Keywords
physician - online medical records - patient portal - facilitators and barriers -
disparity - public health - health policy
Background and Significance
Background and Significance
Online medical record (OMR) systems—alternatively, patient portals—provide patients
with secure access to personal health information contained in their electronic health
record (EHR).[1] OMR offers an accessible and useful platform for patients and care delivery or coordination.
Over the past year or so, health care providers have utilized OMR's ability to enable
telehealth and other care access options.[2] For example, many providers have utilized these platforms to alert patients about
COVID-19 vaccine eligibility or to set up visits.[2] Advances in Internet-enabled technology use or health care delivery call for reassessments
of provider- or person-centered determinants of OMR use to ensure equity in health
management in the postpandemic era.
OMR use among American adults has increased over years but only modestly[3]; while 25.6% of adults reported use in 2014, 31.4% did so in 2018 as noted by national
surveys in the U.S.[4] Studies have extensively investigated patient-side barriers to OMR adoption or use[1]
[5]
[6] and issues associated with such barriers among population segments defined by demographic
attributes (e.g., older adults),[7]
[8] social determinants (e.g., disadvantaged population),[9] or clinical attributes (e.g., people living with chronic health conditions).[10]
[11] As for providers, attitudes and barriers include disruption to workflow and concerns
about OMR causing cognitive overload for patients and patient anxiety.[12]
[13]
[14] Physician-perceived barriers were echoed in patient surveys,[10] including lack of providers' willingness to support patient use.[15] While patients expected physicians to get more involved in patients' OMR use,[16] patients reported that physicians did not view patients' OMR use as issues physicians
could resolve.[16]
[17] Only 39% of patients reported to have discussed health information technology use
with their providers[18] and 59% reported no provider encouragement for OMR use.[19]
Despite patient interest in OMRs, widespread use has not happened.[1]
[3]
[12] Provider endorsement or encouragement may act as a stimulus for patient OMR use[20] but providers were not often found to discuss OMR use with patients.[18] Yet, what predicts provider encouragement and, in turn, how provider encouragement
affects OMR use remain understudied. Prior work noted a gap in understanding how follow-ups
and reinforcements relate to patient engagement via OMRs.[3]
[10] Indeed, provider encouragement needs to be investigated due to significant changes
in more recent times (e.g., provider EHR adoption,[3] EHR incentive programs,[12] Internet/smart device use, or rise in digital literacy[12]
[21]). The study aims to examine (1) temporal trends in provider encouragement for patient
use of OMR, (2) predictors of provider encouragement, and (3) association of provider
encouragement with patient use of OMR. Chronic diseases (e.g., hypertension) are inflicting
more people globally or often so do early.[22] Thus, the study examines provider encouragement by disease status.
The findings of this research should help advance understanding of determinants of
OMR use. Pains and disruptions inflicted by the COVID-19 pandemic will not go anytime
soon and certainly not the need for care available or managed digitally. With the
pandemic obviating the need for digital care advancement efforts, the study should
help reduce the current disparity in technology-driven health management. This study
extends understanding of a key motivator of OMR use and holds implications for implementation
scientists, and health systems and public health leaders. Specifically, insights from
analyses of subgroups defined by disease or OMR use attributes are novel. Overall,
the study will serve to rethink roles, identify opportunities to improve access to
care, and facilitate patient engagement with OMR, including those for patients in
need of more proactive management.
Methods
Study Design and Participants
The Health Information National Trends Survey (HINTS) explores noninstitutionalized
U.S. adults' health information and communication behavior.[23] HINTS-5, cycle 1 and 4 iterations administered in early 2017 and 2020 were used.
In 2020, data were collected during the pandemic from March through June, 2020. Each
iteration used a two-stage sampling design—selecting a stratified sample of households
followed by selecting an adult respondent from each sampled household. Inclusion criteria
were imposed to improve the findings' validity. First, subjects having information
on provider office visits in the past year were included. Office visit was measured
by frequencies a respondent went to doctors or other health professionals to get care
for the respondent in the past year without counting emergency room visits. This is
to compare by or control for ongoing care needs in recent times and minimize biases
(e.g., one could argue providers did not have a chance to ask). Second, respondents
reporting having access to the Internet or smart devices were selected. This should
exclude barriers not of interest and better explicate the effects of focal variable.
