Patient Portal Use and Experience Among Older Adults: Systematic Review
Bibliographic record
Abstract
BACKGROUND: The older adult population (65 years or older) in the United States is growing, and it is important for communities to consider ways to support the aging population. Patient portals and electronic personal health records (ePHRs) are technologies that could better serve populations with the highest health care needs, such as older adults. OBJECTIVE: The aim of this study was to assess the existing research landscape related to patient portal and ePHR use and experience among older adults and to understand the benefits and barriers to older adults' use and adoption of patient portals and ePHRs. METHODS: We searched six pertinent bibliographic databases for papers, published from 2006 to 2016 and written in English, that focused on adults 60 years or older and their use of or experience with patient portals or ePHRs. We adapted preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines to review papers based on exclusion and inclusion criteria. We then applied thematic analysis to identify key themes around use, experience, and adoption. RESULTS: We retrieved 199 papers after an initial screening and removal of duplicate papers. Then we applied an inclusion and exclusion criteria, resulting in a final set of 17 papers that focused on 15 separate projects. The majority of papers described studies involving qualitative research, including interviews and focus groups. They looked at the experience and use of ePHRs and patient portals. Overall, we found 2 main barriers to use: (1) privacy and security and (2) access to and ability to use technology and the Internet. We found 2 facilitators: (1) technical assistance and (2) family and provider advice. We also reported on older adults' experience, including satisfaction with the system and improvement of the quality of their health care. Several studies captured features that older adults wanted from these systems such as further assistance managing health-related tasks and contextual health advice and tips. CONCLUSIONS: More research is needed to better understand the patient portal experience among older adults from initial use to adoption. There are also opportunities to explore the role of design in addressing barriers and supporting facilitators to patient portal and ePHR use. Finally, the future use of these systems by older adults should be anticipated and considered in the design process.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".