; and (3) to determine if there are differences in PHR usage based on socio-demographic variables that influence ART adherence.
Methods PHRs come in many forms including paper-based or electronic and standalone or tethered. For this review, we are focusing on electronic PHRs, which have unique features different from paper-based PHRs, such as viewing provider notes or using secure messaging. We are including both standalone and tethered PHRs in our definition of electronic PHRs. Standalone refers to a PHR where a patient fills in health information from her or his own records, and stores the information on the Internet or a personal computer (ONC, 2014). Standalone PHRs, in some cases, can also accept data from health care providers and laboratories. A tethered PHR is linked to a health care organization’s EHR or health care plan’s information system, and allows patients to access their information through a secured portal (ONC, 2014). Search strategy After consulting with a librarian, a search strategy was developed based on key terms and MeSH headings for PHRs and PLHIV. Articles indexed from 1 January 2009 to 18 July 2015 in the following databases were
included: PubMed, CINAHL, Web of Science, Scopus, EMBASE, and PsycINFO. Search results were exported and duplicate articles were removed using the citation manager software (EndNote X7). Study selection Abstracts were included in this review if the study met the following criteria: (1) examined use of electronic PHRs among adult PLHIV; (2) reported original results; (3) was conducted in the US; and (4) was written in English. One investigator reviewed all abstracts and another investigator reviewed 40% of the abstracts to ensure that the inclusion and exclusion criteria were consistently applied. The two investigators did not have any disagreements on included articles. The full text of retained articles were then reviewed based on two criteria: (1) provided a description of the self-management tools available through the PHR intervention and (2) reported outcomes for HIV-related health beliefs, ART adherence, or the role of socio-demographic variables in PLHIV’s PHR usage. Two investigators reviewed the full text of selected abstracts, disagreed over one article to include, re-reviewed the inclusion criteria, and came to a consensus on the final set of articles to include in the study. Assessment of study quality The reporting quality of the studies was assessed (Tables 1 and 2). Criteria for these assessments were adapted from previous systematic reviews of HIV research (Mills et al., 2006). Data analysis We constructed an abstraction table in Excel, which included study objectives, methods, results organized by intervention characteristics and outcome measure, and study limitations. Findings from articles retained for full-text review were abstracted based on the following intervention characteristics and outcome measures. Personal health record features Each study was evaluated to assess the characteristics of the PHR intervention based on a framework of common PHR features and to determine whether the PHR was a standalone or tethered system. HIV-related health beliefs Studies were examined to assess how PHR usage impacts HIV-related health beliefs. HIV-related health beliefs
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Table 1. Quality criteria of qualitative studies. Study criteria Were the data transcribed verbatim?
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Was an interview guide created for in-depth interviews? Was the focus group facilitator trained for focus groups? Did the study include a health behavior theory? Was there a description of how the research themes were identified? Did more than one individual analyze the qualitative data? Did study participants review the analysis to verify the findings (i.e., member check)? Were the original data presented (i. e., quotations)?
Studies that met criteria Dhanireddy et al. (2014); McInnes, Solomon, et al. (2013); Odlum et al. (2012) McInnes, Solomon, et al. (2013); Odlum et al. (2012) None
.
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Gordon et al. (2012); Odlum et al. (2012) McInnes, Solomon, et al. (2013); Odlum et al. (2012) None Dhanireddy et al. (2014); McInnes, Solomon, et al. (2013); Odlum et al. (2012)
HIV awareness and knowledge: awareness of HIVrelated health outcomes including CD4 count and viral load, and knowledge of healthy ranges for CD4 count and viral load. Perceived severity: belief that non-adherence of ART will result in a negative health outcome, such as changes in CD4 count or viral load. Self-efficacy: an individual’s confidence in their ability to adhere to ART, such as taking the proper dosage of medication at the correct times.
