Keywords
Medical informatics applications - consumer health information - ethnic groups - socioeconomic
factors - minority groups - health disparities
Introduction
The digital divide describes “the gap between those who have and do not have access
to computers and the Internet”[1]. Systematic reviews of consumer health informatics (CHI) adoption have placed the
digital divide as one of the core barriers to securing equal participation in technology-based
health management solutions, specifically among underserved populations—groups that
disproportionately experience difficulty accessing care due to social, economic, geographic,
racial, or ethnic status[2]
[3]
[4]
[5].
Internet adoption around the globe, however, has rapidly increased over the past 10
years, including in developing countries[6], groups with low socioeconomic status, and racial and ethnic minority groups in
developed countries[7]
[8]. Between 2010 and 2012, Hispanic and African-American populations in the U.S. represented
the populations with the highest smartphone ownership rates, at 61% and 59%, respectively[9]. Between 2013 and 2015, adults in developing countries who use the Internet at least
occasionally or report owning a smartphone increased significantly, from 6% in India
up to 31% in Turkey[6].
Tasks previously only possible on desktop computers, such as Internet and e-Health
access, are now widely available through mobile phones and tablets. The global trend
of increased Internet access and mobile phone ownership offers low-cost, scalable
opportunities for CHI to empower individuals. A randomized controlled trial of MyHealthKeeper,
a personal health record system from South Korea that allowed for sharing between
patients and healthcare providers, resulted in significant improvements in weight-loss
and triglyceride levels among users[10]. The Finnish National Archive of Health Information (KanTa), the national health
data repository, was developed with the goal for citizens to access their own health
information electronically[11]. In Sub-Saharan Africa, mobile and Internet technology penetration has resulted
in increased female economic participation[12]. Mobile health applications in developing countries have shown effectiveness in
many areas of medical care: improvement in patient follow-up[13], uptake of counseling and testing[14], and improved patient adherence and response to treatment[10]
[15].
Given the increased penetration of Internet and mobile technologies across the globe,
continuing to assume that basic technology access is the main contributor to health
disparity vis-à-vis the digital divide may be in sufficient. This lack of context
may potentially increase health disparities over time if left unchecked. Improving
CHI adoption requires users to remain highly committed and motivated[2]
[16]
[17]
[18]. Sustained engagement, necessary for the adoption of any technology, has proved
challenging in other scenarios[19]
[20]
[21].
In this review, we survey articles that were published between 2012 and 2017 about
CHI use. Given the changing atmosphere of underserved populations’ technology use,
we concentrate on identifying barriers and facilitators. We suggest facilitators for
developing future CHI systems that are sensitive to diverse user populations.
Methods
Our goal is to review the past five years of literature to follow up on persistent
qualitative barriers and facilitators to CHI use among underserved populations. The
goal is not to systematically review all possible literature, but rather update whether
more recent literature continues to view the digital divide and motivation to use as the major barriers. We aim to enrich the discussion regarding what facilitators
we can employ for future work in developing and evaluating CHI and overcome barriers
to this end.
Operationalizing the Terms CHI and Underserved Population
We adapted the definition of CHI from the most recently updated publication on CHI
ontologies[22], which describes CHI as a technology that is: (1) consumer facing, where consumers refer to patients, caregiv-ers, or healthy individuals with prevention
needs; (2) interactive for the consumer, including features such as buttons or links that enable retrieval
of further information initiated by the consumer; and (3) providing tailored information, where the tailoring should happen for each consumer (e.g., providing
personal health records, rather than general health information from Internet search)
or the consumer group (e.g., tailored for cancer survivors group).
Defining underserved populations varies by country-specific political, cultural, and
socioeconomic factors. Our review of a Cochrane study and national and international
agencies publications settled around a common definition of underserved, or medically
underserved, as those groups experiencing barriers to basic health needs due to social
(including racial/ethnic minorities), economic, and geographic factors[23]
[24]
[25]
[26]. In the U.S., some agencies like the National Institute for Minority Health and
Health Disparities (NIMHD), and the Health Resources and Services Administration's
Medically Underserved Areas (HRSA-MUA), which defines underserved as those experiencing a lack of access to basic health care, have operationalized
these key characteristics linked to social, economic, and geographic vulnerabilities
that are in-step with these global definitions for purposes of research and funding[27]
[28]
[29].
