Leveraging EHRs for Patient Engagement: Perspectives on Tailored Program Outreach

Susan D. Brown, PhD; Christina S. Grijalva, MA; and Assiamira Ferrara, MD, PhD

Lifestyle programs, or behavioral interventions for weight management, healthy eating, and physical activity, can prevent type 2 diabetes (T2D)1,2 and are recommended for high-risk adults.3-5 Such programs are offered by healthcare delivery systems, yet little consensus exists on strategies to enhance patient engagement. Expanding the modest uptake observed in large health systems6,7 could further increase the programs’ public health impact. These programs are particularly vital for women with a history of gestational diabetes mellitus (GDM). Despite high rates of progression from GDM to T2D,8 many women may not appreciate this risk9 and healthcare providers may be unaware of patients’ GDM history due to fragmented care. Such factors potentially influence low program uptake in this high-risk population. Of further concern, considering racial/ethnic disparities in GDM10 and progression to T2D,11 minority participation in lifestyle programs appears low12,13 and minorities appear less likely to use recommended approaches to lifestyle changes.14-16
Electronic health records (EHRs) present opportunities to promote lifestyle programs using tailored messages. In contrast to broad patient outreach, tailored outreach—using messages with content personalized to an individual patient based on demographic, behavioral, and/or theoretical constructs17—is a well-established, theory-based strategy for increasing the persuasiveness,17 appeal,18-20 and effectiveness21 of health communications, which may also foster meaningful use of EHRs.22,23 For example, beyond identifying recipients for a given message, patient-level EHR data could be used to personalize the content of that message (eg, by referencing a specific patient’s combination of disease risk factors).
Automated tailoring has potential as a cost-effective strategy that can be delivered consistently in large health systems, which are key advantages given the potential expense of recruiting eligible patients to available programs.4 Still, concerns about privacy call for caution.24 Furthermore, tailored content in messages inviting participation in a lifestyle program could backfire by linking key aspects of the self to negative disease states,25,26 resulting in defensive resistance and dismissal of threatening information.25,27,28 Patient input is thus critical to inform implementation.
Given gaps in the patient engagement literature, we examined perspectives on tailored outreach messages encouraging women at high risk for T2D to participate in health system-based lifestyle programs. We took a mixed-methods approach to obtain more comprehensive patient perspectives29 and inform future research to disseminate evidence-based lifestyle interventions.30

Using a convergent mixed methods design, in 2015, we collected qualitative data via focus groups and quantitative data via survey. Quantitative data served to describe the sample and contextualize focus group responses.
Kaiser Permanente Northern California (KPNC) is a large integrated healthcare delivery system serving 3.7 million members who are demographically similar to the underlying population, except at the extremes of income and education.31
The sampling frame consisted of nearly all women diagnosed with GDM in 2011 to 2012 across 44 KPNC medical facilities. These women were previously identified as part of Gestational Diabetes’ Effects on Moms (GEM), a pragmatic cluster randomized clinical trial comparing postpartum T2D prevention strategies. Cluster randomization at the facility level had assigned women to usual care or a 13-session postpartum lifestyle intervention offered as optional routine care on behalf of the health system.32,33 Here, we used stratified sampling within the GEM cohort to identify women from 6 ethnic groups representative of northern California and among those with the highest prevalence and/or absolute frequencies of GDM.10 Eligibility criteria included being aged 18 to 50 years; comfort reading and speaking English; not currently pregnant; absence of recognized overt diabetes, confirmed by the KPNC diabetes registry34 and self-report; and body mass index (BMI) of 25 to 40 kg/m2 among African American, Mexican American, and non-Hispanic white women and 23 to 40 kg/m2 among Asian Indian, Chinese American, and Filipina women who are at greater risk for diabetes at a lower BMI.35
Recruitment included a letter/e-mail followed by telephone invitation and a $40 incentive. Participants provided written or verbal consent, including permission to link data with GEM. Both studies were approved by the KPNC Institutional Review Board.
