Keywords family health - health risk assessment - health care disparities - patient engagement
Background and Significance
Background and Significance
Racial and socioeconomic disparities in health care have been well documented.[1 ] While there are numerous factors contributing to these disparities, one potential
contributor is associated with health care providers' subconscious biases in risk
assessment and referral patterns.[2 ]
[3 ] Systematic risk assessment using information technology (IT) platforms has the potential
to help mitigate racial and socioeconomic differences in health care access and outcomes.
Through the use of IT platforms, populations can be assessed systematically for disease
risk so that care is evaluated and distributed based on underlying risk without the
influence of subconscious biases.
The potential value of an IT-based risk assessment directing health services to those
at greatest need while reducing use of services among those least likely to benefit
has been increasingly recognized. The purpose of risk assessment will vary across
settings, ranging from decreasing hospital readmission rates to selecting appropriate
medications for patients.[4 ]
[5 ]
[6 ] Within the precision medicine context, risk assessment has great potential benefit.[7 ]
[8 ]
[9 ] Although genetic testing for disease risk and medication adverse events is becoming
more available, the health system still needs systematic ways to identify patients
most likely to benefit from such interventions. Family health history (FHH)-based
risk assessment is an important way to identify those at greatest risk for inherited
conditions and most in need of increased screening and genetic testing services.[10 ] FHH as currently collected in routine care is frequently inadequate to perform risk
assessment.[11 ] There are numerous barriers to adequate collection and use of this information including
lack of patient education and awareness, lack of provider time and resources, and
inadequate electronic health record systems for FHH collection and assessment.[12 ]
[13 ]
[14 ]
[15 ] Numerous IT risk assessment tools (e.g., Family Healthware, Health Heritage) have
been developed and have been shown to: improve the quality of FHH collected as compared
with usual care,[7 ]
[16 ]
[17 ] increase the identification of at-risk individuals,[7 ]
[18 ]
[19 ] and impact health care practices of clinicians and patients.[20 ]
[21 ] One of these tools, MeTree, is a web-based, patient-facing FHH risk assessment platform
with guideline-informed clinical decision support that was developed by our group.[22 ] Little work has been done to evaluate the impact of patient demographics on the
ability to complete FHH risk assessment tools. One small study of 70 individuals comparing
an animated virtual counselor (VICKY) to the Surgeon General's MyFamilyHealthPortrait
(MFHP) among an underserved population showed that VICKY was easier to use and identified
more medical conditions among relatives than MFHP.[23 ] This was the first study to identify a concern about usability among underserved
populations and suggested that a deeper evaluation of the impact of sociodemographics
on feasibility was warranted.
To date, MeTree has been assessed in limited practice settings. Given patients' range
of experiences and comfort with IT-based health applications, knowledge of and sharing
of health information between family members, health information-seeking, cultural
practices, and other patient demographics, we desired to ascertain differences in
utilization of the platform in more diverse patient populations. We recently evaluated
utilization of the platform in four diverse health care systems in different regions
of the United States.[24 ]
Objective
In this paper, we report our findings regarding the usability of a FHH platform as
measured by (1) the degree to which uptake of the intervention varied by patient demographics
and (2) the impact of sociodemographics on the quality of the data collected within
the platform.
Methods
Overview and Study Design
This was a pragmatic type III hybrid implementation-effectiveness trial across four
diverse health care systems in the United States.[24 ]
[25 ] The study design and outcomes were organized based on the Reach, Effectiveness,
Adoption, Implementation, and Maintenance (RE-AIM) framework.[26 ] Full details of the study protocol have been published and are summarized below.[24 ] This is a secondary analysis of impact of sociodemographics on study participation.
Setting
The four participating health care systems had diverse operational profiles, missions,
and patient populations: Duke University, a suburban academic medical system with
a moderately racially diverse patient population in central North Carolina; Essentia
Health (Essentia), a rural integrated health system serving a predominantly white
population across the upper Midwest; Medical College of Wisconsin (MCW), an urban
academic medical system with a large black population in Milwaukee, Wisconsin; and
University of North Texas Health Science Center (UNTHSC), an academic urban medical
system with a large Hispanic, underserved population in Texas.