For the present review, we define under-served populations as racial/ethnic minorities
in the context of country (e.g., Turkish in Germany, Hispanics and African-Americans
in the U.S.), social (e.g., education, literacy, language), economic (e.g., employment,
poverty, insurance), or geographic (e.g., rural) barriers.
Data Sources and Searches
We searched PubMed from October 2012 to October 2017 for full-text studies published
in the English language regarding barriers and facilitators to CHI use in underserved
populations. Staying within the scope of a survey, we restricted keywords to only
Medical Subject Headings (MeSH) terms representative of “barriers and facilitators
of CHI use among underserved populations” as operationalized above and we avoided
custom keywords that might potentially bias the search results if not systematically
chosen. We summarize the search strategy in [Table 1].
Table 1
Medical Subject Headings terms used for searching abstracts on PubMed
|
Consumer health informatics technology
|
▪ Consumer facing
▪ Interactive, such as buttons or links that enable retrieval of further information
initiated by the consumer
▪ Providing tailored information, where the tailoring should happen for each consumer
(e.g., providing personal health information) or the consumer group (e.g., tailored
for Spanish speaking groups)
|
|
Consumer
|
Consumers could refer to patients, caregivers, or healthy individuals with prevention
needs
|
|
Underserved population
|
▪ Racial/ethnic minorities in the study context of the country (e.g., Turkish in Germany,
Hispanics and African-Americans in the U.S.)
▪ Social barriers (e.g., education, literacy)
▪ Economic barriers (e.g., employment, poverty, insurance)
▪ Geographic barriers (e.g., rural)
|
|
Barriers and facilitators
|
Keywords related to access, tailoring, or user-centered design
|
|
Constraints
|
▪ Full text
▪ Published in the past 5 years
▪ Written in English
|
Data Extraction and Synthesis
After a reviewer training phase to ensure inter-annotator agreement, we performed
a single review for title and abstract screening. A full review of the selected text
was performed by MS and JH, and data abstraction was performed by JH.
From each study selected for full-text review, we abstracted the following: population
characteristics, setting, number of subjects, health conditions studied, study type,
and barriers/facilitators to CHI use. To identify the paper's definition for un-derserved
population we abstracted race/ ethnicity, income and insurance indicators, education
level, and geography. We followed the thematic analysis commonly used in qualitative
research methods[30].
We searched for themes that “emerge as being important to the description of the phenomenon”[31] such as, in our case, facilitators and barriers to CHI adoption among the target
population. We identified the themes through “careful reading and re-reading of the
data”[32]. We recognized patterns within the data, allowing emerging themes to become the
categories for analysis. We adapted PRISMA guidelines for our protocol specification,
data abstraction, and synthesis[33].
Results
We obtained 639 abstracts after removing missing abstracts from 645 search results.
There were no duplicates. We removed 586 abstracts because there was no CHI involved
in the study—either because the study did not examine implemented CHI (e.g., interviews
and questionnaires about future CHI) or because the CHI being studied did not involve
direct interaction with users (e.g., educational videos, one-way text message alert
system). We then removed an additional 22 abstracts because the studies reported lacked
an underserved population perspective on CHI even if the study did involve CHI.
As a result, after the abstract screening, 31 articles remained, from which we excluded
18 articles through the full-text screening (see [Figure 1]). Reasons for exclusion during full-text review included one or more of the following
reasons: no data on the underserved (n=5) or on facilitators and barriers to technology
adoption (n=5), the article was only about the study protocol and was lacking outcome
data (n=3), the technology was not CHI (n=4), or the study did not empirically test
technology on human subjects (n=2). As a result, 13 articles finally remained for
full-text analysis.
Fig. 1 Selecting articles related to the use of CHI by underserved populations. Diagram
adapted from PRISMA guidelines.