Data Collection
Quantitative. Self-reported demographic characteristics, participation in a health system-based lifestyle program in the last 6 months (yes/no), and likelihood of participating in the next 6 months (on a 4-point scale ranging from very likely to very unlikely) were assessed using single items. Patient trust in the medical profession was assessed using a validated 5-item questionnaire; summed responses on a 5-point scale range from 5 to 25, with higher scores indicating greater trust.36 Perceived risk for developing diabetes in the next 10 years was assessed using an item originally derived from the validated Risk Perception Survey for Developing Diabetes (RPS-DD).37 Responses on this 4-point scale range from almost no chance to high chance, with higher scores indicating greater perceived risk. Personal control over developing diabetes was assessed using a 4-item RPS-DD subscale. Averaged responses on a 4-point scale range from 1 to 4, with higher scores indicating greater control. Data from GEM included time elapsed since delivery of the index GDM pregnancy, GEM condition assignment (ie, being in a facility assigned to usual care vs intervention), and whether women in facilities assigned to the GEM intervention had participated in at least 1 session.32
Qualitative. Focus group methods were explicitly chosen to elicit an array of ideas among participants from diverse backgrounds. Rather than interpreting results as confirming consensus or strength of endorsement of specific ideas, qualitative themes can suggest topics for future hypothesis-testing research.
Two researchers co-moderated 6 ethnic-specific focus groups among African American, Asian Indian, Chinese American, Filipina, Mexican American, and non-Hispanic white women. We conducted 1 group in person and 5 via Web-enabled conference calls to maximize participation across a large geographical area. A standardized semi-structured interview protocol using the same moderator prompts38 and predetermined open-ended questions enhanced consistency, neutrality, and comparability of responses across groups.39 Key questions explored outreach message content that would help women decide whether to participate in a lifestyle program and the acceptability of individually tailored messages using EHR data. As a sub-section of the latter topic, participants were asked about the message sender and inclusion of personal diabetes risk factors available in EHRs: history of GDM, blood glucose laboratory results, weight status, and ethnicity (for racial/ethnic minority women). To facilitate discussion, participants were asked about their reactions to a 280-word hypothetical tailored message addressed to a fictional patient that referenced the personal risk factors previously noted; the message was purportedly co-signed by the clinical director of a lifestyle program and department chief of obstetrics/gynecology, and delivered via secure e-mail. Focus groups were digitally recorded and professionally transcribed, verbatim.
Quantitative data were analyzed using SAS 9.3 software (SAS Institute Inc; Cary, North Carolina). We tested for group differences in patient trust, perceived risk for diabetes, and personal control using relevant tests for small sample sizes (eg, Kruskal-Wallis and Fisher’s exact tests). Qualitative data were analyzed using NVivo 10 software (QSR Intl Pty Ltd; Doncaster, Australia) and thematic analysis40 to inductively and deductively derive themes from the data.41 A doctoral-level clinical psychologist and master’s-level researcher reviewed all transcripts to develop a coding scheme of broad meaningful themes. One researcher independently coded transcripts; 2 researchers together then reviewed all coded text and selected key quotes, with discrepancies resolved by consensus. Finally, we integrated quantitative and qualitative data by creating joint displays.42
Sample Characteristics

Recruited participants (N = 35) had a mean age of 36 years (standard deviation [SD] = 5.3) and were a mean of 3.6 (SD = 0.3) years postpartum from their index GDM pregnancy. As designed, the sample was ethnically diverse (Table 1). Mean patient trust in the medical profession was 16.6 (SD = 3.8). Regarding perceived risk, 17% (n = 6) believed they had a high chance of developing diabetes, 29% (n = 10) a moderate chance, 49% (n = 17) a slight chance, and 6% (n = 2) almost no chance. Mean personal control over developing diabetes was 3.4 (SD = 0.5). Whereas 6% (n = 2) reported participating in a health system-based lifestyle program in the last 6 months, many endorsed intentions for the next 6 months, with 69% (n = 24) somewhat or very likely and 31% (n = 11) somewhat or very unlikely to participate. There were no significant differences across focus groups in any of the above domains (P ≥.07). Of 17 women in facilities assigned to the GEM intervention, 88% (n = 15) had participated in at least 1 session; participation was 50% among Filipina women (n = 2/4) and 100% within all other groups.
Qualitative Findings
Six broad themes emerged regarding tailored content in outreach messages, encompassing preferences and concerns. Sample quotes and the focus groups in which each theme emerged appear in Table 2.
