Abstract

Objectives

To determine whether use of a patient portal during hospitalization is associated with improvement in hospital outcomes, 30-day readmissions, inpatient mortality, and 30-day mortality.

Materials and Methods

We performed a retrospective propensity score–matched study that included all adult patients admitted to Mayo Clinic Hospital in Jacksonville, Florida, from August 1, 2012, to July 31, 2014, who had signed up for a patient portal account prior to hospitalization (N = 7538).

Results

Out of the admitted patients with a portal account, 1566 (20.8%) accessed the portal while in the hospital. Compared to patients who did not access the portal, patients who accessed the portal were younger (58.8 years vs 62.3 years), had fewer elective admissions (54.2% vs 64.1%), were more frequently admitted to medical services (45.8% vs 35.2%), and were more likely to have liver disease (21.9% vs 12.9%) and higher disease severity scores (0.653 vs 0.456). After propensity score matching, there was no statistically significant difference between the 2 cohorts with respect to 30-day readmission (P = .13), inpatient mortality (P = .82), or 30-day mortality (P = .082).

Conclusion

Use of the patient portal in the inpatient setting may not improve hospital outcomes. Future research should examine the association of portal use with more immediate inpatient health outcomes such as patient experience, patient engagement, medication reconciliation, and prevention of adverse events.

BACKGROUND

In 1996, the Health Insurance Portability and Accountability Act gave patients the legal right to view and own their medical records.1 Unrestricted patient access to medical records was somewhat difficult in the era of paper charts, but after the introduction of the electronic health record (EHR) and, subsequently, EHR-tethered personal health record electronic entry, or “patient portals,” patients’ access to their records became less challenging.

In 2009, the Health Information Technology for Economic and Clinical Health Act published meaningful use rules for using the EHR,2 which included that the EHR should be used to provide effective health information exchange to improve health care quality and patient-centered care. In light of these rules, it is expected that patient portal use will be more associated with medical outcomes.3

Over the past several years, patient portal use has increased in both the outpatient and inpatient settings.4,5 Online, patients can review labs, provider notes, and medication and problem lists, request prescription renewals, manage appointments, and communicate with their physicians. While patient portal use is increasing,6 the inpatient environment presents additional challenges for patients in receiving information about their care, which may affect use. The clinical conditions leading to hospitalization are often acute, at times with devastating consequences. The amount of medical information generated during a hospitalization can be extensive and delivered to the patient rapidly over a short period of time. Consequently, patients may have difficulty comprehending and integrating this information, as well as understanding the plan of care.7 Furthermore, it is not uncommon for care teams to be new to patients and their families, further complicating communication. It is known that the majority of patients are unable to identify their primary inpatient providers.8

Hospitalized patients’ immediate access to the online portal is an additional challenge, since devices to access the Internet are not always available in the hospital. The advent of Internet-connected smartphones and tablets has facilitated online access.9 Despite the barriers, patients want to receive and review health information electronically during their hospitalization and after discharge.9–13

There is limited information regarding patient portal use in the inpatient setting and its association with clinical outcomes.14 Most of the information regarding inpatient portal use is obtained from survey-based quasi-experimental studies of limited numbers of patients.14 These studies report usability and satisfaction outcomes: patients’ intention to use the portal,9 inpatient portal utilization,15 and satisfaction with use of the portal tool.13 Offering hospitalized patients access to a web portal via tablet computers increased their ability to correctly name more than one physician and their roles.15 While these studies offer an indication that immediate health outcomes may be positive, to date no study has examined the association between hospital outcomes of 30-day readmissions, inpatient mortality, and 30-day mortality and patient portal use. Patients who use the portal to access their medical records while hospitalized may have an active interest in their outcome and information about their hospital stay. We pose the question of whether this engagement would reduce 30-day readmissions or decrease mortality.

We asked two main questions in this study:

  1. How do patients who access their portal accounts in the inpatient setting differ from those with access who do not?

  2. Is there a relationship between inpatient portal use and hospital outcomes of 30-day readmission, 30-day mortality, and inpatient mortality?

To our knowledge, this is the first study to examine hospital outcomes associated with inpatient portal use.

MATERIALS AND METHODS

Study design and subjects

This retrospective study included all adult patients admitted to Mayo Clinic Hospital in Jacksonville, Florida, from August 1, 2012, to July 31, 2014, who had a patient portal account prior to hospitalization. If a patient had more than one inpatient admission during this time frame, only the demographic and clinical data from the first encounter was collected and analyzed. Patients were excluded if they were under 18 years of age at the time of admission. Hospital observation stays were not included. The study was approved by the Mayo Clinic Institutional Review Board.

Data collection

The EHR was used to obtain information on patient demographics (age at admission, sex, race, ethnicity, marital status, primary language, payor information, and employment status) and clinical information known at the time of admission (admission status, admission service, and comorbidities), and hospital outcomes (30-day readmission, 30-day mortality, and inpatient mortality). Principal and secondary diagnoses and procedures, comorbidities, and All-Patient Refined Diagnosis Related Group (APR-DRG) weight16 were collected based on International Classification of Diseases, Ninth Revision, Clinical Modification codes extracted from hospital discharge abstracts. Hospital length of stay (LOS) was calculated using the dates and times of hospital admission and hospital discharge.