Study Process
All adult patients (age ≥ 18) of participating clinicians with an upcoming primary
care visit were eligible to participate. Those with email addresses on file were invited
to participate electronically by email. Patients without an email address on file
and at sites not approved for email recruitment were sent paper invitations by post
mail. Invitations were sent 3 weeks prior to an upcoming nonurgent appointment. Invitees
were told that their medical provider had enrolled in the study, would receive their
risk report, and would discuss their results with them at their next clinic visit.
A follow-up invitation was sent to those who did not respond within 5 days. Interested
patients were instructed to contact the central coordinator by email or phone to enroll.
Once the coordinator was contacted by an eligible patient, the entire enrollment,
consent, and study completion process was done electronically.
At each stage, the participant was emailed a link to complete the next step. Completion
of one step automatically led to receipt of a link to complete the next step ([Fig. 1 ]). Once consented, participants were emailed a link to complete a baseline survey
regarding health behaviors, health literacy, and health information seeking; then
a link to the risk assessment application, which collected demographic information
as well as personal and FHH. Risk reports generated by MeTree were immediately displayed
to the participant within the program and made available to be downloaded; the provider
report was uploaded to the electronic medical record (EMR) for the clinician for review,
discussion, and action as clinically appropriate.
Fig. 1 Study flow diagram.
Measures
Measures reported in this paper were collected from (1) MeTree (demographics, personal
health history, and FHH), (2) the baseline survey (health literacy and information-seeking
confidence), and (3) automated tracking of participant progression through the study.
Participant demographic categories included age, sex, race (Asian, black, mixed race,
American Indian/Alaskan Native, not reported, white), ethnicity (Hispanic, non-Hispanic),
insurance type (Medicare, Medicaid, employer/private, other), and education (high
school [HS] or less, community college, 4-year college, graduate school). Health literacy
and information seeking data were only available for those who completed the baseline
survey, and race and ethnicity data, which were collected by the platform, were available
for those who started it. The baseline survey included a validated screener for health
literacy (“How confident are you in filling out forms by yourself?”),[27 ] and assessment of confidence in information seeking (“Overall, how confident are
you that you could get health-related advice or information if you needed it?”).[28 ]
Outcomes
We evaluated how participants' demographics were associated with two outcomes: (1)
progression through each stage of the study (i.e., enrollment, consent, baseline survey
completion, intervention completion) and (2) quality of FHH entered. Participants
with race “not reported” (n = 327, 13.9%) and “multiracial” (n = 48, 2.0%) categories were kept in the analysis as separate groups to assess the
impact of their education and insurance status on outcomes; however, impacts of race
for these two groups are not reported as these results were considered uninformative.
“Multiracial” was considered uninformative because it included subjects from a wide
range of racial combinations, each of which was too sparse to represent its own group.
A high-quality FHH as defined by Bennett and commonly accepted should include: (1)
three generations of relatives; (2) relatives' lineage (i.e., paternal or maternal);
(3) relatives' gender; (4) an up-to-date FHH; (5) pertinent negatives in FHH noted;
(6) age of disease onset in affected relatives; and for deceased relatives, the (7)
age and (8) cause of death.[29 ] Criteria 1 to 5 are automatically collected based on how the risk assessment platform
is structured. We assessed the remaining criteria of the number and percentage of
relatives: (1) with age of onset reported on relevant conditions and (2) if deceased,
with cause of death and (3) age of death reported. We also evaluated the percentage
of relatives for which medical history was marked as unknown.
