Keywords
health records - patient portals - informatics - patient engagement
Background and Significance
Background and Significance
The percentage of health care organizations offering online patient portals rose from
43% in 2013 to 92% in 2015.[1]
[2]
[3] The rapid adoption of portals reflects health care organizations' increasing focus
on patient engagement, person-centered care, and shared decision making.[4]
[5] In 2016, a national survey found that hospital leaders view portals as the most
effective tool to engage patients with their health care.[6] The federal Meaningful Use program has driven portal adoption, by incentivizing organizations to provide patients
with electronic access to their health information.[7] As portals become increasingly available, more patients use them.[8]
[9]
[10]
[11] In the United States, the rate of self-reported portal use rose from 17% in 2014
to 28% in 2017.[12]
[13]
The increasing popularity of portals and their potential utility to hospitalized patients
has motivated some organizations to adopt inpatient portals, or patient portals accessed in the hospital setting.[14]
[15]
[16]
[17]
[18]
[19] Vendors now offer portals intended specifically for in-hospital use, such as MyChart
Bedside from Epic Systems Corporation.[20] Institutions use inpatient portals to address patients' information needs, engage
patients in decision making, facilitate patient–provider communication, improve transparency,
provide health education, increase patient safety, and enable transitions of care.[14]
[15] Research suggests that inpatient portals may improve patient safety and satisfaction.[21]
[22]
[23]
[24]
[25]
[26] Inpatient portals empower patients to report safety concerns, facilitate patient
recognition of errors, improve patients' perceptions of safety and quality, and fulfill
patients' information needs.
Many studies report that disadvantaged populations such as racial and ethnic minorities
and low income, low literacy, elderly, and disabled persons use patient portals less
often, in both outpatient and inpatient settings.[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36] Research suggests that limited access to and experience with technology, as well
as limited assistance with the portal, contribute to disparities in portal use.[27]
[31]
[37]
[38]
[39]
[40]
[41] According to the Pew Research Center, in 2018, 23% of Americans do not own a smartphone,[42] and 11% never use the Internet.[43] Disadvantaged populations experience a higher burden of disease, and relatively
low portal use among disadvantaged groups may worsen existing health disparities.[41]
[44] As portals gain in popularity, identifying strategies to reduce disparities in use
is essential, to ensure portals benefit all populations.[41]
To date, interventions designed to reduce disparities in portal use include universal
access policies,[44] caregiver enrollment,[37] and computer literacy training.[45]
[46] Intervention success is hampered by (1) difficulty reaching disadvantaged populations
for enrollment and training, (2) limited technology access in disadvantaged populations,
and (3) difficulty achieving scale. The inpatient setting offers unique opportunities
to overcome such limitations. Hospitalized patients may be more easily reached with
hands-on interventions, and represent the sickest and most vulnerable population.
Because portals improve access to information, engaging hospitalized patients with
portals should have far-reaching consequences such as improved patient safety and
trust.[21]
[47]
We conducted a pragmatic randomized controlled trial (RCT) of a locally developed
inpatient portal delivered to hospitalized patients.[16]
[48] In the trial, we implemented strategies to ensure every intervention-arm participant
could use the portal. Specifically, portal users received (1) free access to hospital-provided
iPads and the Internet, (2) assistance in establishing their portal account, (3) basic
training on how to use the portal, and (4) regular troubleshooting. In this subgroup
analysis, we examine characteristics associated with frequent and infrequent portal use in the intervention arm, specifically (1) health-equity-relevant characteristics
such as age, gender, race, ethnicity, socioeconomic status, health literacy, technology
experience, and illness severity, (2) engagement and satisfaction with health care,
and (3) perceived usefulness and ease-of-use of both the iPad and portal.
