Background: Patient portals tethered to electronic health records (EHR) have become vital to patient engagement and better disease management, specifically among adults with multimorbidity. We determined individual and neighborhood factors associated with patient portal use (MyChart) among adult patients with multimorbidity seen in an Emergency Department (ED). Methods: This study adopted a cross-sectional study design and used a linked database of EHR from a single ED site to patients’ neighborhood characteristics (i.e., zip code level) from the American Community Survey. The study population included all adults (age > 18 years), with at least one visit to an ED and multimorbidity between 1 January 2019 to 31 December 2020 (N = 40,544). Patient and neighborhood characteristics were compared among patients with and without MyChart use. Random-intercept multi-level logistic regressions were used to analyze the associations of patient and neighborhood factors with MyChart use. Results: Only 19% (N = 7757) of adults with multimorbidity used the patient portal. In the fully adjusted multi-level model, at the patient level, having a primary care physician (AOR = 5.55, 95% CI 5.07–6.07, p < 0.001) and health insurance coverage (AOR = 2.41, 95% CI 2.23–2.61, p < 0.001) were associated with MyChart use. At the neighborhood level, 4.73% of the variation in MyChart use was due to differences in neighborhood factors. However, significant heterogeneity existed in patient portal use when neighborhood characteristics were included in the model. Conclusions: Among ED patients with multimorbidity, one in five adults used patient portals. Patient-level factors, such as having primary care physicians and insurance, may promote patient portal use.
Background Different machine learning (ML) technologies have been applied in healthcare systems with diverse applications. We aimed to determine the model feasibility and accuracy of predicting patient portal use among diabetic patients by using six different ML algorithms. In addition, we also compared model performance accuracy with the use of only essential variables. Methods This was a single-center retrospective observational study. From March 1, 2019 to February 28, 2020, we included all diabetic patients from the study emergency department (ED). The primary outcome was the status of patient portal use. A total of 18 variables consisting of patient sociodemographic characteristics, ED and clinic information, and patient medical conditions were included to predict patient portal use. Six ML algorithms (logistic regression, random forest (RF), deep forest, decision tree, multilayer perception, and support vector machine) were used for such predictions. During the initial step, ML predictions were performed with all variables. Then, the essential variables were chosen via feature selection. Patient portal use predictions were repeated with only essential variables. The performance accuracies (overall accuracy, sensitivity, specificity, and area under receiver operating characteristic curve (AUC)) of patient portal predictions were compared. Results A total of 77,977 unique patients were placed in our final analysis. Among them, 23.4% (18,223) patients were diabetic mellitus (DM). Patient portal use was found in 26.9% of DM patients. Overall, the accuracy of predicting patient portal use was above 80% among five out of six ML algorithms. The RF outperformed the others when all variables were used for patient portal predictions (accuracy 0.9876, sensitivity 0.9454, specificity 0.9969, and AUC 0.9712). When only eight essential variables were chosen, RF still outperformed the others (accuracy 0.9876, sensitivity 0.9374, specificity 0.9932, and AUC 0.9769). Conclusion It is possible to predict patient portal use outcomes when different ML algorithms are used with fair performance accuracy. However, with similar prediction accuracies, the use of feature selection techniques can improve the interpretability of the model by addressing the most relevant features.
Background: Patient portal (PP) use varies among different patient populations, specifically among those with diabetes mellitus (DM). In addition, it is still uncertain whether PP use could be linked to improved clinical outcomes. Therefore, the aim of this paper was to determine PP use status for patients, recognize factors promoting PP use, and further identify the association between PP use and clinical outcome among diabetic patients of different races and ethnicities. Methods:This was a single-center cross-section study. Patients were divided into non-Hispanic white (NHW), non-Hispanic black (NHB), and Hispanic/Latino groups. PP use was compared among these three groups. Multivariate logistic regressions were used to determine factors associated with PP use, serum glycemic control, and emergency department (ED) hospitalizations.Results: A total of 77,977 patients were analyzed. The rate of PP use among patients of NHW (24%) was higher than those of NHB (19%) and Hispanic/Latinos (18%, P < 0.0001). The adjusted odds ratio (AOR) of insurance coverage associated with PP use was 2.12 (2.02 -2.23, P < 0.0001), and having a primary care physician (PCP) associated with PP use was 3.89 (3.71 -4.07, P < 0.0001). In terms of clinical outcomes, the AOR of PP use associated with serum glycemic control was 0.98 (0.90 -1.05, P = 0.547) and ED hospitalization was 0.79 (0.73 -0.86, P < 0.0001). Conclusion:PP use disparity occurred among NHB and Hispanic/ Latino patients in the ED. Having insurance coverage and PCPs seem to correlate with PP use. PP use did not seem to associate with serum glycemic control among DM patients present in the ED but could possibly reduce patient hospitalizations.
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