Third, 18 to 75 years old subjects were included consistent with the literature[18]; far older adults may be too heterogeneous (e.g., caregiver responses, lacking/declining
technical skills, etc.). About 95% also reported doing online activities (e.g., searching
or sharing health information, visiting social media) in the past year.
Variable Description
This study has three outcome measures. Provider encouragement was the primary variable
of interest and measured by yes/no to “(h)ave any of your health care providers, including doctors, nurses, or office staff
ever encouraged you to use an online medical record?” For OMR function use in the past year, viewing results (“have you used your online medical record to (l)ook up test results?”) and messaging (“have you used your online medical record to (s)ecurely message health care provider
and staff (for example, e-mail)?”) were binary outcomes. Of those reporting to have accessed OMR within a year, these
most frequently used functions[8] were asked in both cycles and together should capture OMR use.
“Big five” disease (diabetes, cardiovascular diseases, respiratory disease, cancer,
and stroke) patients stand to benefit from information and communication technology
use.[24] A set of diseases—termed as priority conditions—were previously identified as having
significant population-level impacts due to their incidence rates or cost of management.[25]
[26] HINTS asked whether subjects had some of these conditions: chronic diseases such
as diabetes, hypertension, heart, or lung diseases (defined as CVMD herein) and cancer.
Patient-reported number of CVMDs was calculated as an approximate measure of disease
burden. Furthermore, a binary variable—CVMD status—indicates the presence/absence
of any CVMDs and was used either as a predictor or for subgroup analyses. Frequency
of provider office visits by a patient within a year was used as a measure of health
care utilization. Respondent demographic attributes include race/ethnicity, education,
residential area (per U.S. Department of Agriculture or USDA Rural/Urban Designation,
2013), income, marital status, gender, and age.
Statistical Analysis
The study provides descriptive statistics and population-level estimates (i.e., weighted
frequencies/percent) using HINTS-provided weights. Analysis was undertaken with all
years combined, by survey year (interchangeably specified as year) to examine longitudinal
changes, or by subgroups defined by clinical attributes (i.e., disease profile or
health care utilization) or year. OMR access status (i.e., no [0] vs. yes [≥ 1]),
computed from number of times subjects had accessed OMRs in the past year, was used
for subgroup analysis. Multivariable logistic regression models were run separately
with three outcome variables, provider encouragement and its associations with OMR
feature usage (i.e., secure communication and viewing results), controlling for sociodemographic
determinants (i.e., age, gender, race, education, marital status, income, and area
of residence), clinical predictors (i.e., office visit frequency, number of CVMDs,
cancer history/survivor), and survey year. As survey weights were used in regressions,
standard error estimations applied methods appropriate for weighted analysis. In the
models for OMR feature use, encouragement was the focal predictor. For comparative
reasons and better interpretation, regression analyses were repeated in subgroups;
however, regression models did not include year and/or CVMDs as appropriate. Data
analysis was performed with SAS version 9.4.
Results
From cycle 1 (2017) and cycle 4 (2020), 2,558 and 3,058 respondents were included,
respectively, and these represented approximately 205 and 218 million U.S. adults
in the respective year. On average, respondents were approximately 45 years old and
made 2.5 provider office visits in the past year ([Table 1]). About 44% were CVMD patients and 7% were cancer survivors.
