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Leveraging EHRs for Patient Engagement: Perspectives on Tailored Program Outreach
Susan D. Brown, PhD; Christina S. Grijalva, MA; and Assiamira Ferrara, MD, PhD

Leveraging EHRs for Patient Engagement: Perspectives on Tailored Program Outreach

Susan D. Brown, PhD; Christina S. Grijalva, MA; and Assiamira Ferrara, MD, PhD
Divergent reactions among women at high risk for diabetes highlight challenges of implementing tailored outreach messages, driven by electronic health records, to promote patient engagement in preventive lifestyle programs.
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.

DISCUSSION

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.

CONCLUSIONS

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. 

Acknowledgments

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.

 
Author Affiliations: Division of Research, Kaiser Permanente Northern California (SDB, CSG, AF), Oakland, CA.

Source of Funding: This research was supported by grant K01 DK099404 to Susan D. Brown from the National Institute of Diabetes and Digestive and Kidney Diseases and grant R01 HS019367 to Assiamira Ferrara from the Agency for Healthcare Research and Quality. Assiamira Ferrara also received support from grant P30 DK092924 from the National Institute of Diabetes and Digestive and Kidney Diseases.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (SDB, AF); acquisition of data (SDB, CSG); analysis and interpretation of data (SDB, CSG, AF); drafting of the manuscript (SDB, CSG); critical revision of the manuscript for important intellectual content (SDB, CSG, AF); provision of patients or study materials (SDB, CSG, AF); obtaining funding (SDB, AF); administrative, technical, or logistic support (CSG); and supervision (SDB, AF).

Address Correspondence to: Susan D. Brown, PhD, Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612. E-mail: Susan.D.Brown@kp.org. 
REFERENCES

1. Knowler WC, Barrett-Connor E, Fowler SE, et al; Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002(6):393-403.

2. Ratner RE, Christophi CA, Metzger BE, et al. Prevention of diabetes in women with a history of gestational diabetes: effects of metformin and lifestyle interventions. J Clin Endocrinol Metab. 2008;93(12):4774-4779. doi: 10.1210/jc.2008-0772.

3. Siu AL, on behalf of the US Preventive Services Task Force. Screening for abnormal blood glucose and type 2 diabetes mellitus: US Preventive Services Task Force recommendation statement. Ann Intern Med. 2015;163(11):861-868. doi: 10.7326/L16-0102.

4. Pronk NP, Remington PL, on behalf of the Community Preventive Services Task Force. Combined diet and physical activity promotion programs for prevention of diabetes: Community Preventive Services Task Force Recommendation statement. Ann Intern Med. 2015;163(6):465-468. doi: 10.7326/M15-1029.

5. American Diabetes Association. Classification and diagnosis of diabetes. Diabetes Care. 2015;(suppl 38):S8-S16. doi: 10.2337/dc15-S005.

6. Xiao H, Adams SR, Goler N, et al. Wellness coaching for people with prediabetes: a randomized encouragement trial to evaluate outreach methods at Kaiser Permanente, Northern California, 2013. Prev Chronic Dis. 2015;12:E207. doi: 10.5888/pcd12.150251.

7. Jackson SL, Long Q, Rhee MK, et al. Weight loss and incidence of diabetes with the Veterans Health Administration MOVE! lifestyle change programme: an observational study. Lancet Diabetes Endocrinol. 2015;3(3):173-180. doi: 10.1016/S2213-8587(14)70267-0.

8. Bellamy L, Casas JP, Hingorani AD, Williams D. Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. Lancet. 2009;373(9677):1773-1779. doi: 10.1016/S0140-6736(09)60731-5.

9. Kim C, McEwen LN, Piette JD, Goewey J, Ferrara A, Walker EA. Risk perception for diabetes among women with histories of gestational diabetes mellitus. Diabetes Care. 2007;30(9):2281-2286.

10. Hedderson MM, Darbinian JA, Ferrara A. Disparities in the risk of gestational diabetes by race-ethnicity and country of birth. Paediatr Perinat Epidemiol. 2010;24(5):441-448. doi: 10.1111/j.1365-3016.2010.01140.x

11. Xiang AH, Li BH, Black MH, et al. Racial and ethnic disparities in diabetes risk after gestational diabetes mellitus. Diabetologia. 2011;54(12):3016-3021. doi: 10.1007/s00125-011-2330-2.

