Patient Accessible Electronic Health Records for the Chronically Ill: A Review of the Literature
Bibliographic record
Abstract
Background: Consumers with chronic conditions account for approximately 70% of all healthcare spending. The Chronic Care Model is a healthcare paradigm whose purpose is the achievement of improved patient outcomes by facilitating the delivery of patient-centered, evidence-based care. We conducted a review of the literature to examine the role patient accessible electronic health records (PAEHR) may play in implementing and supporting the Chronic Care Model. Methods: A review of the literature was conducted using multiple databases (1950-2012). Publications included in the review were restricted to those using experimental or quasi-experimental methodology, English language and peer review. Results: Published results indicated that PAEHR facilitated improvements in health literacy and patient-provider communication, and that personalization of content was viewed favourably. Research on the use of PAEHR by some disease groups suggest improvements in clinical outcomes. Conclusions: The literature reviewed indicated that the patient experience for individuals with chronic illnesses could be enhanced through access to PAEHR. Improved satisfaction was noted for individuals with access to PAEHR with personalized content (e.g lab results etc). Use of PAEHR also improved patient-provider communication and increased personal knowledge and comprehension concerning individual condition and state of health. PAEHR for individuals living with chronic illnesses are an effective management technique that can help patients better manage the challenges of living with a chronic illness. These results indicate PAEHR have the potential to be a key component for actualizing the theoretical constructs of the Chronic Care Model by providing a platform for increased patient-provider collaboration.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".