Table 1
Respondent attributes
|
Variable
|
Overall
N (weighted %)
|
2017
N (weighted %)
|
2020
N (weighted %)
|
|
N/weighted N
|
5,616
|
2,558/205M
|
3,058/218M
|
|
Age (weighted mean/SE)
|
45.4 (0.24)
|
45.6 (0.36)
|
45.1 (0.32)
|
|
Male
|
2,214 (49.1)
|
1,008 (48.6)
|
1,206 (49.5)
|
|
Race
|
|
White
|
3,930 (76.5)
|
1,805 (76.9)
|
2,125 (76.1)
|
|
African American
|
818 (12.5)
|
368 (11.9)
|
450 (12.9)
|
|
Others
|
586 (11.0)
|
277 (11.1)
|
309 (11.0)
|
|
Education
|
|
≤ High school
|
1,133 (27.0)
|
497 (26.5)
|
636 (27.4)
|
|
Some college
|
1,657 (37.0)
|
772 (34.0)
|
885 (39.9)
|
|
≥ College
|
2,758 (36.0)
|
1,275 (39.6)
|
1,483 (32.7)
|
|
Married (yes)
|
3,227 (56.7)
|
1,523 (57.7)
|
1,704 (55.8)
|
|
Household income ($)
|
|
< 20K
|
764 (13.4)
|
342 (13.8)
|
422 (13.1)
|
|
20K–34K
|
625 (10.7)
|
291 (11.1)
|
334 (10.4)
|
|
35K–49K
|
657 (13.4)
|
301 (15.0)
|
356 (11.9)
|
|
50K–74K
|
970 (19.3)
|
461 (19.9)
|
509 (18.8)
|
|
≥ 75K
|
2,195 (43.1)
|
980 (40.2)
|
1,215 (45.8)
|
|
Residence
|
|
Nonmetro
|
246 (5.3)
|
118 (5.2)
|
128 (5.3)
|
|
Metro
|
4,983 (87.7)
|
2,239 (86.6)
|
2,744 (88.7)
|
|
Rural
|
387 (7.0)
|
201 (8.2)
|
186 (6.0)
|
|
Visit frequency (mean/SE)
|
2.5 (0.04)
|
2.4 (0.06)
|
2.6 (0.05)
|
|
Number of CVMDs
|
|
0
|
2,728 (55.9)
|
1,256 (56.9)
|
1,472 (54.9)
|
|
1
|
1,720 (27.9)
|
779 (27.4)
|
941 (28.3)
|
|
2
|
856 (12.2)
|
391 (11.9)
|
465 (12.4)
|
|
3
|
250 (3.2)
|
102 (2.7)
|
148 (3.6)
|
|
4
|
62 (0.9)
|
30 (1.1)
|
32 (0.7)
|
|
Cancer survivor
|
750 (7.2)
|
327 (6.8)
|
423 (7.7)
|
|
Provider encouraged
|
2,885 (47.1)
|
1,198 (41.3)
|
1,687 (52.8)
|
|
Accessed OMRs
|
2,312 (37.9)
|
914 (32.2)
|
1,398 (43.1)
|
Abbreviations: CVMD, chronic diseases include diabetes, high blood pressure, heart,
or lung diseases; OMR, online medical record; SE, standard error computed utilizing
survey weights.
Abbreviations: Area of residence was measured per U.S. Department of Agriculture (USDA)
Rural/Urban Designation (2013) as metro (≥ 250K population or in metro counties),
nonmetro (urban or ≥ 20K population), and rural (< 20K); visit frequency: provider
office visit frequency in the past year.
Trends in Provider Encouragement
In 2020, 52.8% (representing ∼113 million adults) reported providers encouraging OMR
use within the past year compared with 41.3% (representing ∼84 million adults) in
2017 (p < 0.001). The trends increased among respondents with select CVMDs (57.2% in 2020
vs. 45.5% in 2017; p < 0.001) and without select diseases (49.2% in 2020 vs. 38% in 2017, p < 0.001) as well. Of 2,878 subjects with chronic conditions or cancer survivors that
made an office visit within the past year, the trend increased from 48.5 to 58.7%
(p < 0.001).
Predictors of Provider Encouragement
Multivariable analysis reveals ([Table 2]) that year significantly predicted provider encouragement (odds ratio [OR] for 2020
vs. 2017 = 1.52, p < 0.001). Among sociodemographic determinants, gender (male vs. female OR = 0.58),
education (e.g., high school [HS] or less vs. college graduate OR = 0.54), marital
status (currently married vs. not OR = 1.46), and income (< 20K vs. ≥ 75K OR = 0.41)
were significant (p < 0.05). In addition, visit frequency (OR = 1.17, p < 0.001), number of CVMDs (OR = 1.2, p = 0.006), and cancer survivorship (OR = 1.38, p = 0.028) were significant predictors.
