Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

Patient portal adoption and use by hospitalized cancer patients: a retrospective study of its impact on adverse events, utilization, and patient satisfaction

BMC Medical Informatics and Decision Making201818:70

https://doi.org/10.1186/s12911-018-0644-4

  • Received: 9 August 2017
  • Accepted: 26 June 2018
  • Published:
Open Peer Review reports

Abstract

Background

Portal use has been studied among outpatients, but its utility and impact on inpatients is unclear. This study describes portal adoption and use among hospitalized cancer patients and investigates associations with selected safety, utilization, and satisfaction measures.

Methods

A retrospective review of 4594 adult hospitalized cancer patients was conducted between 2012 and 2014 at Mayo Clinic in Jacksonville, Florida, comparing portal adopters, who registered for a portal account prior to hospitalization, with nonadopters. Adopters were classified by their portal activity during hospitalization as active or inactive inpatient users. Univariate and several logistic and linear regression models were used for analysis.

Results

Of total patients, 2352 (51.2%) were portal adopters, and of them, 632 (26.8%) were active inpatient users. Portal adoption was associated with patients who were young, female, married, with higher income, and had more frequent hospitalizations (P < .05). Active inpatient use was associated with patients who were young, married, nonlocals, with higher disease severity, and were hospitalized for medical treatment (P < .05). In univariate analyses, self-management knowledge scores were higher among adopters vs nonadopters (84.3 and 80.0, respectively; P = .01) and among active vs inactive inpatient users (87.0 and 83.3, respectively; P = .04). In regression models adjusted for age and disease severity, the association between portal behaviors and majority of measures were not significant (P > .05).

Conclusions

Over half of our cancer inpatients adopted a portal prior to hospitalization, with increased adoption associated with predisposing and enabling determinants (eg: age, sex, marital status, income), and increased inpatient use associated with need (eg: nonlocal residence and disease severity). Additional research and greater effort to expand the portal functionality is needed to impact inpatient outcomes.

Keywords

  • Adverse events
  • Cancer
  • Hospitalization
  • Portal
  • Satisfaction
  • Utilization

Background

Two decades ago, the National Academy of Medicine (formerly the Institute of Medicine) recommended implementation of electronic health records to improve quality of care in the United States [1]. Since then, health information technologies have been rapidly adopted, with a focus on providers rather than patients. In 1996, the Health Insurance Portability and Accountability Act legally allowed patients to access their own clinical records. However, record retrieval fees, illegible handwriting, and time delays hindered accessibility [2]. An additional challenge is the fragmented health system with many independently owned and operated health care service locations [35]. An integrated information system that aggregates and offers updated health information to patients through a single access point was needed. In 2009, the Health Information Technology for Economic and Clinical Health Act incentivized clinicians to provide patients with electronic access to clinical records through meaningful use rules, administered by the Centers for Medicare & Medicaid Services [6]. This incentive program remains the principal driver of patient portal development by funding nearly $30 billion in provider incentives to encourage appropriate use [7, 8]. Investigations where information access was offered via patient portals in the outpatient settings showed encouraging positive effects in patient satisfaction and self-management behaviors [4, 918]. However, providing patients access to information is important not only in home and outpatient settings, but also when patients are hospitalized [19].

When patients are able to see their own health information during the hospital stay, they become more informed, empowered to ask questions, and gain ownership of their health care [20, 21]. Despite daily bedside rounds, important patient informational needs may not be met due to the cost of reviewing tailored information with each patient individually [22]. Thus, the portal technology may provide opportunity for inpatients to meet informational needs, facilitate awareness, and improve understanding of their care during hospitalization and after discharge [2, 23]. Meeting informational needs could reduce uncertainties surrounding the care process, reduce information asymmetry between patients and providers, promote shared decision-making, and increase patient self-management and adherence to care [24, 25].

Unfortunately, assessments of patient portal use among hospitalized cancer patients are limited [8, 2630]. For many patients, the hospital is a challenging and intimidating setting, compounded by unmet information needs and limited patient engagement [24, 31]. The rapid dynamic and pace of clinical care, changing medical teams, reliance on verbal communication, and absence of an established relationship with the care providers further challenge patients’ effective participation in their own care [32, 33]. Additional affective and emotional challenges are faced by inpatients with cancer due to the nature of their disease, frequently uncertain outcomes of treatments, and the need to understand their multiple active conditions to make treatment decisions [34, 35]. In a study of breast cancer patients, those who desired an active role in treatment decision making also desired detailed information of their diagnosis, treatment procedures, and alternatives [36]. Similar information needs were vital to gynecologic and colorectal cancer patients who felt that information about the likelihood of cure, spread of disease, and treatment options were priorities for decision making [37]. Providing clinical information through patient portals may have the potential to transform the patient-physician relationship and help patients to become active in their disease management [38]. Recent documentation on hospital-based patient portals is encouraging [3942]. Creber et al. published a protocol for developing a personalized inpatient portal at an urban academic medical center to improve cardiology inpatients engagement [2]. Greysen et al. conducted pilot interviews showing patients’ enthusiasm for a tablet application that provides health information during their inpatient stay [43]. Vawdrey et al. assessed the patient-perceived efficacy of tablets to improve cardiothoracic surgery patients’ engagement in care, showing a favorable response regarding usability of the application [19]. Several other studies assessed the feasibility of web-based applications to increase patient engagement in both pediatric and adult care [4446]. Yet, the evaluation of patient portals among cancer inpatients is still limited, a knowledge gap addressed by this study. We hypothesized that patient adoption of a portal and active use during a hospital stay may be associated with greater patient safety, postdischarge care utilization and satisfaction, similar to outpatient settings. According to Karahanna et al., adoption and continued use represent different behaviors [47]. Adoption is the initial enrollment and signifies receptivity to the portal, while usage represents active engagement, continued use after adoption. Therefore we distinguish between these 2 behaviors and evaluate them separately. Our specific aims were to 1) identify the key patient factors predicting adoption and active inpatient use behaviors, and 2) examine the association between portal use behaviors and adverse events, postdischarge utilization (emergency visits and readmissions), and selected patient satisfaction measures (self-health management knowledge and satisfaction with the overall hospital experience).

Methods

Study setting and description of the portal

The site of the study was Mayo Clinic, Jacksonville, Florida (MCF), a large nonprofit, specialized tertiary care practice and medical research center with more than 1.3 million domestic and international patients seen each year. Physicians are salaried, not linked to care volume, thus reducing monetary incentives in patient treatment. MCF contracted with Cerner Solutions (Cerner Corp) to implement the patient portal and integrate it with the system-wide electronic health record in 2010. When patients schedule an appointment at MCF, they are invited to register for a portal account and are provided with information on why and how to register. With each appointment reminder, patients receive a reminder message to register for the portal. Portal invitations are also offered in all outpatient waiting areas and displayed on electronic screens around the clinic.

Once registered, patients’ are able to access informational functions, such as viewing lab results, current medications, allergies, and diagnostic reports from clinic visits and hospitalizations, and administrative functions, such as paying bills, processing prescription refills, and coordinating appointments. A Continuity of Care Document, a complete summary of patient current health status and history, is also available to view, download, or forward to physicians at other hospitals. Additional information on MCF patient portal is documented elsewhere [48]. Although the portal is designed for outpatients, some functions are applicable to inpatient health information needs during the hospital stay. Hospitalized patients potentially have time to access the portal when they are not occupied with diagnostic testing or other activities [49]. For example, the portal gives inpatients real-time access to laboratory results, admission notes, consultation reports, and surgical notes, to view on their own time and between bedside rounds. This functionality potentially facilitates patient communication and interaction with the health care team during their stay, and empowers the patient to be more attentive toward errors in documentation [20]. In addition, the medication function provides patients with information on the type and purpose of their medications, including in-hospital medication intake, which could enable patients to ask questions, review for accuracy, or report medication discrepancies [50, 51]. Before home discharge, a discharge summary and discharge instructions is uploaded to the portal, giving patients time to review closely and ensure their understanding of home self-management instructions. While the development of portal functionality for inpatients is in early stages, the offered content may still help patients become more activated and improve postdischarge care.