Materials and Methods
Study Design
The RCT is registered with ClinicalTrials.gov (NCT01970852). The protocol and primary
results have been published separately.[16]
[48] Between March 2014 and May 2017, patients at a large urban hospital were recruited
and randomized into three arms: (1) usual care, (2) iPad with general Internet access,
(3) iPad with inpatient portal. In the RCT, the primary outcome was change in patient
activation from baseline. Patient activation is an individual's knowledge, skills, and confidence in managing their health and
health care. Secondary outcomes included (1) potential medical errors that patients
noticed during their hospital stay, (2) patient satisfaction and engagement with health
care, (3) perceived usefulness and ease-of-use of the iPad and portal, and (4) medical
record inaccuracies that patients identified using the portal. Participants completed
a baseline questionnaire to assess demographics, socioeconomic status, health literacy,
patient activation, and technology experience. Three to five days later, participants
completed a follow-up questionnaire to assess primary and secondary outcomes. For
this paper, we conducted a retrospective subgroup analysis of participants in the
third arm, hereby called portal users. The Columbia University Institutional Review
Board approved the trial and all patients provided informed consent.
Intervention
We designed and developed an inpatient portal that provides hospitalized patients
with real-time access to their clinical data, sourced directly from the electronic
health record. A comprehensive description of the portal has been published[16] and is summarized here. Portal features included (1) names, photos, and roles of
care team members, (2) medications, (3) allergies, (4) diagnostic laboratory test
orders and results, (5) current diet, (6) vital signs, (7) glucose levels, (8) weights,
(9) patient-reported pain levels, (10) patient-generated messages to the portal team
and hospital staff, (11) written and video educational materials on medications and
tests, (12) portal navigation tutorials, and (13) Spanish translation ([Fig. 1]). User actions were recorded in a detailed system use log.
Fig. 1 Screenshots of the inpatient portal.
Participants
Participants included adult patients aged 18 years or older who spoke English or Spanish,
admitted to one of two medical and surgical cardiac units at an urban academic medical
center. We excluded patients with a Mini Mental Status Examination score less than
9, patients placed in contact isolation, patients involved in another research study,
patients unable to provide written informed consent, and patients already admitted
for more than 2 weeks. The exclusion criteria were limited to improve generalizability.
Recruitment
The research coordinators identified new admissions from the electronic health record,
confirmed eligibility, obtained written informed consent, and administered the questionnaires.
The coordinators enrolled users in the portal system, and provided their unique username
and password. Each intervention-arm participant underwent a brief training session
to familiarize them with the iPad and portal as appropriate to their level of technology
experience. Training included basic iPad gestures (tapping, scrolling, and zooming),
signing into the portal, and an overview of portal features. Portal users also received
a handout with instructions ([Fig. 2]). The coordinators visited participants daily to address potential issues with the
iPad, Internet, or portal, such as difficulty signing in.
Fig. 2 Technology assistance handout.
Measures and Data Sources
The baseline and follow-up questionnaires have been previously described.[16] To assess health literacy, we used Chew and colleagues' health literacy screening
questions.[49] To assess patient activation, we used the 13-item Patient Activation Measure (PAM-13).[50]
[51]
[52]
[53]
[54]
[55]
[56] Our team has previously validated the PAM-13 in the inpatient setting.[57] To assess patient-identified medical errors, we asked patients to describe and categorize
errors they noticed while enrolled in the trial. To assess patient satisfaction, patient
engagement, perceived usefulness, and perceived ease-of-use, we adapted the Telemedicine
Satisfaction and Usefulness Questionnaire.[58]
We obtained information on unit and hospital demographics, insurance type, mailing
address, diagnoses, length of hospital stay, All Patient Refined Diagnosis Related
Group (APR-DRG) Severity of Illness,[59] and APR-DRG Risk of Mortality[60] from our institution's clinical data warehouse. APR-DRG Severity of Illness is defined
as “the extent of organ system loss of function or physiologic decompensation,” and
Risk of Mortality is defined as “the likelihood of dying.” The APR-DRG categorizes
both Severity of Illness and Risk of Mortality as minor (level 1), moderate (level
2), major (level 3), or extreme (level 4). We used diagnosis information to calculate
each participant's Charlson Comorbidity Index,[61] which predicts 10-year mortality. We geocoded participant mailing addresses to obtain
their census tracts, which enabled linkage with the 2016 Centers for Disease Control
and Prevention (CDC) Social Vulnerability Index,[62] an overall score for census tracts based on social determinants. We excluded post
office boxes since these addresses may not reflect the actual residence.