The CHI solutions evaluated in these 13 articles included patient education tools
(n=7)[34]
[35]
[36]
[37]
[38]
[39]
[40], patient portals (n=4)[41]
[42]
[43]
[44], and technology-based illness intervention (n=2)[45]
[46]. Nine articles presented mixed methods[35]
[36]
[37]
[38]
[39]
[42]
[43]
[44]
[46], nine included interviews and focus groups[34]
[35]
[36]
[37]
[38]
[39]
[43]
[44]
[46], five included surveys[2]
[36]
[39]
[42]
[44], three included trials[37]
[45]
[46], and three included a cohort analysis[41]
[42]
[46]. The total sample size ranged from 21 patients to more than 200,000 patients. The
studies were from two countries: U.S. (n=12) and Netherlands (n=1)[34]. Six health areas were covered: cancer (n=4)[35]
[36]
[38]
[39], cardiometabolic risk and nutrition (n=2)[34]
[40], HIV/AIDS (n=1)[44], environmental health for prenatal patients (n=1)[37], medication adherence (n=1)[46], and dementia (n=1)[45]. Two articles covered the use of general patient portals, not specific to an illness[41]
[42] and two specified “chronic conditions” without further detail[42]
[43]. Eligibility criteria included older adults in five studies[40]
[42]
[43]
[45]
[46]. Four articles focused only on Hispanic populations[35]
[37]
[44]
[46], four only on African-Americans[36]
[38]
[39]
[43], two on Hispanic and African-Americans[40]
[45], and the rest on one or more ethnic minorities[34]
[41]
[42]. Nine focused on low-income patients[36]
[37]
[38]
[40]
[41]
[42]
[43]
[44]
[46], and 11 on low health literacy or low education populations[34]
[35]
[36]
[37]
[38]
[39]
[40]
[42]
[43]
[44]
[45]. [Tables 2] and [3] display a summary of the inal articles.
Table 2
Characteristics of CHI and population in the selected studies
|
Author, year
|
Type of CHI applications
|
Total number of participants
|
Reported age of participants
|
Indicators for underserved populations
|
|
Ethnicities of interest
|
Income indicators
|
Reported education level
|
Reported insurance status
|
|
Ancker, 2017[41]
|
Patient portal: Medline connected links in medical records
|
12,877
|
18-24: 18.5% 25-44: 43.5% 45-64: 30% 65+: 8%
|
Latinos by language preference, Black, White, Other, Unknown
|
Patients at Federally Qualified Health Centers (FQHC)
|
n/a
|
Private, Medicaid, uninsured, Medicare
|
|
Gordon, 2016[42]
|
Patient portal: Kaiser North California
|
231,082
|
65-79
|
English speaking non-Hispanic White, Black, Hispanic, Filipino, and Chinese
|
30.3% had low income
|
[Survey] 22% Latino seniors and 4% others did not graduate from high school
|
Kaiser HMO
|
|
Damman, 2016[34]
|
Patient education tool: Web-based cardiometabolic disease risk calculator and information
|
23
|
40-66
|
n/a
|
n/a
|
Low health literacy
|
|
|
Kukafka, 2015[35]
|
Patient education tool: Web-based decision aid for breast cancer prevention
|
34
|
Mean age: 53.4 (SD=n/a)
|
Hispanic
|
n/a
|
41% had low numeracy
|
n/a
|
|
Owens, 2015[36]
|
Patient education tool: Computer-based decision aid
|
21
|
37-66
|
African-American
|
More than half had income lower than $39,999/yr
|
14% finished high school
|
20% Medicaid or no coverage or other insurance
|
|
Smith, 2015[43]
|
Patient portal: Registration and utilization of a patient portal
|
534
|
55-74
|
African-American
|
FQHC and ambulatory care clinic
|
17.4% had low health literacy, 14.8% graduated from high school or less
|
n/a
|
|
Rosas, 2014[37]
|
Patient education tool: Kiosk, interactive game for prenatal and environmental health
|
152
|
n/a
|
Hispanic
|
Low income, FQHC
|
Low literacy
|
n/a
|
|
Odlum, 2014[44]
|
Patient portal: Internet-based electronic personal health, management tools
|
42 [Survey], 15 [Focus groups]
|
24-63
|
Hispanic
|
80.9% earned less than $10,000/yr
|
83% graduated from high school or General Educational Development (GED)
|
Medicaid Special Needs Plan
|
|
Cogbill, 2014[38]
|
