Inhibitors to personal digital assistant adoption in healthcare: a secondary analysis of the research literature, 1995--2005
Bibliographic record
Abstract
Personal digital assistants (PDAs) have become a valuable communication tool for physicians by allowing them to have instant access to clinical references; to calculate important parameters; and to retrieve, record, and store patient data at the point-of-care. Despite the apparent efficacy of PDA-based point-of-care systems, research has indicated that the PDA adoption rate in healthcare sector slowed during 2004. This study employed secondary analysis of published research studies involving PDA-based systems implementation in a clinical setting from 1995 to June 2005 to examine the barriers that discourage PDA adoption by healthcare practitioners. The sample included 121 studies---83 conducted in the United States, 18 in Canada, 11 in Europe, and 9 in Asia. As a secondary analysis study, this research used a combination of quantitative and qualitative perspectives with cross-tabulation, chi-square analysis, Fisher's exact test, and content analysis techniques. Following Ajzen's theory of planned behavior, Davis' technology acceptance model, and DeLone and McLean's updated information systems success model, the barriers identification process was focused on the following major factors influencing potential and existing users' decisions toward use of particular technology: (a) perceived usefulness, (b) ease of use, (c) behavioral beliefs and outcome evaluations, (d) normative beliefs and motivation to comply, (e) control beliefs and perceived facilitation, and (f) service quality. The study found that the tested barriers to PDA adoption had been changing during the last decade. The changing trends were qualitatively different in clinician surveys compared to experiments in the clinical setting and in experiments with Pocket PCs compared to experiments with Palm OS devices. Based on the findings of this investigation, recommendations for healthcare organizations and PDA technology vendors were provided to facilitate increased PDA usage by healthcare practitioners.
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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 | 0.014 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".