Statistical Analyses
To categorize portal use as frequent or infrequent for each participant, a composite use variable was utilized.[63] We utilized the composite variable to capture multiple aspects of patients' portal
use and provide the best overall assessment. Specifically, participants' portal use
was classified as frequent if it exceeded thresholds in four out of four metrics: (1) total number of logins,
(2) logins per day, (3) total minutes using the portal, and (4) minutes using the
portal per day. The thresholds were (1) two or more logins total, (2) one or more
logins per day on average, (3) 50 minutes using the portal total, and (4) 20 minutes
using the portal per day on average. We determined thresholds based on logical breaks
in the data, since descriptive analyses found bimodal or multimodal distributions
for all four metrics.
After portal users were classified into subgroups, we conducted bivariate analyses
to assess whether each baseline characteristic or outcome differed between frequent and infrequent users. Nominal variables were compared using chi-squared tests or Fisher's exact
tests with Monte Carlo approximation, while ordinal and numerical variables were compared
using Wilcoxon rank-sum tests.
We further used multivariable binomial logistic regression to examine the relationship
between baseline characteristics and portal use (frequent vs. infrequent). First, we constructed a preliminary main effect model using all independent variables
where p < 0.25 in bivariate analyses.[64] Then, we used the R package glmulti
[65] to perform exhaustive automated model selection using Akaike information criteria
(AIC) for model comparison. glmulti builds all possible unique models and selects the highest quality one as per AIC
to reward goodness-of-fit, penalize overfitting, and avoid collinearity. We performed
preliminary experiments testing interaction terms and regularization approaches, but
ultimately excluded them to prevent overfitting and support parsimony in model selection.
We assessed predictive ability using area under the ROC curve (AUC), calculated by
averaging results from 1,000 random five-fold cross-validation cycles to improve the
metric's stability. All analyses were conducted in R version 3.3.3 and SAS version
9.4.
Results
Of the 478 individuals assessed for eligibility, 23 did not meet the inclusion criteria
and 1 declined to participate. We enrolled the remaining 454 individuals, 155 of whom
were randomized to the portal arm. Nine portal users did not complete the trial, including
4 who withdrew consent, 3 deceased, and 2 lost to follow-up. Of the 146 portal users
who completed the trial, 93 (63.7%) met the criteria for frequent use of the inpatient portal, and the remaining 53 (36.3%) had infrequent use. Among infrequent users, 25 (47%) never used the portal. A comparison of baseline
characteristics between frequent and infrequent user groups is presented in [Table 1]. Overall, portal users had an average age of 56 years (range: 22–89). Portal users
were 16.0% Black, 22.1% Latino, and 8.3% preferred Spanish as a primary language.
Portal users were representative of our institution's hospitalized population based
on gender, race, and ethnicity, but were slightly younger ([Supplementary Table 1], available in the online version).
Table 1
Baseline characteristics
|
Variable
|
All users, n = 146
|