Patient education tool: Online colorectal cancer education website
|
18 [Focus groups], 60 [Trial]
|
45-75
|
African-American
|
[Focus groups] 16.7% earned $10,000/yr or less, [Trial] 33.3% earned $10,000/yr or
less
|
[Focus groups] 33.3% graduated from high school or less, [Trial] 31.7% graduated from
high school or less
|
n/a
|
|
Czaja, 2013[45]
|
Technology-based intervention: In-home and videophone technology, multi-component
psychosocial intervention
|
110
|
Mean age: 60.9
(SD: 12.8)
|
Hispanic, African-American
|
n/a
|
40% of the intervention group and 36.5% of the control group had less than high school
education
|
n/a
|
|
Bass, 2013[39]
|
Patient education tool: Low-literacy computer touch-screen colonoscopy decision aid
|
102
|
50-74
|
African-American
|
n/a
|
Low literacy; less than 6th grade REALM score[87]
|
n/a
|
|
Lapane, 2012[46]
|
Technology-based intervention: Tailored DVDs on medication adherence
|
326 [Telephone survey], 106 [First focus group], 16 [Second focus group]
|
Eligibility:
At least 65 years old; n/a for actual participants
|
Hispanic
|
Low-income
|
n/a
|
n/a
|
|
Neuen-schwander, 2012[40]
|
Patient education tool: Web-based nutrition education program
|
123
|
18-30: 48%
31-50: 39.8% 51-70: 10.6% 71+: 1.6%
|
Hispanic, African-American
|
SNAP-Ed eligible
|
43% graduated from high school or less
|
n/a
|
Table 3
Barriers, facilitators, and study context of the reviewed articles
|
Author, year
|
Health condition of interest
|
Study or instrument type
|
Barriers
|
Facilitators
|
|
Ancker, 2017[41]
|
n/a
|
Cohort study
|
Low health and computer literacy
|
Needs for more information
|
|
Gordon, 2016[42]
|
Chronic disease
|
Cohort study, survey
|
Low health and computer literacy
|
Help from proxy users
|
|
Damman, 2016[34]
|
Type 2 diabetes, cardiovascular disease, chronic kidney disease
|
Interviews
|
Low health and computer literacy, challenges accepting the presented information
|
|
|
Kukafka, 2015[35]
|
Breast cancer
|
Focus groups, survey
|
Challenges accepting the presented information
|
|
|
Owens, 2015[36]
|
Prostate cancer
|
Focus groups, survey
|
|
Early user engagement In design
|
|
Smith, 2015[43]
|
One or more chronic conditions
|
Interviews
|
Low health and computer literacy, challenges accepting the presented information,
poor usability and clarity
|
Early user engagement in design
|
|
Rosas, 2014[37]
|
Pregnancy, environmental health
|
Pre/post test, open-ended interviews
|
|
Early user engagement in design
|
|
Odlum, 2014[44]
|
HIV/AIDS
|
Focus groups, survey
|
Challenges accepting the presented information, poor usability and clarity
|
Needs for more information
|
|
Cogbill, 2014[38]
|
Colorectal cancer
|
Focus groups, a 3-week feasibility trial
|
Low health and computer literacy, challenges accepting the presented information,
poor usability and clarity
|
Early user engagement in design, needs for more information
|
|
Czaja, 2013[45]
|
Caregivers of patients with dementia
|
A 5-month randomized clinical trial
|
|
Early user engagement in design, needs for more information, proxy users’ help
|
|
Bass, 2013[39]
|
Colorectal cancer
|
Focus groups, survey, segmentation analysis
|
|
Early user engagement in design
|
|
Lapane, 2012[46]
|
Medication adherence
|
Cohort study, focus groups, survey
|
Challenges accepting the presented information, poor usability and clarity
|
Early user engagement in design
|
|
Neuenschwander, 2012[40]
|
Nutrition
|
1-month randomized, block equivalence trial
|
Low health and computer literacy
|
|
Barriers
We found three main barriers to CHI adoption among underserved populations: (1) low
health literacy[34]
[40]
[41]
[42]
[43] and lack of experience with information technology use[38]
[42]
[43]; (2) challenges in accepting the presented information[34]
[35]
[38]
[43]
[44]
[46]; and (3) poor usability and clarity of content[38]
[43]
[44]
[46].