Statistical Analysis
Participant demographics were summarized using counts and percentages for ethnicity,
race, education level, and insurance type and using mean and standard deviation for
age in years. Bivariate analysis of differences in participants' demographics by study
progression was assessed using Pearson's chi-squared tests or ANOVA F-tests. Intervention
completion (i.e., of patients who started and completed it) was modeled as a function
of education level, insurance, race, ethnicity, gender, age, health literacy, and
information-seeking confidence, while controlling for study site. Backward stepwise
logistic mixed effect regression was used to identify and eliminate nonsignificant
fixed-effect model terms (p > 0.05) until all remaining terms were significant. Significance of each model term
was assessed using likelihood ratio tests of nested models, and pairwise contrasts
between levels of categorical model terms with three or more levels (e.g., education,
insurance) were assessed using generalized linear hypothesis testing with Benjamini–Hochberg
multiple testing corrections.
FHH quality measures of age of disease onset, cause of death, age at death, and unknown
health history were aggregated to the family level, summarizing the number of instances
with and without the feature reported in each family. For example, the number of deceased
relatives with an age at death reported and not reported was determined for each participant.
Proportions were aggregated at the family level, and then assessed for differences
by the participant's demographics using grouped logistic fixed effect regression,
with weights such that each family had equal weight, regardless of family size. Significance
was assessed using likelihood ratio tests of nested models.
Results
All primary care patients from 19 primary care clinics across four health systems
with upcoming nonurgent primary care appointments (N = 55,738) were invited to participate in the study. We enrolled 2,514 (4.51%) patients
with a mean age of 57 ([Table 1 ]). Data on how enrolled participants differed from the underlying clinic populations
have been previously reported.[30 ]
Table 1
Participant characteristics and study progression by demographics
Demographic group
Consented
N (%)
Completed baseline survey
N (%)
Started intervention
N (%)
Completed intervention
N (%)
Age (mean [SD])
56.76 (14.13)
57.00 (14.07)
57.03 (14.00)
57.02 (14.06)
Sex
Female
1,722 (72.6)
1,680 (97.6)
1,611 (95.9)
1,309 (81.3)
[a ]
Male
791 (72.3)
772 (97.6)
736 (95.3)
578 (78.5)
[a ]
Insurance
Employer/private
1,629 (100)
1,603 (98.4)
[b ]
1,541 (96.1)
[b ]
1,280 (83.1)
[b ]
Medicaid
67 (100)
63 (94)
[b ]
54 (85.7)
[b ]
36 (66.7)
[b ]
Medicare
741 (100)
733 (98.9)
[b ]
703 (95.9)
[b ]
536 (76.2)
[b ]
Other
22 (100)
19 (86.4)
[b ]
18 (94.7)
[b ]
14 (77.8)
[b ]
Education
HS or less
235 (100)
228 (97)
213 (93.4)
150 (70.4)
[a ]
[b ]
Community college
391 (100)
380 (97.2)
362 (95.3)
276 (76.2)
[b ]
4-year college
709 (100)
700 (98.7)
682 (97.4)
563 (82.6)
[a ]
[b ]
Graduate school
1,119 (100)
1,106 (98.8)
1,056 (95.5)
875 (82.9)
[a ]
[b ]
Race[c ]
Asian
–
–
29 (100)
21 (72.4)
[a ]
[b ]
Black
–
–
165 (100)
143 (86.7)
[b ]
Native American/Alaskan
–
–
3 (100)
3 (100)
[b ]
White
–
–
1,776 (100)
1,606 (90.4)
[a ]
[b ]
Health literacy[d ]
Never
–
9 (100)
8 (88.9)
[b ]
5 (62.5)
[b ]
Occasionally
–
8 (100)
7 (87.5)
[b ]
3 (42.9)
[b ]
Sometimes
–
48 (100)
44 (91.7)
[b ]
31 (70.5)
[b ]
Often
–
251 (100)
234 (93.2)
[b ]
171 (73.1)
[b ]
Always
–
1,807 (100)
1,740 (96.3)
[b ]
1,424 (81.8)
[b ]
Information-seeking confidence[e ]
Not confident at all
–
7 (100)
6 (85.7)
3 (50)
[b ]
A little confident
–
36 (100)
33 (91.7)
21 (63.6)
[b ]
Somewhat confident
–
311 (100)
297 (95.5)
234 (78.8)
[b ]
Very confident
–
923 (100)
885 (95.9)
710 (80.2)
[b ]
Completely confident
–
850 (100)
817 (96.1)
666 (81.5)
[b ]
Note: All percentages in parentheses use the number from the preceding column as the
denominator for the calculation. Statistically significant values are presented in
bold.