Patient Online Services (patient portal)

The Florida campus of Mayo Clinic implemented the Patient Online Services (patient portal) in 2010, initially providing patients with secure Internet access to their laboratory data. On March 1, 2012, access to outpatient physician notes was added. In May 2011, Mayo Clinic launched the Mayo Clinic Mobile app, which allows patients the same access as the web portal. This app was compatible with Apple iOS initially, and then expanded to Android as well. The app gives patients the flexibility to view their information on the go, to sync appointments to electronic calendars, and to set appointment reminders. The patient portal is tethered to Mayo Clinic Florida’s EHR, PowerChart (Cerner Corporation, North Kansas City, MO, USA). Information is extracted from PowerChart and displayed by Mayo’s proprietary online and mobile-based applications. The date and time that patients access the portal can be determined; however, the content or modules accessed cannot, due to a system limitation with PowerChart.

Patients routinely create portal accounts in the outpatient setting. Once an account is created, the portal gives a patient the opportunity to view lab results, clinic progress notes, and medication and allergy lists, and to send messages to care providers. When a patient who signed up for an account is hospitalized, the patient can then use the portal to access inpatient labs, inpatient admission notes, consultation reports, operative notes, and discharge summaries in real time. Radiology and pathology reports are available with a 72-h delay. Providers’ daily progress notes are not viewable. While patient-to-provider electronic messaging is encouraged in the outpatient setting, communication between hospitalized patients and hospital care teams is not available.

Statistical analysis

Preadmission characteristics were summarized according to portal use during hospitalization among the cohort of patients who had a portal account prior to admission. We aimed to estimate the effect of portal use during hospitalization on hospital outcomes (inpatient mortality, 30-day readmission, and 30-day mortality). In an attempt to control confounding, propensity score matching was used to identify a cohort of patients with similar baseline characteristics. Propensity score is defined here as the conditional probability of an inpatient using his or her existing portal account given a set of covariates known at the time of admission (baseline). A multivariable logistic regression model with inpatient portal use as the dependent variable and all the baseline characteristics displayed in Table 1 as covariates was used to estimate propensity score. Up to 3 non–portal users17 for each portal user were selected utilizing a greedy matching algorithm without replacement18 with a caliper width equal to 0.2 of the standard deviation (SD) of the logit of the propensity score.19,20 To assess potential imbalance in baseline characteristics between the 2 groups, standardized differences were estimated before and after matching. A standardized difference of <10% for a given baseline characteristic was considered a negligible imbalance between groups.20 In the matched cohort of non–portal users, summary statistics were weighted such that each patient was assigned a weight of 1 divided by the number of non–portal users matched to the corresponding portal user for the given analysis.

Table 1.

Preadmission characteristics of the full cohort of patients who had a portal account established prior to hospital admission according to portal use during the inpatient stay