Privacy. According to one perspective, tailored messages are acceptable and nonintrusive—as long as they are sent securely, by their personal physician, and/or within the boundaries of the health system. As one participant described tensions related to privacy:
“…to sort of pinpoint the people that would most benefit from a [program]…the only way to do that is to get some of that information together about the different people to see who would be the ones that would benefit the most. So, I understand that it’s sort of a really tricky balance between that privacy, but also establishing the access to these programs…it’s a very tricky balance because sometimes those of us who need it most aren’t that quick…to go and get that help.”
Concerns over privacy included wariness about the number of people who might have access to one’s medical records; still, participants acknowledged the advantages of such broad access (eg, the ability to receive integrated care from multiple providers).
Authenticity. Some participants perceived tailored messages as personal, appealing, and persuasive, creating a sense of authenticity that was interpreted as a genuine expression of professional care—that is, appreciating that “someone took the time to see what my information is.” In contrast, others perceived tailored messages as impersonal, generic, or spam, unlikely to be opened or read; and expressed disdain for ostensibly computer-generated content.
Sender. Participants perceived their physician as trustworthy and concerned about their health and noted that they were more likely to read a tailored message that came directly from that individual. Positive viewpoints also emerged about senders other than a personal physician, founded on the notion that the entire health system is working collaboratively for one’s benefit as a patient. Negative viewpoints about senders other than a personal physician were tied to concerns about privacy, inauthenticity, and perceptions that such messages would lack the caring or potential for follow-up offered by one’s physician.
Risk communication. Participants described preferences for strong, clear, and hopeful messages that clearly communicated their risk for diabetes and actions needed to prevent it, thus empowering women with the information and resources needed to “get it fixed.” Subthemes included the appeal of messages that recognized women’s past efforts to take good care of their health and a suggestion that messages should convey a sense of urgency. Contrasting perspectives included negative reactions to diabetes risk communication, including taking offense at messages perceived as unhelpful and “telling me what I already know.”
Clinical risk factors. According to one perspective, inclusion of diabetes risk factors, such as history of GDM, laboratory results, and weight status, was viewed as a caring and persuasive basis upon which to recommend preventive programs. Participants described having “forgotten” that they had a history of GDM and regarded even “unappealing” risk factor information as an important health reminder—particularly in the context of being asymptomatic and engrossed in the competing demands of day-to-day life. A contrasting perspective viewed risk factors as an unwelcome reminder of a physically and emotionally difficult time of life (as standard GDM treatment involves intensive glucose monitoring and control via diet, physical activity, and, occasionally, medication43). Subthemes included feelings of sadness, upset, and taking offense—or imagining that others might take offense, even if they themselves did not—and preference for messages that focus on the program being offered rather than recipients’ medical histories.
Ethnicity as a risk factor. Perspectives regarding the mention of ethnicity as a diabetes risk factor included seeing it as relevant, useful, and offering a more complete picture of risk factors that acknowledge cultural influences on health, beyond a sole focus on individual choices. Contrasting perspectives included viewing it as uninformative and potentially reflecting cultural stereotypes about health behaviors (eg, negative perceptions of ethnic food preferences).
Programmatic message content. In addition to themes relevant to tailored message content, participants preferred that messages specify a range of both program features (eg, cost, staff expertise, behavior change techniques used) and program outcomes (benefits of participating, such as anecdotal and quantitative evidence of effectiveness). The first theme emerged across all focus groups; the second emerged in groups with Asian Indian, Chinese American, Mexican American, and non-Hispanic white women.
Integration of Quantitative and Qualitative Findings
We created a descriptive joint display addressing diabetes risk. The Figure displays the mean proportions of women reporting high perceived risk for diabetes across the focus groups in which risk-related themes emerged. Proportions were somewhat greater across focus groups that expressed preferences for risk information compared with concerns. For example, 19% of women reported high perceived risk in groups that expressed preferences for tailored messages containing clinical risk factors versus 10% in groups that expressed concerns. Patient trust did not markedly differ between groups that expressed preferences versus concerns related to privacy, authenticity, and the trustworthiness of senders (data not shown). For example, trust scores averaged 15.8 across groups that expressed privacy-related preferences and 16.7 across groups that expressed privacy-related concerns.
