Electronic health record access by patients as an indicator of information seeking and sharing for cardiovascular health promotion in social networks: Secondary analysis of a randomized clinical trial

Sherry-Ann N Brown, Hayan Jouni, Iftikhar J Kullo, Sherry-Ann N Brown, Hayan Jouni, Iftikhar J Kullo

Abstract

We investigated electronic health record (EHR) access as an indicator of cardiovascular health promotion by patients in their social networks, by identifying individuals who viewed their coronary heart disease (CHD) risk information in the EHR and shared this information in their social networks among various spheres of influence. In a secondary analysis of the Myocardial Infarction Genes trial, Olmsted County MN residents (2013-2015; n = 203; whites, ages 45-65 years) at intermediate CHD risk were randomized to receive their conventional risk score (CRS; based on traditional risk factors) alone or also their genetic risk score (GRS; based on 28 genomic variants). We assessed self-reported and objectively quantified EHR access via a patient portal at three and six months after risk disclosure, and determined whether this differed by GRS disclosure. Data were analyzed using logistic regression and adjusted for sociodemographic characteristics, family history, and baseline CRS/GRS. Self-reported EHR access to view CHD risk information was associated with a high frequency of objectively quantified EHR access (71(10) versus 37(5) logins; P = 0.0025) and a high likelihood of encouraging others to be screened for their CHD risk (OR 2.936, CI 1.443-5.973, P = 0.0030), compared to the absence of self-reported EHR access to view CHD risk information. We thereby used EHR access trends to identify individuals who may function as disseminators of CHD risk information in social networks, compared to individuals on the periphery of their social networks who did not exhibit this behavior. Partnering with such individuals could amplify CHD health promotion. Clinical Trial Registration: Myocardial Infarction Genes (MI-GENES) Study, NCT01936675, https://ichgcp.net/clinical-trials-registry/NCT01936675.

Keywords: Behavior modification; Electronic health records; Genetics; Patient engagement; Patient portals; Personal health records; Risk assessment; Risk factors; Social network.

Figures

Fig. 1
Fig. 1
The MI-GENES study design. Approximately 2000 of 30,000 individuals available in the Mayo Clinic BioBank met the screening criteria for our study (ages 45–65 years, with no history of CHD or statin use and at intermediate risk for CHD; Olmsted County, MN; 2013–2015). A random 1000 individuals were selected from among the 2000. Of these, 966 individuals were successfully genotyped. Targeted recruitment of at least 110 individuals with high GRS and 110 with average/low GRS led to enrolment of 216 participants; 9 withdrew. Ultimately, 207 individuals were randomized to the CRS group to receive their CRS alone, or the GRS group to also receive a GRS; 4 withdrew and 203 remained at follow-up. Surveys for EHR access and Internet Use were completed at baseline and at 3 months post-disclosure (internet use outside of the patient portal only) and 6 months post-disclosure (internet use outside of the patient portal as well as for patient portal access). Surveys for information sharing and social network were completed at three and six months post-disclosure. CHD = coronary heart disease; CRS = conventional risk score; GRS = genetic risk score.
Fig. 2
Fig. 2
Information Exchange Trends in Social Networks (Olmsted County, MN; 2013–2015). Internet use for information seeking outside of the patient portal at 3 months after risk disclosure for (a) GRS participants, and (b) CRS participants. (c) Information sharing at 3 months after risk disclosure for GRS participants. (d) Social network at baseline, 3 months, and 6 months after risk disclosure for GRS participants. CRS = conventional risk score; EUCRs = EHR Users for CHD Risk; GRS = genetic risk score; NEUCRs = NOT EHR Users for CHD Risk. * P < 0.05.
Fig. 3
Fig. 3
A Provisional Information Sharing Radius Score and a Conceptual Model for Information Exchange in Social Networks (Olmsted County, MN; 2013–2015). (a) Percentage of participants with a particular information sharing radius: EUCRs as a single class among all 203 trial participants had a wider information sharing radius than NEUCRs, with a maximum possible score of 4 (∑; sum) demonstrated to the right of the vertical dashed line. * P < 0.05. (b) A conceptual model of EUCRs and NEUCRs in social networks: EUCRs had a high frequency of quantified EHR access via a patient portal, with a skew towards a higher number of communication ties or connections; NEUCRs had a low frequency of quantified EHR access via a patient portal, with no skew towards a higher number of ties. EUCRs: EHR Users for CHD Risk; NEUCRs: NOT EHR Users for CHD Risk.