Subgroup analysis
|
p-Value
|
|
Infrequent use, n = 53
|
Frequent use, n = 93
|
|
Demographics
|
|
Age (mean, SD)
|
56.3 ± 15.2
|
58.6 ± 16.6
|
55.0 ± 14.3
|
0.140
|
|
Female sex (n, %)
|
59 (40.4)
|
18 (34.0)
|
41 (44.1)
|
0.231
|
|
Race (n, %)
|
|
Asian or Pacific Islander
|
4 (2.8)
|
2 (3.8)
|
2 (2.2)
|
0.064
|
|
Black or African American
|
23 (16.0)
|
13 (24.5)
|
10 (11.0)
|
|
Multi-race
|
4 (2.8)
|
3 (5.7)
|
1 (1.1)
|
|
Other
|
29 (20.1)
|
8 (15.1)
|
21 (23.1)
|
|
White
|
84 (58.3)
|
27 (50.9)
|
57 (62.6)
|
|
Hispanic, Latino, or Spanish origin (n, %)
|
32 (22.1)
|
10 (18.9)
|
22 (23.9)
|
0.481
|
|
Country of origin (n, %)
|
|
Dominican Republic or Puerto Rico
|
10 (6.9)
|
3 (5.7)
|
7 (7.6)
|
0.183
|
|
United States
|
108 (74.5)
|
36 (67.9)
|
72 (78.3)
|
|
Other
|
27 (18.6)
|
14 (26.4)
|
13 (14.1)
|
|
Spanish as preferred language (n, %)
|
12 (8.3)
|
5 (9.6)
|
7 (7.6)
|
0.757
|
|
Socioeconomic status
|
|
Social vulnerability index (mean, SD)
|
0.54 ± 0.32
|
0.58 ± 0.33
|
0.51 ± 0.31
|
0.222
|
|
Education (n, %)
|
|
< High school graduate or GED
|
12 (8.3)
|
4 (7.5)
|
8 (8.7)
|
0.419
|
|
High school graduate or GED
|
24 (16.6)
|
8 (15.1)
|
16 (17.4)
|
|
Associate's degree, technical school, some college
|
53 (36.6)
|
24 (45.3)
|
29 (31.5)
|
|
College graduate or higher
|
56 (38.6)
|
17 (32.1)
|
39 (42.4)
|
|
Employment status (n, %)
|
|
Employed
|
79 (55.6)
|
23 (44.2)
|
56 (62.2)
|
0.054
|
|
Unemployed
|
29 (20.4)
|
11 (21.2)
|
18 (20.0)
|
|
Retired
|
34 (23.9)
|
18 (34.6)
|
16 (17.8)
|
|
Insurance type (n, %)
|
|
Public (Medicare or Medicaid) only
|
28 (19.3)
|
13 (25.0)
|
15 (16.1)
|
0.013[a]
|
|
Public plus private supplement
|
51 (35.2)
|
24 (46.2)
|
27 (29.0)
|
|
Private or commercial only
|
54 (37.2)
|
14 (26.9)
|
40 (43.0)
|
|
Self-pay[b]
|
12 (8.3)
|
1 (1.9)
|
11 (11.8)
|
|
Annual household income (n, %)
|
|
Less than 15,000 USD
|
11 (14.5)
|
2 (8.3)
|
9 (17.3)
|
0.010[a]
|
|
15,000–50,000 USD
|
17 (22.4)
|
11 (45.8)
|
6 (11.5)
|
|
50,000–100,000 USD
|
19 (25.0)
|
5 (20.8%)
|
14 (26.9)
|
|
More than 100,000 USD
|
29 (38.2)
|
6 (25.0)
|
23 (44.2)
|
|
Inadequate health literacy (n, %)
|
55 (37.7)
|
22 (41.5)
|
33 (35.5)
|
0.470
|
|
Technology experience
|
|
Can access desktop, laptop, or tablet at home (n, %)
|
136 (93.2)
|
48 (90.6)
|
88 (94.6)
|
0.497
|
|
Can access the Internet at home (n, %)
|
133 (91.1)
|
42 (79.2)
|
91 (97.8)
|
<0.001[a]
|
|
Daily Internet use in past 30 days (n, %)
|
|
<1 hour
|
41 (28.1)
|
18 (34.0)
|
23 (24.7)
|
0.312
|
|
1–3 hours
|
47 (32.2)
|
18 (34.0)
|
29 (31.2)
|
|
>3 hours
|
58 (39.7)
|
17 (32.1)
|
41 (44.1)
|
|
Has email address (n, %)
|
128 (87.7)
|
41 (77.4)
|
87 (93.5)
|
0.004[a]
|
|
Looks up health information online (n, %)
|
121 (82.9)
|
42 (79.2)
|
79 (84.9)
|
0.379
|
|
Illness severity
|
|
Charlson Comorbidity Index (mean, SD)
|
2.4 ± 2.0
|
2.8 ± 2.1
|
2.1 ± 1.9
|
0.036[a]
|
|
Severity of Illness level (mean, SD)
|
2.7 ± 1.0
|
2.9 ± 1.0
|
2.6 ± 0.9
|
0.020[a]
|
|
Risk of Mortality level (mean, SD)
|
2.4 ± 1.0
|
2.6 ± 0.9
|
2.3 ± 1.0
|
0.028[a]
|
|
Length of hospital stay, days (mean, SD)
|
11.9 ± 14.7
|
14.7 ± 14.7
|
10.4 ± 14.5
|
0.005[a]
|
|
Patient activation
|
|
PAM score, pre (mean, SD)
|
69.9 ± 14.6
|
71.7 ± 14.6
|
68.9 ± 14.7
|
0.306
|
Abbreviations: GED, General Education Diploma; n, number; PAM, Patient Activation Measure; SD, standard deviation; USD, United States
dollars.