Study design and participants

This was a retrospective review of patients satisfying the following criteria: 1) adults 18 years of age or older, 2) cancer as a primary or secondary diagnosis at time of hospitalization identified through the International Classification of Diseases, Ninth Revision (ICD-9) codes, and 3) admitted to MCF between August 1, 2012, and July 31, 2014 (N = 4594). Per the unified theory of acceptance of use of technology, user acceptance and intention to use a technology is followed by actual use [52]. Therefore, we included the first hospitalization where a portal account had been established prior to admission to examine consequent inpatient use. If the patient had not established a portal account prior to any admission, then the first hospitalization in the study period was selected. Patients who had a portal account prior to admission were defined as adopters, and those without a portal account were nonadopters. Among adopters, inpatients who logged in their portal during the hospital stay were active inpatient users and those who never logged in were referred to as inactive inpatient users. The study was approved by the Mayo Clinic Institutional Review Board.

Study model

Our study was informed by Andersen’s Model of Healthcare Utilization [53]. The model was initially developed in 1968 to understand health services use and later revised to include consumer satisfaction and dimensions of health status [54]. Shortly after the model was developed, health services use was portrayed as a health behavior influenced by multiple factors [55]. According to the World Health Organization, health behavior is defined as “any activity undertaken by an individual, regardless of actual or perceived health status, for the purpose of promoting, protecting or maintaining health, whether or not such behavior is objectively effective towards that end” [56]. Because the portal is a tool to maintain and promote health, we considered portal adoption and use as health behaviors that could be studied using Andersen’s model. As shown in Fig. 1, we examined the influence of patients’ characteristics in three major components: predisposing, enabling, and need factors, on portal adoption and use behaviors.
Fig. 1
Fig. 1

Study Theoretical Model Derived from Andersen’s Model of Healthcare Utilization. 1 Predisposing factors: age, sex, and race. 2 Enabling factors: marital status, employment status, health insurance type, and income. 3 Need factors: geographic area of residence, comorbidities, and frequency of hospitalizations. Additional need factors related to the admission: MSDRG type and APRDRG disease severity weight. APRDRG indicates All Patients Refined Diagnostic Related Group; MSDRG, Medicare Severity-Diagnosis Related Group

Measures

Environment and patient characteristics

In this study, we assumed that all study participants had a common environmental context, as all patients in MCF received their care in the same structure. Predisposing determinants included age, sex, and race. Enabling determinants included marital status, employment status, health insurance type, and median income in the residential ZIP code less than Florida’s state median income, a surrogate for socioeconomic status. Need factors included geographic area of residence, comorbidities, and frequency of hospitalizations in the study period. Additional need determinants related to the hospital admission included patient’s disease severity weight as measured by the 3 M All Patients Refined Diagnosis Related Groups (APDRG) classification system, and whether the hospitalization was for medical or surgical treatment, based on the Medicare Severity-Diagnosis Related Group (MSDRG) codes [57].

Demographic data were extracted from the patient electronic health records. The ZIP code median income was obtained from the 2006–2010 American Community Survey and matched to the patient sample at the ZIP code level [58]. A count of comorbidities included in Charlson Comorbidity Index during the 12 months prior to hospitalization was documented [59].

Patient safety, utilization, and satisfaction

We examined selected patients’ measures to investigate associations with portal use. For patient safety, we studied the occurrence or otherwise of provider-reported, in-hospital, adverse events, such as falls, accidental self-injuries, or other events related to the surgery, vascular, equipment or devices, medication, or skin events, obtained from quality management services. For postdischarge utilization, we examined the occurrence of emergency department visits within 14 days and unplanned readmissions within 30 days, both obtained from the hospital records. We measured patient satisfaction by obtaining data from the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. The survey is a validated instrument used since 2006 to assess patients’ perspectives of hospital care, and distributed to a random sample of discharged patients between 2 days and 6 weeks after discharge [60]. While the survey included many important questions, we selected the relevant items with highest response. We measured patient self-health management knowledge with 2 questions: “When I left the hospital, I had a good understanding of the things I was responsible for in managing my health”, and “When I left the hospital, I clearly understood the purpose for taking each of my medications”, and measured overall hospital satisfaction with 1 question: “Using any number from 0 to 10, where 0 is the worst hospital possible and 10 is the best hospital possible, what number would you use to rate this hospital during your stay” [61]. Responses were transformed and averaged, resulting in a 0 to 100 linear-scaled score.

Data analysis

We described the characteristics of cancer patients according to their portal adoption and inpatient activity behaviors, and examined differences between groups using Pearson χ2 and Wilcoxon nonparametric tests. Multivariate regression models were conducted to predict factors associated with portal adoption and active inpatient use, as well as to examine the association between selected outcomes and portal behaviors. All analyses were conducted in SAS Version 9.4 (SAS Institute Inc., Cary, North Carolina, USA), and significance was defined as P < .05.

Results

Participants, adopters, and active inpatient users

Of the 4594 study-eligible hospitalized patients with cancer, 2352 (51.2%) had a portal account prior to admission (ie, adopters), of whom 632 (26.8%) used the portal account during their hospital stay (ie, active inpatient users). Patient characteristics at admission were reported in Table 1. Significant differences in patient characteristics were present among portal adoption and inpatient use behaviors (Table 2). Adoption was influenced by a majority of predisposing and enabling factors, such as age, sex, race, marital status, employment status, income, and type of health insurance. While active inpatient use was similarly influenced by predisposing and enabling factors, such as age, race, and marital status, we found greater influence associated with need, such as having greater disease severity, being nonlocal, and admitted for medical rather than surgical treatment.
Table 1

Sample Baseline Characteristics by Portal Behaviora

Characteristics

Adopters (n = 2,352)

Nonadopters (n = 2,242)

P value

Active Inpatient Users (n = 632)

Inactive Inpatient Users (n = 1,720)

P value

Age group (years)

Mean (SD)

62.3 (14.0)

65.4 (14.8)

<.01b

60.2 (14.3)

63.0 (13.8)

<.01b

≤44

259 (11.0)

191 (8.5)

<.01c

82 (13.0)

177 (10.3)

<.01c

45-54

339 (14.4)

281 (12.5)

106 (16.8)

233 (13.5)

55-64

632 (26.9)

480 (21.4)

185 (29.3)

447 (26.0)

65-74

702 (29.8)

652 (29.1)

166 (26.3)

536 (31.2)

75-84

339 (14.4)

454 (20.2)

80 (12.7)

259 (15.1)

≥85

81 (3.4)

184 (8.2)

13 (2.1)

68 (4.0)

Sex

Female

1,148 (48.8)

1,055 (47.0)

.25c

295 (46.7)

852 (49.5)

.22c

Male

1,204 (51.2)

1,188 (53.0)

337 (53.3)

869 (50.5)

Race

African American

121 (5.2)

273 (12.4)

<.01c

18 (2.9)

103 (6.1)

<.01c

White

2,120 (91.3)

1847 (84.0)

575 (91.9)

1,545 (91.2)

