Thesis
Impact of a large-scale robotics adoption on the hospital pharmacy workforce
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2023
- Thesis identifier
- T16790
- Person Identifier (Local)
- 201268315
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- The National Health Service (NHS) regularly adopts new technologies which often result in the redesign of services, where large numbers of staff undergo organisational change. The NHS is made up of teams of people, all of whom continue to work interdependently providing safe and effective care throughout these times of change. Automation in pharmacy is becoming popular, with recent advancements involving the automation of the medicines supply chain. Previous ventures involving Automated Dispensing Systems (ADS) have been small-scale. Maximising efficiencies through automation relies on the effective introduction of technologies as well as the alignment of technical and social change, and there has been little exploration of how automation impacts on the staff experience and team effectiveness. In the literature there are numerous models available against which to compare and analyse the success of teams more generally. Underpinning many of these models is the Hackman model which proposes that team effectiveness is influenced by: the effort team members exhibit; the knowledge and skills team members possess; and the appropriateness of the performance strategies implemented. There is a gap in the literature on the impacts large-scale automation has on teams (and their success) in healthcare, specifically in pharmacy. Approved in August 2008, NHS Greater Glasgow & Clyde (GG&C) initiated a large-scale redesign (the PPSU Acute Pharmacy Redesign Programme). The Programme aimed to; provide a single procurement department for Glasgow pharmacy; have a centralised Pharmacy Distribution Centre (PDC); introduce ward-level ordering; and improve the current staff skill-mix while promoting the use of patients’ own medicines in hospital (Making the Most of Your Medicines or MMyM). Since opening in September 2010, the PDC (comprising 9 robots in total) is now the single facility responsible for the procurement and distribution of medicines to approximately 4000 destinations, and affected approximately 530 hospital pharmacy staff. This scale of pharmacy redesign has not been seen in any other automated schemes in the UK. The aim of the first study was to describe and evaluate NHS GG&C pharmacy staff experiences over the programme duration by different job roles/locations. Interviews were conducted with 36 pharmacy staff members from 4 hospital sites and the PDC, and 9 stakeholders, identified by members of the project Steering Group. Staff were interviewed about their experiences before, during and after the redesign. An inductive content analysis was performed, which produced two main themes: “The Work I Do” and “The Context of My Work”. The first theme allowed the exploration of the changes in staff job role, with a focus on tasks, work pace/control, morale, training/progression opportunities and voice/relationships. The second theme focused on social impacts of the redesign, including support, leadership, praise, reliability and trust of co-workers. Results showed that there was a lack of training available and morale was low in part due to this. There was no cohesive vision among participants as to why the redesign was happening. Hospital staff training was in theory available, yet completing training, and progressing into higher pay bands was not always feasible. Management were concerned with PDC technicians losing their clinical-skills as a result of a change in job location. PDC support workers experienced a gradual depletion of medicines knowledge due to this transition. The pharmacist role was seen as more social. Experiences between MMyM and non-MMyM staff were different in terms of how challenging, varied and social the work was. All roles within the PDC appeared to be less social compared with hospital roles. The aims of the second study were to apply Hackman’s model of team effectiveness in the context of the pharmacy team dynamics and performance and (based on this model) discuss the extent to which these teams were successful in the adoption of the automation. Hackman’s characteristics were applied to the pharmacy staff interviews (n=36). The results indicated that PDC and hospital teams exhibited 8 of the 23 characteristics: members have a variety of high-level skills; members contribute and are motivated equally; members are equally committed; members have personal and professional skills; relevant education and training is present; learning should be collective; members self-regulate; and there is clarity about task requirements, constraints, resources available and who the service user is. The “minimising of performance slippages” characteristic could be observed in one hospital team but not in the PDC. The teams did not exhibit 5 of the characteristics, indicating less success in these areas: autonomy is available; adequate feedback is available; excellent performance is rewarded; team size is appropriate; and relevant education and training is actually available. Nine of Hackman’s characteristics could not be commented on due to a lack of illustrative data. This thesis adds to the limited literature on the exploration of automation in healthcare, specifically pharmacy. Three main lessons can be concluded: staff consultation and engagement is critical to the successful redesign of services driven by technology; ensuring job role components are appropriate for job tasks is essential- technology adoption may require new skill sets and also cause other pre-existing skill sets to become lost; team effectiveness is an important focus within any organisational change programme, but less up-to-date models of team effectiveness may not be ideally applicable to teams utilising technology. These lessons align with current Scottish Government policy on pharmacy innovation and provide valuable key points for change implementers to support the continued adoption of automation locally, nationally and internationally.
- Advisor / supervisor
- Meer, R. van der (Robert)
- Bennie, Marion
- Resource Type
- DOI
- Date Created
- 2015
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PDF of thesis T16790 | 2023-12-19 | 公开 | 下载 |