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.

To investigate critical care delivery in intensive care units (ICUs), we propose a qualitative and quantitative coupling approach to developing an ICU digital twin model. The Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model was adapted to conceptualise the current ICU system. A hybrid simulation model was developed to characterise major care delivery processes as discrete-time events, feature patients, clinicians, and other artifacts as autonomous agents, and integrate them in the same simulation environment to capture their interactions under a variety of ICU production conditions. Electronic health record (EHR) data from a medical ICU of Mayo Clinic Rochester, Minnesota, were used to calibrate model parameters. Upon iterative refinement and validation, the model has the potential to be integrated with the hospital information system to simulate real-life events as a full-fledged digital twin of the system. It can be used as an in-silico testbed to investigate the real-time allocation of ICU resources such as medical equipment, flexible staffing, workflow change, and support decisions of patient admission, discharge, and transfer, for healthcare delivery innovation. The interdisciplinary nature of this framework demonstrates and promotes the partnership between healthcare and engineering communities to building a better delivery system.

The Centers for Medicare & Medicaid Services currently incentivizes hospitals to reduce postdischarge adverse events such as unplanned hospital readmissions for patients who underwent total joint arthroplasty (TJA). This study aimed to predict 90-day TJA readmissions from our comprehensive electronic health record data and routinely collected patient-reported outcome measures. We retrospectively queried all TJA-related readmissions in our tertiary care center between 2016 and 2019. A total of 104-episode care characteristics and preoperative patient-reported outcome measures were used to develop several machine learning models for prediction performance evaluation and comparison. For interpretability, a logistic regression model was built to investigate the statistical significance, magnitudes, and directions of associations between risk factors and readmission. Given the significant imbalanced outcome (5.8% of patients were readmitted), our models robustly predicted the outcome, yielding areas under the receiver operating characteristic curves over 0.8, recalls over 0.5, and precisions over 0.5. In addition, the logistic regression model identified risk factors predicting readmission: diabetes, preadmission medication prescriptions (ie, nonsteroidal anti-inflammatory drug, corticosteroid, and narcotic), discharge to a skilled nursing facility, and postdischarge care behaviors within 90 days. Notably, low self-reported confidence to carry out social activities accurately predicted readmission. A machine learning model can help identify patients who are at substantially increased risk of a readmission after TJA. This finding may allow for health-care providers to increase resources targeting these patients. In addition, a poor response to the “social activities” question may be a useful indicator that predicts a significant increased risk of readmission after TJA.

Background: Depression is a substantial global health problem, affecting >300 million people and resulting in 12.7% of all deaths. Depression causes various physical and cognitive problems, leading to a 5-year to 10-year decrease in life expectancy compared with the general population. Physical activity is known to be an effective, evidence-based treatment for depression. However, people generally have difficulties with participating in physical activity owing to limitations in time and accessibility.Objective: To address this issue, this study aimed to contribute to the development of alternative and innovative intervention methods for depression and stress management in adults. More specifically, we attempted to investigate the effectiveness of a mobile phone–based physical activity program on depression, perceived stress, psychological well-being, and quality of life among adults in South Korea.Methods: Participants were recruited and randomly assigned to the mobile phone intervention or waitlist group. Self-report questionnaires were used to assess variables before and after treatment. The treatment group used the program around 3 times per week at home for 4 weeks, with each session lasting about 30 minutes. To evaluate the program’s impact, a 2 (condition) × 2 (time) repeated-measures ANOVA was conducted, considering pretreatment and posttreatment measures along with group as independent variables. For a more detailed analysis, paired-samples 2-tailed t tests were used to compare pretreatment and posttreatment measurements within each group. Independent-samples 2-tailed t tests were conducted to assess intergroup differences in pretreatment measurements.Results: The study included a total of 68 adults aged between 18 and 65 years, who were recruited both through web-based and offline methods. Of these 68 individuals, 41 (60%) were randomly assigned to the treatment group and 27 (40%) to the waitlist group. The attrition rate was 10.2% after 4 weeks. The findings indicated that there is a significant main effect of time (F1,60=15.63; P=.003; ηp2=0.21) in participants’ depression scores, indicating that there were changes in depression level across time. No significant changes were observed in perceived stress (P=.25), psychological well-being (P=.35), or quality of life (P=.07). Furthermore, depression scores significantly decreased in the treatment group (from 7.08 to 4.64; P=.03; Cohen d=0.50) but not in the waitlist group (from 6.72 to 5.08; P=.20; Cohen d=0.36). Perceived stress score of the treatment group also significantly decreased (from 2.95 to 2.72; P=.04; Cohen d=0.46) but not in the waitlist group (from 2.82 to 2.74; P=.55; Cohen d=0.15).Conclusions: This study provided experimental evidence that mobile phone–based physical activity program affects depression significantly. By exploring the potential of mobile phone–based physical activity programs as a treatment option, this study sought to improve accessibility and encourage participation in physical activity, ultimately promoting better mental health outcomes for individuals with depression and stress.

