Quantifying Patient Portal Use: Systematic Review of Utilization Metrics.

BACKGROUND: Use of patient portals has been associated with positive outcomes in patient engagement and satisfaction. Portal studies have also connected portal use, as well as the nature of users' interactions with portals, and the contents of their generated data to meaningful cost and quality outcomes. Incentive programs in the United States have encouraged uptake of health information technology, including patient portals, by setting standards for meaningful use of such technology. However, despite widespread interest in patient portal use and adoption, studies on patient portals differ in actual metrics used to operationalize and track utilization, leading to unsystematic and incommensurable characterizations of use. No known review has systematically assessed the measurements used to investigate patient portal utilization. OBJECTIVE: The objective of this study was to apply systematic review criteria to identify and compare methods for quantifying and reporting patient portal use. METHODS: Original studies with quantifiable metrics of portal use published in English between 2014 and the search date of October 17, 2018, were obtained from PubMed using the Medical Subject Heading term "Patient Portals" and related keyword searches. The first search round included full text review of all results to confirm a priori data charting elements of interest and suggest additional categories inductively; this round was supplemented by the retrieval of works cited in systematic reviews (based on title screening of all citations). An additional search round included broader keywords identified during the full-text review of the first round. Second round results were screened at abstract level for inclusion and confirmed by at least two raters. Included studies were analyzed for metrics related to basic use/adoption, frequency of use, duration metrics, intensity of use, and stratification of users into "super user" or high utilizers. Additional categories related to provider (including care team/administrative) use of the portal were identified inductively. Additional analyses included metrics aligned with meaningful use stage 2 (MU-2) categories employed by the US Centers for Medicare and Medicaid Services and the association between the number of portal metrics examined and the number of citations and the journal impact factor. RESULTS: Of 315 distinct search results, 87 met the inclusion criteria. Of the a priori metrics, plus provider use, most studies included either three (26 studies, 30%) or four (23 studies, 26%) metrics. Nine studies (10%) only reported the patient use/adoption metric and only one study (1%) reported all six metrics. Of the US-based studies (n=76), 18 (24%) were explicitly motivated by MU-2 compliance; 40 studies (53%) at least mentioned these incentives, but only 6 studies (8%) presented metrics from which compliance rates could be inferred. Finally, the number of metrics examined was not associated with either the number of citations or the publishing journal's impact factor. CONCLUSIONS: Portal utilization measures in the research literature can fall below established standards for "meaningful" or they can substantively exceed those standards in the type and number of utilization properties measured. Understanding how patient portal use has been defined and operationalized may encourage more consistent, well-defined, and perhaps more meaningful standards for utilization, informing future portal development.

Assessing the Impact of Patient-Facing Mobile Health Technology on Patient Outcomes: Retrospective Observational Cohort Study.

BACKGROUND: Despite the growth of and media hype about mobile health (mHealth), there is a paucity of literature supporting the effectiveness of widespread implementation of mHealth technologies. OBJECTIVE: This study aimed to assess whether an innovative mHealth technology system with several overlapping purposes can impact (1) clinical outcomes (ie, readmission rates, revisit rates, and length of stay) and (2) patient-centered care outcomes (ie, patient engagement, patient experience, and patient satisfaction). METHODS: We compared all patients (2059 patients) of participating orthopedic surgeons using mHealth technology with all patients of nonparticipating orthopedic surgeons (2554 patients). The analyses included Wilcoxon rank-sum tests, Kruskal-Wallis tests for continuous variables, and chi-square tests for categorical variables. Logistic regression models were performed on categorical outcomes and a gamma-distributed model for continuous variables. All models were adjusted for patient demographics and comorbidities. RESULTS: The inpatient readmission rates for the nonparticipating group when compared with the participating group were higher and demonstrated higher odds ratios (ORs) for 30-day inpatient readmissions (nonparticipating group 106/2636, 4.02% and participating group 54/2048, 2.64%; OR 1.48, 95% CI 1.03 to 2.13; P=.04), 60-day inpatient readmissions (nonparticipating group 194/2636, 7.36% and participating group 85/2048, 4.15%; OR 1.79, 95% CI 1.32 to 2.39; P<.001), and 90-day inpatient readmissions (nonparticipating group 261/2636, 9.90% and participating group 115/2048, 5.62%; OR 1.81, 95% CI 1.40 to 2.34; P<.001). The length of stay for the nonparticipating cohort was longer at 1.90 days, whereas the length of stay for the participating cohort was 1.50 days (mean 1.87, SD 2 vs mean 1.50, SD 1.37; P<.001). Patients treated by participating surgeons received and read text messages using mHealth 83% of the time and read emails 84% of the time. Patients responded to 60% of the text messages and 53% of the email surveys. Patients were least responsive to digital monitoring questions when the hospital asked them to do something, and they were most engaged with emails that did not require action, including informational content. A total of 96% (558/580) of patients indicated high satisfaction with using mHealth technology to support their care. Only 0.40% (75/2059) patients opted-out of the mHealth technology program after enrollment. CONCLUSIONS: A novel, multicomponent, pathway-driven, patient-facing mHealth technology can positively impact patient outcomes and patient-reported experiences. These technologies can empower patients to play a more active and meaningful role in improving their outcomes. There is a deep need, however, for a better understanding of the interactions between patients, technology, and health care providers. Future research is needed to (1) help identify, address, and improve technology usability and effectiveness; (2) understand patient and provider attributes that support adoption, uptake, and sustainability; and (3) understand the factors that contribute to barriers of technology adoption and how best to overcome them.

Multiple Comorbid Conditions among Middle-Aged and Elderly Hemophilia Patients: Prevalence Estimates and Implications for Future Care.

Introduction. Advances in hemophilia care and treatment have led to increases in the life expectancy among hemophiliacs. As a result, persons with hemophilia are reaching an older age and experiencing various age-related health conditions never seen before in this population. Aim. To determine the prevalence of comorbidities among middle-aged and elderly hemophilia A and hemophilia B patients. Methods. Retrospective chart review among all hemophilia patients, who attended the Gulf States Hemophilia and Thrombophilia Center. Results. All patients had at least one comorbid condition other than hemophilia, and the majority had between 3 and 6 comorbidities. The most common conditions identified were chronic hepatitis C, hypertension, HIV, chronic arthropathy, and overweight/obesity. Conclusions. Since persons with comorbidities are more likely to have poorer health outcomes and require greater care in managing their health needs, caring for aging hemophiliacs is likely to pose various social and economic challenges for both patients and providers.