• Low health literacy and lack of experience with information technology use
Many study participants did not have experience in using the Internet from school
or work, lacked cellphones with Internet access, and had little contact with Internet
technology[38]
[42]
[43]. This inexperience hindered their ability to adopt and use CHI solutions without
appropriate training. In a trial involving nutrition education websites in a low-income
community in the U.S. Midwest[40], understanding medical language, or health literacy, was a barrier to using the
tool. Conversely, an in-person meeting was perceived as being more useful than the
CHI intervention. Furthermore, participants did not think they could use a CHI application[38]
[42]. This result was supported by a patient portal use study among older adults in Northern
California[42] and a feasibility trial of an online colorectal cancer education program with African-American
older adults[38]. Focus groups conducted in the latter study showed that texting might not be feasible
for the population because the participants either did not own cell phones or, when
they owned one, did not know how to use it or feared that texting would constitute
a financial burden[38].
• Challenges in accepting the presented information
In some studies, even when participants had access to technology, some of them did
not find the presented information useful[38]
[44]. In another study[34], the presented information contradicted what participants believed about their own
health, or they misinterpreted the materials. For instance, in a Dutch study involving
low health literacy individuals, participants were provided their cardiovascular risk
using technology-based educational materials[34]. The participants either did not believe or misconstrued their risk based on how
the information was visualized. Similarly, a study displayed breast cancer risk using
a web-based decision aid tool where the majority of participants were Hispanic women
with low numeracy. The participants, who felt uncertain about the models presented
to them, attributed the reason of their distrust with their healthcare providers to
their past interaction with the providers[35]. Odlum et al.[44] studied the use of Internet-based electronic personal health management tools among
a mostly minority, low-income HIV/AIDS urban clinic population. The participants preferred
to enter their own health history rather than accepting the data generated by the
clinic, which they found confusing.
• Poor usability and clarity of content
Lastly, usability problems and a lack of message clarity hindered CHI adoption. For
instance, losing an access code after registration deterred older adults from using
a patient portal[43]. Confusing user interfaces made it difficult to use patient portals or to benefit
from patient education materials[38]
[43]
[44]. Tailored educational materials (DVD) for medication adherence aimed at older adults
were mainly critiqued for their background color rather than their content[46]. In a study of a colorectal cancer screening education tool with African-American
men, the participants felt that the messages were vague and should be further tailored—otherwise
they would not motivate behavior change[38].
Facilitators
We found three main CHI facilitators: (1) early user engagement through iterative
user-centered design[36]
[37]
[38]
[39]
[43]
[45]
[46]; (2) engaging users early in the design development process and identifying their
health information needs[38]
[41]
[44]
[45]; and (3) proxies, such as caregivers or family members, who are more familiar with
technology, and use CHI on behalf of the users[42]
[45].
• Early user engagement through iterative user-centered design
Participants were more willing to use CHI when the system was usable, engaging, trusted,
and tailored[36]
[37]
[38]
[39]
[43]
[45]
[46]. To meet these requirements, one aspect frequently discussed was that CHI should
allow for customized communication modes. In a study evaluating an environmental health
education intervention for pregnant Hispanic women via a kiosk, researchers communicated
information through both audio and text on the screen. Some participants preferred
voice to text whereas others preferred reading the information on-screen[37]. Conversely, participants in a colorectal cancer screening study that used text
messages versus emails to assess an educational tool had contrasting preferences for
receiving reminders and learning materials[38]. Factors influencing their decisions included perceived cost of texting, ease of
use, annoyance, and likelihood to grab attention. Bass et al. developed a colonoscopy
decision aid for African-American men[39]; survey and focus group results showed that photographs were preferred over graphics
in depicting educational materials. As a result, the aid included photographs coupled
with testament videos from the actual clinic patients. While web-based nutrition education
was as effective as in-person counseling for low-income participants in Neuenschwander
et al.'s study[40], some topics (e.g., nutrition facts labeling) benefited from a combination of web-based
and in-person approaches.
