Pharmacy decision support: where is it? A systematic literature review
Bibliographic record
Abstract
Abstract Objective The objectives of this study were: to identify electronic decision supported systems that directly support pharmacists or pharmacy practice, in either the hospital or community settings; to ascertain the type of research in this area; and to identify any observable gaps for electronic decision support. Method A systematic literature review of PubMed, journals with known publication of decision support, databases including the Cochrane Reviews Database, selected websites and conference proceedings was conducted. Criteria for inclusion were electronic decision support systems that: were in routine use; had been used or published since 1998; provided clinical support; were not embedded into medical instruments; and were used by pharmacists in the community or hospital setting. Key findings Four publications met the criteria from 386 references. Of these, three described alerting systems for pharmacists, and one described the effect on pharmacist workflow of computerised prescribing. Investigation of selected websites revealed a further 20 pharmacy-related projects. It was difficult to ascertain to what extent to these (unpublished) activities were occurring in countries outside Australia (the main practice setting considered), such as the UK and Canada. Conclusion There is scant literature describing electronic decision support system activities specifically for pharmacy or pharmacists, in comparison to the substantial quantity of similar literature for healthcare in general. Electronic decision support activities are evident in the commercial environment for systems supporting traditional pharmacy roles. Selected conference proceedings and web-based information indicate that activity is occurring in the area of pharmacy decision support, although for relatively simple systems. The emerging patient-focused roles for pharmacists suggest that they may require similar knowledge and information to those of medical professionals. There is now a need to fully understand, define and support these pharmacist requirements, to enable appropriate decision support tools to be developed, and provide the opportunity for improved healthcare.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".