Table 2
Predictors of provider encouragement from overall and subgroups analyses
|
Predictor
|
Overall
|
No CVMD
|
CVMD
|
2017
|
2020
|
|
Year [ref: 2017]
|
|
2020
|
1.52[c]
|
1.46[b]
|
1.61[c]
|
–
|
–
|
|
Age
|
1
|
1
|
0.99
|
1
|
1
|
|
Gender [ref: Female]
|
|
Male
|
0.58[c]
|
0.50[c]
|
0.65[b]
|
0.53[c]
|
0.63[b]
|
|
Race [ref: white]
|
|
|
|
|
|
|
African American
|
1.08
|
2.06[b]
|
0.68[a]
|
0.89
|
1.3
|
|
Others
|
1.06
|
1.13
|
1.01
|
0.93
|
1.18
|
|
Education [ref: ≥college]
|
|
≤ High school
|
0.54[c]
|
0.45[c]
|
0.69[a]
|
0.56[b]
|
0.52[c]
|
|
Some college
|
0.75[b]
|
0.64[b]
|
0.93
|
0.72[b]
|
0.78
|
|
Marital status [ref: not married]
|
|
Married
|
1.46[b]
|
1.58[b]
|
1.35[a]
|
1.74[b]
|
1.27
|
|
Household income ($) [ref: ≥75K]
|
|
< 20K
|
0.41[c]
|
0.34[c]
|
0.49[b]
|
0.46[b]
|
0.36[c]
|
|
20K–34K
|
0.49[c]
|
0.40[c]
|
0.59[b]
|
0.47[c]
|
0.49[b]
|
|
35K–49K
|
0.62[b]
|
0.63[a]
|
0.61[a]
|
0.64[b]
|
0.63[a]
|
|
50K–74K
|
0.69[b]
|
0.69[a]
|
0.70[b]
|
0.84
|
0.58[b]
|
|
Residence [ref: rural]
|
|
Nonmetro
|
1.12
|
0.62
|
1.88
|
1.62
|
0.73
|
|
Metro
|
1.15
|
0.95
|
1.33
|
1.89[b]
|
0.68
|
|
Visit frequency
|
1.17[c]
|
1.20[c]
|
1.13[b]
|
1.19[c]
|
1.16[c]
|
|
Number of CVMDs
|
1.20[b]
|
|
1.08
|
1.23[b]
|
1.18[a]
|
|
Cancer survivor [ref: no]
|
|
|
|
|
|
|
Yes
|
1.38[b]
|
1.57[a]
|
1.31
|
1.03
|
1.71[b]
|
Abbreviation: CVMD, chronic diseases include diabetes, high blood pressure, heart,
or lung diseases.
Note: Numbers are odds ratios derived from weighted multivariable logistic regression
models. Area of residence was measured per U.S. Department of Agriculture (USDA) Rural/Urban
Designation (2013) as metro (≥ 250K population or in metro counties), nonmetro (urban
or ≥ 20K population), and rural (< 20K); visit frequency: provider office visit frequency
in the past year.
a
p < 0.1.
b
p < 0.05.
c
p < 0.001.
Among those without select CVMDs, year had a significant effect (OR = 1.46, p = 0.005). In addition, gender (male vs. female OR = 0.50, p < 0.001), race (African American [AA] vs. white OR = 2.1, p = 0.03), education (e.g., HS or less vs. college graduate OR = 0.45, p = 0.008), marital status (currently married vs. not OR = 1.58, p = 0.009), income (< 20K vs. ≥ 75K OR = 0.34, p < 0.001 and 20–35K vs. ≥ 75K OR = 0.40, p < 0.001), and visit frequency (OR = 1.2, p < 0.001) were significant. For those with CVMDs, year (2020 vs. 2017 OR = 1.61, p = 0.001) had a strong effect along with gender (male vs. female OR = 0.65, p = 0.006), income, and visit frequency (OR = 1.13, p = 0.002).