Table 2. Quality criteria of quasi-experimental and crosssectional studies. Study criteria Were participants randomly selected? Was the recruitment strategy described in sufficient detail? Was the subject/comparison group selection sufficiently described (i.e., participant demographics)? Did the study include a theory of change or conceptual model? Did the study include a comparison or control group? Did the study report a response rate greater than 50% for surveys? Were survey instruments tested for validity? Were relevant confounding variables controlled for? Were the results reported in sufficient detail (i.e., consistent reporting of significance level and estimates of variance)? Did the study include objective outcome measures (i.e., non-selfreported measures)?
.
None
were categorized based on knowledge of HIV and HBM constructs, which are defined below. .
.
Studies that met criteria None Crouch et al. (2015); Kahn et al. (2010); McInnes, Shimada, et al. (2013); Wicks et al. (2010) Crouch et al. (2015); Gordon et al. (2012); McInnes, Shimada, et al. (2013); Oster et al. (2015); Ralston et al. (2013); Wicks et al. (2010) Crouch et al. (2015) Crouch et al. (2015); Oster et al. (2015) McInnes, Shimada, et al. (2013) Crouch et al. (2015); McInnes, Shimada, et al. (2013) Crouch et al. (2015); McInnes, Shimada, et al. (2013); Oster et al. (2015); Ralston et al. (2013) Crouch et al. (2015); McInnes, Shimada, et al. (2013); Oster et al. (2015); Ralston et al. (2013) McInnes, Shimada, et al. (2013); Ralston et al. (2013)
.
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Perceived benefit: belief that engaging in self-management behaviors, such as viewing laboratory results within PHRs, will reduce the likelihood of negative HIV health outcomes. Perceived barrier: belief that the negative aspects of self-management behaviors, such as privacy concerns when viewing health information within the PHR, serve as impediments to engaging in self-management behaviors. Cues to action: strategies that prepare an individual to engage in self-management behavior, such as communication with a health care provider.
Changes in ART adherence Information about the impact of PHRs on changes in ART adherence was abstracted for each study. Role of socio-demographic variables known to predict ART adherence Information about PHR usage and the role of sociodemographic variables known to predict ART adherence were summarized for each study. Research has shown that Black race, younger age, female gender, lower socioeconomic status, substance use, and mental health conditions are associated with lower ART adherence (Beer & Skarbinski, 2014; Golin et al., 2002). Therefore, demographic variables such as race, age, gender, socioeconomic status (including education and income), history of substance use, and mental health conditions were included.
Results The search resulted in 1123 citations (Figure 1). After duplicate studies were removed, 434 abstracts and 19 full-text articles were reviewed and irrelevant articles were excluded, leaving 12 studies to be included in this review. The study designs included two qualitative (Dhanireddy et al., 2014; Odlum et al., 2012), two mixed-methods (Gordon et al., 2012; McInnes, Solomon, et al., 2013), eight cross-sectional (Crouch, Rose, Johnson, & Janson, 2015; Kahn et al., 2010; Luque et al., 2013; McInnes, Shimada, et al., 2013; Ralston et al., 2013; Saberi et al., 2015; Wicks et al., 2010), and one quasi-experimental (Oster et al., 2015). The assessment criteria are summarized in Tables 1 and 2. Personal health record features Across the studies, PHRs were similar in design but varied in features. Nearly all PHRs, for example, were
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The same study found that 41% of participants believed that they had reduced HIV-related risk behaviors as a result of using the PHR. ART self-efficacy One cross-sectional study (n = 30) assessed how PHR usage affects ART self-efficacy, and found that self-efficacy scores significantly increased from pre- to postintervention (Luque et al., 2013).