Other

80 (3.4)

78 (3.5)

33 (5.3)

47 (2.8)

Marital status

Married

1,786 (75.9)

1,454 (64.9)

<.01c

506 (80.1)

1,280 (74.4)

<.01c

Single/divorced/widowed

566 (24.1)

788 (35.1)

126 (19.9)

440 (25.6)

Employment status

Employed

739 (37.7)

547 (28.9)

<.01c

206 (38.1)

533 (37.5)

.79c

Retired

836 (42.6)

935 (49.4)

208 (38.5)

628 (44.2)

Not employed/disabled

386 (19.7)

411 (21.7)

126 (23.3)

260 (18.3)

Income

Below Florida median income

645 (28.3)

803 (37.2)

<.01c

167 (26.5)

478 (27.8)

.54c

At or above Florida median income

1,707 (71.7)

1,439 (64.1)

465 (73.5)

1,242 (72.2)

Health insurance

Commercial insurance/self pay

1,145 (48.7)

815 (36.4)

<.01c

339 (53.6)

806 (46.9)

<.01c

Medicare/Medicaid/other government assistance

1,207 (51.3)

1,427 (63.6)

293 (46.4)

914 (53.1)

Geographic area of residence

Nonlocal

521 (22.2)

493 (22.0)

.89c

166 (26.3)

335 (20.6)

<.01c

Local

1,831 (77.8)

1,749 (78.0)

466 (73.7)

1,365 (79.4)

Comorbidity d

None

1,115 (47.4)

1,052 (46.9)

.58c

289 (45.7)

826 (48.0)

.32c

One or more

1,237 (52.6)

1,190 (53.1)

343 (54.3)

894 (52.0)

Comorbidity typee

Congestive heart failure

145 (6.2)

171 (7.6)

.05c

51 (8.1)

94 (5.5)

.02c

Peripheral vascular disease

322 (13.7)

338 (15.1)

.18c

83 (13.1)

239 (13.9)

.63c

Cerebrovascular disease

165 (7.0)

224 (10.0)

<.01c

42 (6.6)

123 (7.2)

.67c

Chronic pulmonary disease

262 (11.1)

256 (11.4)

.77c

61 (9.7)

201 (11.7)

.16c

Mild liver disease

432 (18.4)

291 (13.0)

<.01c

129 (20.4)

303 (17.6)

.12c

Diabetes mellitus

392 (16.7)

351 (15.7)

.35c

115 (18.2)

277 (16.1)

.23c

Moderate/severe renal disease

196 (8.3)

179 (8.0)

.66c

53 (8.4)

143 (8.3)

.96c

Moderate/severe liver disease

121 (5.1)

86 (3.8)

.03c

49 (7.8)

72 (4.2)

<.01c

Admission type based on MSDRG

Surgical

1,504 (63.9)

1,315 (58.7)

<.01b

348 (55.1)

1,156 (67.2)

<.01b

Medical

848 (36.1)

927 (41.3)

284 (44.9)

564 (32.8)

Frequency of hospitalizations, mean (SD)

1.8 (1.5)

1.4 (1.1)

<.01b

2.0 (1.7)

1.7 (1.4)

<.01b

APRDRG weight, mean, (median, SD)

2.5 (1.5, 2.9)

2.3 (1.5, 2.3)

.24b

3.1 (1.9, 3.6)

2.3 (1.4, 2.6)

<.01b

Abbreviations: APRDRG All Patients Refined Diagnosis Related Group, MSDRG Medicare Severity-Diagnosis Related Group

aData are reported as No. (%) for count variables and mean (SD) for continuous variables

bWilcoxon nonparmetric

cPearson χ2 test

dComorbidity groups are not mutually exclusive as a patient may have more than 1 comorbidity diagnosis

eComorbidity type was reported for diseases with > 5% of patients

Table 2

Logistic Regression Analysis Showing the Predictors of Portal Behaviors

Factors

Characteristics

Odds Ratio (95% CI)a

Adoptionb

Active Inpatient Usec

Predisposing

Age

44-

1.42 (1.03, 1.95)

1.89 (1.16, 3.06)

45-54

1.10 (0.82, 1.47)

1.73 (1.10, 2.72)

55-64

1.22 (0.95, 1.57)

1.48 (1.00, 2.19)

65-74 (reference)

1.00

1.00

75-84

0.71 (0.57, 0.87)

1.08 (0.75, 1.56)

85+

0.36 (0.24, 0.54)

0.77 (0.34, 1.75)

Sex

Male (reference)

1.00

1.00

Female

1.26 (1.10, 1.45)

0.97 (0.78, 1.21)

Race

White (reference)

1.00

1.00

African American

0.34 (0.27, 0.45)

0.51 (0.29, 0.89)

Others

0.84 (0.58, 1.23)

1.39 (0.82, 2.37)

Enabling

Marital status

Divorced/single/widowed (reference)

1.00

1.00

Married

1.60 (1.37, 1.87)

1.49 (1.14, 1.94)

Employment

Employed (reference)

1.00

1.00

Retired

1.04 (0.83, 1.30)

1.04 (0.75, 1.46)

Not employed/disabled

0.70 (0.57, 0.86)

1.04 (0.77, 1.40)

Health insurance

Commercial insurance (reference)

1.00

1.00

Medicaid/Medicare

0.76 (0.61, 0.95)

1.07 (0.77, 1.50)

Income

Below FL median income (reference)

1.00

1.00

At or above FL median income

1.39 (1.20, 1.60)

1.10 (0.87, 1.39)

Need

Geographic area of residence

Local (reference)

1.00

1.00

Nonlocal

1.13 (0.96, 1.33)

1.34 (1.04, 1.71)

Comorbidities

None (reference)

1.00

1.00

1+

1.05 (0.91, 1.21)

0.97 (0.77, 1.22)

MSDRG type d

Surgical (reference)

-

1.00

Medical

-

2.17 (1.68, 2.78)

Frequency of hospitalizations

1.43 (1.33, 1.55)

1.08 (0.99, 1.19)

APRDRG weight d

-

1.13 (1.09, 1.17)

Abbreviations: APRDRG All Patients Refined Diagnosis Related Group, MSDRG Medicare Severity-Diagnosis Related Group

aBold values are statistically significant at P<.05. Odds ratios greater than 1 imply increased chance for behavior; less than 1 imply decreased chance for behavior

bPredictors for adoption: age, sex, race, marital status, employment status, income, health insurance type, and frequency of hospitalizations

cPredictors for active inpatient use: age, race, marital status, geographic area of residence, APRDRG weight, and MSDRG type

dVariables related to the hospital admission were not examined among adopters as the adoption behavior was established prior to admission

Bivariate associations of portal behaviors with adverse events, care utilization, and patient satisfaction

Bivariate associations of portal adoption with our selected measures (Table 3) showed that adopters had more emergency visits and readmissions than nonadopters, while reporting higher self-health management knowledge. Similarly, active inpatient users had more readmissions than inactive inpatient users, and marginally higher self-management scores. Logistic and linear regression analyses showed that after adjusting for age and disease severity, the association between portal behaviors and majority of our assessed measures were not significant (Table 4). Adverse events and overall hospital experience did not differ among groups in either univariate or multivariate regression analyses (P > .05).
Table 3

Adverse Events, Postdischarge Care Utilization and Patient Satisfaction Among the portal Behavior Groups

Measures

Outcomes

Adopters (n = 2352)

Nonadopters (n = 2242)

P value

Active Inpatient Users (n = 632)

Inactive Inpatient Users (n = 1720)

P value

Patient safety

Had an adverse event, No. (%)