Three studies used varied methodological contexts and study scales (sample size ranging
from 21 to 534)[36]
[43]
[46]. These studies emphasized the importance of early engagement of end-users into the
design process through user-testing and improvement of functionality[36]
[43]. This process helped add tailored information that met user needs. For instance,
a mixed methods study presented the development of a touch-screen decision aid for
low health literate African-Americans with colorectal cancer[39]. The study revealed that psychosocial issues related to the colonoscopy rather than
medical information on colorectal cancer were the more critical factors in decision-making.
In the health-education study with prenatal Hispanic women, adding games helped children,
partners, and all family members engage in learning about environmental exposures
using a kiosk[37]. Further examples of tailoring included adding actors for a video intervention,
who were relatable to the user population[46].
• Intrinsic needs for more information
Consumer health informatics use was facilitated when participants had an intrinsic
need for more information[44]. The works by Ancker et al.[41] and Odlum et al.[44] demonstrated that participants found that practical tips for provider engagement
and health management were most useful. In a study with Medicaid users, participants
felt that information on facilitating provider visits was useful as a personal health
management tool[44]. African-American participants from a colorectal cancer screening tool study expressed
that they wanted tips on free or low-cost screening[38]. A videophone-based intervention for dementia patients and their caregivers showed
that having access to a support group was helpful and that some participants wanted
more information on accessing support groups as part of these interventions[45].
• Proxy users’ help
For those with low computer literacy, having a delegated person who could help use
the CHI had an impact on CHI adoption[42]
[45]. As evidenced by the videophone study involving dementia patients and their caregivers,
caregivers felt motivated to use the technology when it allowed them to better understand
their patients’ illness. CHI could contribute to an increase in caregivers’ abilities
to help patients take care of the illness[45].
Discussion
The barriers and facilitators discussed in the present review—low health literacy,
tailoring, and the digital divide —have all been considered at length in prior literature[3]
[46]
[47]
[48]
[49]. Studies published in the past 5 years shows a lag in CHI adoption among the underserved
when compared to the general public. Additionally, our results further show that a
digital divide persists[50]
[51]
[52]
[53]. At the same time, increased mobile technology adoption by underserved populations
has slowly changed the state and nature of the digital divide[9]
[54]. Furthermore, newer studies suggest an increased willingness to engage with CHI
tools among underserved populations[41]
[55]. Our findings lead us to discuss how the digital divide, literacy, and user-centered
design of CHI should be approached.
Re-thinking the Digital Divide, Motivation, and Perceived Usefulness
A 2008 systematic review report to the Agency for Healthcare Research and Quality
(AHRQ)[3] concluded that users found the majority of the evaluated CHI tools to be usable.
A 2010 report to the Office of the National Coordinator for Health Information Technology
(ONC)[4] showed that health and technological literacy, culture and language, level of comfort
in interacting with the health care system, and digital divide added to the evidence
of these factors as barriers to CHI adoption. A more recent systematic review in 2011[5] further confirmed these findings, identifying perceived benefits of health information
technology, and conversely highlighting a lack of trust, technical problems, limited
access to computers or hardware, technology fears, and cognitive and physical disabilities
as persistent barriers to CHI adoption among underserved populations. These reports
emphasize a lack of user-motivation and barriers to technology access rather than
the design of CHI systems as the main drivers of the low adoption rate.
However, 10 years later, the digital divide does not appear to persist due to a lack
of technology adoption, especially given the increased mobile technology use and Internet
adoption among underserved populations globally[9]
[54]
[56]. Instead, the digital divide is driven by more complex, multi-dimensional factors.
Ancker et al. showed from their 2017 study on patient portals at Federally Qualified
Health Centers (FQHCs)[41], that ethnic minorities, such as Hispanics and African Americans, were more likely
to use hyperlinked patient education materials in patient portals than were Caucasian
users. Ancker et al. posited two possible causes: first, users’ motivation to understand
the medical jargon, and second, a strong association between low health literacy and
the need for further explanations. In either case, the study shows intrinsic motivation
to learn by those often labeled a ‘disengaged’ population[57]
[58]
[59]. All people, regardless of their privilege status, were highly motivated, had intrinsic
needs for information[41], wanted to manage their own health information and to share it with their providers[44], wanted tips on free or low cost ways of accessing care[38], or wanted to learn more on how to use CHI[41]
[44]
[60].