Note: Categorical variables reported as n (%), and p-values calculated using chi-squared tests or Fisher's exact tests with Monte Carlo
approximation. Continuous variables reported as mean ± SD, and p-values calculated using Wilcoxon rank-sum tests. Percentages exclude missing data.
a Significant at p = 0.05.
b In this clinical setting, “self-pay” reflects high-income patients who pay out-of-pocket
for elective care.
No significant differences in age, gender, race, ethnicity, level of education, employment
status, or baseline patient activation existed between the frequent and infrequent users. As shown in [Table 1], frequent use was associated with more technology experience (indicated by Internet access
at home and having an email address), higher socioeconomic status (indicated by insurance
type and household income), and less severe illness (as measured by the Charlson Comorbidity
Index, Severity of Illness level, and Risk of Mortality level).
Insurance type, Internet access at home, and Severity of Illness level were included
in the multivariable logistic regression model ([Table 2]), reflecting portal users' socioeconomic status, technology experience, and illness
severity, respectively. Predictive performance (AUC) for the model was 65.6%, indicating
moderate predictive ability for frequent versus infrequent portal use.
Table 2
Predictors of frequent acute care portal use
|
Variable
|
Adjusted OR (95% CI)
|
p-Value
|
|
Private insurance or self-pay (vs. public)
|
2.25 (1.03–5.02)
|
0.043
|
|
Can access the Internet at home (vs. cannot)
|
5.39 (1.15–38.94)
|
0.049
|
|
Severity of illness level 1 or 2 (vs. 3 or 4)
|
2.07 (0.93–4.78)
|
0.077
|
Abbreviations: CI, confidence interval; OR, odds ratio.
Note: We included only complete cases in our analysis, meaning we excluded 15 out
of 146 portal users due to missing data values (n = 131, 47 infrequent users and 84 frequent users).
An exploratory analysis of outcomes from the RCT, comparing frequent and infrequent users, is presented in [Table 3]. No significant differences in patient activation, satisfaction, and engagement
existed between frequent and infrequent users. A significantly greater percentage of frequent users reported using the iPad to look up health information online (97 vs. 76%; p < 0.001).
Table 3
Outcomes
|
Variable
|
All users, n = 146
|
Subgroup analysis
|
p-Value
|
|
Infrequent use, n = 53
|
Frequent use, n = 93
|
|
Patient activation
|
|
PAM score, post
|
74.3 ± 16.0
|
74.8 ± 16.5
|
74.0 ± 15.8
|
0.789
|
|
PAM score, pre–post difference
|
4.4 ± 14.4
|
3.2 ± 15.9
|
5.1 ± 13.6
|
0.977
|
|
PAM score, pre–post difference > 3[a]
|
70 (48.3%)
|
26 (50.0%)
|
44 (47.3%)
|
0.756
|
|
Medical errors
|
|
I noticed medical errors while hospitalized
|
21 (15.9%)
|
2 (4.4%)
|
19 (21.8%)
|
0.010[b]
|
|
I noticed inaccurate information in my record
|
20 (16.5%)
|
3 (8.6%)
|
17 (19.8%)
|
0.133
|
|
If yes, I reported the inaccuracy
|
7 (41.2%)
|
1 (50.0%)
|
6 (40.0%)
|
1.000
|
|
If yes, the inaccuracy was amended
|
3 (60.0%)
|
0 (0.0%)
|
3 (75.0%)
|
0.400
|
|
Patient satisfaction and engagement with care
|
|
Patient satisfaction and engagement with care
|
4.4 ± 0.5
|
4.4 ± 0.5
|
4.4 ± 0.5
|
–
|
|
Perceived usefulness and ease-of-use of iPad
|
|
I used the iPad to …
|
|
access my email
|
20 (14.8%)
|
5 (11.1%)
|
15 (16.7%)
|
0.392
|
|
entertain myself
|
92 (68.1%)
|
30 (66.7%)
|
62 (68.9%)
|
0.794
|
|
look up health information online
|
121 (89.6%)
|
34 (75.6%)
|
87 (96.7%)
|
<0.001[b]
|
|
The iPad is easy to use
|
4.4 ± 0.7
|
4.1 ± 0.8
|
4.5 ± 0.6
|
–
|
|
Perceived usefulness and ease-of-use of portal
|
|
The portal is easy to use
|
4.5 ± 0.7
|
4.3 ± 0.8
|
4.5 ± 0.6
|
–
|
|
The portal made it easier to contact my care team
|
3.3 ± 1.2
|
3.4 ± 1.2
|
3.2 ± 1.2
|
0.411
|
|
I entered questions or comments into my portal
|
32 (26.4%)
|
7 (20.0%)
|
25 (29.1%)
|
0.305
|
Abbreviations: n, number; PAM, Patient Activation Measure.