40 (1.7)

47 (2.1)

.36a

13 (2.1)

27 (1.6)

.42a

Postdischarge care utilization

Emergency visit within 14-days of discharge, No. (%)

272 (11.6)

214 (9.5)

.03a

75 (11.9)

197 (11.5)

.78a

30-day unplanned readmission, No. (%)

299 (12.7)

222 (9.9)

<.01a

96 (15.2)

203 (11.8)

.03a

Patient satisfactionc

Understand responsibilities for self-health management, mean score (SD)

87.6 (19.6)

85.5 (20.1)

.02b

89.1 (18.4)

87.1 (20.0)

.22b

Understand the purpose for taking each medication, mean score (SD)

90.0 (18.8)

87.8 (20.9)

.05b

91.8 (16.0)

89.4 (19.7)

.21b

Aggregate self-health management knowledge score

84.3 (21.3)

80.0 (23.1)

<.01b

87.0 (19.2)

83.3 (21.9)

.05b

Overall rating of the hospital stay, mean score (SD)

95.6 (9.1)

95.3 (10.3)

.75b

95.6 (10.5)

95.7 (8.6)

.56b

aPearson χ2 test

bWilcoxon nonparametric

cSatisfaction survey was distributed to a random sample of discharges; thus, the sample size was as follows: adopters; n = 788, nonadopters; n = 646, active inpatient users; n = 205, and inactive inpatient users; n = 577

Table 4

Association Between Patient Outcomes and Portal Behaviors: Results From Regression Models

 

Independent Variables

Active Inpatient Users vs Nonadopters

Inactive inpatient Users vs Nonadopters

Dependent Variablesa

OR (95% CI)

P value

OR (95% CI)

P value

 [adverse events = yes]

0.76 (0.40, 1.45)

.41

0.71 (0.44–1.16)

.17

 [emergency visits = yes]

1.28 (0.97, 1.70)

.08

1.23 (1.00–1.51)

.08

 [readmissions = yes]

1.60 (1.23, 2.08)

<.01

1.21 (0.99–1.48)

.06

Dependent Variablesa

Beta Coefficient

P value

Beta Coefficient

P value

 Self-health management knowledge

2.18

.07

1.15

.17

 Overall hospital experience

0.16

.83

0.28

.60

aFive regression models were conducted adjusting for age and disease severity. Logistic regression was used for adverse events, emergency visits, and readmissions. Linear regression was used for self-health management knowledge and overall hospital experience scores

Discussion

To date there remains a gap in the literature evaluating the use of inpatient portals among cancer patients. This study provides important information to clinicians, administrators, and researchers, on the key patient determinants associated with portal adoption and use. Prior studies reported significant interest in patient portals among oncology populations [28, 29, 62]. Yet, to our knowledge, this is the first study to examine portal use in a large inpatient oncology cohort. In this sample, we found that portal adoption and use during hospitalization has reached modest levels and somewhat higher usage than published reports on inpatient portal use. Over half of our inpatient oncology population voluntarily adopted the portal before hospital admission and 27% actively used the portal during the stay. Dumitrascu et al. found that of 44.2% patients who had a portal account at the time of admission, only 20.8% accessed the portal during their stay [48]. Davis et al. found that of 34.4% registered portal patients, 23.4% used it while hospitalized [39]. Robinson et al. reported that 16% of surgical inpatients with a portal account used it while being in the hospital [63].

There were noteworthy differences in patient characteristics between adopters and nonadopters in a majority of predisposing and enabling factors. Portal adoption increased among patients who were female, married, and with higher income, and decreased among patients who were African American, unemployed, and had governmental health insurance. Interestingly, the likelihood of portal adoption was similar for patients aged 65 to 75 years as the middle-aged adults 45 to 55 years, contradicting popular beliefs that older patients were less likely to engage in health technologies [64]. Portal adoption, however, considerably decreased among patients aged over 75 years. Similar to our findings, portal use among outpatient oncology patients was reported to be greater among younger, white patients, and those with upper aerodigestive malignancy diagnosis, greater disease severity, and case complexity [8]. Among nononcology populations, a similar digital divide was reported by age groups, race/ethnicity, income, and education [6570]. Our findings showed higher portal adoption among those with more frequent hospitalizations, which was the only notable need determinant. Other studies have reported higher interest in the portal among those with more medical problems, greater severity of illness, or higher than average clinical need [10, 15, 71, 72].

Similarly, inpatient portal use increased with younger age and being married, but more influenced with need determinants. Active access was associated with residing outside the city of Jacksonville (nonlocals); suggesting that commuting patients found health information important to view during the stay. Additionally, access was greater among those with higher disease severity and those admitted for medical rather than surgical treatment. Medical admissions for cancer patients are usually associated with investigating the origin and cause of disease, or evaluating chemo or radiation treatments, compared to surgical admissions that involve typical procedural routines and surgical recovery that may fully occupy the patient’s time in the hospital [73]. Because a cancer diagnosis is a stressful life event, patients’ information-seeking behavior was thought to become more active, possibly as a coping strategy to overcome uncertainties [27, 29, 74].

Patient safety

Several studies have assumed that information technology systems have the potential to improve patient safety by identifying errors in medications and preventing adverse drug reactions. Yet, limited evidence exists regarding the effectiveness of a portal as a tool in reducing adverse events. One recent study by Kelly et al. found that 8% of parents with hospitalized children recognized errors in their child’s medication list after using an inpatient portal application [46]. Further optimistic views about the ability of portals to reduce errors were derived from patient participation in care, where patients could notify clinicians of their medication allergies, unexpected toxicity symptoms, and lapses in care to prevent adverse events [50, 7577]. Among surgical inpatients who were portal users, postoperative infection was their most frequent ICD-9 code, suggesting that experiencing a safety-event may activate patients to follow up their personal health information to avoid further complications [63]. In contrast to this evidence, our study did not find an association between portal adoption or use and adverse events. Likewise, a randomized controlled trial by Weingart et al. did not find sufficient evidence to support an association between adverse drug events and portal use [51]. Earlier research reported that patient history evaluation in cancer care is more focused, providing the patient an opportunity to recall medical and medication information to prevent errors. [78, 79] In addition, most adverse events at hospitals are underreported and the events in our data were limited to those reported by providers. A new initiative within the portal that is gaining popularity and has the potential to prevent errors is the OpenNotes national movement, which invites patients to read their clinicians’ notes online and report back errors or safety concerns that, in turn, may avert mistakes from happening [80, 81]. Hence, it opens up a new possibility to engage patients as safety partners through their reported documentation errors.

Utilization

Studies that examined the effect of portal use on subsequent utilization of health services showed mixed results [10, 67, 82]. A study using propensity score matching found no difference between portal users and nonusers on clinical service utilization [83]. Among members of Kaiser Permanente, a retrospective study in the Northwest found that patient access to an online portal was associated with decreased rates of primary care office visits and phone calls [84], whereas the opposite was found by Palen et al. where portal users had higher rates of office visits, phone encounters, after-hour clinic visits, emergency department visits, and hospitalizations [85]. The assumption was that if patients could view personal health information, they will be more aware, able to manage their health, and need less emergency service or hospitalizations. This expectation was not validated in our study, suggesting that a portal technology may be a complementary technology and does not substitute for health services needs of oncology patients. Mayer et al. reported 77.2% of cancer patients’ visits to the emergency department were due to pain, respiratory problems, and gastrointestinal issues, with 63.2% of those visits resulting in hospital admission [86]. Barbera et al. reported that 83.8% of cancer patients who died had visited the emergency department during their final 6 months of life with issues related to abdominal pain, dyspnea, pneumonia, fatigue, and pleural effusion [87]. Shapiro et al. found that those who had surgery during their index admission were 3 times more likely to be readmitted [88]. Weaver et al. examined cancer inpatients and found 48% of readmissions were within 1 to 2 days of discharge [89]. Donze et al. developed a predictive model and found that discharge from an oncology service was a significant predictor of unplanned readmission [90]. Similarly, a recent systematic review reported that comorbidities, older age, advanced disease, and index hospitalization length of hospital stay were significant predictors for readmission in oncology [91]. Thus, emergency department visits and readmissions may be influenced more by the nature of illness, treatment-related complications, and other such factors than avoidable reasons by portal use.