CharacteristicNo portal use (n = 5972)Portal use (n = 1566)Standardized difference before matching, %
Mean age, years62.3 ± 15.158.8 ± 15.722.7
Female sex (%)3121 (52.3)739 (47.2)10.2
Race (%)
 Black361 (6.0)53 (3.4)12.6
 White5374 (90.0)1428 (91.2)4.1
 Other144 (2.4)71 (4.5)11.6
 Unknown93 (1.6)14 (0.9)6.0
Ethnic group (%)
 Hispanic or Latino202 (3.4)81 (5.2)8.9
 Not Hispanic or Latino5625 (94.2)1457 (93.0)4.7
 Unknown145 (2.4)28 (1.8)4.5
Marital status (%)
 Married or life partner4371 (73.2)1194 (76.2)7.0
 Divorced or separated502 (8.4)103 (6.6)7.0
 Widowed491 (8.2)95 (6.1)8.4
 Single603 (10.1)171 (10.9)2.7
 Unknown5 (0.1)3 (0.2)2.9
Language (%)
 English5857 (98.1)1504 (96.0)12.1
 Spanish49 (0.8)29 (1.9)9.0
 Other31 (0.5)19 (1.2)7.5
 Unknown35 (0.6)14 (0.9)3.6
Government payor (%)3315 (55.5)723 (46.2)18.8
Employment status (%)
 Employed1730 (29.0)488 (31.2)4.8
 Not employed663 (11.1)249 (15.9)14.1
 Retired2040 (34.2)489 (31.2)6.3
 Disabled351 (5.9)107 (6.8)3.9
 Unknown1188 (19.9)233 (14.9)13.3
Admission status (%)
 Routine/elective3831 (64.1)848 (54.2)20.4
 Semi-urgent161 (2.7)80 (5.1)12.5
 Urgent1718 (28.8)564 (36.0)15.5
 Emergency239 (4.0)61 (3.9)0.5
 Other23 (0.4)13 (0.8)5.7
Admission service (%)
 Critical244 (4.1)85 (5.4)6.3
 Medical2104 (35.2)717 (45.8)21.6
 Surgical3624 (60.7)764 (48.8)24.1
Myocardial infarct (%)291 (4.9)87 (5.6)3.1
Congestive heart failure (%)541 (9.1)147 (9.4)1.1
Peripheral vascular disease (%)1045 (17.5)262 (16.7)2.0
Cerebrovascular disease (%)718 (12.0)162 (10.3)5.3
Dementia (%)253 (4.2)67 (4.3)0.2
Chronic pulmonary disease (%)851 (14.2)196 (12.5)5.1
Ulcer (%)150 (2.5)47 (3.0)3.0
Mild liver disease (%)766 (12.8)335 (21.4)22.9
Diabetes (%)1050 (17.6)285 (18.2)1.6
Diabetes with organ damage (%)130 (2.2)39 (2.5)2.1
Hemiplegia (%)78 (1.3)22 (1.4)0.9
Moderate/severe renal disease (%)690 (11.6)196 (12.5)3.0
Moderate/severe liver disease (%)273 (4.6)168 (10.7)23.3
Metastatic solid tumor (%)473 (7.9)178 (11.4)11.7
AIDS (%)8 (0.1)4 (0.3)2.8
Rheumatologic disease (%)208 (3.5)52 (3.3)0.9
Other cancer (%)1743 (29.2)569 (36.3)15.3
Mean APR-DRG weight, log scale0.456 ± 0.7250.653 ± 0.85924.7
CharacteristicNo portal use (n = 5972)Portal use (n = 1566)Standardized difference before matching, %
Mean age, years62.3 ± 15.158.8 ± 15.722.7
Female sex (%)3121 (52.3)739 (47.2)10.2
Race (%)
 Black361 (6.0)53 (3.4)12.6
 White5374 (90.0)1428 (91.2)4.1
 Other144 (2.4)71 (4.5)11.6
 Unknown93 (1.6)14 (0.9)6.0
Ethnic group (%)
 Hispanic or Latino202 (3.4)81 (5.2)8.9
 Not Hispanic or Latino5625 (94.2)1457 (93.0)4.7
 Unknown145 (2.4)28 (1.8)4.5
Marital status (%)
 Married or life partner4371 (73.2)1194 (76.2)7.0
 Divorced or separated502 (8.4)103 (6.6)7.0
 Widowed491 (8.2)95 (6.1)8.4
 Single603 (10.1)171 (10.9)2.7
 Unknown5 (0.1)3 (0.2)2.9
Language (%)
 English5857 (98.1)1504 (96.0)12.1
 Spanish49 (0.8)29 (1.9)9.0
 Other31 (0.5)19 (1.2)7.5
 Unknown35 (0.6)14 (0.9)3.6
Government payor (%)3315 (55.5)723 (46.2)18.8
Employment status (%)
 Employed1730 (29.0)488 (31.2)4.8
 Not employed663 (11.1)249 (15.9)14.1
 Retired2040 (34.2)489 (31.2)6.3
 Disabled351 (5.9)107 (6.8)3.9
 Unknown1188 (19.9)233 (14.9)13.3
Admission status (%)
 Routine/elective3831 (64.1)848 (54.2)20.4
 Semi-urgent161 (2.7)80 (5.1)12.5
 Urgent1718 (28.8)564 (36.0)15.5
 Emergency239 (4.0)61 (3.9)0.5
 Other23 (0.4)13 (0.8)5.7
Admission service (%)
 Critical244 (4.1)85 (5.4)6.3
 Medical2104 (35.2)717 (45.8)21.6
 Surgical3624 (60.7)764 (48.8)24.1
Myocardial infarct (%)291 (4.9)87 (5.6)3.1
Congestive heart failure (%)541 (9.1)147 (9.4)1.1
Peripheral vascular disease (%)1045 (17.5)262 (16.7)2.0
Cerebrovascular disease (%)718 (12.0)162 (10.3)5.3
Dementia (%)253 (4.2)67 (4.3)0.2
Chronic pulmonary disease (%)851 (14.2)196 (12.5)5.1
Ulcer (%)150 (2.5)47 (3.0)3.0
Mild liver disease (%)766 (12.8)335 (21.4)22.9
Diabetes (%)1050 (17.6)285 (18.2)1.6
Diabetes with organ damage (%)130 (2.2)39 (2.5)2.1
Hemiplegia (%)78 (1.3)22 (1.4)0.9
Moderate/severe renal disease (%)690 (11.6)196 (12.5)3.0
Moderate/severe liver disease (%)273 (4.6)168 (10.7)23.3
Metastatic solid tumor (%)473 (7.9)178 (11.4)11.7
AIDS (%)8 (0.1)4 (0.3)2.8
Rheumatologic disease (%)208 (3.5)52 (3.3)0.9
Other cancer (%)1743 (29.2)569 (36.3)15.3
Mean APR-DRG weight, log scale0.456 ± 0.7250.653 ± 0.85924.7

Categorical characteristics are given as the number and percentage of patients, while numerical characteristics are given as mean ± SD. The sample mean of APR-DRG weight (0.497, log scale) was imputed for 14 patients (12 with no portal use and 2 with portal use) who were missing that information.

Abbreviations: APR-DRG, All-Patient Refined Diagnosis Related Group.

Table 1.

Preadmission characteristics of the full cohort of patients who had a portal account established prior to hospital admission according to portal use during the inpatient stay

