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

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Pages (from-to)180-188
Number of pages9
JournalApplied Clinical Informatics
Volume10
Issue number2
DOIs
StatePublished - 1 Jan 2019

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Medical History Taking
Facings
Risk assessment
Information technology
Health
Technology
Age of Onset
Cause of Death
Logistics
Health Literacy
Medical Informatics
Electronic Health Records
Electronic medical equipment
Health risks
Population
Logistic Models

Keywords

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

Cite this

Wu, R. Ryanne ; Myers, Rachel A. ; Buchanan, Adam H. ; Dimmock, David ; Fulda, Kimberly G. ; Haller, Irina V. ; Haga, Susanne B. ; Harry, Melissa L. ; McCarty, Catherine ; Neuner, Joan ; Rakhra-Burris, Teji ; Sperber, Nina ; Voils, Corrine I. ; Ginsburg, Geoffrey S. ; Orlando, Lori A. / Effect of Sociodemographic Factors on Uptake of a Patient-Facing Information Technology Family Health History Risk Assessment Platform. In: Applied Clinical Informatics. 2019 ; Vol. 10, No. 2. pp. 180-188.
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title = "Effect of Sociodemographic Factors on Uptake of a Patient-Facing Information Technology Family Health History Risk Assessment Platform",
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 care disparities, health risk assessment, patient engagement",
author = "Wu, {R. Ryanne} and Myers, {Rachel A.} and Buchanan, {Adam H.} and David Dimmock and Fulda, {Kimberly G.} and Haller, {Irina V.} and Haga, {Susanne B.} and Harry, {Melissa L.} and Catherine McCarty and Joan Neuner and Teji Rakhra-Burris and Nina Sperber and Voils, {Corrine I.} and Ginsburg, {Geoffrey S.} and Orlando, {Lori A.}",
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Wu, RR, Myers, RA, Buchanan, AH, Dimmock, D, Fulda, KG, Haller, IV, Haga, SB, Harry, ML, McCarty, C, Neuner, J, Rakhra-Burris, T, Sperber, N, Voils, CI, Ginsburg, GS & Orlando, LA 2019, 'Effect of Sociodemographic Factors on Uptake of a Patient-Facing Information Technology Family Health History Risk Assessment Platform' Applied Clinical Informatics, vol. 10, no. 2, pp. 180-188. https://doi.org/10.1055/s-0039-1679926

Effect of Sociodemographic Factors on Uptake of a Patient-Facing Information Technology Family Health History Risk Assessment Platform. / Wu, R. Ryanne; Myers, Rachel A.; Buchanan, Adam H.; Dimmock, David; Fulda, Kimberly G.; Haller, Irina V.; Haga, Susanne B.; Harry, Melissa L.; McCarty, Catherine; Neuner, Joan; Rakhra-Burris, Teji; Sperber, Nina; Voils, Corrine I.; Ginsburg, Geoffrey S.; Orlando, Lori A.

In: Applied Clinical Informatics, Vol. 10, No. 2, 01.01.2019, p. 180-188.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Effect of Sociodemographic Factors on Uptake of a Patient-Facing Information Technology Family Health History Risk Assessment Platform

AU - Wu, R. Ryanne

AU - Myers, Rachel A.

AU - Buchanan, Adam H.

AU - Dimmock, David

AU - Fulda, Kimberly G.

AU - Haller, Irina V.

AU - Haga, Susanne B.

AU - Harry, Melissa L.

AU - McCarty, Catherine

AU - Neuner, Joan

AU - Rakhra-Burris, Teji

AU - Sperber, Nina

AU - Voils, Corrine I.

AU - Ginsburg, Geoffrey S.

AU - Orlando, Lori A.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - family health

KW - health care disparities

KW - health risk assessment

KW - patient engagement

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U2 - 10.1055/s-0039-1679926

DO - 10.1055/s-0039-1679926

M3 - Article

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SP - 180

EP - 188

JO - Applied Clinical Informatics

JF - Applied Clinical Informatics

SN - 1869-0327

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ER -