Investigating digital determinants shaping pharmacists’ preparedness for interoperability and health informatics practice evolution: a systematic review
Bibliographic record
Abstract
BACKGROUND: Fragmented healthcare systems hinder pharmacists' access to comprehensive patient data, limiting their clinical role and posing health risks. Enhancing system interoperability and evaluating factors influencing pharmacists' readiness for technology-driven practice change is a crucial step. AIM: This systematic review aimed to investigate the digital determinants of pharmacists' readiness for technology-oriented practice change and interoperability. METHOD: A systematic search of PubMed, Scopus, and Cochrane Library was conducted on August 7, 2023, with registration number INPLASY202380071. Search method was developed, and quality was assessed using the Boynton and Greenhalgh Quality Checklist (BGQC) and Critical Appraisal Skills Programme (CASP). RESULTS: The review included 13 studies, of which 7 (53.8%) included the study's setting. Of the seven studies, most discussed the community pharmacy setting (n = 3, 23.1%), followed by hospital pharmacy (n = 1, 7.6%), and both settings (n = 3, 23.1%). The studies included several countries: the United Kingdom (UK), Canada, the United States of America (USA), Australia, India, Sweden, and Saudi Arabia. The studies discussed several medical health informatic technologies such as electronic health records and e-prescribing. The three most reported technology-related influencing factors were related to digital literacy and technology-oriented tailored training (n = 9, 69.2%), followed by technical system features (n = 6, 46.2%) and technology operations (n = 5, 38.5%). The overall readiness level for technology-related practice change was intermediate (n = 7, 53.8%), high (n = 3, 23.1%), and low (n = 3, 23.1%). CONCLUSION: Digital literacy, tailored training, and system features are crucial for enhancing pharmacists' readiness for technology adoption, highlighting the need for improved digital infrastructure and interoperability in clinical practice.
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How this classification was reachedexpand
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 | 0.029 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".