Trend Analysis on Effects of Provider Encouragement on Patient Use of OMRs
[Fig. 1] presents OMR accessing rates by years stratified by provider encouragement. Among
those reporting providers encouraging them, a large percent used OMR (62.9% in 2017
vs. 67.4% in 2020) in both years; a significant increase in OMR use over years is
seen among subjects with CVMDs (60.6% in 2017 to 73.3% in 2020, p = 0.002) but increase among cancer survivors (63.3 to 72.4%) was not significant.
For those reporting no encouragement, there is an increase (p = 0.009) in use over years; a similar trend in use is noted among CVMD subjects (11.3
to 21.4%, p = 0.006).
Fig. 1 Rates of accessing online medical record (OMR) in 2020 and 2017 stratified by provider
encouragement. Estimates used survey weights. 20xx, survey year 20xx; Ca, cancer survivor;
CVMD, chronic diseases include diabetes, high blood pressure, heart, or lung diseases;
MDN, subjects not reporting provider encouragement; MDY, subjects reporting provider
encouragement for OMR use; OMR, online medical records systems.
Associations of Provider Encouragement with Patient Use of OMR Features
[Table 3] describes patterns of effects of encouragement on secure communication and viewing
results using OMR. Provider encouragement was significantly associated (OR = 2, p < 0.001) with patient communication securely using OMR after controlling for other
predictors, including year. A similar effect of encouragement (OR = 2.71, p = 0.002) was noted among CVMD subjects. An effect of encouragement on viewing results
via OMR was found overall (OR = 1.74, p = 0.03) and among CVMD subjects (OR = 2.13, p = 0.01).
Table 3
Association (odds ratio) of provider encouragement with online medical records feature
use
|
Year
|
Secure messaging
|
View results
|
|
Overall
|
No CVMD
|
CVMD
|
Overall
|
No CVMD
|
CVMD
|
|
Combined
|
2.00[c]
|
1.58[a]
|
2.71[c]
|
1.74[b]
|
1.38
|
2.13[b]
|
|
2017
|
–
|
1.51
|
2.52[b]
|
–
|
1.63
|
1.88
|
|
2020
|
–
|
2.05[a]
|
3.34[b]
|
–
|
1.27
|
2.87[b]
|
Abbreviation: CVMD, chronic diseases include diabetes, high blood pressure, heart,
or lung diseases.
Note: Odds ratios were derived from weighted multivariable logistic regression models
controlling for age, gender, race, education, marital status, income, residence, provider
office visit frequency in the past year, cancer history, CVMDs (as appropriate), and
survey year (as appropriate).
a
p < 0.1.
b
p < 0.05.
c
p < 0.001.
Discussion
Nationally representative data collected 3 years apart reveal the growth in provider
encouragement, which was associated with social or demographic determinants. The study
confirms variability in associations between provider encouragement and OMR feature
usage. This study makes several contributions. First, this is the first study to longitudinally
assess both determinants of provider encouragement and associations of OMR feature
use with encouragement and those within disease-based or other attributes-based subgroups.
Second, this work occurs amid the ongoing COVID pandemic and included data before
and after meaningful use stage 3, which had quality measures to capture patient education
and engagement objectives.[27] That is measuring the provider impact amid changing contexts (e.g., smartphone use
growth) in recent years. The results are relevant for bridging the gap in or advancing
OMR use in the postpandemic era, especially among subjects with digital access and/or
literacy. As providers are the reliable source for patient education or persuading
patients[8]
[28] regarding health care decisions, favorable usage patterns are expected in this population—including
those who may access OMRs through smartphone apps—if patients are appropriately—more
nondiscriminatorily—targeted.
Trends in Provider Encouragement: Contextual Attributes and Their Interactions
Patient-reported provider encouragement improved in the 2020 cohort compared with
those in 2017 after controlling for sociodemographic or clinical determinants; such
a trend remains steady and was seen across those without or with CVMDs. This work
complements the literature on OMR use[29] by assessing the trend in a key driver of OMR use. General temporal comparisons
look positive especially if one considers that for some 2020 respondents providers
might have postponed nonurgent visits early in the pandemic; however, it is also not
unreasonable to wonder whether the rate of increase is as expected given the pandemic
often required providers offering Internet-enabled services. It is for policymakers
to undertake comparative benchmark assessments on the role in the future. Of the 2020
respondents who had a provider visit in the past year, many (43%) did not specify
encouragement; a similar finding is noted among CVMD subjects or cancer survivors.
