Evidence for handheld electronic medical records in improving care: a systematic review
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
BACKGROUND: Handheld electronic medical records are expected to improve physician performance and patient care. To confirm this, we performed a systematic review of the evidence assessing the effects of handheld electronic medical records on clinical care. METHODS: To conduct the systematic review, we searched MEDLINE, EMBASE, CINAHL, and the Cochrane library from 1966 through September 2005. We included randomized controlled trials that evaluated effects on practitioner performance or patient outcomes of handheld electronic medical records compared to either paper medical records or desktop electronic medical records. Two reviewers independently reviewed citations, assessed full text articles and abstracted data from the studies. RESULTS: Two studies met our inclusion criteria. No other randomized controlled studies or non-randomized controlled trials were found that met our inclusion criteria. Both studies were methodologically strong. The studies examined changes in documentation in orthopedic patients with handheld electronic medical records compared to paper charts, and both found an increase in documentation. Other effects noted with handheld electronic medical records were an increase in time to document and an increase in wrong or redundant diagnoses. CONCLUSION: Handheld electronic medical records may improve documentation, but as yet, the number of studies is small and the data is restricted to one group of patients and a small group of practitioners. Further study is required to determine the benefits with handheld electronic medical records especially in assessing clinical outcomes.
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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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