Our review also found that motivated use was not limited to the target user of CHI.
If the patients themselves could not use the CHI tool, caregivers became highly motivated,
assuming CHI user roles[42]
[45]. This concept of a proxy user aligns with past findings that refer to caregivers’
effective use of technology either for themselves[60] or as helpers for patients[61]
[62]. A systematic review of older adults’ use of patient portals identified technical
assistance and family and provider advices as the main facilitators for patient portal
use[51]. In an interview study about patient portal use in safety net hospitals, caregivers
expressed interest in using patient portals to interpret health information, advocate
for quality care, or manage health behaviors and medical care of patients[63].
Engaged and motivated users should receive sustainable and culturally appropriate
support to help improve computer and health literacy. A recent study by the University
of Kansas[64] found that low-income African-Americans wanted to learn how to use computers but
study participants felt that the educators were condescending and hence they lost
their motivation to return to the education sessions. A similar report was published
in 1991, where African-American women engaged in a literacy improvement program were
discouraged from going back to the classes because “the instructors were too mean”[65]. Such breach of trust between innovation disseminators and end-users must be repaired,
and assumptions around technology adoption in underserved populations should be reconsidered
and addressed in design and dissemination.
Institutional response to health disparities, or variation in access, quality, and
care, has been incremental and piecemeal with respect to underserved populations.
For example, language services are mandated for all hospitals that receive federal
support like Medicare reimbursement, which is nearly all hospitals in the U.S.; however,
uptake is less than 70%[66]. Thus, CHI interventions must not only consider barriers related to technology adoption,
but also culturally competent care delivery, health equity, and significant institutional
barriers. Understanding the values and beliefs of underserved populations must be
a priority given the growing racial, ethnic, and linguistic diversity of many countries.
Internationally, similar principles apply. For instance, McBride et al. used SMS to
help with maternal health among ethnic minorities in Vietnam[67]. A U.K. based systematic review reported, when developing ethnic-specific dietary
assessment tools, using customized portion sizes by sex and age, household utensil
usage, and literacy levels are critical[48]. The growing focus on patient-centered care serves as an opportunity to secure institutional
buy-in to tailor healthcare[68]
[69]
[70]. Culturally and linguistically appropriate care could aid CHI diffusion for underserved
populations across the globe given universal health care, along with rapid Internet
for low-resource and rural areas[66].
Although the digital divide still exists, barriers to technology access will likely
diminish, but general computer literacy will likely continue to impede progress globally.
Research groups such as the 2G Lab at the University of Michigan[71] are responding to emerging digital literacy gaps by redesigning and repurposing
older technologies (e.g., non-smart phones). Such endeavors consider tailoring needs
to the individual a technology innovation in and of itself. This focus on the individual
may be the key to CHI development for underserved populations.
It is no longer sufficient to state that the core barriers to CHI adoption by under-served
populations are the lack of access to technology, or the lack of motivation or perceived
usefulness of CHI applications by the end users as past studies suggested[72]
[73]
[74]. Rather, it is currently more important to determine how CHI can be tailored to
support culturally relevant, intrinsic, and personalized information needs.
Re-thinking CHI Usability Evaluations for Underserved Populations
Usability and design problems can discourage even highly motivated users. In some
of the studies reviewed[38]
[43]
[44]
[46], participants did not find information useful because the display was confusing
or they could not relate to the content or the actors communicating the information.
These findings contradict the general consensus of the AHRQ report[3], which found the reviewed CHI systems’ usability to be high.
This contradiction may be explained by differing variables and tasks chosen for the
usability evaluation. For instance, Greenberg and Buxton described this phenomenon
in their seminal article, “Usability Evaluation Considered Harmful (Some of the Time)”[75]. They discussed the importance of choosing appropriate evaluation techniques to
the problem and the stage of the design cycle. Otherwise, the results can be meaningless.
