Effect of Sociodemographic Factors on Uptake of a Patient-Facing Information Technology Family Health History Risk Assessment Platform

R. Ryanne Wu; Rachel A. Myers; Adam H. Buchanan; David Dimmock; Kimberly G. Fulda; Irina V. Haller; Susanne B. Haga; Melissa L. Harry; Catherine McCarty; Joan Neuner; Teji Rakhra-Burris; Nina Sperber; Corrine I. Voils; Geoffrey S. Ginsburg; Lori A. Orlando

doi:10.1055/s-0039-1679926

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2019; 10(02): 180-188
DOI: 10.1055/s-0039-1679926

Research Article

Georg Thieme Verlag KG Stuttgart · New York

Effect of Sociodemographic Factors on Uptake of a Patient-Facing Information Technology Family Health History Risk Assessment Platform

R. Ryanne Wu

¹Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States

²Durham VA Cooperative Studies Program Epidemiology Center, Durham, North Carolina, United States

,

Rachel A. Myers

¹Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States

,

Adam H. Buchanan

³Genomic Medicine Institute, Geisinger, Danville, Pennsylvania, United States

,

David Dimmock

⁴Rady Children's Institute for Genomic Medicine, San Diego, California, United States

,

Kimberly G. Fulda

⁵The North Texas Primary Care Practice-Based Research Network and Family Medicine, University of North Texas Health Science Center, Fort Worth, Texas, United States

,

Irina V. Haller

⁶Essentia Institute of Rural Health, Essentia, Duluth, Minnesota, United States

,

Susanne B. Haga

¹Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States

,

Melissa L. Harry

⁶Essentia Institute of Rural Health, Essentia, Duluth, Minnesota, United States

,

Catherine McCarty

⁷University of Minnesota Medical School, Duluth Campus, Duluth, Minnesota, United States

,

Joan Neuner

⁸Department of Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin, United States

⁹Center for Patient Care and Outcomes Research, Medical College of Wisconsin, Milwaukee, Wisconsin, United States

,

Teji Rakhra-Burris

¹Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States

,

Nina Sperber

¹Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States

¹⁰Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, United States

¹¹Durham VA Health Services & Development Service, Durham, North Carolina, United States

,

Corrine I. Voils

¹²William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, United States

¹³Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States

,

Geoffrey S. Ginsburg

¹Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States

,

Lori A. Orlando

¹Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States

› Author Affiliations

Abstract

Objective Investigate sociodemographic differences in the use of a patient-facing family health history (FHH)-based risk assessment platform.

Methods In this large multisite trial with a diverse patient population, we evaluated the relationship between sociodemographic factors and FHH health risk assessment uptake using an information technology (IT) platform. The entire study was administered online, including consent, baseline survey, and risk assessment completion. We used multivariate logistic regression to model effect of sociodemographic factors on study progression. Quality of FHH data entered as defined as relatives: (1) with age of onset reported on relevant conditions; (2) if deceased, with cause of death and (3) age of death reported; and (4) percentage of relatives with medical history marked as unknown was analyzed using grouped logistic fixed effect regression.

Results A total of 2,514 participants consented with a mean age of 57 and 10.4% minority. Multivariate modeling showed that progression through study stages was more likely for younger (p-value = 0.005), more educated (p-value = 0.004), non-Asian (p-value = 0.009), and female (p-value = 0.005) participants. Those with lower health literacy or information-seeking confidence were also less likely to complete the study. Most significant drop-out occurred during the risk assessment completion phase. Overall, quality of FHH data entered was high with condition's age of onset reported 87.85%, relative's cause of death 85.55% and age of death 93.76%, and relative's medical history marked as unknown 19.75% of the time.

Conclusion A demographically diverse population was able to complete an IT-based risk assessment but there were differences in attrition by sociodemographic factors. More attention should be given to ensure end-user functionality of health IT and leverage electronic medical records to lessen patient burden.

