Focused Bedside Education May Improve Engagement of Hospitalized Patients with Their Patient Portals
Bibliographic record
Abstract
A Review of: Greysen, S.R., Harrison, J.D., Rareshide, C., Magan, Y., Seghal, N., Rosenthal, J., Jacolbia, R., & Auerbach, A.D. (2018). A randomized controlled trial to improve engagement of hospitalized patients with their patient portals. Journal of the American Medical Informatics Association, 25(12), 1626-1633. https://doi.org/10.1093/jamia/ocy125 Abstract Objectives – To study hospitalized patients who were provided with tablet computers and the extent to which having access to these computers increased their patient portal engagement during hospitalization and following their discharge. Design – Prospective, randomized controlled trial (RCT) within a larger, observational study of patient engagement in discharge planning. Setting – A large, academic medical centre in the Western United States of America. Subjects – Of a total of 250 potential subjects from a larger observational study, 137 declined to participate in this one; of the remaining 113 subjects, 16 were unable to access the patient portal, leaving 97 adult (18 years of age or older) patients in the final group. All subjects (50 intervention and 47 control) were randomized but not blinded, had been admitted to medical service, and spoke English. In addition, all participants were supplied with tablet computers for one day during their inpatient stay and were provided with limited assistance to the portal registration and login process as needed. They were also required to have access to a tablet or home computer when discharged. Methods – The intervention group participants received focused bedside structured education by trained research assistants (RAs) who demonstrated portal key functions and explained the importance of these functions for their upcoming transition to post-discharge care. Following enrolment and consent, RAs administered a brief pre-study survey to assess baseline technology use. Then, at the end of the observation day, the RAs performed a debrief interview in which participants were asked to demonstrate their ability to perform key portal tasks. The RAs recorded which tasks were accomplished or if the RAs had provided assistance. Patient demographics and clinical information were obtained from the Electronic Health Record (EHR). Main results – Of the 97 patients who were enrolled in the RCT, 57% logged into their portals at least once within seven days of their discharge. The mean number of logins and specific portal tasks performed was higher for the intervention group than for the control group. In addition, while in the hospital, the intervention group was better able to log in and navigate the portal. Only one specific portal task reached statistical significance—the use of the tab for viewing the messaging interaction with the provider. The time needed to deliver the intervention was brief—less than 15 minutes for 80% of participants. The intervention group’s overall satisfaction with the bedside tablet to access the portal was high. Conclusion – Data analysis revealed that the bedside tablet educational intervention succeeded in increasing patient engagement in the use of the patient portal, both during hospitalization and following discharge. As the interest and demand for patient access to EHRs increases among patients, caregivers, and healthcare providers, more rigorous studies will be needed to guide the implementation of patient portals during and after hospitalization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".