a Significant differences (p -value < 0.05) seen on pairwise comparisons in multivariate model of intervention
completion.
b Significant differences (p -value < 0.05) across the category in bivariate analysis of study stage progression.
c Race recorded within MeTree, so only available from that study stage. Participants
in “no race reported” or “mixed race” category not included in race analysis.
d “How confident are you in filling out forms by yourself?”[27 ]
e “Overall, how confident are you that you could get health-related advice or information
if you needed it?”[28 ]
Study Progression
Participants' progression through the study varied significantly by age, race, insurance
type, education level, health literacy, and information-seeking confidence in bivariate
analyses ([Table 1 ]). Age did not correlate with progression through initial stages of the study, but
those of older age were less likely to finish the final step of completing the risk
assessment once it was started (for every 10-year increase in participant age, odds
of not completing increased by a factor of 1.22, p -value < 0.001). Intervention completion rates varied by race (p -value < 0.001). Progression by insurance type and education varied at every study
stage (insurance: p -value < 0.001 at all stages; education: completed baseline survey, p -value = 0.04; started MeTree, p -value = 0.04; completed MeTree, p -value < 0.001). Low health literacy, as measured in the baseline survey, correlated
with lack of progression at all subsequent steps (started MeTree, p -value = 0.05; completed MeTree, p -value < 0.001). Low information-seeking confidence also correlated with lack of intervention
completion (p -value = 0.03). Progression did not vary by sex or ethnicity.
In multivariate logistic mixed effect modeling of the transition from intervention
started to intervention completed, lack of completion was more commonly seen among
those of older age (for every 10-year increase in age, odds of not completing increased by a factor of 1.22, p -value = 0.005), less education (p -value = 0.004), male sex (odds ratio [OR]female vs. male = 1.75, 95% confidence interval [CI]: 1.19–2.56, p -value = 0.005), and was variable by race (p -value = 0.009). Insurance type, health literacy, and information seeking were no
longer significant covariates and eliminated from the model via backward stepwise
regression. Pairwise comparisons of racial groups showed that Asians had the lowest
odds of completing while whites had the highest odds. After multiple testing corrections,
whites were significantly different from Asians (ORwhite vs. Asian = 5.51, 95% CI: 2.14–14.17, p = 0.006); black versus Asians was trending toward significance (ORblack vs. Asian = 4.66, 95% CI: 1.43–15.3, p = 0.08). Pairwise comparisons of education levels show that differences in intervention
completion were driven by lower completion rates in those with HS education versus
all other levels of education: community college (ORComm. coll. vs. HS = 2.24, 95% CI: 1.12–4.48, p -value = 0.04), 4-year college (OR4-year. coll. vs. HS = 2.91, 95% CI: 1.57–5.41, p -value = 0.002), or graduate level degrees (ORGrad. level vs. HS = 2.95, 95% CI: 1.67–5.23, p -value < 0.001).
Family Health History Quality
FHH quality metrics ([Table 2 ]) were used to evaluate any disparities in FHH reporting.[29 ] Some sociodemographic factors had significant correlation with quality metrics.
Health literacy and information-seeking confidence had no correlation with any of
the FHH quality metrics assessed.