That a large proportion reportedly not receiving encouragement indicates the extent
to which this key motivator can help expand OMR use.
Sociodemographic determinants were associated with receiving encouragement in the
entire cohort or among subjects without CVMDs. As for the race effects, whites reported
significantly lesser odds among subjects without CVMDs. However, this pattern is not
seen among subjects with CVMDs; AA were marginally (p = 0.06) less likely to receive encouragement compared with whites. Such a pattern
remains to be observed in future studies with more subjects. Also, alternative operationalization
of race (e.g., non-Hispanic white, etc.) may change results (see [Appendix Table A1]). The current study suggests an interaction between disease status and race. Social
or demographic determinants have been consistently associated with disparity in OMR
adoption or usage patterns[11]
[30]; the results of this study imply that such disparity may partially be rooted in
differential encouragement. Providers may operate in systems where implicit bias or
racism is not uncommon.[31] Some providers may be aware of disparities and encouraging AAs to use OMR more than
others or implicitly making efforts to engage certain patients in some contexts and
not others. Only gender and income emerged as significant among CVMD patients. While
providers may perceive that subjects with CVMDs need no further encouragement as many
already adopted OMR, it may still be good practice to encourage continued use because
adoption of OMR is known to not guarantee usage.[11] Visit frequency remained consistently a significant predictor and seems to interact
with disease status; while the no select CVMD group reported lesser odds, those with
diseases reported higher odds of encouragement with increasing visits. It remains
to assess whether patient visits are optimally utilized for encouragement, including
the state of alerts from EHR for providers.
Appendix Table A1
Predictors of provider encouragement from overall and subgroups analyses
|
Predictor
|
Overall
|
No CVMD
|
CVMD
|
2017
|
2020
|
|
Year [ref: 2017]
|
|
2020
|
1.55[c]
|
1.53[b]
|
1.58[c]
|
–
|
–
|
|
Age
|
1
|
1
|
0.99
|
1
|
1
|
|
Gender [ref: female]
|
|
Male
|
0.59[c]
|
0.53[c]
|
0.64[b]
|
0.51[c]
|
0.67[b]
|
|
Race [ref: non-Hispanic white]
|
|
Non-Hispanic African American
|
1.08
|
2.52[c]
|
0.59[b]
|
0.92
|
1.26
|
|
Others
|
0.87
|
1.05
|
0.69[b]
|
0.77
|
0.96
|
|
Education [ref: ≥college]
|
|
≤ High school
|
0.50[c]
|
0.43[c]
|
0.60[b]
|
0.51[b]
|
0.50[c]
|
|
Some college
|
0.77[b]
|
0.69[b]
|
0.90
|
0.70[b]
|
0.83
|
|
Marital status [ref: not married]
|
|
Married
|
1.51[b]
|
1.53[b]
|
1.53[b]
|
1.95[b]
|
1.24
|
|
Household income ($) [ref: ≥75K]
|
|
< 20K
|
0.42[c]
|
0.31[c]
|
0.59[b]
|
0.50[b]
|
0.36[c]
|
|
20K–34K
|
0.49[c]
|
0.38[c]
|
0.67
|
0.53[b]
|
0.45[b]
|
|
35K–49K
|
0.65[b]
|
0.62[a]
|
0.71
|
0.75
|
0.59[b]
|
|
50K–74K
|
0.68[b]
|
0.64[b]
|
0.75
|
0.83
|
0.57[b]
|
|
Residence [ref: rural]
|
|
Nonmetro
|
1.20
|
0.64
|
2.22[a]
|
1.83
|
0.70
|
|
Metro
|
1.20
|
0.93
|
1.54[a]
|
2.20[b]
|
0.62
|
|
Visit frequency
|
1.17[c]
|
1.19[c]
|
1.13[b]
|
1.18[c]
|
1.16[c]
|
|
Number of CVMDs
|
1.20[b]
|
|
1.12
|
1.26[b]
|
1.16[a]
|
|
Cancer survivor [ref: no]
|
|
|
|
|
|
|
Yes
|
1.40[b]
|
1.57[a]
|
1.34
|
1.05
|
1.71[b]
|
Abbreviations: CVMD, chronic diseases include diabetes, high blood pressure, heart,
or lung diseases.