CharacteristicNo portal use (n = 5972)Portal use (n = 1566)Standardized difference before matching, %
Mean age, years62.3 ± 15.158.8 ± 15.722.7
Female sex (%)3121 (52.3)739 (47.2)10.2
Race (%)
 Black361 (6.0)53 (3.4)12.6
 White5374 (90.0)1428 (91.2)4.1
 Other144 (2.4)71 (4.5)11.6
 Unknown93 (1.6)14 (0.9)6.0
Ethnic group (%)
 Hispanic or Latino202 (3.4)81 (5.2)8.9
 Not Hispanic or Latino5625 (94.2)1457 (93.0)4.7
 Unknown145 (2.4)28 (1.8)4.5
Marital status (%)
 Married or life partner4371 (73.2)1194 (76.2)7.0
 Divorced or separated502 (8.4)103 (6.6)7.0
 Widowed491 (8.2)95 (6.1)8.4
 Single603 (10.1)171 (10.9)2.7
 Unknown5 (0.1)3 (0.2)2.9
Language (%)
 English5857 (98.1)1504 (96.0)12.1
 Spanish49 (0.8)29 (1.9)9.0
 Other31 (0.5)19 (1.2)7.5
 Unknown35 (0.6)14 (0.9)3.6
Government payor (%)3315 (55.5)723 (46.2)18.8
Employment status (%)
 Employed1730 (29.0)488 (31.2)4.8
 Not employed663 (11.1)249 (15.9)14.1
 Retired2040 (34.2)489 (31.2)6.3
 Disabled351 (5.9)107 (6.8)3.9
 Unknown1188 (19.9)233 (14.9)13.3
Admission status (%)
 Routine/elective3831 (64.1)848 (54.2)20.4
 Semi-urgent161 (2.7)80 (5.1)12.5
 Urgent1718 (28.8)564 (36.0)15.5
 Emergency239 (4.0)61 (3.9)0.5
 Other23 (0.4)13 (0.8)5.7
Admission service (%)
 Critical244 (4.1)85 (5.4)6.3
 Medical2104 (35.2)717 (45.8)21.6
 Surgical3624 (60.7)764 (48.8)24.1
Myocardial infarct (%)291 (4.9)87 (5.6)3.1
Congestive heart failure (%)541 (9.1)147 (9.4)1.1
Peripheral vascular disease (%)1045 (17.5)262 (16.7)2.0
Cerebrovascular disease (%)718 (12.0)162 (10.3)5.3
Dementia (%)253 (4.2)67 (4.3)0.2
Chronic pulmonary disease (%)851 (14.2)196 (12.5)5.1
Ulcer (%)150 (2.5)47 (3.0)3.0
Mild liver disease (%)766 (12.8)335 (21.4)22.9
Diabetes (%)1050 (17.6)285 (18.2)1.6
Diabetes with organ damage (%)130 (2.2)39 (2.5)2.1
Hemiplegia (%)78 (1.3)22 (1.4)0.9
Moderate/severe renal disease (%)690 (11.6)196 (12.5)3.0
Moderate/severe liver disease (%)273 (4.6)168 (10.7)23.3
Metastatic solid tumor (%)473 (7.9)178 (11.4)11.7
AIDS (%)8 (0.1)4 (0.3)2.8
Rheumatologic disease (%)208 (3.5)52 (3.3)0.9
Other cancer (%)1743 (29.2)569 (36.3)15.3
Mean APR-DRG weight, log scale0.456 ± 0.7250.653 ± 0.85924.7
CharacteristicNo portal use (n = 5972)Portal use (n = 1566)Standardized difference before matching, %
Mean age, years62.3 ± 15.158.8 ± 15.722.7
Female sex (%)3121 (52.3)739 (47.2)10.2
Race (%)
 Black361 (6.0)53 (3.4)12.6
 White5374 (90.0)1428 (91.2)4.1
 Other144 (2.4)71 (4.5)11.6
 Unknown93 (1.6)14 (0.9)6.0
Ethnic group (%)
 Hispanic or Latino202 (3.4)81 (5.2)8.9
 Not Hispanic or Latino5625 (94.2)1457 (93.0)4.7
 Unknown145 (2.4)28 (1.8)4.5
Marital status (%)
 Married or life partner4371 (73.2)1194 (76.2)7.0
 Divorced or separated502 (8.4)103 (6.6)7.0
 Widowed491 (8.2)95 (6.1)8.4
 Single603 (10.1)171 (10.9)2.7
 Unknown5 (0.1)3 (0.2)2.9
Language (%)
 English5857 (98.1)1504 (96.0)12.1
 Spanish49 (0.8)29 (1.9)9.0
 Other31 (0.5)19 (1.2)7.5
 Unknown35 (0.6)14 (0.9)3.6
Government payor (%)3315 (55.5)723 (46.2)18.8
Employment status (%)
 Employed1730 (29.0)488 (31.2)4.8
 Not employed663 (11.1)249 (15.9)14.1
 Retired2040 (34.2)489 (31.2)6.3
 Disabled351 (5.9)107 (6.8)3.9
 Unknown1188 (19.9)233 (14.9)13.3
Admission status (%)
 Routine/elective3831 (64.1)848 (54.2)20.4
 Semi-urgent161 (2.7)80 (5.1)12.5
 Urgent1718 (28.8)564 (36.0)15.5
 Emergency239 (4.0)61 (3.9)0.5
 Other23 (0.4)13 (0.8)5.7
Admission service (%)
 Critical244 (4.1)85 (5.4)6.3
 Medical2104 (35.2)717 (45.8)21.6
 Surgical3624 (60.7)764 (48.8)24.1
Myocardial infarct (%)291 (4.9)87 (5.6)3.1
Congestive heart failure (%)541 (9.1)147 (9.4)1.1
Peripheral vascular disease (%)1045 (17.5)262 (16.7)2.0
Cerebrovascular disease (%)718 (12.0)162 (10.3)5.3
Dementia (%)253 (4.2)67 (4.3)0.2
Chronic pulmonary disease (%)851 (14.2)196 (12.5)5.1
Ulcer (%)150 (2.5)47 (3.0)3.0
Mild liver disease (%)766 (12.8)335 (21.4)22.9
Diabetes (%)1050 (17.6)285 (18.2)1.6
Diabetes with organ damage (%)130 (2.2)39 (2.5)2.1
Hemiplegia (%)78 (1.3)22 (1.4)0.9
Moderate/severe renal disease (%)690 (11.6)196 (12.5)3.0
Moderate/severe liver disease (%)273 (4.6)168 (10.7)23.3
Metastatic solid tumor (%)473 (7.9)178 (11.4)11.7
AIDS (%)8 (0.1)4 (0.3)2.8
Rheumatologic disease (%)208 (3.5)52 (3.3)0.9
Other cancer (%)1743 (29.2)569 (36.3)15.3
Mean APR-DRG weight, log scale0.456 ± 0.7250.653 ± 0.85924.7