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Cues to action for seeking out HIV-related information
Figure 1. Review of articles for inclusion in review.
tethered to EHRs (n = 11 studies). Most PHRs allowed PLHIV to view their medication lists (81.8%), laboratory results (72.7%), and provider notes (63.6%). Many PHRs had features, such as viewing upcoming medical appointments and online prescription refill (45.4%). Very few PHRs allowed patients to view problem lists (27.2%) or schedule appointments online (18.1%). HIV awareness and knowledge Four studies evaluated how PHR usage impacts HIV awareness and knowledge among PLHIV (Crouch et al., 2015; Dhanireddy et al., 2014; McInnes, Solomon, et al., 2013; Wicks et al., 2010). Researchers found that PHR usage improved awareness of HIV-related health outcomes, including CD4 count and viral load (Dhanireddy et al., 2014; McInnes, Solomon, et al., 2013; Wicks et al., 2010). Researchers also found that PHR users were significantly more likely to correctly estimate their CD4 count and viral load compared to non-users (80% vs. 68%, p = .003) (Crouch et al., 2015). Additionally, participants found that using PHRs helped to reinforce HIV-related information shared by a provider during a recent visit. Perceived severity of non-adherence One study (n = 177) evaluated how PHR usage impacts beliefs about the severity of non-adherence to ART, and found that 63% of participants agreed that PHR usage helped them to better understand the consequences of non-adherence to ART (Wicks et al., 2010).
In four studies, researchers examined how PHR usage served as a cue to action for PLHIV to engage in HIV self-management (Dhanireddy et al., 2014; Gordon et al., 2012; McInnes, Solomon, et al., 2013; Oster et al., 2015). Two studies discussed how viewing health information within the PHR prompted PLHIV to seek out additional HIV-related health information on the Internet to answer questions, particularly regarding their laboratory values (Gordon et al., 2012; McInnes, Solomon, et al., 2013). Two studies found that PLHIV used health information from their PHR to initiate conversations with their providers (Dhanireddy et al., 2014; Oster et al., 2015). Perceived benefits of personal health records for HIV self-management Five studies assessed the perceived benefits of PHRs for HIV self-management (Kahn et al., 2010; Luque et al., 2013; McInnes, Solomon, et al., 2013; Odlum et al., 2012; Oster et al., 2015). Perceived benefits included increased transparency of health information and ability to share information with other providers and caregivers. Additionally, PLHIV described how access to their own health information within the PHR gave them a greater sense of control over their health. Perceived barriers of using personal health records for HIV self-management Across the studies, similar barriers were reported, including lack of home Internet access, concerns about confidentiality, and lack of computer literacy (Dhanireddy et al., 2014; Gordon et al., 2012; Kahn et al., 2010; Odlum et al., 2012; Oster et al., 2015). Researchers found that PLHIV were more likely to access the Internet through public library computers than other patients (22% vs. 4%, p = .0001) (Oster et al., 2015). PLHIV explained that lack of home
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Internet access served as a barrier to PHR usage because it prevented them from being able to access their PHR in a private location (Dhanireddy et al., 2014; Odlum et al., 2012). Patients were specifically concerned that sharing HIV data with additional health care workers could lead to accidental or purposeful disclosure of an individual’s HIV status (Odlum et al., 2012). Additionally, PLHIV were concerned about the security of PHR data and whether health insurers would try to obtain health information within the PHR to deny medical claims (Dhanireddy et al., 2014). Another barrier to PHR usage was inadequate computer literacy, as computer literacy was found to be associated with higher PHR usage (Gordon et al., 2012). PHR-associated changes in ART adherence Four studies evaluated the impact of PHRs on ART adherence (Crouch et al., 2015; McInnes, Shimada, et al., 2013; Oster et al., 2015; Wicks et al., 2010). Two studies examined the impact of PHRs on intentions to take ART as recommended and found that PHRs had a significant effect on patients’ intentions (Oster et al., 2015; Wicks et al., 2010). Two studies examined the impact of PHRs on ART adherence (Crouch et al., 2015; McInnes, Shimada, et al., 2013). After controlling for socio-demographic characteristics and health status, one study reported that PHR users were significantly more likely to adhere to ART (OR = 1.80; CI = 1.35– 2.38). The second study did not find that PHR usage impacted adherence but reported that baseline levels of adherence were high among the study population (Crouch et al., 2015). Role of socio-demographic variables known to predict ART adherence Studies consistently reported that PLHIV who identified as Black, individuals with lower socioeconomic status, and individuals with substance use disorders were less likely to adopt PHRs (McInnes, Shimada, et al., 2013; Ralston et al., 2013). One the other hand, PLHIV with depression were more likely to use PHRs than PLHIV without depression (RR = 1.24, p = .0001) (Ralston et al., 2013). There were inconsistent findings on the impact of age and gender on PHR adoption. Two studies found that younger patients were more likely to adopt PHRs, whereas one study found that older patients were more likely to adopt PHRs (McInnes, Shimada, et al., 2013; Ralston et al., 2013). Additionally, one study reported that women were less likely to use PHRs, while another study reported that men were less
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likely to use PHRs (McInnes, Shimada, et al., 2013; Ralston et al., 2013).