Keywords

family health - health risk assessment - health care disparities - patient engagement

Full Text

References

References
1 Baicker K, Chandra A, Skinner JS. Geographic variation in health care and the problem of measuring racial disparities. Perspect Biol Med 2005; 48 (1, Suppl): S42-S53
2 Williams DR, Wyatt R. Racial bias in health care and health: challenges and opportunities. JAMA 2015; 314 (06) 555-556
3 Paradies Y, Truong M, Priest N. A systematic review of the extent and measurement of healthcare provider racism. J Gen Intern Med 2014; 29 (02) 364-387
4 Kansagara D, Englander H, Salanitro A. , et al. Risk prediction models for hospital readmission: a systematic review. JAMA 2011; 306 (15) 1688-1698
5 Pisters R, Lane DA, Nieuwlaat R, de Vos CB, Crijns HJGM, Lip GYH. A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: the Euro Heart Survey. Chest 2010; 138 (05) 1093-1100
6 Subramanian S, Bobashev G, Morris RJ, Hoover S. Personalized medicine for prevention: can risk stratified screening decrease colorectal cancer mortality at an acceptable cost?. Cancer Causes Control 2017; 28 (04) 299-308
7 Cohn WF, Ropka ME, Pelletier SL. , et al. Health Heritage© a web-based tool for the collection and assessment of family health history: initial user experience and analytic validity. Public Health Genomics 2010; 13 (7–8): 477-491
8 Elliott LS, Henderson JC, Neradilek MB, Moyer NA, Ashcraft KC, Thirumaran RK. Clinical impact of pharmacogenetic profiling with a clinical decision support tool in polypharmacy home health patients: a prospective pilot randomized controlled trial. PLoS One 2017; 12 (02) e0170905
9 Larson EA, Wilke RA. Integration of genomics in primary care. Am J Med 2015; 128 (11) 1251.e1-1251.e5
10 Welch BM, Wiley K, Pflieger L. , et al. Review and comparison of electronic patient-facing family health history tools. J Genet Couns 2018; 27 (02) 381-391
11 Qureshi N, Wilson B, Santaguida P. , et al. Collection and use of cancer family history in primary care. Evid Rep Technol Assess (Full Rep) 2007; (159) 1-84
12 Mikat-Stevens NA, Larson IA, Tarini BA. Primary-care providers' perceived barriers to integration of genetics services: a systematic review of the literature. Genet Med 2015; 17 (03) 169-176
13 Berg AO, Baird MA, Botkin JR. , et al. National Institutes of Health state-of-the-science conference statement: family history and improving health. Ann Intern Med 2009; 151 (12) 872-877
14 Gramling R, Nash J, Siren K, Eaton C, Culpepper L. Family physician self-efficacy with screening for inherited cancer risk. Ann Fam Med 2004; 2 (02) 130-132
15 de Hoog CL, Portegijs PJ, Stoffers HE. Family history tools for primary care are not ready yet to be implemented. a systematic review. Eur J Gen Pract 2014; 20 (02) 125-133
16 Hulse NC, Ranade-Kharkar P, Post H, Wood GM, Williams MS, Haug PJ. Development and early usage patterns of a consumer-facing family health history tool. AMIA Annu Symp Proc 2011; 2011: 578-587
17 Wu RR, Himmel TL, Buchanan AH. , et al. Quality of family history collection with use of a patient facing family history assessment tool. BMC Fam Pract 2014; 15: 31
18 Rubinstein WS, Acheson LS, O'Neill SM. , et al; Family Healthware Impact Trial (FHITr) Group. Clinical utility of family history for cancer screening and referral in primary care: a report from the Family Healthware Impact Trial. Genet Med 2011; 13 (11) 956-965
19 Orlando LA, Wu RR, Beadles C. , et al. Implementing family health history risk stratification in primary care: impact of guideline criteria on populations and resource demand. Am J Med Genet C Semin Med Genet 2014; 166C (01) 24-33
20 Ruffin IV MT, Nease Jr DE, Sen A. , et al; Family History Impact Trial (FHITr) Group. Effect of preventive messages tailored to family history on health behaviors: the Family Healthware Impact Trial. Ann Fam Med 2011; 9 (01) 3-11
21 Orlando LA, Wu RR, Myers RA. , et al. Clinical utility of a Web-enabled risk-assessment and clinical decision support program. Genet Med 2016; 18 (10) 1020-1028
22 Orlando LA, Buchanan AH, Hahn SE. , et al. Development and validation of a primary care-based family health history and decision support program (MeTree). N C Med J 2013; 74 (04) 287-296