Categorical characteristics are given as the number and percentage of patients, while numerical characteristics are given as mean ± SD. The sample mean of APR-DRG weight (0.497, log scale) was imputed for 14 patients (12 with no portal use and 2 with portal use) who were missing that information.

Abbreviations: APR-DRG, All-Patient Refined Diagnosis Related Group.

Conditional logistic regression was used to evaluate the impact of portal use on inpatient mortality, 30-day readmission, and 30-day mortality. All models included a variable representing the match identifier as a stratification factor in order to preserve the benefit of matching. Models evaluating 30-day outcomes were adjusted for LOS (log scale). Patients were excluded from analyses evaluating 30-day outcomes if they died during their hospital stay or if they did not have a corresponding match after patients who died during their hospital stay were excluded.

Statistical analyses were performed using SAS statistical software (version 9.4, SAS Institute Inc., Cary, NC, USA), and all reported P-values are 2-sided without adjustment for multiple testing.

RESULTS

We identified 17 050 patients in the study period: 7538 (44.2%) had a portal account established at the time of admission and 9512 (55.8%) did not. Of the patients who already had portal accounts, 1566 (20.8%) accessed the portal during hospitalization. Patients’ preadmission characteristics before matching are summarized in Table 1 and show substantial differences (standardized difference >20%) among those who accessed the portal by age, elective admission status, admission to medical or surgical services, comorbidity of liver disease, and disease severity (APR-DRG weight). Patients who accessed their portal account were more likely to be younger, to have more elective vs urgent admissions, to be admitted to medical services versus surgical services, and to have liver disease, and had higher disease severity scores than patients who did not access the portal during their stay.

We included 7538 patients with a portal account at the time of admission in the logistic regression model to estimate the propensity score for using the portal account during hospitalization. The c index from that model was 0.67. The mean propensity score among the patients who accessed their portal account during hospitalization was 0.260 (SD = 0.125), compared with 0.194 (SD = 0.094) for patients who did not access their portal account during hospitalization. As seen in Table 2, all differences in preadmission patient characteristics between those who did and did not use the portal during hospitalization were considered negligible (all standardized differences ≤2.0%) after propensity score matching.

Table 2.

Preadmission characteristics after propensity score matching

CharacteristicNo in-hospital portal use (n = 3922)In-hospital portal use (n = 1559)Standardized difference after matching, %
Age, years58.7 ± 10.058.9 ± 15.71.5
Female sex47.447.20.3
Race
 Black3.33.40.4
 White91.591.40.2
 Other4.24.30.5
 Unknown1.00.91.1
Ethnic group
 Hispanic or Latino4.95.10.6
 Not Hispanic or Latino93.293.10.4
 Unknown1.81.80.2
Marital status
 Married or life partner75.676.31.7
 Divorced or separated6.76.60.4
 Widowed6.16.10.0
 Single11.510.82.0
 Unknown0.20.20.2
Language
 English96.396.20.6
 Spanish1.81.80.0
 Other1.21.20.1
 Unknown0.80.91.4
Government payor47.046.31.5
Employment status
 Employed31.031.20.6
 Not employed15.815.70.5
 Retired31.131.30.4
 Disabled7.36.91.5
 Unknown14.814.90.3
Admission status
 Routine/elective53.754.20.9
 Semi-urgent5.05.00.0
 Urgent36.736.01.3
 Emergency3.83.90.5
 Other0.70.81.0
Admission service
 Critical5.65.31.2
 Medical45.645.70.2
 Surgical48.849.00.3
Myocardial infarct5.85.60.8
Congestive heart failure9.79.40.9
Peripheral vascular disease16.516.80.8
Cerebrovascular disease10.210.40.7
Dementia4.44.30.4
Chronic pulmonary disease12.512.60.2
Ulcer2.83.01.1
Mild liver disease21.021.00.0
Diabetes18.518.20.7
Diabetes with organ damage2.62.50.9
Hemiplegia1.51.40.9
Moderate/severe renal disease12.612.60.1
Moderate/severe liver disease10.210.51.1
Metastatic solid tumor11.511.20.9
AIDS0.20.31.4
Rheumatologic disease3.23.30.5
Other cancer36.336.20.2
APR-DRG weight, log scale0.639 ± 0.5550.645 ± 0.8520.8
CharacteristicNo in-hospital portal use (n = 3922)In-hospital portal use (n = 1559)Standardized difference after matching, %
Age, years58.7 ± 10.058.9 ± 15.71.5
Female sex47.447.20.3
Race
 Black3.33.40.4
 White91.591.40.2
 Other4.24.30.5
 Unknown1.00.91.1
Ethnic group
 Hispanic or Latino4.95.10.6
 Not Hispanic or Latino93.293.10.4
 Unknown1.81.80.2
Marital status
 Married or life partner75.676.31.7
 Divorced or separated6.76.60.4
 Widowed6.16.10.0
 Single11.510.82.0
 Unknown0.20.20.2
Language
 English96.396.20.6
 Spanish1.81.80.0
 Other1.21.20.1
 Unknown0.80.91.4
Government payor47.046.31.5
Employment status
 Employed31.031.20.6
 Not employed15.815.70.5
 Retired31.131.30.4
 Disabled7.36.91.5
 Unknown14.814.90.3
Admission status
 Routine/elective53.754.20.9
 Semi-urgent5.05.00.0
 Urgent36.736.01.3
 Emergency3.83.90.5
 Other0.70.81.0
Admission service
 Critical5.65.31.2
 Medical45.645.70.2
 Surgical48.849.00.3
Myocardial infarct5.85.60.8
Congestive heart failure9.79.40.9
Peripheral vascular disease16.516.80.8
Cerebrovascular disease10.210.40.7
Dementia4.44.30.4
Chronic pulmonary disease12.512.60.2
Ulcer2.83.01.1
Mild liver disease21.021.00.0
Diabetes18.518.20.7
Diabetes with organ damage2.62.50.9
Hemiplegia1.51.40.9
Moderate/severe renal disease12.612.60.1
Moderate/severe liver disease10.210.51.1
Metastatic solid tumor11.511.20.9
AIDS0.20.31.4
Rheumatologic disease3.23.30.5
Other cancer36.336.20.2
APR-DRG weight, log scale0.639 ± 0.5550.645 ± 0.8520.8

