Evidence map›Paper›PMID 39525040›Full record

ArticleExploratory research in clinical and social pharmacy2024

Understanding the social networks that contribute to diversion in hospital inpatient pharmacies: A social network analysis.

Troy Francis, Maaike de Vries, Mark Fan, Sonia Pinkney, Reza Yousefi-Nooraie, Mathieu Ouimet, Valeria E Rac, Patricia Trbovich

Abstract read
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Article in Exploratory research in clinical and social pharmacy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Troy FrancisHumanEra, Research and Innovation, North York General Hospital, Toronto, Ontario, Canada.
Maaike de VriesHumanEra, Research and Innovation, North York General Hospital, Toronto, Ontario, Canada.
Mark FanHumanEra, Research and Innovation, North York General Hospital, Toronto, Ontario, Canada.
Sonia PinkneyHumanEra, Research and Innovation, North York General Hospital, Toronto, Ontario, Canada.
Reza Yousefi-NooraieDepartment of Public Health Sciences, University of Rochester, New York, USA.
Mathieu OuimetDepartment of Political Science at Université Laval, Quebec, Canada.
Valeria E RacInstitute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Canada.
Patricia TrbovichHumanEra, Research and Innovation, North York General Hospital, Toronto, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Controlled substances (CS) are 'diverted' (stolen) from healthcare facilities via many integrated and diverse mechanisms due to a lack of safeguards. There remains a gap in understanding how healthcare workers (HCWs) leverage their social networks (e.g., their role/tasks and interactions with other roles/tasks) within the medication use process (MUP) that contribute to diversion. Social network analysis (SNA) is an analytic approach used to map and analyze social connections, which can help identify influential interdependence between HCWs and tasks susceptible to drug diversion. Objectives: To map the social network structures of MUP tasks vulnerable to CS diversion in two Inpatient pharmacies and compare diversion risks by identifying influential tasks and HCWs. Methods: This was an exploratory sequential mixed methods study conducted in the Inpatient pharmacies at two large hospitals in Toronto, Canada. Initial analysis used previously collected clinical observation data to identify key pharmacy roles and tasks vulnerable to CS diversion. Subsequently, a cross-sectional survey was conducted to collect demographic information on HCWs and assess their engagement in the identified vulnerable tasks. Clinical observations and survey data were used to perform two-mode SNA to identify connections between HCWs and tasks susceptible to drug diversion. Results: The analysis identified different network structures across both sites but highlighted the importance of strategic Pharmacist or Technician Supervisor oversight to moderate-high vulnerability tasks. Pharmacy technicians were found to be the network's most central actors, while Pharmacists had a more supportive role on the network's periphery, providing oversight. Across both sites, there was strong connectivity between HCWs and tasks, indicating a higher level of security against potential undetected diversion. Conclusion: By strategically involving Pharmacists or Technician Supervisors, diversion risk can be mitigated through cross-checking and quality control. Through identifying the network structure of each unit, hospitals can identify opportunities for future interventions to prevent diversion.

Indexed as

DiversionHealthcare safetyHuman factorsPharmacySocial network analysis

Identifiers

PMID39525040
PMCPMC11543554

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.