Although EHR-driven tailored messages have potential as an efficient and cost-effective outreach strategy, divergent patient reactions highlight key challenges. In the present study, diverse women at high risk for T2D raised contrasting ideas about privacy and authenticity. Participants appreciated the size and complexity of integrated health systems and appeared savvy about the ways in which tailored messages could be generated. Yet, whereas some were unconcerned about security, others were strongly critical. Indeed, prior research shows that patients endorse opposing views of what EHR data they want made available to their healthcare team44 and a substantial proportion remain concerned about the privacy of EHRs.45 Seeking input from patient stakeholders and explicitly addressing these issues in tailored messages (eg, by stating how and by whom they have been generated and sent—preferably, by personal physicians) could enhance acceptability and maintain trust.
In terms of risk communication, some participants welcomed it as a caring prompt for preventive action whereas others viewed it as unhelpful, uninformative, or an unpleasant reminder of challenges related to lifestyle behavior change. Similarly, some minority participants appreciated the mention of ethnicity as a diabetes risk factor to acknowledge culture’s impact on health; others raised concerns over stereotyping. These perspectives related to risk factors highlight, first, the complexities of developing sensitive and culturally relevant communications.46 Second, results echo literature on the threatening nature of personally relevant health messages.27,28 This effect could be counteracted by self-affirmation,25  or asking patients to reflect on a positive aspect of the self—a technique shown to increase attention to diabetes risk information.47 Third, results suggest the need for a positive tone and careful use of clinical information. Patients’ desire for messages that highlight the benefits of lifestyle programs corroborates theoretical48 and empirical49 emphasis on positive consequences and developing favorable expectations (eg, gain-framed messaging) to promote new preventive behaviors. Future research should determine whether theory-based approaches, such as self-affirmation and gain-framed messaging, could mitigate negative reactions to tailored risk communication and increase patient engagement in preventive services. Of note, descriptive findings integrating quantitative and qualitative data raise the possibility that higher self-reported perceived risk may be associated with greater openness to tailored risk communication. If confirmed, health systems could consider surveying patient subsamples to estimate the acceptability of such messages.
Some themes emerged consistently across focus groups (eg, preferences for physician senders), whereas others emerged across fewer groups (eg, concerns about risk communication). Often, the same groups expressed both positive and negative viewpoints signaling within-group heterogeneity (ie, viewpoints differing from person to person) or ambivalence about tailored approaches (ie, the same person expressing conflicting viewpoints), or both. Exceptions include focus groups with Chinese American women, in which themes more often clustered around concerns, and African American women, in which themes more often clustered around preferences. Still, such comparisons must be tentative given that the data came from single focus groups for each racial/ethnic background. Although qualitative focus group methods are well suited to elicit a range of ideas about a phenomenon, identify issues worthy of further investigation, and develop hypotheses, they should be followed by hypothesis-testing research to make definitive cross- and within-group comparisons.
Limitations and Strengths
Study limitations include the sample’s relatively high level of education, their membership in a single health system, and, as noted, our limited ability to make cross-group comparisons. Strengths include stratified sampling by race/ethnicity and ethnic-specific focus groups to foster a range of diverse perspectives and the mixed methods design, whereby quantitative data provided context in which to interpret qualitative themes. For example, evidence that participants’ trust in the medical profession was similar to a national sample36 and similar between focus groups describing preferences versus concerns about privacy and authenticity suggests that these themes did not arise from undue suspicion. Similarly, perceived risk for, and personal control over, diabetes, reflected levels in comparable samples.9 Of note, the sample was evenly split between women who had and had not been offered a lifestyle intervention in the GEM trial. In responding to focus group prompts, women in the former category may have drawn on their direct experience of having received outreach messages in that context.
In an era of big data, health systems are well poised to discover ways of leveraging increasingly prevalent and powerful EHRs to engage high-risk patients. Results among women at high risk for diabetes suggested that patients acknowledge both the advantages and pitfalls of tailored approaches to outreach. Optimal outreach may require communication from personal physicians, mitigating privacy and authenticity concerns, and applying theory-based approaches to counteract the threatening nature of personalized risk communication. 

The authors thank the study participants and analysts Emily Han, MPH, and Ai-Lin Tsai, MS.  The authors also thank Joseph J. Gallo, MD, MPH, and Karen M. Emmons, PhD, for their scientific input.
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