Categorical characteristics are given as the percentage of patients, while numerical characteristics are given as mean ± SD. Summary statistics in the group with no in-hospital portal use are weighted such that each patient was assigned a weight of 1 divided by the number of patients with no in-hospital portal use matched to the corresponding portal user.

Abbreviations: APR-DRG, All-Patient Refined Diagnosis Related Group.

Table 2.

Preadmission characteristics after propensity score matching

CharacteristicNo in-hospital portal use (n = 3922)In-hospital portal use (n = 1559)Standardized difference after matching, %
Age, years58.7 ± 10.058.9 ± 15.71.5
Female sex47.447.20.3
Race
 Black3.33.40.4
 White91.591.40.2
 Other4.24.30.5
 Unknown1.00.91.1
Ethnic group
 Hispanic or Latino4.95.10.6
 Not Hispanic or Latino93.293.10.4
 Unknown1.81.80.2
Marital status
 Married or life partner75.676.31.7
 Divorced or separated6.76.60.4
 Widowed6.16.10.0
 Single11.510.82.0
 Unknown0.20.20.2
Language
 English96.396.20.6
 Spanish1.81.80.0
 Other1.21.20.1
 Unknown0.80.91.4
Government payor47.046.31.5
Employment status
 Employed31.031.20.6
 Not employed15.815.70.5
 Retired31.131.30.4
 Disabled7.36.91.5
 Unknown14.814.90.3
Admission status
 Routine/elective53.754.20.9
 Semi-urgent5.05.00.0
 Urgent36.736.01.3
 Emergency3.83.90.5
 Other0.70.81.0
Admission service
 Critical5.65.31.2
 Medical45.645.70.2
 Surgical48.849.00.3
Myocardial infarct5.85.60.8
Congestive heart failure9.79.40.9
Peripheral vascular disease16.516.80.8
Cerebrovascular disease10.210.40.7
Dementia4.44.30.4
Chronic pulmonary disease12.512.60.2
Ulcer2.83.01.1
Mild liver disease21.021.00.0
Diabetes18.518.20.7
Diabetes with organ damage2.62.50.9
Hemiplegia1.51.40.9
Moderate/severe renal disease12.612.60.1
Moderate/severe liver disease10.210.51.1
Metastatic solid tumor11.511.20.9
AIDS0.20.31.4
Rheumatologic disease3.23.30.5
Other cancer36.336.20.2
APR-DRG weight, log scale0.639 ± 0.5550.645 ± 0.8520.8
CharacteristicNo in-hospital portal use (n = 3922)In-hospital portal use (n = 1559)Standardized difference after matching, %
Age, years58.7 ± 10.058.9 ± 15.71.5
Female sex47.447.20.3
Race
 Black3.33.40.4
 White91.591.40.2
 Other4.24.30.5
 Unknown1.00.91.1
Ethnic group
 Hispanic or Latino4.95.10.6
 Not Hispanic or Latino93.293.10.4
 Unknown1.81.80.2
Marital status
 Married or life partner75.676.31.7
 Divorced or separated6.76.60.4
 Widowed6.16.10.0
 Single11.510.82.0
 Unknown0.20.20.2
Language
 English96.396.20.6
 Spanish1.81.80.0
 Other1.21.20.1
 Unknown0.80.91.4
Government payor47.046.31.5
Employment status
 Employed31.031.20.6
 Not employed15.815.70.5
 Retired31.131.30.4
 Disabled7.36.91.5
 Unknown14.814.90.3
Admission status
 Routine/elective53.754.20.9
 Semi-urgent5.05.00.0
 Urgent36.736.01.3
 Emergency3.83.90.5
 Other0.70.81.0
Admission service
 Critical5.65.31.2
 Medical45.645.70.2
 Surgical48.849.00.3
Myocardial infarct5.85.60.8
Congestive heart failure9.79.40.9
Peripheral vascular disease16.516.80.8
Cerebrovascular disease10.210.40.7
Dementia4.44.30.4
Chronic pulmonary disease12.512.60.2
Ulcer2.83.01.1
Mild liver disease21.021.00.0
Diabetes18.518.20.7
Diabetes with organ damage2.62.50.9
Hemiplegia1.51.40.9
Moderate/severe renal disease12.612.60.1
Moderate/severe liver disease10.210.51.1
Metastatic solid tumor11.511.20.9
AIDS0.20.31.4
Rheumatologic disease3.23.30.5
Other cancer36.336.20.2
APR-DRG weight, log scale0.639 ± 0.5550.645 ± 0.8520.8