Note: Categorical variables reported as n (%), and p-values calculated using chi-squared tests or Fisher's exact tests with Monte Carlo
approximation. Continuous variables reported as mean ± SD, and p-values calculated using Wilcoxon rank-sum tests. Scores based on questionnaires with
Likert-type rating scales (satisfaction, ease-of-use) reported as numbers between
1 and 5. Percentages exclude missing data.
a Studies suggest a pre–post difference greater than 3 in the PAM score is clinically
significant.
b Significant at p = 0.05.
A greater percentage of frequent users noticed potential medical errors during their hospital stay (22 vs. 4%; p = 0.010). About half (51%) of potential medical errors related to medication dosage
or administration. Other potential errors related to laboratory tests or procedures
(21%), pain control (9%), diet (8%), allergies (6%), diagnosis (<2%), hospital-acquired
infections (<2%), and sanitation (<2%). A greater percentage of frequent users noticed
inaccurate information in their medical record (20 vs. 9%, p = 0.133), although the difference lacked statistical significance in this trial.
Overall, only 41% of portal users who noticed inaccurate information reported it to
their health care provider.
Discussion
Although hospitalized patients experience high information needs, illness severity
and stress due to hospitalization may obstruct their use of an inpatient portal. On
the other hand, the hospital setting offers unique opportunities to promote portal
use. Hospitals can offer free devices and Internet to inpatients, help set up portal
accounts, and teach patients to use the portal. Such interventions may offset the
barriers that contribute to disparities in portal use, in both inpatient and outpatient
settings. Under the technology access and assistance conditions of our pragmatic RCT,
many expected disparities were not evident. Specifically, age, gender, race, ethnicity,
level of education, employment status, and patient activation were not associated
with frequent use. However, more severe illness, preexisting limited technology experience, and
public insurance were significantly associated with infrequent use, despite technology access and assistance.
In the outpatient setting, research suggests that low technology literacy, alongside
limited computer and Internet access, impacts outpatients' adoption of portals as
well as their continued use.[31]
[38]
[39]
[40] Currently, many patients access inpatient portals through hospital-provided devices,
as in this RCT. However, hospital-provided devices require maintenance and disinfection,
prompting some organizations to consider “bring your own device” (BYOD) options.[14] Unfortunately, BYOD may exacerbate disparities in portal use, by adding the additional
barriers of computer and Internet access. Therefore, organizations should consider
maintaining hospital-provided device programs.
The relationship between illness severity and portal use is more complex.[31] Some studies suggest that having multiple comorbidities is associated with increased
portal adoption,[66]
[67] while others indicate that poorer overall health is associated with decreased portal
adoption.[67] One hypothesis is that patients use portals more as their health condition worsens,
until they become too sick to use it. At that point, their use declines. We studied
an overall sicker population, hospitalized patients, and found that use declines as
illness severity increases. These data are consistent with the hypothesis that the
relationship between illness severity and portal use is parabolic.
Since disparities in use of inpatient portals exist despite technology access and
assistance, health care organizations must consider additional strategies that promote
equal access. One possible strategy is enrolling caregivers, such as surrogate decision
makers or health care proxies. Caregivers may help patients with low technology literacy
or severe illness access their information. This strategy may be of limited use among
the most severely socioeconomically disadvantaged patients, whose family and caregivers
may themselves have limited technology literacy. An alternative strategy is additional
in-hospital training on portal use. The hospital setting offers an opportunity for
patients to develop familiarity with the portal software while assistance is available.