Discussion Our findings suggest that PHRs could be an effective intervention for influencing HIV-related health beliefs and improving ART adherence. However, additional research is needed to understand how to maximize the effectiveness of PHR interventions for PLHIV. In particular, future studies should evaluate whether specific PHR features are more effective than others at changing HIV-related health beliefs and adherence outcomes. Health behavior theories, such as the HBM, could be useful for informing this future research. Consistent with past research (Archer, Fevrier-Thomas, Lokker, McKibbon, & Straus, 2011), the majority of studies included in this review reported low PHR adoption rates. To improve PHR adoption rates, researchers should test interventions that minimize adoption-related barriers for PLHIV, such as lack of home Internet access. Additionally, our review found that PLHIV were concerned that PHRs might increase risk of HIV status disclosure. Future studies could identify the informational needs of PLHIV regarding PHR privacy and security. Furthermore, studies have shown that Black patients are less likely to adopt PHRs than White patients (Roblin, Houston, Allison, Joski, & Becker, 2009; Yamin et al., 2011). Further research is needed to understand how race impacts PHR adoption since HIV disproportionately impacts Black Americans (CDC, 2011b), for example, by building upon previous research indicating that racial differences in patient–provider trust contribute to disparities in ART non-adherence (Saha, Jacobs, Moore, & Beach, 2010). Future studies could examine whether the relationship between patient–provider trust and PHR adoption differs between Black and White PLHIV. Findings from this review indicated that few studies have used a theory-informed approach to maximize the effectiveness of PHR interventions on ART adherence. The HBM is a particularly useful theory since its constructs have been shown to predict ART adherence. Future studies could use HBM constructs to guide PHR interventions. For example, studies in this review found that patients who experience a negative change in their HIV-related health status are more likely to activate and view information in their PHR. This finding is consistent with the HBM, which suggests that a cue to action, such as a change in health status, is often needed to prepare patients to engage in self-management (Janz & Becker, 1984). Future studies could examine the
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optimal time for prompting PLHIV to use a PHR to increase the likelihood of PHRs influencing adherence behaviors. Our review has some limitations. First, relatively few studies have been conducted that examine PHR usage among PLHIV, which may limit the generalizability of the review. However, the review highlights specific issues that require further investigation to strengthen the evidence base. Second, the review did not examine provider perspectives on the benefits and barriers of PHRs, such as changes in provider workload and efficiency. Although these topics were beyond the scope of this review, they are important for researchers to consider when designing programs to promote PHR adoption.
Conclusion Prior to the implementation of the Patient Protection and Affordable Care Act, limited access to health care coverage prevented many PLHIV from maintaining consistent access to ART (Kates, Garfield, & Young, 2014). As coverage is expanded, and access barriers are reduced, there is a window of opportunity to better engage individuals in their own care and ensure that all PLHIV are achieving the maximum benefit of ART. Initial evidence suggests that PHRs offer promising tools for improving ART adherence among PLHIV; however, more research is needed to determine how to optimize and effectively implement these tools.
Disclosure statement No potential conflict of interest was reported by the authors.
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