Categorical characteristics are given as the percentage of patients, while numerical characteristics are given as mean ± SD. Summary statistics in the group with no in-hospital portal use are weighted such that each patient was assigned a weight of 1 divided by the number of patients with no in-hospital portal use matched to the corresponding portal user.

Abbreviations: APR-DRG, All-Patient Refined Diagnosis Related Group.

All but 7 patients who used the portal were matched to one or more patients who did not use the portal during their hospital stay. Matching retained 72.7% of the original cohort. The mean propensity score in the matched groups was 0.258 in the group that used the portal (SD = 0.120, n = 1559) and 0.227 in the group that did not use the portal (SD = 0.098, n = 3922).

Associations of portal use with hospital outcomes in the propensity-matched cohort are shown in Table 3. There was no significant association of portal use with inpatient mortality (P = .82), and, after adjusting for LOS, no statistically significant association with 30-day readmission (P = .13) or 30-day mortality (P = .087).

Table 3.

Association of patient portal use with hospital outcomes in the propensity-matched cohort

OutcomeNo.OR (95% CI)P-value
Inpatient mortality54810.94 (0.55, 1.61).82
30-day readmission53741.16 (0.96, 1.39).13
30-day mortality53741.37 (0.96, 1.97).087
OutcomeNo.OR (95% CI)P-value
Inpatient mortality54810.94 (0.55, 1.61).82
30-day readmission53741.16 (0.96, 1.39).13
30-day mortality53741.37 (0.96, 1.97).087

Odds ratios (ORs) and 95% confidence intervals (CIs) result from conditional logistic regression models with in-hospital patient portal use as a predictor variable. Propensity match identifier was included in each model as a stratification factor to preserve the benefit of matching. Models of 30-day outcomes include length of hospital stay on the logarithm scale as a covariate. Patients were excluded from the 30-day outcomes analysis (n = 107) if they died in the hospital (n = 65: 44 patients with no in-hospital portal use and 21 patients with in-hospital portal use) or if they did not have a corresponding match after patients who died in the hospital were excluded (n = 42: 34 patients with no in-hospital portal use and 8 patients with in-hospital portal). Among the 5374 patients included in the 30-day outcomes analyses, 609 patients were readmitted within 30 days after hospital discharge (403 patients with no in-hospital portal use and 206 patients with in-hospital portal use) and 142 patients died within 30 days after hospital discharge (83 patients with no in-hospital portal use and 59 patients with in-hospital portal use).

Table 3.

Association of patient portal use with hospital outcomes in the propensity-matched cohort

OutcomeNo.OR (95% CI)P-value
Inpatient mortality54810.94 (0.55, 1.61).82
30-day readmission53741.16 (0.96, 1.39).13
30-day mortality53741.37 (0.96, 1.97).087
OutcomeNo.OR (95% CI)P-value
Inpatient mortality54810.94 (0.55, 1.61).82
30-day readmission53741.16 (0.96, 1.39).13
30-day mortality53741.37 (0.96, 1.97).087

Odds ratios (ORs) and 95% confidence intervals (CIs) result from conditional logistic regression models with in-hospital patient portal use as a predictor variable. Propensity match identifier was included in each model as a stratification factor to preserve the benefit of matching. Models of 30-day outcomes include length of hospital stay on the logarithm scale as a covariate. Patients were excluded from the 30-day outcomes analysis (n = 107) if they died in the hospital (n = 65: 44 patients with no in-hospital portal use and 21 patients with in-hospital portal use) or if they did not have a corresponding match after patients who died in the hospital were excluded (n = 42: 34 patients with no in-hospital portal use and 8 patients with in-hospital portal). Among the 5374 patients included in the 30-day outcomes analyses, 609 patients were readmitted within 30 days after hospital discharge (403 patients with no in-hospital portal use and 206 patients with in-hospital portal use) and 142 patients died within 30 days after hospital discharge (83 patients with no in-hospital portal use and 59 patients with in-hospital portal use).

DISCUSSION

The primary objective of this study was to compare patients with portal accounts who accessed and did not access the portal during hospitalization and to determine if there was an association between inpatient portal use and 30-day readmissions and mortality. In this study, patients who accessed their portal account were younger and had greater disease severity and more urgent admissions. It is possible that patients (or their families) access the portal when they are uncertain of the hospital course. This would account for lower use among surgical patients (large portion of elective surgery) and those with shorter stays. After propensity matching, there were no differences in 30-day readmissions, inpatient mortality, or 30-day mortality after adjusting for LOS.

