Patient portals and quality of care: A literature review using the BC Health Quality Matrix (Preprint)
Bibliographic record
Abstract
BACKGROUND Electronic patient portals in healthcare are quickly becoming the preference of clinicians, patients, and caregivers alike. In times where the demand for all things to be online appears limitless, it is no surprise that portals are requested when updating health information systems worldwide. However, there are barriers to implementing online portals and many countries are lagging behind in updating their systems. With the interest in increasing investment in online portals by the Canadian public healthcare system, decision makers should be considering whether patient access to medical records through portals and mobile devices provides any changes to quality of care. OBJECTIVE This literature review examines available research in Canada and globally on health-related online portals and their impact on quality of care and patient access. Also examined are examples of different health portals, including issues or barriers to full implementation and utilization in the health sphere. METHODS A preliminary search was completed in May 2019 to examine the impact of patient portals on quality of care. The resources utilized in the first stage of the review included the University of British Columbia and the Western University library databases, Google, and Google Scholar. Parameters for the search included search terms such as patient portals, personal health records, effectiveness, quality, and access. Recent articles were prioritized, and included articles were generally published in the last five years. The authors reviewed 52 articles or article abstracts and 29 were included in the current review. Of the mixture of Canadian and international references, five are systematic reviews, and 18 are original research studies. RESULTS This study reviewed the available literature and found that there are some positive trends on patient portals’ impact on quality of care, with overall inconclusive or neutral results. Emerging evidence has showcased some benefits of implementing electronic health and patient portals to improve the quality of patient care and access to pertinent health records. However, general consensus is that there is limited available literature that is not considered to be outdated and as such, further investigation is required to support any previous findings. CONCLUSIONS As health care related technology develops further, better quality, quantity, and larger-scale studies will be available in evidence-based research databases. As such, a future follow-up literature review would be relevant to re-examine this topic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.050 | 0.081 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".