References

    1. Ammenwerth E., Lannig S., Hörbst A., Muller G., Schnell-Inderst P. Adult patient access to electronic health records. Cochrane Database Syst. Rev. 2017 Issue 6 Art No: CD012707.
    1. Aral S., Walker D. Identifying influential and susceptible members of social networks. Science. 2012;337:337–341.
    1. Ashida S., Koehly L.M., Roberts J.S., Chen C.A., Hiraki S., Green R.C. Disclosing the disclosure: factors associated with communicating the results of genetic susceptibility testing for Alzheimer's disease. J. Health Commun. 2009;14:768–784.
    1. Borgatti S.P., Mehra A., Brass D.J., Labianca G. Network analysis in the social sciences. Science. 2009;323:892–895.
    1. Brunson E.K. The impact of social networks on parents' vaccination decisions. Pediatrics. 2013;131:e1397–e1404.
    1. Chen D.B., Gao H., Lü L., Zhou T. Identifying influential nodes in large-scale directed networks: the role of clustering. PLoS One. 2013;8
    1. Christakis N.A., Fowler J.H. The collective dynamics of smoking in a large social network. N. Engl. J. Med. 2008;358:2249–2258.
    1. Christensen K.D., Roberts J.S., Whitehouse P.J. Disclosing pleiotropic effects during genetic risk assessment for Alzheimer disease: a randomized trial. Ann. Intern. Med. 2016;164:155–163.
    1. Christophe V., Vennin P., Corbeil M., Adenis C., Reich M. Social sharing of genetic information in the family: a study on hereditary breast and ovarian cancers. J. Health Psychol. 2009;14:855–860.
    1. van den Putte B., Yzer M., Southwell B.G., de Bruijn G.J., Willemsen M.C. Interpersonal communication as an indirect pathway for the effect of antismoking media content on smoking cessation. J. Health Commun. 2011;16:470–485.
    1. van Esch S.C., Nijkamp M.D., Cornel M.C., Snoek F.J. Patients' intentions to inform relatives about Type 2 diabetes risk: the role of worry in the process of family risk disclosure. Diabet. Med. 2012;29:e461–e467.
    1. van Esch S.C., Cornel M.C., Geelhoed-Duijvestijn P.H., Snoek F.J. Family communication as strategy in diabetes prevention: an observational study in families with Dutch and Surinamese South-Asian ancestry. Patient Educ. Couns. 2012;87:23–29.
    1. Ganna A., Magnusson P.K., Pedersen N.L. Multilocus genetic risk scores for coronary heart disease prediction. Arterioscler. Thromb. Vasc. Biol. 2013;33:2267–2272.
    1. Hallowell N., Jenkins N., Douglas M. Patients' experiences and views of cascade screening for familial hypercholesterolemia (FH): a qualitative study. J. Community Genet. 2011;2:249–257.
    1. Harris P.A., Taylor R., Thielke R., Payne J., Gonzalez N., Conde J.G. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform. 2009;42:377–381.
    1. Hughes M.F., Saarela O., Stritzke J. Genetic markers enhance coronary risk prediction in men: the MORGAM prospective cohorts. PLoS One. 2012;7
    1. Iribarren C., Lu M., Jorgenson E. Clinical utility of multimarker genetic risk scores for prediction of incident coronary heart disease: a cohort study among over 51 thousand individuals of European ancestry. Circ. Cardiovasc. Genet. 2016;9:531–540.
    1. Kaphingst K.A., McBride C.M., Wade C. Patients' understanding of and responses to multiplex genetic susceptibility test results. Genet. Med. 2012;14:681–687.
    1. Kempe D., Kleinberg J., Tardos E. Maximizing the Spread of Influence through a Social Network. Theory Comput. 2015;11:105–147.
    1. Khera A.V., Emdin C.A., Drake I. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 2016;375:2349–2358.
    1. Koehly L.M., Peters J.A., Kenen R. Characteristics of health information gatherers, disseminators, and blockers within families at risk of hereditary cancer: implications for family health communication interventions. Am. J. Public Health. 2009;99:2203–2209.