Note: Numbers are odds ratios derived from weighted multivariable logistic regression
models. Area of residence was measured per U.S. Department of Agriculture (USDA) Rural/Urban
Designation (2013) as metro (≥250K population or in metro counties), nonmetro (urban
or ≥20K population), and rural (< 20K); visit frequency: provider office visit frequency
in the past year.
a
p < 0.1.
b
p < 0.05.
c
p < 0.001.
Analyses within individual years reveal that the relative impacts of sociodemographic
determinants on encouragement remained stable over years; as such, effects largely
continued despite no apparent issues related to the digital divide among the subjects.
It is not unlikely for providers to be sensitive to patients' abilities (e.g., understanding
of health information in English, which is the predominant OMR format), or incorrectly
assess abilities; either will lead providers to not encourage OMR use. Yet, these
findings point toward a need for more proactive outreach to preclude sociodemography-based
disparity in or further delaying OMR use. Among clinical predictors, cancer survivorship
was positively associated in 2020. This is a welcome change given that cancer survivors
need care regularly. However, caution is warranted because responses in 2020 came
in or after March when the World Health Organization declared the global pandemic;
such encouragement may have been fueled by the pattern of care during the pandemic
and not solely due to the motivation to encourage technology-based health management.
Given provider concerns about resources to support patient use of OMRs[30]
[32] and ongoing changes in locus of incentives for providers (e.g., proposed elimination
of EHR engagement measure),[33] it is imperative to ensure resources facilitating provider encouragement do not
abate moving forward. Adopting practices and policies responsive to stakeholders'
needs (e.g., some patients need more help with portal setup than others) should help
providers rethink their roles in patient OMR use.[10]
[12]
[34] The study calls for reassessments of policies and priorities (e.g., lacking organizational
priority reported as an impediment[10]) to enable providers to continue to encourage OMR use.
Provider Encouragement: OMR Use Patterns
There is a substantially higher rate of OMR access among those reporting encouragement
compared with no encouragement. Unlike patterns in those receiving encouragement,
a significant increase in OMR use from 2017 to 2020 was noted among those reporting
no encouragement. Such patterns are difficult to explain; it may be just that some
are motivated and see efficiency in care management through the utilization of OMRs.
Among those with CVMDs, there are statistically significant increasing trends in OMR
use in both groups reporting and not reporting encouragement. Among cancer survivors,
rates were much larger in those reporting encouragement although there were no changes
over time. Yet, a large number of subjects did not access OMRs; of those reporting
to not have accessed OMRs despite provider visits in the past year, a large proportion
(i.e., representing tens of millions) of cancer survivors or CVMD patients did not
specify encouragement either. Encouragement should enhance uptake and continued use
among these subjects.
OMR feature usage patterns (i.e., viewing results or secure communication) among different
population segments were associated with encouragement. Prior research qualitatively
observed similar findings,[19] although our study adopted a different methodological and clinically focused approach.
Similar effects of encouragement were noted among CVMD patients. As for longitudinal
evolution, compared with 2017, OMR feature utilization in 2020 showed a general favorable
trend although feature usage by some segments (e.g., CVMD folks viewing results or
no CVMD folks utilizing secure communication) still stands to grow from continued
encouragement if providers reflect positive beliefs about OMRs during patient–provider
interactions. Given the positive relationship between visit frequency and encouragement,
higher frequencies might have signaled providers to encourage those patients, and
in turn, such encouragements lead to OMR usage. The usage pattern among CVMD subjects
is evidence that usage is facilitated by patient engagement or needs as reported previously.[14]
[35] Prior programs that incentivized providers to encourage patient engagement and electronic
health information exchange yielded favorable OMR utilization.[29] This study shows provider encouragement helps realize high-priority public health
goals, including utilization of electronic health information.[36]
Removing access to technology (e.g., Internet) did not always eliminate disparities
in OMR use.[37] Outreaching for encouragement offers providers opportunities to assess issues facing
patients, including barriers associated with demographic and disease attributes,[38] or reeducate patients. As such, a patient-centric approach will help patients perceive
OMR use as an integral part of patient–provider collaborations and facilitate equitable
access to digital care given OMR activation status is related to telemedicine access.[39] In the post-COVID era, this will be important for some (e.g., low income or education
subjects that experience less encouragement) that are disproportionately vulnerable
to poor health outcomes. Making encouragement part of standard office visit interactions,
when possible, may nudge more patients toward utilizing digital care or health services.