Table 2
Frequency of reporting relative: age of onset, unknown medical history, cause and
age of death
Participant demographics
Conditions with age of onset reported[a ] % (CI)
Relatives with unknown medical history % (CI)
Deceased relatives with cause of death reported % (CI)
Deceased relatives with age of death reported % (CI)
Sex
Female
86.25 (84.3–88.0)
[b ]
18.36 (16.3–20.5)
[b ]
86.25 (84.3–88.1)
93.27 (91.8–94.5)
Male
91.46 (89.0–93.6)
[b ]
22.87 (19.6–26.4)
[b ]
83.97 (80.8–86.8)
94.85 (92.8–96.5)
Insurance
Employer/private
87.31 (85.4–89.1)
16.86 (14.9–19.0)
[b ]
86.96 (85.0–88.7)
94.10 (92.7–95.3)
Medicaid
84.05 (69.5–93.7)
25.92 (13.6–41.7)
[b ]
86.10 (72.5–94.8)
87.11 (73.8–95.4)
Medicare
89.42 (86.6–91.8)
26.20 (22.6–30.0)
[b ]
82.05 (78.6–85.2)
93.48 (91.2–95.4)
Other
89.53 (67.2–98.8)
17.37 (3.8–41.8)
[b ]
89.29 (66.9–98.7)
92.26 (71.2–99.5)
Education
HS or less
87.75 (81.8–92.4)
26.54 (19.9–34.0)
81.78 (75.0–87.4)
90.32 (84.9–94.4)
Community college
86.81 (82.5–90.5)
22.17 (17.5–27.3)
85.43 (80.9–89.3)
92.49 (89.0–95.2)
4-year college
86.93 (84.0–90.0)
18.85 (15.8–22.2)
86.59 (83.6–89.3)
93.99 (91.8–95.8)
Graduate school
88.80 (86.6–90.8)
18.36 (15.9–21.0)
85.54 (83.1–87.7)
94.59 (93.0–96.0)
Race
Asian
90.92 (73.5–98.6)
26.38 (11.0–47.2)
80.82 (60.4–93.8)
85.50 (66.2–96.3)
[b ]
Black
81.21 (74.2–87.1)
24.40 (17.9–31.9)
78.68 (71.5–84.8)
83.33 (76.7–88.8)
[b ]
Native American/Alaskan
100 (NA)
36.30 (3.0–85.7)
68.89 (17.5–98.2)
93.33 (40.2–100)
[b ]
White
88.44 (86.8–89.9)
19.26 (17.4–21.2)
86.38 (84.6–88.0)
94.84 (93.7–95.9)
[b ]
Abbreviations: CI, confidence interval; HS, high school.
Note: Statistically significant values are presented in bold.
a Includes only conditions entered where age of onset is requested.
b Significant differences (p -value < 0.05) between groups within the socio-demographic factor.
Age of Onset
Participants reported age of onset for the majority of diseases (87.85%, CI: 86.32–89.27%)
entered for relatives. Women and those who were younger reported significantly fewer
ages of onset for relatives' conditions (p -value = 0.001 for sex and for age, for every 10-year increase in age, odds of providing
age of onset increased by a factor of 1.15, p -value = 0.005).
Cause of Death
Participants reported cause of death for the majority (85.55%, CI: 83.91–87.10%) of
relatives marked as deceased. Older participants were less likely to report cause
of death (for every 10-year increase in age, odds of not reporting cause of death increased by a factor of 1.25, p -value < 0.001). No other demographic factors were associated with significant differences
in reporting.
Age of Death
Participants reported age of death for the majority (93.76%, CI: 92.61–94.80%) of
relatives marked as deceased. Participants' race impacted age of death reporting,
with Asians (85.50%) and blacks (83.33%) being less likely to report age of death
(p -value < 0.001).