23 Wang C, Bickmore T, Bowen DJ. , et al. Acceptability and feasibility of a virtual counselor (VICKY) to collect family health histories. Genet Med 2015; 17 (10) 822-830
24 Wu RR, Myers RA, McCarty CA. , et al; Family Health History Network. Protocol for the “Implementation, adoption, and utility of family history in diverse care settings” study. Implement Sci 2015; 10: 163
25 Curran GM, Bauer M, Mittman B, Pyne JM, Stetler C. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care 2012; 50 (03) 217-226
26 Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health 1999; 89 (09) 1322-1327
27 Chew LD, Bradley KA, Boyko EJ. Brief questions to identify patients with inadequate health literacy. Fam Med 2004; 36 (08) 588-594
28 NCI. Health Information National Trends Survey. Available at: https://hints.cancer.gov/view-questions-topics/question-details.aspx?red=1&qid=413 . Accessed August 29, 2018
29 Bennett RL. The family medical history. Prim Care 2004; 31 (03) 479-495
30 Wu RR, Myers RA, Sperber N. , et al. Implementation, adoption, and utility of family health history risk assessment in diverse care settings: evaluating implementation processes and impact with an implementation framework. Genet Med 2018;
31 Lyles C, Schillinger D, Sarkar U. Connecting the dots: health information technology expansion and health disparities. PLoS Med 2015; 12 (07) e1001852
32 Cunningham TJ, Croft JB, Liu Y, Lu H, Eke PI, Giles WH. Vital signs: racial disparities in age-specific mortality among Blacks or African Americans - United States, 1999-2015. Morb Mortal Wkly Rep 2017; 66 (17) 444-456
33 O'Connell J, Yi R, Wilson C, Manson SM, Acton KJ. Racial disparities in health status: a comparison of the morbidity among American Indian and U.S. adults with diabetes. Diabetes Care 2010; 33 (07) 1463-1470
34 Gordon NP, Hornbrook MC. Differences in access to and preferences for using patient portals and other eHealth technologies based on race, ethnicity, and age: a database and survey study of seniors in a large health plan. J Med Internet Res 2016; 18 (03) e50
35 Choi NG, Dinitto DM. The digital divide among low-income homebound older adults: Internet use patterns, eHealth literacy, and attitudes toward computer/Internet use. J Med Internet Res 2013; 15 (05) e93
36 Estacio EV, Whittle R, Protheroe J. The digital divide: examining socio-demographic factors associated with health literacy, access and use of internet to seek health information. J Health Psychol 2017;
37 Murray E, Lo B, Pollack L. , et al. The impact of health information on the internet on the physician-patient relationship: patient perceptions. Arch Intern Med 2003; 163 (14) 1727-1734
38 Morris A, Goodman J, Brading H. Internet use and non-use: views of older users. Univers Access Inf Soc 2007; 6 (01) 43-57
39 Neves BB, Amaro F, Fonseca J. Coming of (old) age in the digital age: ICT usage and non-usage among olders adults. Sociol Res Online 2013; 18 (02) 1-14
40 Hughes Halbert C, Welch B, Lynch C. , et al. Social determinants of family health history collection. J Community Genet 2016; 7 (01) 57-64
41 Koehly LM, Peters JA, Kenen R. , et al. 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 (12) 2203-2209
42 Chen L-S, Li M, Talwar D, Xu L, Zhao M. Chinese Americans' views and use of family health history: a qualitative study. PLoS One 2016; 11 (09) e0162706
43 Kam S, Bylstra Y, Forrest L, Macciocca I, Foo R. Experience of Asian males communicating cardiac genetic risk within the family. J Community Genet 2018; 9 (03) 293-303
44 Quay TA, Frimer L, Janssen PA, Lamers Y. Barriers and facilitators to recruitment of South Asians to health research: a scoping review. BMJ Open 2017; 7 (05) e014889
45 Ford JG, Howerton MW, Lai GY. , et al. Barriers to recruiting underrepresented populations to cancer clinical trials: a systematic review. Cancer 2008; 112 (02) 228-242
46 Alterovitz G, Warner J, Zhang P. , et al. SMART on FHIR Genomics: facilitating standardized clinico-genomic apps. J Am Med Inform Assoc 2015; 22 (06) 1173-1178
47 Warner JL, Rioth MJ, Mandl KD. , et al. SMART precision cancer medicine: a FHIR-based app to provide genomic information at the point of care. J Am Med Inform Assoc 2016; 23 (04) 701-710