Background: Patient portals have drawn much attention, as they are considered an important tool for health providers in facilitating patient engagement. However, little is known about whether the intensive use of patient portals contributes to improved management of patients’ health in terms of their confidence in acquiring health information and exercising self-care. There is a lack of randomized trials with these outcomes measured both pre- and postadoption of patient portals. Objective: The aim of this study was to examine the causal relationship between the usage of patient portals and patients’ self-efficacy toward obtaining health information and performing self-care. Methods: This study was a secondary data analysis that used data from a US national survey, the National Cancer Institute’s Health Information National Trends Survey 5 Cycle 1. Patient portal usage frequency was used to define the treatment. Survey items measuring self-efficacy on a Likert-type scale were selected as the main outcomes, including patients’ confidence in obtaining health information and performing self-care. To establish causality using survey data, we adopted the instrumental variables method. To determine the direction of the causal relationship in the presence of high-dimensional confounders, we further proposed a novel testing framework that employs conditional independence tests in a directed acyclic graph. The average causal effect was measured using the two-stage least squares regression method. Results: We showed that frequently using patient portals improves patients’ confidence in obtaining health information. The estimand of the weighted average causal effect was 0.14 (95% CI 0.06-0.23; P<.001). This means that when increasing the portal usage intensity, for instance, from 1-2 times to 3-5 times per year, the expected average increase in confidence level measured on a Likert-type scale would be 0.14. However, we could not conclusively determine the causal effect between patient portal usage and patients’ confidence in exercising self-care. Conclusions: The results support the use of patient portals and encourage better support and education to patients. The proposed statistical method can be used to exploit the potential of national survey data for causal inference studies.

The aim of this study was to improve understanding of hospital length of stay (LOS) in patients undergoing total joint arthroplasty (TJA) in a high-efficiency, hospital-based pathway. We retrospectively reviewed 1401 consecutive primary and revision TJA patients across 67 patient and preoperative care characteristics from 2016 to 2019 from the institutional electronic health records. A machine learning approach, testing multiple models, was used to assess predictors of LOS. The median LOS was 1 day; outpatients accounted for 16.5%, 1-day inpatient stays for 38.0%, 2-day stays for 26.4%, and 3-days or more for 19.1%. Patients characteristically fell into 1 of 3 broad categories that contained relatively similar characteristics: outpatient (0-day LOS), short stay (1- to 2-day LOS), and prolonged stay (3 days or greater). The random forest models suggested that a lower Risk Assessment and Prediction Tool score, unplanned admission or hospital transfer, and a medical history of cardiovascular disease were associated with an increased LOS. Documented narcotic use for surgery preparation prior to hospitalization and preoperative corticosteroid use were factors independently associated with a decreased LOS. After TJA, most patients have either an outpatient or short-stay hospital episode. Patients who stay 2 days do not differ substantially from patients who stay 1 day, while there is a distinct group that requires prolonged admission. Our machine learning models support a better understanding of the patient factors associated with different hospital LOS categories for TJA, demonstrating the potential for improved health policy decisions and risk stratification for centers caring for complex patients.