Unknown History
The proportion of relatives with an “unknown medical history” reported (19.75%, CI:
18.00–21.59%) was higher among male participants (p -value = 0.025), those with Medicaid and Medicare insurance (p -value < 0.001), and older participants (for every 10-year increase in participant
age, odds of reporting unknown history increase by a factor of 1.32, p -value < 0.001) on bivariate analyses. Race and education had no effect. Multivariate
fixed effect modeling showed that difference was driven entirely by participant age
(for every 10-year increase in participant age, odds of reporting unknown history
increased by 1.32, p -value < 0.001).
Discussion
While the systematic use of IT-based health applications such as MeTree may improve
use of FHH for risk assessment and targeted prevention strategies, IT-based health
applications could also exacerbate health disparities.[31 ] This study shows that using a web-based technology to collect family health histories
directly from patients results in a high overall completion rate (75%) with no significant
disparities among blacks or Hispanics as compared with white participants. This is
a significant finding given ongoing concerns about health care disparities in general,
and widening gaps in morbidity and longevity in minority patients.[32 ]
[33 ]
However, several factors significantly correlated with lack of completion of the risk
assessment platform, including less education, older age, male sex, and Asian race.
There are several reasons that may have contributed to these findings, including lack
of comfort with IT, lack of knowledge of one's FHH, and lack of time due to competing
demands.[30 ] Older patients who have registered to access a patient portal are less likely to
utilize it.[34 ] In addition, poor digital literacy, health literacy, and lack of internet access
are all interrelated and well-documented barriers among the elderly and lower socioeconomic
groups.[35 ]
[36 ]
[37 ]
[38 ]
[39 ] Collection of and knowledge about FHH among men have been shown to be significantly
lower which may help explain their lower rates of completion.[40 ]
[41 ] Better understanding of these barriers and their relationships will allow investment
in interventions that can solve these challenges. Low numbers of Asian participants
makes it difficult to draw strong conclusions about this group although Asian-Americans
may have some unique cultural barriers that should be considered as highlighted by
Chen and colleagues' study reporting a high perceived value of FHH among Asian-Americans,
yet a significant lack of knowledge due to barriers such as distance from relatives
and the perception of “healthy families.”[42 ]
[43 ] Lastly, the higher engagement in clinical trials by Caucasians and those with higher
education is a well-documented phenomenon, which is a difficult challenge to overcome.[44 ]
[45 ]
For those who did complete the intervention, the quality of the data provided was
high overall, with negligible differences by sociodemographic characteristics. Important
to note, sociodemographic (e.g., race, education, insurance) differences did not correlate
with lack of knowledge about relatives' medical histories. Age was the only significant
correlate. This is likely due to (1) less clear medical diagnoses in previous generations,
(2) the diaspora induced by World Wars I and II and the massive impact these wars
had on mortality in young adults, and (3) higher number of deceased relatives among
other factors.
The limitations of this analysis include the inability to capture the reason why individuals did not progress past a stage in the study. Further work needs to be
done to understand these barriers. In addition, not all sociodemographic factors were
available from the time of enrollment (e.g., race is captured in the platform). Thus
fully understanding the impact of each factor on progression between each stage of
the study was not possible. We also are limited in applying these results more broadly
by the small number of minority participants. Additional studies are in development
to more directly target non-White populations. Furthermore, the collinearity and imbalance
in the features hypothesized to influence completion prohibited us from obtaining
stable estimates for the complete multivariable mixed effect model of study completion.
To overcome this limitation, we used backward stepwise regression to eliminate nonsignificant
features. However, this approach results in p -values that may be biased due to the repeated analyses in stepwise regressions and
parameter estimates that are biased/may not replicate.
Despite these limitations, the stepwise regression complements the bivariate analyses
by estimating the effects in the context of other participant features (e.g., effect
of education while holding race constant) and identifying which features remain associated
with completion.