12. Weiss EC, Galuska DA, Khan LK, Serdula MK. Weight-control practices among US adults, 2001-2002. Am J Prev Med. 2006;31(1):18-24.

13. Burroughs VJ, Nonas C, Sweeney CT, et al. Self-reported weight loss practices among African American and Hispanic adults in the United States. J Natl Med Assoc. 2010;102(6):469-475.

14. Tsai AG, Wadden TA, Pillitteri JL, et al. Disparities by ethnicity and socioeconomic status in the use of weight loss treatments. J Natl Med Assoc. 2009;101(1):62-70.

15. Serdula MK, Williamson DF, Anda RF, Levy A, Heaton A, Byers T. Weight control practices in adults: results of a multistate telephone survey. Am J Public Health. 1994;84(11):1821-1824.

16. Baradel LA, Gillespie C, Kicklighter JR, Doucette MM, Penumetcha M, Blanck HM. Temporal changes in trying to lose weight and recommended weight-loss strategies among overweight and obese Americans, 1996-2003. Prev Med. 2009;49(2-3):158-164. doi: 10.1016/j.ypmed.2009.06.030.

17. Kreuter MW, Strecher VJ, Glassman B. One size does not fit all: the case for tailoring print materials. Ann Behav Med. 1999;21(4):276-283.

18. Kessels LTE, Ruiter RA, Brug J, Jansma BM. The effects of tailored and threatening nutrition information on message attention. Evidence from an event-related potential study. Appetite. 2011;56(1):32-38. doi: 10.1016/j.appet.2010.11.139.

19. Updegraff JA, Sherman DK, Luyster FS, Mann TL. The effects of message quality and congruency on perceptions of tailored health communications. J Exp Soc Psychol. 2007;43(2):249-257.

20. Kreuter MW, Bull FC, Clark EM, Oswald DL. Understanding how people process health information: a comparison of tailored and nontailored weight-loss materials. Health Psychol. 1999;18(5):487-494.

21. Noar SM, Benac CN, Harris MS. Does tailoring matter? meta-analytic review of tailored print health behavior change interventions. Psychol Bull. 2007;133(4):673-693.

22. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med. 2010;363(6):501-504. doi: 10.1056/NEJMp1006114.

23. Meaningful use regulations. HealthIT.gov website. http://www.healthit.gov/policy-researchers-implementers/meaningful-use. Published 2013. Updated July 19, 2016. Accessed July 2017.

24. Blumenthal D, Squires D. Giving patients control of their EHR data. J Gen Intern Med. 2015;30(suppl 1):S42-S43. doi: 10.1007/s11606-014-3071-y.

25. Sherman DAK, Nelson LD, Steele CM. Do messages about health risks threaten the self? increasing the acceptance of threatening health messages via self-affirmation. Pers Soc Psychol Bull. 2000;26(9):1046-1058.

26. Schüz N, Schüz B, Eid M. When risk communication backfires: randomized controlled trial on self-affirmation and reactance to personalized risk feedback in high-risk individuals. Health Psychol. 2013;32(5):561-570. doi: 10.1037/a0029887.

27. Croyle RT, Sun Y-C, Hart M. Processing risk factor information: defensive biases in health-related judgments and memory. In: Petrie KJ, Weinman, JA, eds. Symptom Perception in Health and Disease. Amsterdam, The Netherlands: Harwood Academic Publishers; 1997: 261-286.

28. Kessels LT, Ruiter RA, Jansma BM. Increased attention but more efficient disengagement: neuroscientific evidence for defensive processing of threatening health information. Health Psychol. 2010;29(4):346-354. doi: 10.1037/a0019372.

29. Creswell JW, Klassen AC, Plano Clark VL, Smith KC. Best practices for mixed methods research in the health sciences. National Institutes of Health Office of Behavioral and Social Sciences Research website. https://obssr.od.nih.gov/training/mixed-methods-research/. Published August 2011. Accessed June 28, 2017.

30. Glasgow RE, Emmons KM. How can we increase translation of research into practice? types of evidence needed. Annu Rev Public Health. 2007;28(1):413-433.

31. Gordon NP. Similarity of the adult Kaiser Permanente membership in Northern California to the insured and general population in Northern California: statistics from the 2011 California Health Interview Survey. Kaiser Permanente Division of Research Northern California website. https://divisionofresearch.kaiserpermanente.org/projects/memberhealthsurvey/SiteCollectionDocuments/chis_non_kp_2011.pdf. Published June 2015. Accessed June 28, 2017.