Patient satisfaction

Our findings suggest limited evidence of the relationship between patient satisfaction and portal use. Self-management knowledge scores appear to be considerably higher among both adopters and inpatient users in bivariate associations; however, in regression analyses, associations with satisfaction were somewhat attenuated and no longer statistically significant. Our interpretation of results needs to be cautiously taken as they were limited by the random selection of sample surveyed and the selection of self-management questions. In addition, we have no assessment of self-health management knowledge at baseline. Therefore, the association between portal use and self-health management knowledge may be confounded.

Prior research has shown inconsistent conclusions regarding associations between portal use and patient satisfaction; with wide variability in the offered portal features, the outcomes evaluated, and the populations studied [4, 10, 14, 31]. In addition, the potential of patient portals for patients with chronic conditions is documented, but relatively nascent for cancer [92]. Among chronically ill patients, the portal showed promise for improving diabetic patients’ satisfaction with care, ability to self-manage, and adhering to treatments [93]. This has been accompanied by evidence of improved blood pressure control among people with newly diagnosed hypertension [94]. Patient portal access was also superior in general adherence and satisfaction with doctor-patient communication among patients with congestive heart failure [18, 95]. Yet, not all findings in the literature showed that patients with chronic conditions were amenable to improved outcomes with portal use [29, 9698].

There are many potential recommendations to improve portal functions for inpatients. Hospitals often provide patients and families with standard information on disease and treatment options while being hospitalized, but that is not always enough [99]. An effective tool for awareness and self-management may include problem-solving support, regular education provision, treatment options with cost estimations that aid patient decision making, and consistent patients training on how to take responsibility for their own health [100].

It should be noted that emotional factors, such as anxiety or low self-efficacy, may dramatically influence self-management or symptom-coping behaviors [101, 102]. Of interest, some researchers suggest technology-based applications to provide recreational social supports to help patients cope with their illness. O’Leary et al. reported favorable patient perceptions toward games offered in the hospital-based portal [40]. The same was reported by Jameson et al., who indicated that electronic gaming can be a positive distraction away from pain [103]. Innovative social support approaches offering recreational avenues via the portal may attract more users, which in turn, may improve self-management, symptom-coping, and quality of life [104]. Thus, greater attention is needed to improve the portal content and functionality for inpatients to improve patient outcomes.

This study has a number of limitations. There is limited generalizability given that our oncology cohort was from a single center. Technology limitations restricted our analysis; we could not examine frequency of inpatient log-ins, or distinguish if a portal activity was carried out by the patient or a delegated family member. Further, it would be interesting to understand if there was a dose-response type curve associated with portal use but information on the extent of use was not available. Post-discharge utilization measures were limited to care utilization at MCF, with no data on utilization elsewhere. Conclusions regarding patient safety and satisfaction measures were limited by the range of variable values; adverse events were uncommon, and patient satisfaction was almost uniformly high among all patients. Finally, low response to the HCAHPS resulted in a small sub-sample size to analyze satisfaction, a major limitation, but no other measures were readily available. Despite these limitations, the study uncovered determinants of adoption and use behaviors among a large sample of hospitalized cancer patients. Additionally, it adds new information to the growing body of literature on inpatient engagement using acute care portals. Future research directions should investigate the extent of inpatient portal use, incorporate inpatient-centered education materials, and improve the portal with functions that add the most value for cancer inpatients.

Conclusions

We found that cancer patients had reached modest levels of portal adoption. While portal adoption increased with predisposing and enabling determinants (eg: age, sex, marital status, income), active inpatient use increased with need (eg: commute residence and high disease severity). While these findings should be cautiously interpreted, the study adds to the growing evidence that patient portals should be further addressed for inpatient care. Particularly, the study provides insights for the informatics research community and those interested in improving inpatient care and self-management support through technology.

Abbreviations

HCAHPS: 

Hospital Consumer Assessment of Healthcare Providers and Systems

ICD-9: 

International Classification of Diseases, Ninth Revision

MCF: 

Mayo Clinic Florida

Declarations

Acknowledgments

The authors thank March Rucci and Lisa Nordan from the Center for the Science of Healthcare Delivery at Mayo Clinic in Florida for input and support to complete the study.

Funding

No funding was received from any external source.

Availability of data and materials

Because of variables that would potentially enable patient identification, the data cannot be made publicly available.

Authors’ contributions

DIA led the research and performed all analyses and interpretation of data. AGD and MCB participated in the conception and design of the study. LJW made substantial contributions to the data acquisition. DIA drafted the manuscript and MK, RH, SX, and JMN critically revised the manuscript for important intellectual content. JMN was the senior author, confirmed approach and provided editorial support on the internal drafts of the paper. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This study was a retrospective study of existing records, and a waiver of informed consent was approved by the Mayo Clinic Institutional Review Board in accordance with 45 CFR 46.116 (Approval #14–00603).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA
(2)
Division of Hospital Internal Medicine, Mayo Clinic, Jacksonville, FL, USA
(3)
Division of Biomedical Statistics and Informatics, Mayo Clinic, Jacksonville, FL, USA
(4)
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
(5)
Department of Health Services Policy and Management, University of South Carolina, Columbia, SC, USA