New approaches to improving usability may help reduce training needs.[68]
[69]
Despite the well-documented evidence for disparities in outpatient portal use,[27]
[28]
[29]
[30]
[31]
[32]
[33] fewer studies have investigated potential disparities in inpatient portal use.[34]
[35]
[36] However, this study and the existing literature indicate that disparities in use
occur with inpatient portals as with outpatient ones. Given the development of products
like MyChart Bedside and HealtheLife, both available in Spanish, inpatient portal
availability is expected to increase rapidly in the coming years. Implementing strategies
such as universal access policies,[44] hospital-provided device programs, and technical assistance alongside inpatient
portals will be critical to reduce disparities in use. In this study, 93 (63.7%) patients
demonstrated frequent use, meaning they logged on for over 20 minutes per day on average. These data suggest
that many hospitalized patients will use portals given the opportunity, and organizations
should explore strategies to ensure all populations receive and benefit from this
opportunity.
Significantly more frequent users noticed medical errors during their hospital stay (22 vs. 4%; p = 0.01). It is unclear whether the portal helped users identify medical errors, or
whether noticing errors prompted portal use, or whether a third variable explains
both noticing medical errors and portal use. As with medical errors, frequent users were more likely to notice inaccuracies in their medical record (20 vs. 9%,
p = 0.133), although the difference lacked statistical significance. Recent research
from OpenNotes found that 23% of patients who reviewed their doctor's notes identified
potential safety concerns.[70] Inpatient portal developers should explore methods to engage patients as partners
in improving safety, and consider creating explicit protocols for reporting safety
concerns through patient portals.
We did not find significant differences in patient activation between groups, although
recent research suggests that standardized administration of the PAM is challenging,
especially within our population.[71] Previous research on whether high patient activation correlates with portal use
is mixed.[72]
[73] Hibbard and Greene, developers of the Patient Activation Measure, have reported
that patients with high activation levels use portals more often.[73] However, a smaller study among outpatients at our center failed to find an association
between PAM score and portal use, though it did find the expected associations with
education and socioeconomic status.[72] Future research should further investigate how patient activation interacts with
portal use.
Limitations
We conducted this trial at an urban academic medical center with an advanced informatics
infrastructure, and findings may not generalize to other health care settings. Additional
barriers to inpatient portal use could include (1) literacy factors such as numeracy
or text literacy, (2) portal factors such as usability, utility, and cost, (3) attitudinal
factors such as motivation or concerns about privacy, and (4) patient–provider relationship
factors such as communication and trust.[31] Future work should explore these potential barriers and their impact on inpatient
portal use. Our study excluded visually, physically, and cognitively impaired patients,
despite evidence that these patient populations experience a higher burden of disease.
Future work should explore strategies to engage persons with disabilities in inpatient
portals. Finally, creating standardized methods to assess composite portal use remains
an urgent problem, especially since individual metrics do not reflect actual usage
well. Standardized methods could ensure better comparability between studies of portal
use. This study relied on a simple, transparent, and binary composite use variable;
however, innumerable different composite variables could be constructed.
Conclusion
We conducted a subgroup analysis of frequent versus infrequent inpatient portal users from an RCT. The trial provided free technology access and
assistance, and subsequently many expected health-equity-related disparities were
not observed. However, technology access and assistance did not eliminate disparities
based on illness severity and preexisting technology experience. Future work should
explore strategies, such as enrolling health care proxies and improving usability,
to reduce potential disparities in use of inpatient portals.
Multiple Choice Questions
Multiple Choice Questions
-
Free technology access and technical assistance may ameliorate which previously identified
disparity in inpatient portal use?
-
Age.
-
Technology experience.
-
Illness severity.
-
Insurance status.
Correct Answer: The correct answer option is option a, age. In this study, we found evidence that
free technology access and technical assistance may ameliorate disparities in inpatient
portal use by age. However, disparities in preexisting technology experience, illness
severity, and insurance status remained.
-
Which strategies may ameliorate health-equity-related disparities in patient portal
use?
-
Free technology access and technical assistance.
-
Better access policies such as opt-out enrollment.
-
Enrollment of caregivers and health care proxies.
-
All of the above.
Correct Answer: The correct answer is option d, all of the above. In this study, we demonstrate that
free technology access and technical assistance ameliorate health-equity-related disparities
in patient portal use. Early evidence also indicates that opt-out enrollment policies,
as well as deliberate efforts to engage caregivers, may reduce disparities in use.
