Background Patient portals are now widely available and increasingly adopted by patients and providers. Despite the growing research interest in patient portal adoption, there is a lack of follow-up studies describing the following: whether patients use portals actively; how frequently they use distinct portal functions; and, consequently, what the effects of using them are, the understanding of which is paramount to maximizing the potential of patient portals to enhance care delivery. Objective To investigate the characteristics of primary care patients using different patient portal functions and the impact of various portal usage behaviors on patients’ primary care service utilization and appointment adherence. Methods A retrospective, observational study using a large dataset of 46,544 primary care patients from University of Florida Health was conducted. Patient portal users were defined as patients who adopted a portal, and adoption was defined as the status that a portal account was opened and kept activated during the study period. Then, users were further classified into different user subgroups based on their portal usage of messaging, laboratory, appointment, and medication functions. The intervention outcomes were the rates of primary care office visits categorized as arrived, telephone encounters, cancellations, and no-shows per quarter as the measures of primary care service utilization and appointment adherence. Generalized linear models with a panel difference-in-differences study design were then developed to estimate the rate ratios between the users and the matched nonusers of the four measurements with an observational window of up to 10 quarters after portal adoption. Results Interestingly, a high propensity to adopt patient portals does not necessarily imply more frequent use of portals. In particular, the number of active health problems one had was significantly negatively associated with portal adoption (odds ratios [ORs] 0.57-0.86, 95% CIs 0.51-0.94, all P<.001) but was positively associated with portal usage (ORs 1.37-1.76, 95% CIs 1.11-2.22, all P≤.01). The same was true for being enrolled in Medicare for portal adoption (OR 0.47, 95% CI 0.41-0.54, P<.001) and message usage (OR 1.44, 95% CI 1.03-2.03, P=.04). On the impact of portal usage, the effects were time-dependent and specific to the user subgroup. The most salient change was the improvement in appointment adherence, and patients who used messaging and laboratory functions more often exhibited a larger reduction in no-shows compared to other user subgroups. Conclusions Patients differ in their portal adoption and usage behaviors, and the portal usage effects are heterogeneous and dynamic. However, there exists a lack of match in the patient portal market where patients who benefit the most from patient portals are not active portal adopters. Our findings suggest that health care delivery planners and administrators should remove the barriers of adoption for the portal beneficiaries; in addition, they should incorporate the impact of portal usage into care coordination and workflow design, ultimately aligning patients’ and providers’ needs and functionalities to effectively deliver patient-centric care.

BackgroundThe objective of this study was to investigate the impact of patient portal adoption on patients’ primary care utilization and appointment adherence.MethodsWe conducted a retrospective observational study using a panel difference-in-differences (DID) framework to investigate the use of primary care services by patients, adjusting for their disease burden and allowing for time-dependent portal effect. A large dataset with 46,544 patients of University of Florida (UF) Health during the study period July 2013 – June 2016 was used. The main outcome measures are disease burden adjusted rates of office visits arrived, no-show, and cancellation to primary care physicians (PCPs) per quarter between patient portal adopters (denoted as users) and non-users.ResultsAt the time of adoption, the quarterly PCP office visit rate ratio (RR) of patient portal users to non-users was 1.33 (95% CI, 1.27–1.39; p < 0.001). The RRs were between 0.94 to 0.99 up to four quarters after portal adoption (p = 0.749, 0.100, 0.131, and 0.091, respectively), and were significantly less than one at the seventh (RR =0.82; 95% CI, 0.73–0.91; p < 0.001) and the eighth (RR = 0.80; 95% CI, 0.70–0.90; p < 0.001) quarters post adoption. The quarterly no-show rates of the users were significantly smaller (RRs were between 0.60 and 0.83) except for the seventh, eighth and tenth quarters post adoption. In these three quarters, the no-show rates were not significantly changed (p = 0.645, 0.295, and 0.436, respectively). Quarterly cancellation rates were not significantly affected by portal adoption (p > 0.05 for all cases).ConclusionsPatient portal users’ disease burden adjusted PCP office visit rate was significantly reduced in one and a half year and thereafter post portal adoption. PCP appointment no-show rate was also significantly reduced and cancellation rate was not affected, implying improved care engagement of patients.

Readmission or death soon after heart failure (HF) admission is a significant problem. Traditional analyses for predicting such events often fail to consider the gamut of characteristics that may contribute– tending to focus on 30‐day outcomes even though the window of increased vulnerability may last up to 90 days. Risk assessments incorporating machine learning (ML) methods may be better suited than traditional statistical analyses alone to sort through multitude of data in the electronic health record (EHR) and identify patients at higher risk. Hypothesis ML‐based decision analysis may better identify patients at increased risk for 90‐day acute HF readmission or death after incident HF admission. Methods and Results Among 3189 patients who underwent index HF hospitalization, 15.2% experienced primary or acute HF readmission and 11.5% died within 90 days. For risk assessment models, 98 variables were considered across nine data categories. ML techniques were used to help select variables for a final logistic regression (LR) model. The final model's AUC was 0.760 (95% CI 0.752 to 0.767), with sensitivity of 83%. This proved superior to an LR model alone [AUC 0.744 (95% CI 0.732 to 0.755)]. Eighteen variables were identified as risk factors including dilated inferior vena cava, elevated blood pressure, elevated BUN, reduced albumin, abnormal sodium or bicarbonate, and NT pro‐BNP elevation. A risk prediction ML‐based model developed from comprehensive characteristics within the EHR can efficiently identify patients at elevated risk of 90‐day acute HF readmission or death for whom closer follow‐up or further interventions may be considered.

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