Evidence for handheld electronic medical records in improving care: a systematic review
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.
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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 | 0.020 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".