Of the 44.2% of patients who had a portal account at the time of admission, only 20.8% accessed the portal while hospitalized. There are several possible reasons for low inpatient portal use. Patient education regarding portal access and the potential benefits of portal use was not provided. The inpatient portal lacks specific features that allow communication between patients and care teams. Only admission notes, operative notes, consultations, and laboratory studies are available in real time. Daily progress notes cannot be viewed, and there is a 72-h delay in the viewing of radiology and pathology reports. Finally, the inpatient portal does not provide education regarding patient-specific disease processes.

Other studies similarly report low inpatient use. Davis noted that 34.4% of admitted patients were registered for the portal, but only 23.4% used it during hospitalization.21 Among surgical patients admitted to a tertiary-care hospital, 25.3% had a portal account but only 16% of registered users accessed their account.22 The lack of features designed specifically for inpatient use was previously emphasized in a systematic review.14 Consequently, several medical centers designed hospital-specific applications aimed at improving the use and usability of inpatient portals.23–25 In a realistic review, Roberts indicated that patient participation with inpatient health information technology (including patient portals) can be augmented by interactive learning focused on information sharing, self-assessment and feedback, tailored education, user-centered design, and user support.26

Studies show that in the outpatient setting, patients with severe disease use portals more frequently,27,28 and patients who access portals have better outcomes in certain chronic medical conditions such as diabetes (lower HbA1c at 6 months),29 hypertension (improved blood pressure control at 12 months),30 depression management (increased medication adherence),31 and preventative care (up-to-date immunizations and mammograms).32,33 Our study indicates that unprompted and unguided patient portal use does not have any benefit with respect to 30-day readmissions and mortality. There are several possible explanations for this. First, use of a patient portal may play a more important role in the longitudinal management of chronic medical issues than in the outcomes associated with acute management of a decompensating clinical situation. While patients and their families can review clinical information via patient portals, this passive, unguided review of clinical data may have little bearing on the overall hospital and immediate posthospital course. In a systematic review, Goldzweig et al.34 noted that positive outcomes for patients with chronic diseases such as diabetes, hypertension, and depression were obtained only when portal use was enhanced with case management intervention, and the cohorts that showed clinical benefit received tailored education about portal use. Our patients were not specifically trained to use the portal, nor did they receive targeted intervention such as case management via the portal. Secondly, in the outpatient setting, outcome improvement was noted mostly for chronic diseases where the disease course is influenced by self-monitoring and behavioral modifications (diabetes, hypertension, asthma, HIV, fertility management, glaucoma, and hyperlipidemia).12 Due to the acute nature of inpatient conditions, behavioral modification is less feasible in the short time associated with hospitalization. Use of the patient portal is strongly encouraged by the Centers for Medicare and Medicaid Services, presumably to better inform patients and improve outcomes.35 Our study shows that clinical outcomes are not improved.

Our study has a number of limitations. The population cohorts were selected from patients admitted to a single academic tertiary-care referral center, which precludes generalizability of our findings. Education level, which could affect portal use, is not routinely recorded in the EHR, so we could not use this information. Another limitation is that only certain information can be accessed by patients in real time, while there is a 72-h delay with pathology reports and radiology testing. The effect of patient portal use may be different in hospitals that embrace the “open notes” concept, allowing patients immediate access to provider notes and test results. Due to a system limitation, we could not verify the type of information accessed by patients in the portal. Also, we did not control the device type (PC, tablet, phone) used to access the patient portal. Free Internet connection is ubiquitous in the study institution, and the patient portal has iterations that can be accessed on different platforms under multiple operating systems and via web browsing. Mobile device availability and patient familiarity with the mobile version of the patient portal were not evaluated. Although the information in the patient portal is private, some hospitalized patients may have shared their log-in information with their family, friends, or primary care physician, and we did not account for the patient’s level of alertness at the time of portal access, nor did we evaluate who actually accessed the information. Prior inpatient or ambulatory use may be associated with inpatient portal use, and this was not assessed. Finally, there may be immediate outcomes that we did not directly examine where use of an inpatient portal may be of benefit, such as medication reconciliation, prevention of adverse events, patient satisfaction, and patient engagement.

CONCLUSION

This is the first study to examine the association between 30-day readmissions, 30-day mortality, and inpatient mortality and inpatient portal use. We found that there was no difference in 30-day readmissions, inpatient mortality, or 30-day mortality between portal and non–portal users and that inpatient portal use was low. The first step in determining whether patient portal use can improve hospital outcomes is to increase adoption and use by designing inpatient-specific portal tools that can engage patients and make them active participants in their health care. Future research should examine the role of portal use on more immediate outcomes such as patient experience and engagement to help clarify how patient portals can be used to improve outcomes.

FUNDING

This work was supported by the Robert D and Patricia E Kern Center for the Science of Health Care Delivery, Mayo Clinic, Jacksonville, Florida.

COMPETING INTERESTS

The authors have no competing interests to declare.

CONTRIBUTORS

AGD, MCB, and NLD conceived and designed the study. AGD, LMN, and CST collected the data. CST and JMN analyzed the data, designed the statistical method, and drafted the tables. AGD, MCB, NLD, and CST drafted the article. HEG, DIA, and JMN critically revised the article for important intellectual content. All co-authors contributed to writing, proofreading, and editing the manuscript.

ACKNOWLEDGMENTS

Launia J. White for her help with data extraction.

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