Health care organizations and policymakers should determine strategies to keep the
trajectory of encouragement optimal and serving patient needs across social strata.
The study has a few limitations. A generic question on provider encouragement was
asked. Qualitative contents of encouragement that consider patient or disease attributes
or other barriers (e.g., knowledge, self-efficacy)[37] should matter. Future studies should investigate content effects. Of many OMR functionalities,
this study examined only two that were common to both years; however, these features
served to unfold the role of encouragement. Of note, clinical outcomes of encouragement
remain out of scope for the study because OMR use is just one of many dimensions of
health management. Finally, encouragement may work differently among subjects excluded
in this work (e.g., > 75 years, having access barriers such as no Internet). Furthermore,
provider behaviors in 2020 that may have been influenced by the pandemic or other
determinants not controlled for may have affected results. Despite limitations, the
study results are likely conservative because of restricted inclusions yet affirm
the importance of provider encouragement.
Conclusion
An upward trajectory of provider encouragement for use of OMR systems or patient portals
was observed. Sociodemographic and clinical factors predicted encouragement and many
of such determinants remained stable predictors over years. Variability in OMR access
was noted by provider encouragement, which had mixed effects on feature use ranging
from a strong one to none depending on disease status. That many subjects reported
a lack of provider encouragement—some being cancer survivors, living with one or more
chronic diseases, or having visited provider office within the past year—points toward
a potential intervention task for providers. Lessening of intensification of provider
encouragement may lessen OMR utilization and potentially widen disparity by perpetuating
the problematic utilization of health technologies.
Clinical Relevance Statement
Clinical Relevance Statement
The recent disease outbreak has laid bare challenges to care providing for those who
are vulnerable or need continuity of care (e.g., people with chronic diseases such
as diabetes, high blood pressure, etc., or multiple chronic conditions). Public health
initiatives (e.g., Million Hearts of CDC, Healthy People 2030, etc.) have advocated
the use of health information technology (HIT) to optimize care. Through a deeper
understanding of an important driver of HIT use, the findings should serve to expand
equity in technology-enabled health management as envisioned by public health programs/initiatives
and more so in the postpandemic times.
Multiple Choice Questions
Multiple Choice Questions
-
Electronic health records (EHRs) adoption among U.S. providers has largely grown in
recent years. What do we observe regarding provider encouragement for online medical
records (OMR) or patient portal use? It has
Correct Answer: The correct answer is option c. An upward trajectory in provider encouragement for
OMR use was observed. Sociodemographic determinants and clinical factors predicted
provider encouragement even among those with access to the Internet or Internet-enabled
technologies.
-
How Important is to study provider encouragement for OMR use?
-
Unrelated to patient OMR use.
-
Important but affects providers only.
-
Irrelevant as patients are always encouraged.
-
Important for patients and other stakeholders.
Correct Answer: The correct answer is option d. There is a significant and positive relationship
between portal feature use (e.g., secure communication via OMR) and provider encouragement.
Yet, a substantial proportion of subjects reported a lack of encouragement, including
those during the COVID pandemic or those with a history of cancers or chronic diseases.
Lessening of intensification of provider encouragement may lessen OMR utilization
and potentially widen disparity in utilization of health technologies. Public health,
implementation science, and health systems leaders can use the results to understand
how encouragement facilitates OMR use overall or in clinical subgroups to prevent
widening the current disparity in technology-driven health management among patients,
including those in need of proactive management.
