Who’s Using PDAs? Estimates of PDA Use by Health Care Providers: A Systematic Review of Surveys
Bibliographic record
Abstract
BACKGROUND: Personal digital assistants (PDAs) find many uses in health care. Knowing rates of collective PDA use among health care providers is an important guiding step to further understanding those health care contexts that are most suited to PDA use and whether PDAs provide improved health outcomes. OBJECTIVES: The objectives of this study were to estimate current and future PDA use among health care providers and to discuss possible implications of that use on choice of technology in clinical practice and research. METHODS: This study was a systematic review of PDA usage surveys. Surveys were identified as part of an ongoing systematic review on the use of handheld devices. Reports from eight databases covering both biomedical sciences and engineering (1993-2006) were screened against distinct eligibility criteria. Data from included surveys were extracted and verified in a standardized way and were assessed descriptively. RESULTS: We identified 23 relevant surveys, 15 of which were derived from peer-reviewed journals. This cohort of surveys was published between 2000 and 2005. Overall, since 1999, there is clear evidence of an increasing trend in PDA use. The current overall adoption rate for individual professional use ranges between 45% and 85%, indicating high but somewhat variable adoption, primarily among physicians. CONCLUSIONS: Younger physicians and residents and those working in large and hospital-based practices are more likely to use a PDA. The adoption rate is now at its highest rate of increase according to a commonly accepted diffusion of innovations model. A common problem with the evaluation of information technology is that use frequently precedes research. This is the case here, in which PDA adoption rates are already high and projections are for rapid growth in the short term. In general, it appears that professional PDA use in health care settings involves more administrative and organizational tasks than those related to patient care, perhaps signaling where the growth in adoption is most likely to occur. We conclude that physicians are likely accustomed to using a PDA, and, therefore, technology expertise will probably not be a barrier to implementing PDA applications. However, there is an urgent need to evaluate the effectiveness and efficiency of specific tasks using handheld technology to inform those developing and those using PDA applications.
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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 | 0.061 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".