References

  1. Crossing the Quality Chasm. A new health system for the 21st century. In: Washington (DC): Institute of Medicine (US) committee on quality of health care in America; 2001.Google Scholar
  2. Masterson Creber R, Prey J, Ryan B, Alarcon I, Qian M, Bakken S, et al. Engaging hospitalized patients in clinical care: study protocol for a pragmatic randomized controlled trial. Contemp Clin Trials. 2016;47:165–71.View ArticlePubMedGoogle Scholar
  3. Furukawa MF, King J, Patel V, Hsiao CJ, Adler-Milstein J, Jha AK. Despite substantial progress in EHR adoption, health information exchange and patient engagement remain low in office settings. Health Aff (Millwood). 2014;33:1672–9.View ArticleGoogle Scholar
  4. Kruse CS, Bolton K, Freriks G. The effect of patient portals on quality outcomes and its implications to meaningful use: a systematic review. J Med Internet Res. 2015;17:e44.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Rittenhouse DR, Ramsay PP, Casalino LP, McClellan S, Kandel ZK, Shortell SM. Increased health information technology adoption and use among small primary care physician practices over time: a National Cohort Study. Ann Fam Med. 2017;15:56–62.View ArticlePubMedPubMed CentralGoogle Scholar
  6. CMS. Electronic health records incentive programs. 2017. http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/index.html. Accessed 23 Feb 2018.
  7. Blumenthal D. Launching HITECH. N Engl J Med. 2010;362:382–5.View ArticlePubMedGoogle Scholar
  8. Gerber DE, Laccetti AL, Chen B, Yan J, Cai J, Gates S, et al. Predictors and intensity of online access to electronic medical records among patients with cancer. J Oncol Pract. 2014;10:e307–12.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Ammenwerth E, Schnell-Inderst P, Hoerbst A. The impact of electronic patient portals on patient care: a systematic review of controlled trials. J Med Internet Res. 2012;14:e162.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Goldzweig CL, Orshansky G, Paige NM, Towfigh AA, Haggstrom DA, Miake-Lye I, et al. Electronic patient portals: evidence on health outcomes, satisfaction, efficiency, and attitudes: a systematic review. Ann Intern Med. 2013;159:677–87.View ArticlePubMedGoogle Scholar
  11. Hibbard JH, Greene J, Overton V. Patients with lower activation associated with higher costs; delivery systems should know their patients’ ‘scores’. Health Aff (Millwood). 2013;32:216–22.View ArticleGoogle Scholar
  12. Greene J, Hibbard JH. Why does patient activation matter? An examination of the relationships between patient activation and health-related outcomes. J Gen Intern Med. 2012;27:520–6.View ArticlePubMedGoogle Scholar
  13. Hibbard JH, Mahoney ER, Stock R, Tusler M. Do increases in patient activation result in improved self-management behaviors? Health Serv Res. 2007;42:1443–63.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Neuner J, Fedders M, Caravella M, Bradford L, Schapira M. Meaningful use and the patient portal: patient enrollment, use, and satisfaction with patient portals at a later-adopting center. Am J Med Qual. 2015;30:105–13.View ArticlePubMedGoogle Scholar
  15. Ralston JD, Carrell D, Reid R, Anderson M, Moran M, Hereford J. Patient web services integrated with a shared medical record: patient use and satisfaction. J Am Med Inform Assoc. 2007;14:798–806.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Osborn CY, Mayberry LS, Wallston KA, Johnson KB, Elasy TA. Understanding patient portal use: implications for medication management. J Med Internet Res. 2013;15:e133.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Horvath M, Levy J, L'Engle P, Carlson B, Ahmad A, Ferranti J. Impact of health portal enrollment with email reminders on adherence to clinic appointments: a pilot study. J Med Internet Res. 2011;13:e41.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Ross SE, Moore LA, Earnest MA, Wittevrongel L, Lin CT. Providing a web-based online medical record with electronic communication capabilities to patients with congestive heart failure: randomized trial. J Med Internet Res. 2004;6:e12.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Vawdrey DK, Wilcox LG, Collins SA, Bakken S, Feiner S, Boyer A, et al. A tablet computer application for patients to participate in their hospital care. AMIA Annu Symp Proc. 2011;2011:1428–35.PubMedPubMed CentralGoogle Scholar
  20. Gentles SJ, Lokker C, McKibbon KA. Health information technology to facilitate communication involving health care providers, caregivers, and pediatric patients: a scoping review. J Med Internet Res. 2010;12:e22.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Walker DM, Sieck CJ, Menser T, Huerta TR, Scheck McAlearney A. Information technology to support patient engagement: where do we stand and where can we go? J Am Med Inform Assoc. 2017;24:1088–94.View ArticlePubMedGoogle Scholar
  22. Bickmore TW, Pfeifer LM, Jack BW. Taking the time to care: empowering low health literacy hospital patients with virtual nurse agents. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '09). 2009; doi: https://doi.org/10.1145/1518701.1518891.
  23. Roberts S, Chaboyer W, Gonzalez R, Marshall A. Using technology to engage hospitalised patients in their care: a realist review. BMC Health Serv Res. 2017;17:388.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Kaziunas E, Hanauer DA, Ackerman MS, Choi SW. Identifying unmet informational needs in the inpatient setting to increase patient and caregiver engagement in the context of pediatric hematopoietic stem cell transplantation. J Am Med Inform Assoc. 2016;23:94–104.View ArticlePubMedGoogle Scholar
  25. Prey JE, Restaino S, Vawdrey DK. Providing hospital patients with access to their medical records. AMIA Annu Symp Proc. 2014;2014:1884–93.PubMedPubMed CentralGoogle Scholar
  26. Borosund E, Cvancarova M, Moore SM, Ekstedt M, Ruland CM. Comparing effects in regular practice of e-communication and web-based self-management support among breast cancer patients: preliminary results from a randomized controlled trial. J Med Internet Res. 2014;16:e295.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Cahill JE, Lin L, LoBiondo-Wood G, Armstrong TS, Acquaye AA, Vera-Bolanos E, et al. Personal health records, symptoms, uncertainty, and mood in brain tumor patients. Neurooncol Pract. 2014;1:64–70.PubMedPubMed CentralGoogle Scholar
  28. Pai HH, Lau F, Barnett J, Jones S. Meeting the health information needs of prostate cancer patients using personal health records. Curr Oncol. 2013;20:e561–9.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Wiljer D, Leonard KJ, Urowitz S, Apatu E, Massey C, Quartey NK, et al. The anxious wait: assessing the impact of patient accessible EHRs for breast cancer patients. BMC Med Inform Decis Mak. 2010;10:46.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Wiljer D, Bogomilsky S, Catton P, Murray C, Stewart J, Minden M. Getting results for hematology patients through access to the electronic health record. Can Oncol Nurs J. 2006;16:154–64.View ArticlePubMedGoogle Scholar
  31. Woollen J, Prey J, Wilcox L, Sackeim A, Restaino S, Raza ST, et al. Patient experiences using an inpatient personal health record. Appl Clin Inform. 2016;7:446–60.View ArticlePubMedPubMed CentralGoogle Scholar
  32. O'Leary KJ, Kulkarni N, Landler MP, Jeon J, Hahn KJ, Englert KM, et al. Hospitalized patients’ understanding of their plan of care. Mayo Clin Proc. 2010;85:47–52.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Olson DP, Windish DM. Communication discrepancies between physicians and hospitalized patients. Arch Intern Med. 2010;170:1302–7.View ArticlePubMedGoogle Scholar