    1. Kullo I.J., Jouni H., Olson J.E., Montori V.M., Bailey K.R. Design of a randomized controlled trial of disclosing genomic risk of coronary heart disease: the Myocardial Infarction Genes (MI-GENES) study. BMC Med. Genet. 2015;8:51.
    1. Kullo I.J., Jouni H., Austin E.E. Incorporating a genetic risk score into coronary heart disease risk estimates: effect on low-density lipoprotein cholesterol levels (the MI-GENES Clinical Trial) Circulation. 2016;133:1181–1188.
    1. Lambert S.D., Loiselle C.G. Health information seeking behavior. Qual. Health Res. 2007;17:1006–1019.
    1. de Lusignan S., Mold F., Sheikh A. Patients' online access to their electronic health records and linked online services: a systematic interpretative review. BMJ Open. 2014;4
    1. Mega J.L., Stitziel N.O., Smith J.G. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet. 2015;385:2264–2271.
    1. Mills R., Powell J., Barry W., Haga S.B. Information-seeking and sharing behavior following genomic testing for diabetes risk. J. Genet. Couns. 2015;24:58–66.
    1. National Cholesterol Education Program ATP III Guidelines At-A-Glance Quick Desk Reference. 2001.
    1. Patel V., Barker W., Siminerio E. Office of the National Coordinator for Health Information Technology; Washington DC: 2014. Individuals' Access and Use of Their Online Medical Record Nationwide. ONC Data Brief, No. 20.
    1. Patenaude A.F., Dorval M., DiGianni L.S., Schneider K.A., Chittenden A., Garber J.E. Sharing BRCA1/2 test results with first-degree relatives: factors predicting who women tell. J. Clin. Oncol. 2006;24:700–706.
    1. Ripatti S., Tikkanen E., Orho-Melander M. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet. 2010;376:1393–1400.
    1. Ryan C., Bauman K. Current Population Reports. 2016. Educational attainment in the United States: 2015.
    1. Scheinfeldt L., Schmidlen T., Gharani N. Coronary artery disease genetic risk awareness motivates heart health behaviors in the coriell personalized medicine collaborative. Expert. Rev. Precis. Med. Drug Dev. 2016;1:407–413.
    1. Smart A. Impediments to DNA testing and cascade screening for hypertrophic cardiomyopathy and Long QT syndrome: a qualitative study of patient experiences. J. Genet. Couns. 2010;19:630–639.
    1. Social Networks and Vaccination Decisions. 2007. Accessed December 27, 2016, at.
    1. Stoffel E.M., Ford B., Mercado R.C. Sharing genetic test results in Lynch syndrome: communication with close and distant relatives. Clin. Gastroenterol. Hepatol. 2008;6:333–338.
    1. Sturm A.C. Cardiovascular cascade genetic testing: exploring the role of direct contact and technology. Front. Cardiovasc. Med. 2016;3:11.
    1. Sun Y. Rethinking public health: promoting public engagement through a new discursive environment. Am. J. Public Health. 2014;104:e6–13.
    1. Tikkanen E., Havulinna A.S., Palotie A., Salomaa V., Ripatti S. Genetic risk prediction and a 2-stage risk screening strategy for coronary heart disease. Arterioscler. Thromb. Vasc. Biol. 2013;33:2261–2266.
    1. Whitford D.L., McGee H., O'Sullivan B. Reducing health risk in family members of patients with type 2 diabetes: views of first degree relatives. BMC Public Health. 2009;9:455.
    1. Zhang J.X., Chen D.B., Dong Q., Zhao Z.D. Identifying a set of influential spreaders in complex networks. Sci. Rep. 2016;6
    1. Zhu X., Yao N., Mishra Identifying health care teams using electronic health records access data and social network analysis [abstract] J. Hosp. Med. 2016;11(Suppl. 1)
    1. Zulman D.M., Nazi K.M., Turvey C.L., Wagner T.H., Woods S.S., An L.C. Patient interest in sharing personal health record information: a web-based survey. Ann. Intern. Med. 2011;155:805–810.

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe