Evaluation of feasibility and potential impact of a clinical innovative software tool to support pharmacists to prescribe for minor ailments
Bibliographic record
Abstract
Background: \nThe role of community pharmacists has shifted from dispensing prescribed medications to providing patient-focused clinical services such as Pharmacists Prescribing for Minor Ailments (PPMA). These services are expected to improve patient care. Pharmacists across provinces have been given some level of prescriptive authority since 2005. The PPMA service was first introduced in Alberta. Currently, nine out ten Canadian provinces have the authority for PPMA service. Ontario recently adopted PPMA, in January 2023. However, there are several barriers such as workload, time constraints and integration to workflow that can hinder pharmacists’ ability to implement PPMA into existing practice. Technological solutions such as computerized decision support system (CDSS) can help pharmacists and facilitate performing PPMA service. PharmAssess Diagnostics developed a CDSS that provides a digital software platform for pharmacists to provide clinical services such as minor ailment prescribing. Although previous research examined the acceptability and impact of CDSS in other professions, there is paucity of research about the feasibility and impact of implementing supporting software tools into the workflow of community pharmacies on adapting PPMA. \nObjective: \nThe aim of this project is to evaluate the feasibility and potential impact of a clinical innovative software tool to support pharmacists for minor ailment prescribing. \nMethod: \nThis project followed a mixed method design. It included an anonymous online survey and semi-structured interview with community pharmacists/pharmacy student interns to explore their perspectives on the usability, acceptability, potential impact on integration in workflow and overall effect on workload while using the PharmAssess Diagnostics software for PPMA. In Ontario, pharmacists were recruited via the list of pharmacists in the Ontario College of Pharmacist (OCP) who agreed to be contacted for research purposes. Additionally, the co-op supervisors list from the School of Pharmacy University of Waterloo, and clients of the software company were contacted to be recruited. Demographic (age, gender) and professional characteristics (academic qualification, type of community pharmacy, current position in the pharmacy) were collected from participating pharmacists. Then, pharmacists’ perspectives were examined using a survey with Likert’s scale questions and were summarized. Descriptive statistics were used to describe the distribution of the responses. \nSemi-structured interviews were conducted with pharmacists/student interns who were further interested in providing their detailed perspectives with the use of this software tool in community pharmacy settings. The interviews were analyzed thematically. Then both forms of data were merged using the side-by-side approach to draw conclusion. \nResults: A total of 11 survey responses were collected. Pharmacists agreed that the software tool was usable (72%), acceptable (81%), had positive impact on workload (63%) and had positive impact on workflow (45%). Overall, 90% of participants stated that the average time per consultation using the software tool ranged between 5 to 15 minutes. Seven pharmacists were interviewed virtually. Three major themes emerged from the interviews, revealing usability of the software tool, facilitators, and barriers to the implementation of the software tool and impact of the software tool implementation into community pharmacy.
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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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".