Veterinarian-Pharmacist Collaboration for Medication Safety

Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. The PLoS One scoping review by Kambayashi, Suzuki, and Hirohara (2026) maps how pharmacists currently contribute to veterinary teams and where evidence is missing. For qualitative researchers and veterinary practice analysts, the primary keyword veterinarian-pharmacist collaboration defines the topic and the payoff: use structured qualitative methods plus AI tools to convert fragmented, perception-based evidence into actionable models for practice integration.

Key Takeaways

According to the PLoS One scoping review (Kambayashi et al., PLoS One, 2026) the global evidence base on veterinarian-pharmacist collaboration is small but descriptive, with 239 records screened and 16 studies included. PLoS One

The PLoS One review (published 31 July 2026) screened 239 records and included 16 studies published between 2007 and 2024.
The PLoS One review (search finalized 30 August 2025) found compounding/dispensing reported in 62.5% of included studies (10 of 16) and drug information/consultation in 37.5% (6 of 16).
Kambayashi et al., PLoS One (2026) identified veterinarians' concern about pharmacists' species-specific training as the principal barrier to deeper clinical collaboration.
Kambayashi et al., PLoS One (2026) concluded, "Pharmacists are expanding their role in veterinary medicine, " and recommended that "addressing this gap through specialized veterinary education for pharmacists is a critical step."

What Happened: scope, methods, and core findings

Answer: The PLoS One scoping review mapped published and gray literature on veterinarian-pharmacist collaboration to identify roles, practices, and perceptions.

According to Kambayashi et al., PLoS One (2026), two reviewers searched PubMed, Web of Science, the Cochrane Library, Ichushi-Web, and Google Scholar from database inception through 30 August 2025, identifying 239 records and including 16 studies after screening and full-text review.

According to Kambayashi et al., PLoS One (2026), most included studies were cross-sectional surveys, with study locations concentrated in the United States (7 studies), New Zealand (3), and Japan (3), and publication years spanning 2007 to 2024.

According to Kambayashi et al., PLoS One (2026), pharmacists most frequently contributed via compounding and dispensing (62.5%, 10/16), followed by drug information and consultation (37.5%, 6/16), inventory/supply management (25.0%, 4/16), client education (18.8%, 3/16), and safety/exposure control (12.5%, 2/16).

Findings Snapshot

Date / Source	Metric	Value	Implication
30 Aug 2025, search (Kambayashi et al., PLoS One 2026)	Records retrieved	239	Limited but searchable literature base for qualitative synthesis
After deduplication and screening (Kambayashi et al., PLoS One 2026)	Studies included	16	Small corpus, dominated by perception surveys
Included studies (published 2007–2024) (Kambayashi et al., PLoS One 2026)	Compounding/dispensing reported in	62.5% (10/16)	Technical compounding is the dominant pharmacist contribution
Included studies (Kambayashi et al., PLoS One 2026)	Drug information/consultation reported in	37.5% (6/16)	Opportunity to expand clinical advisory roles with training
Geographic distribution (Kambayashi et al., PLoS One 2026)	United States contributions	7 studies	North American practice models heavily represented

Implications for qualitative researchers studying veterinary teams

Answer: Qualitative researchers should treat the existing literature as a fragmented, perception-heavy evidence base that needs structured thematic and comparative analysis.

According to Kambayashi et al., PLoS One (2026), most evidence consists of cross-sectional surveys and small qualitative reports, so researchers should prioritize collecting rich interview and ethnographic data to evaluate trust, role perception, and educational needs.

Researchers should explicitly document context, for example regulatory environment and usual-dispensing model, because Kambayashi et al., PLoS One (2026) found strong regional variation: compounding-focused models in the United States and Oceania, and management/information models in parts of Asia.

Researchers should also plan for multilingual sources and translation: Kambayashi et al., PLoS One (2026) included Japanese-language studies and recommended active translation to surface non-English innovations.

How Evidano Helps: AI tools for thematic, cross-segment, and mixed-method synthesis

Problem: Small, scattered qualitative evidence → Solution: Rapid corpus ingestion and coding

Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.

Evidano feature mapping: ingest the 16 included studies, gray literature extracts, and interview transcripts, then run automated thematic extraction to identify recurring barriers such as the species-specific training gap documented by Kambayashi et al., PLoS One (2026).