Depending on the user groups tested and the tasks chosen, the results may not reflect
how the technology would actually evolve for its intended audience and actual use.
Aspects of user-centered design other than usability, such as understanding requirements,
considering cultural aspects, and developing and showing stakeholders design alternatives,
should be taken into account when evaluating technology use.
Many “quick and dirty” usability evaluation solutions exist that have proven to be
as equally reliable and powerful as more comprehensive measurements[76]
[77]
[78]
[79]. However, these methods should be carefully chosen when involving populations who
may have linguistic, cultural, and literacy challenges. For instance, Bangor et al.
discussed how simpler usability measurements, such as the System Usability Scale Survey
(SUS)[80], should be accompanied by other measurements[81]. The language used for the survey items in the SUS, because of its terseness, can
cause comprehension problems for non-native English speakers[82]
[83]
[84]. Peres et al. warned that shorter surveys meant to be designed for non-usability
specialists can in fact hinder a correct interpretation of the results if a facilitator
was not present for clarifications[85]. These studies demonstrated the need for careful consideration while interpreting
scores in evaluating a system.
Lessons learned for improving CHI adoption among the underserved include assuring
user-centered design has been deployed before dissemination and evaluation. In their
2012 systematic review[47], Montague and Perchonok suggested providing tailored, relevant, and contextu-ally
situated health technology to enable behavior change among underserved populations.
Simply translating English to Spanish, for instance, has been shown to be an ineffective
solution to increasing technology adoption[86]. Personalizing the tool for each individual and understanding intrinsic needs of
users and utilizing proxy users, such as caregivers or younger family members, should
help motivate CHI adoption by underserved populations.
Limitations and Future Directions
We confined our search to PubMed indexed publications and our search strategy was
very specific. We adopted a definition of underserved population that, though promulgated
by U.S. health agencies[27]
[28]
[29], is race/ethnicity neutral. However, we acknowledge that this perspective might
not agree with other definitions developed outside the U.S. While we attempted to
be as inclusive as possible of the international context, several studies were excluded
during the abstract or full text screening process. We restricted publications to
English only, which may have eliminated studies of CHI in underserved populations.
We did not employ pre-specified procedures to assess the risk of bias in individual
studies. Nevertheless, we have referenced selected studies within this review outside
of the context of our data synthesis. Future research may consider using inclusion
and exclusion criteria that are specifically designed to address non-U.S. contexts
to complement what might have been lost in this review. We need more empirical research
reporting facilitators and barriers that can apply to a broader international context
and address the ‘research divide’ shown from the results of our screening process.
On the 31 screened articles, many did not discuss factors that impacted CHI adoption.
However, studies that incorporated qualitative methods, such as conducting focus groups
after a trial or coupling surveys with interviews provided insights into what might
have been barriers or facilitators to CHI adoption but were not generalizable. These
studies did not empirically confirm factors around barriers and facilitators for generalization,
perhaps because they were derived from qualitative feedbacks from a small number of
individuals. Future studies should consider testing the effectiveness of facilitators
and barriers in CHI adoption in a larger, confirmatory study setting to understand
scalability and generalizability issues that are predominant among under-served population
groups. Furthermore, user-centered design techniques that result in reliable methods
for tailoring, such as expected scenarios of use, reflections, case studies, and participatory
critique should be considered in addition to usability methods.
Conclusion
The digital divide and few perceived benefits of CHI use were previously considered
as the dominant barriers to CHI adoption among underserved populations. The narrowing
digital divide, due to increasing technology access, will not by itself solve the
problem of low adoption rates. Digital divide can come from a variety of factors,
including lack of net neutrality and geographic constraints that require resolution
before asserting improved technology access as a solution. Contrary to misleading
assumptions that underserved populations, who may suffer from low health and computer
literacy, are largely disinterested in engaging with technology, studies published
in the past five years indicate high motivation to adopt technology and improve literacy.
CHI development should benefit from varied user-centered design techniques that address
context and individualized needs of each user. At the same time, there is still much
to be learned about underserved populations’ CHI use. Future studies should develop
systematic methods of evaluating effective user-centered design and adoptability of
CHI use among underserved populations.