32. Ferrara A, Hedderson MM, Albright CL, et al. A pragmatic cluster randomized clinical trial of diabetes prevention strategies for women with gestational diabetes: design and rationale of the Gestational Diabetes’ Effects on Moms (GEM) study. BMC Pregnancy Childbirth. 2014;14:21. doi: 10.1186/1471-2393-14-21.

33. Ferrara A, Hedderson MM, Brown SD, et al. The comparative effectiveness of diabetes prevention strategies to reduce postpartum weight retention in women with gestational diabetes: the Gestational Diabetes’ Effects on Moms (GEM) cluster randomized controlled trial. Diabetes Care. 2016;39(1):65-74. doi: 10.2337/dc15-1254.

34. Karter AJ, Ferrara A, Liu JY, Moffet HH, Ackerson LM, Selby JV. Ethnic disparities in diabetic complications in an insured population. J Amer Med Assoc. 2002;287(19):2519-2527.

35. Hsu WC, Araneta MR, Kanaya AM, Chiang JL, Fujimoto W. BMI cut points to identify at-risk Asian Americans for type 2 diabetes screening. Diabetes Care. 2015;38(1):150-158. doi: 10.2337/dc14-2391.

36. Dugan E, Trachtenberg F, Hall MA. Development of abbreviated measures to assess patient trust in a physician, a health insurer, and the medical profession. BMC Health Serv Res. 2005;5:64.

37. Walker EA, Mertz CK, Kalten MR, Flynn J. Risk perception for developing diabetes: comparative risk judgments of physicians. Diabetes Care. 2003;26(9):2543-2548.

38. Kiernan M, Kiernan NE, Goldberg J. Program evaluation: tipsheet #69. Using standard phrases in qualitative interviews. Penn State Cooperative Extension website. http://comm.eval.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=9da20813-dbdc-496d-a9e8-2ea5a9bbd73d&forceDialog=0. Published 2003. Accessed June 14, 2014.

39. Patton MQ. Qualitative Research and Evaluation Methods. 3rd ed. Thousand Oaks, CA: Sage Publications Inc; 2002.

40. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77-101.

41. Strauss A, Corbin JM. Basics of Qualitative Research: Grounded Theory Procedures and Techniques. 3rd ed. Thousand Oaks, CA: Sage Publications, Inc; 1990.

42. Fetters MD, Curry LA, Creswell JW. Achieving integration in mixed methods designs-principles and practices. Health Serv Res. 2013;48(6, pt 2):2134-2156. doi: 10.1111/1475-6773.12117.

43. Ferrara A, Hedderson MM, Ching J, Kim C, Peng T, Crites YM. Referral to telephonic nurse management improves outcomes in women with gestational diabetes. Am J Obstet Gynecol. 2012;206(6):491.e1-e5. doi: 10.1016/j.ajog.2012.04.019.

44. Schwartz PH, Caine K, Alpert SA, Meslin EM, Carroll AE, Tierney WM. Patient preferences in controlling access to their electronic health records: a prospective cohort study in primary care. J Gen Intern Med. 2015;30(suppl 1):S25-S30. doi: 10.1007/s11606-014-3054-z.

45. Ancker JS, Brenner S, Richardson JE, Silver M, Kaushal R. Trends in public perceptions of electronic health records during early years of meaningful use. Am J Manag Care. 2015;21(8):e487-e493.

46. Resnicow K, Baranowski T, Ahluwalia JS, Braithwaite RL. Cultural sensitivity in public health: defined and demystified. Ethn Dis. 1999;9(1):10-21.

47. van Koningsbruggen GM, Das E. Don’t derogate this message! Self-affirmation promotes online type 2 diabetes risk test taking. Psychol Health. 2009;24(6):635-649. doi: 10.1080/08870440802340156.

48. Rothman AJ. Toward a theory-based analysis of behavioral maintenance. Health Psychol. 2000;19(1S):64-69.

49. Gallagher KM, Updegraff JA. Health message framing effects on attitudes, intentions, and behavior: a meta-analytic review. Ann Behav Med. 2012;43(1):101-116. doi: 10.1007/s12160-011-9308-7. 
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