  34. Beckjord EB, Arora NK, McLaughlin W, Oakley-Girvan I, Hamilton AS, Hesse BW. Health-related information needs in a large and diverse sample of adult cancer survivors: implications for cancer care. J Cancer Surviv. 2008;2:179–89.View ArticlePubMedGoogle Scholar
  35. Kowalski C, Lee SY, Ansmann L, Wesselmann S, Pfaff H. Meeting patients’ health information needs in breast cancer center hospitals - a multilevel analysis. BMC Health Serv Res. 2014;14:601.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Hack TF, Degner LF, Dyck DG. Relationship between preferences for decisional control and illness information among women with breast cancer: a quantitative and qualitative analysis. Soc Sci Med. 1994;39:279–89.View ArticlePubMedGoogle Scholar
  37. Beaver K, Booth K. Information needs and decision-making preferences: comparing findings for gynaecological, breast and colorectal cancer. Eur J Oncol Nurs. 2007;11:409–16.View ArticlePubMedGoogle Scholar
  38. Tang PC, Ash JS, Bates DW, Overhage JM, Sands DZ. Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption. J Am Med Inform Assoc. 2006;13:121–6.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Davis SE, Osborn CY, Kripalani S, Goggins KM, Jackson GP. Health literacy, education levels, and patient portal usage during hospitalizations. AMIA Annu Symp Proc. 2015;2015:1871–80.PubMedPubMed CentralGoogle Scholar
  40. O'Leary KJ, Sharma RK, Killarney A, O'Hara LS, Lohman ME, Culver E, et al. Patients’ and healthcare providers’ perceptions of a mobile portal application for hospitalized patients. BMC Med Inform Decis Mak. 2016;16:123.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Pell JM, Mancuso M, Limon S, Oman K, Lin CT. Patient access to electronic health records during hospitalization. JAMA Intern Med. 2015;175:856–8.View ArticlePubMedGoogle Scholar
  42. Dalal AK, Dykes PC, Collins S, Lehmann LS, Ohashi K, Rozenblum R, et al. A web-based, patient-centered toolkit to engage patients and caregivers in the acute care setting: a preliminary evaluation. J Am Med Inform Assoc. 2016;23:80–7.View ArticlePubMedGoogle Scholar
  43. Greysen SR, Khanna RR, Jacolbia R, Lee HM, Auerbach AD. Tablet computers for hospitalized patients: a pilot study to improve inpatient engagement. J Hosp Med. 2014;9:396–9.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Maher M, Hanauer DA, Kaziunas E, Ackerman MS, Derry H, Forringer R, et al. A novel health information technology communication system to increase caregiver activation in the context of hospital-based pediatric hematopoietic cell transplantation: a pilot study. JMIR Res Protoc. 2015;4:e119.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Runaas L, Hanauer D, Maher M, Bischoff E, Fauer A, Hoang T, et al. BMT roadmap: a user-centered design health information technology tool to promote patient-centered Care in Pediatric Hematopoietic Cell Transplantation. Biol Blood Marrow Transplant. 2017;23:813–9.View ArticlePubMedGoogle Scholar
  46. Kelly MM, Hoonakker PL, Dean SM. Using an inpatient portal to engage families in pediatric hospital care. J Am Med Inform Assoc. 2017;24:153–61.View ArticlePubMedGoogle Scholar
  47. Karahanna ESD, Chervany NL. Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Q. 1999;23:183–213.View ArticleGoogle Scholar
  48. Dumitrascu AG, Burton MC, Dawson NL, Thomas CS, Nordan LM, Greig HE, et al. Patient portal use and hospital outcomes. J Am Med Inform Assoc. 2017;0:1–7.Google Scholar
  49. Chu ES, Hakkarinen D, Evig C, Page S, Keniston A, Dickinson M, et al. Underutilized time for health education of hospitalized patients. J Hosp Med. 2008;3:238–46.View ArticlePubMedGoogle Scholar
  50. Wasson JH, MacKenzie TA, Hall M. Patients use an internet technology to report when things go wrong. Qual Saf Health Care. 2007;16:213–5.View ArticlePubMedPubMed CentralGoogle Scholar
  51. Weingart SN, Carbo A, Tess A, Chiappetta L, Tutkus S, Morway L, et al. Using a patient internet portal to prevent adverse drug events: a randomized, controlled trial. J Patient Saf. 2013;9:169–75.View ArticlePubMedGoogle Scholar
  52. Venkatesh VMMGDG, Davis FD. User acceptance of information technology: toward a unified view. MIS Q. 2003;27:425–78.View ArticleGoogle Scholar
  53. Andersen R. A behavioral model of families’ use of health services. Chicago: Center for Health Administration Studies, University of Chicago; 1968.Google Scholar
  54. Andersen R, Newman JF. Societal and individual determinants of medical care utilization in the United States. Milbank Q. 2005;83 https://doi.org/10.1111/j.468-0009.2005.00428.x.
  55. Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav. 1995;36:1–10.View ArticlePubMedGoogle Scholar
  56. Nutbeam D. Health promotion glossary. Health Promotion (Oxford, England). 1986;1:113–27.View ArticleGoogle Scholar
  57. 3M Health Information Systems. All patients refined diagnosis related groups (APR-DRGs). Version 20.0. Methodology Overview. 2003. https://www.hcup-us.ahrq.gov/db/nation/nis/APR-DRGsV20MethodologyOverviewandBibliography.pdf. Accessed 23 Feb 2018.
  58. United States Census Bureau. American Community Survey (ACS). https://www.census.gov/programs-surveys/acs.
  59. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–83.View ArticlePubMedGoogle Scholar
  60. Agency for Healthcare Research and Quality. HCAHPS Survey. http://www.hcahpsonline.org/en/survey-instruments. Accessed 23 Feb 2018.
  61. Agency for Healthcare Research and Quality. HCAHPS Fact Sheet CAHPS Hospital Survey. http://www.hcahpsonline.org/globalassets/hcahps/facts/hcahps_fact_sheet_november_2017a.pdf. Accessed 11 Feb 2017.
  62. Yu PP. The evolution of oncology electronic health records. Cancer J. 2011;17:197–202.View ArticlePubMedGoogle Scholar
  63. Robinson JR, Davis SE, Cronin RM, Jackson GP. Use of a patient portal during hospital admissions to surgical services. AMIA Annu Symp Proc. 2016;2016:1967–76.PubMedGoogle Scholar
  64. Smith SG, Pandit A, Rush SR, Wolf MS, Simon C. The association between patient activation and accessing online health information: results from a national survey of US adults. Health Expect. 2015;18:3262–73.View ArticlePubMedGoogle Scholar
  65. Goel MS, Brown TL, Williams A, Cooper AJ, Hasnain-Wynia R, Baker DW. Patient reported barriers to enrolling in a patient portal. J Am Med Inform Assoc. 2011;18(Suppl 1):i8–12.View ArticlePubMedPubMed CentralGoogle Scholar
  66. Smith SG, O'Conor R, Aitken W, Curtis LM, Wolf MS, Goel MS. Disparities in registration and use of an online patient portal among older adults: findings from the LitCog cohort. J Am Med Inform Assoc. 2015;22:888–95.View ArticlePubMedPubMed CentralGoogle Scholar
  67. Irizarry T, DeVito Dabbs A, Curran CR. Patient portals and patient engagement: a state of the science review. J Med Internet Res. 2015;17:e148.View ArticlePubMedPubMed CentralGoogle Scholar
  68. Ancker JS, Barron Y, Rockoff ML, Hauser D, Pichardo M, Szerencsy A, et al. Use of an electronic patient portal among disadvantaged populations. J Gen Intern Med. 2011;26:1117–23.View ArticlePubMedPubMed CentralGoogle Scholar
  69. Gordon NP, Hornbrook MC. Differences in access to and preferences for using patient portals and other eHealth technologies based on race, ethnicity, and age: a database and survey study of seniors in a large health plan. J Med Internet Res. 2016;18:e50.View ArticlePubMedPubMed CentralGoogle Scholar
