Shared decision-making using personal health record technology: a scoping review at the crossroads
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: This scoping review aims to determine the size and scope of the published literature on shared decision-making (SDM) using personal health record (PHR) technology and to map the literature in terms of system design and outcomes. MATERIALS AND METHODS: Literature from Medline, Google Scholar, Cumulative Index to Nursing and Allied Health Literature, Engineering Village, and Web of Science (2005-2015) using the search terms "personal health records," "shared decision making," "patient-provider communication," "decision aid," and "decision support" was included. Articles ( n = 38) addressed the efficacy or effectiveness of PHRs for SDM in engaging patients in self-care and decision-making or ways patients can be supported in SDM via PHR. RESULTS: Analysis resulted in an integrated SDM-PHR conceptual framework. An increased interest in SDM via PHR is apparent, with 55% of articles published within last 3 years. Sixty percent of the literature originates from the United States. Twenty-six articles address a particular clinical condition, with 10 focused on diabetes, and one-third offer empirical evidence of patient outcomes. The tethered and standalone PHR architectural types were most studied, while the interconnected PHR type was the focus of more recently published methodological approaches and discussion articles. DISCUSSION: The study reveals a scarcity of rigorous research on SDM via PHR. Research has focused on one or a few of the SDM elements and not on the intended complete process. CONCLUSION: Just as PHR technology designed on an interconnected architecture has the potential to facilitate SDM, integrating the SDM process into PHR technology has the potential to drive PHR value.
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.
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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 it