Given the potential benefits of technology to overcome barriers of access and implicit
bias, future efforts to address these and other technology-related barriers that hinder
enrollment and engagement with minorities and lower literacy groups are essential
for improving the health of the U.S. population. Risk assessment tools like MeTree
frequently already include tips to gather information from relatives, and education
on the importance of specific types of information such as age of onset, glossaries
and pop-up reminders to help clarify the type of information, or remind patients about
missing data, respectively. These could be further enhanced. In addition, some potential
solutions to address the technology barriers and expand utilization include development
of additional patient resources, such as short step-by-step videos demonstrating how
to complete an online survey or use a tablet (if applicable) and how to complete a
FHH-based risk assessment. A small community pilot with 20 low-literacy American-Indians
showed that they were highly engaged in collecting and documenting their FHH. However,
when using a technology, like MeTree, their limited awareness of the body and diseases
was a barrier for identifying where to enter disease information. Going forward, understanding
these challenges in greater detail will allow developers to build technology that
overcomes these difficulties, e.g., using augmented reality to select the location
of the disease or speech to text so that the program enters the data for them. In
addition, integration with the EMR through SMART-on-FHIR-enabled applications that
allow seamless integration for patients and providers may help overcome some of the
existing challenges.[46 ]
[47 ] EMR integration has great potential as it permits access to data within the EMR,
reducing the burden of data entry and improving the quality of risk stratification
by using verified laboratory and diagnosis data imported from the patient's medical
record.
Conclusion
There is potential to make a significant impact on population health by systematic
identification of presymptomatic disease risk. Although there was little evidence
of racial disparity in risk assessment completion in this study, there is room to
improve in addressing barriers for older and less-educated individuals. Systematic,
unbiased risk assessment has great potential to mitigate disparities currently seen
in our health care systems at least at the stage of assessment (although it is only
one piece of the puzzle and work must continue to address disparities in referral
and care decision making). Yet even as we address racial disparities, we must be mindful
of the risk of introducing new disparities for the elderly and less educated based
on IT literacy in the process.
Clinical Relevance Statement
Clinical Relevance Statement
This study shows that a patient-facing risk assessment IT platform is of interest
and accessible by patients from a wide range of racial and educational backgrounds.
There are potential barriers to FHH-based health IT use among those who are older,
with low education levels, and who are male. The quality of the FHH information that
patients can provide is minimally impacted by their sociodemographics.
Multiple Choice Questions
Multiple Choice Questions
Family health history based risk assessment platforms have been shown to:
Identify disease risk at equal rates as routine care.
Change practice patterns.
Diagnose conditions in participants.
All of the above.
Correct Answer: The correct answer is option b, change practice patterns. Use of these platforms
has been shown to change screening and referral patterns within clinics so that they
are more in line with current practice guidelines. FHH-based risk assessment platforms
have been shown to increase identification of at-risk individuals as compared with
current identification rates in routine care. The purpose of risk assessment is not
to diagnose medical conditions in those who complete them but to identify what conditions
they are at most risk for based on their family health history.
In this study, it was shown that information technology based risk assessment platforms
are less likely to be completed by:
Blacks.
Medicaid recipients.
Elderly.
Women.
Correct Answer: The correct answer is option b. In multivariate modeling of intervention completion,
those who were of older age, male sex, less education, and Asian race were less likely
to complete the risk assessment. Insurance type did not have an effect.
Which of the following is not a measure of family health history data quality?
Correct Answer: The correct answer is option d. A high-quality FHH as defined by Bennett should include
(1) three generations of relatives; (2) relatives' lineage (i.e., paternal or maternal);
(3) relatives' gender; (4) an up-to-date FHH; (5) pertinent negatives in FHH noted;
(6) age of disease onset in affected relatives; and for deceased relatives, the (7)
age and (8) cause of death. Relatives' current age is not a key quality indicator.
Which of the following groups were shown to be less likely to know their relatives'
medical history (i.e., report “unknown history”)?
Correct Answer: The correct answer is option b. On multivariate modeling, being older was the only
significant factor associated with reporting “unknown history” for a relative. Race
and health literacy had no impact on this outcome. Men were less likely to know relatives'
medical history on bivariate analyses but this association was no longer present on
multivariate modeling.