  70. Yamin CK, Emani S, Williams DH, Lipsitz SR, Karson AS, Wald JS, et al. The digital divide in adoption and use of a personal health record. Arch Intern Med. 2011;171:568–74.View ArticlePubMedGoogle Scholar
  71. Phelps RG, Taylor J, Simpson K, Samuel J, Turner AN. Patients’ continuing use of an online health record: a quantitative evaluation of 14,000 patient years of access data. J Med Internet Res. 2014;16:e241.View ArticlePubMedPubMed CentralGoogle Scholar
  72. Miller H, Vandenbosch B, Ivanov D, Black P. Determinants of personal health record use: a large population study at Cleveland Clinic. J Healthc Inf Manag. 2007;21:44–8.PubMedGoogle Scholar
  73. Numico G, Cristofano A, Mozzicafreddo A, Cursio OE, Franco P, Courthod G, et al. Hospital admission of cancer patients: avoidable practice or necessary care? PLoS One. 2015;10:e0120827.View ArticlePubMedPubMed CentralGoogle Scholar
  74. Catt S, Chalmers A, Fallowfield L. Psychosocial and supportive-care needs in high-grade glioma. Lancet Oncol. 2008;9:884–91.View ArticlePubMedGoogle Scholar
  75. Weingart SN, Cleary A, Seger A, Eng TK, Saadeh M, Gross A, et al. Medication reconciliation in ambulatory oncology. Jt Comm J Qual Patient Saf. 2007;33:750–7.View ArticlePubMedGoogle Scholar
  76. Basch E, Artz D, Dulko D, Scher K, Sabbatini P, Hensley M, et al. Patient online self-reporting of toxicity symptoms during chemotherapy. J Clin Oncol. 2005;23:3552–61.View ArticlePubMedGoogle Scholar
  77. Hibbard JH, Peters E, Slovic P, Tusler M. Can patients be part of the solution? Views on their role in preventing medical errors. Med Care Res Rev. 2005;62:601–16.View ArticlePubMedGoogle Scholar
  78. Adler HM. The history of the present illness as treatment: who’s listening, and why does it matter? J Am Board Fam Pract. 1997;10:28–35.PubMedGoogle Scholar
  79. Rosenzweig MQ, Gardner D, Griffith B. The history and physical in Cancer care: a primer for the oncology advanced practitioner. J Adv Pract Oncol. 2014;5:262–8.PubMedPubMed CentralGoogle Scholar
  80. Esch T, Mejilla R, Anselmo M, Podtschaske B, Delbanco T, Walker J. Engaging patients through open notes: an evaluation using mixed methods. BMJ Open. 2016;6:e010034.View ArticlePubMedPubMed CentralGoogle Scholar
  81. Delbanco T, Walker J, Bell SK, Darer JD, Elmore JG, Farag N, et al. Inviting patients to read their doctors’ notes: a quasi-experimental study and a look ahead. Ann Intern Med. 2012;157:461–70.View ArticlePubMedPubMed CentralGoogle Scholar
  82. Pillemer F, Price RA, Paone S, Martich GD, Albert S, Haidari L, et al. Direct release of test results to patients increases patient engagement and utilization of care. PLoS One. 2016;11:e0154743.View ArticlePubMedPubMed CentralGoogle Scholar
  83. Meng D, Palen TE, Tsai J, McLeod M, Garrido T, Qian H. Association between secure patient-clinician email and clinical services utilisation in a US integrated health system: a retrospective cohort study. BMJ Open. 2015;5:e009557.View ArticlePubMedPubMed CentralGoogle Scholar
  84. Zhou YY, Garrido T, Chin HL, Wiesenthal AM, Liang LL. Patient access to an electronic health record with secure messaging: impact on primary care utilization. Am J Manag Care. 2007;13:418–24.PubMedGoogle Scholar
  85. Palen TE, Ross C, Powers JD, Xu S. Association of online patient access to clinicians and medical records with use of clinical services. JAMA. 2012;308:2012–9.View ArticlePubMedGoogle Scholar
  86. Mayer DK, Travers D, Wyss A, Leak A, Waller A. Why do patients with cancer visit emergency departments? Results of a 2008 population study in North Carolina. J Clin Oncol. 2011;29:2683–8.View ArticlePubMedPubMed CentralGoogle Scholar
  87. Barbera L, Taylor C, Dudgeon D. Why do patients with cancer visit the emergency department near the end of life? CMAJ. 2010;182:563–8.View ArticlePubMedPubMed CentralGoogle Scholar
  88. Shapiro JS, Humeniuk MS, Siddiqui MA, Bonthu N, Schroeder DR, Kashiwagi DT. Risk factors for readmission in patients with Cancer Comanaged by hospitalists. Am J Med Qual. 2017;32:526–31.View ArticlePubMedGoogle Scholar
  89. Weaver C, Schiech L, Held-Warmkessel J, Kedziera P, Haney E, DiLullo G, et al. Risk for unplanned hospital readmission of patients with cancer: results of a retrospective medical record review. Oncol Nurs Forum. 2006;33:E44–52.View ArticlePubMedGoogle Scholar
  90. Donze J, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med. 2013;173:632–8.View ArticlePubMedGoogle Scholar
  91. Bell JF, Whitney RL, Reed SC, Poghosyan H, Lash RS, Kim KK, et al. Systematic review of hospital readmissions among patients with Cancer in the United States. Oncol Nurs Forum. 2017;44:176–91.PubMedGoogle Scholar
  92. Coughlin SS, Prochaska JJ, Williams LB, Besenyi GM, Heboyan V, Goggans DS, et al. Patient web portals, disease management, and primary prevention. Risk Manag Healthc Policy. 2017;10:33–40.View ArticlePubMedPubMed CentralGoogle Scholar
  93. Ralston JD, Hirsch IB, Hoath J, Mullen M, Cheadle A, Goldberg HI. Web-based collaborative care for type 2 diabetes: a pilot randomized trial. Diabetes Care. 2009;32:234–9.View ArticlePubMedPubMed CentralGoogle Scholar
  94. Manard W, Scherrer JF, Salas J, Schneider FD. Patient portal use and blood pressure control in newly diagnosed hypertension. J Am Board Fam Med. 2016;29:452–9.View ArticlePubMedGoogle Scholar
  95. Earnest MA, Ross SE, Wittevrongel L, Moore LA, Lin CT. Use of a patient-accessible electronic medical record in a practice for congestive heart failure: patient and physician experiences. J Am Med Inform Assoc. 2004;11:410–7.View ArticlePubMedPubMed CentralGoogle Scholar
  96. Wagner PJ, Dias J, Howard S, Kintziger KW, Hudson MF, Seol YH, et al. Personal health records and hypertension control: a randomized trial. J Am Med Inform Assoc. 2012;19:626–34.View ArticlePubMedPubMed CentralGoogle Scholar
  97. Grant RW, Wald JS, Schnipper JL, Gandhi TK, Poon EG, Orav EJ, et al. Practice-linked online personal health records for type 2 diabetes mellitus: a randomized controlled trial. Arch Intern Med. 2008;168:1776–82.View ArticlePubMedGoogle Scholar
  98. Price M, Bellwood P, Kitson N, Davies I, Weber J, Lau F. Conditions potentially sensitive to a personal health record (PHR) intervention, a systematic review. BMC Med Inform Decis Mak. 2015;15:32.View ArticlePubMedPubMed CentralGoogle Scholar
  99. Holmstrom I, Roing M. The relation between patient-centeredness and patient empowerment: a discussion on concepts. Patient Educ Couns. 2010;79:167–72.View ArticlePubMedGoogle Scholar
  100. Lovell MR, Luckett T, Boyle FM, Phillips J, Agar M, Davidson PM. Patient education, coaching, and self-Management for Cancer Pain. J Clin Oncol. 2014;32:1712–20.View ArticlePubMedGoogle Scholar
  101. Lev EL. Bandura's theory of self-efficacy: applications to oncology. Sch Inq Nurs Pract. 1997;11:21–37. discussion 9-43PubMedGoogle Scholar
  102. Haugland T, Wahl AK, Hofoss D, DeVon HA. Association between general self-efficacy, social support, cancer-related stress and physical health-related quality of life: a path model study in patients with neuroendocrine tumors. Health Qual Life Outcomes. 2016;14:11.View ArticlePubMedPubMed CentralGoogle Scholar
  103. Jameson E, Trevena J, Swain N. Electronic gaming as pain distraction. Pain Res Manag. 2011;16:27–32.View ArticlePubMedPubMed CentralGoogle Scholar
  104. Foster C, Breckons M, Cotterell P, Barbosa D, Calman L, Corner J, et al. Cancer survivors’ self-efficacy to self-manage in the year following primary treatment. J Cancer Surviv. 2015;9:11–9.View ArticlePubMedGoogle Scholar

Copyright

Advertisement