Evidano links: Use the features page to learn about automated coding, and the speech-to-text tools to transcribe interviews for analysis.

Problem: Regional variation and language barriers → Solution: Translation plus cross-segment analysis

Evidano supports translation workflows so teams can include non-English sources that Kambayashi et al., PLoS One (2026) identified as important, such as Japanese reports on Kampo medicine.

Evidano feature mapping: translate Japanese and English documents, harmonize codes across languages, and run cross-segment frequency analysis to compare themes by country, setting, or stakeholder group.

Problem: Perception-based data with few clinical endpoints → Solution: Mixed-methods synthesis

Evidano supports thematic, content, frequency, and cross-segment analyses so qualitative findings can be summarized and presented alongside any available quantitative indicators (for example, counts of compounding incidents from institutional audits).

Evidano feature mapping: create visualizations such as co-occurrence networks to show how concerns about species-specific pharmacology co-occur with mentions of compounding errors in the corpus identified by Kambayashi et al., PLoS One (2026).

FAQ: veterinarian-pharmacist collaboration

What roles do pharmacists currently play in veterinary medicine?

Answer: Pharmacists most commonly perform compounding and dispensing, followed by drug information, inventory management, client education, and safety roles.

According to Kambayashi et al., PLoS One (2026), compounding and dispensing were reported in 62.5% (10/16) of included studies, while drug information/consultation appeared in 37.5% (6/16).

What is the principal barrier to deeper clinical collaboration?

Answer: Veterinarians’ concern about pharmacists’ species-specific pharmacology training is the principal barrier.

According to Kambayashi et al., PLoS One (2026), multiple studies cited a perceived knowledge gap about non-human physiology as a reason veterinarians hesitate to delegate clinical tasks to pharmacists.

How can AI-enabled qualitative methods improve research on this topic?

Answer: AI-enabled qualitative methods can accelerate coding, reveal cross-segment patterns, and translate non-English studies to build a more representative evidence base.

For example, Kambayashi et al., PLoS One (2026) highlighted language barriers and small sample sizes; AI tools can harmonize multilingual documents and extract themes from a broader corpus for higher-confidence inferences.

Where can researchers find guidance on professional practice and policy?

Answer: Professional guidance and regulatory context are available from organizations such as the American Veterinary Medical Association for practice-level considerations.

For regulatory and professional resources see the American Veterinary Medical Association guidance on pharmacy and prescription issues, cited in Kambayashi et al., PLoS One (2026) and available at AVMA pharmacy guidance.

Conclusion & Next Steps

The PLoS One scoping review (Kambayashi et al., 2026) shows a small but diverse literature on veterinarian-pharmacist collaboration, dominated by compounding roles and perception-based studies.

Qualitative researchers can use AI-enabled workflows to translate non-English work, synthesize themes across regions, and produce the structured evidence Kambayashi et al., PLoS One (2026) call for.

If you are planning a mixed-methods or qualitative study of interprofessional collaboration in veterinary settings, use Evidano to ingest transcripts, run thematic and cross-segment analyses, and create presentation-ready visualizations; for more details see the features page.

Next step: Try Evidano for free to upload your interviews, abstracts, and survey open-ends and generate reproducible thematic analyses.

Topics

veterinarian-pharmacist collaboration
veterinary pharmacy collaboration
veterinary pharmacist roles
qualitative analysis veterinary collaboration

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Veterinarian-Pharmacist Collaboration for Medication Safety

Key Takeaways

What Happened: scope, methods, and core findings

Findings Snapshot

Implications for qualitative researchers studying veterinary teams

How Evidano Helps: AI tools for thematic, cross-segment, and mixed-method synthesis

Problem: Small, scattered qualitative evidence → Solution: Rapid corpus ingestion and coding

Problem: Regional variation and language barriers → Solution: Translation plus cross-segment analysis

Problem: Perception-based data with few clinical endpoints → Solution: Mixed-methods synthesis

FAQ: veterinarian-pharmacist collaboration

What roles do pharmacists currently play in veterinary medicine?

What is the principal barrier to deeper clinical collaboration?

How can AI-enabled qualitative methods improve research on this topic?

Where can researchers find guidance on professional practice and policy?

Conclusion & Next Steps

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