Major UK Hospital Research Articles

Negotiating Order: Experts and Control in Bureaucratic Organizations

This two-year ethnographic study of a major UK hospital’s response to the Covid-19 pandemic examines how control is negotiated between managers and experts in bureaucratic organizations. Previous studies have shown that experts lose control in bureaucratic organizations as managers come to dominate. In this study, the locus of control shifted between managers and experts through a process that we label ‘negotiating order’. Specifically, we show that managerial control was temporarily challenged by the experts through a set of expert control practices – creating expert groups, framing emerging issues, and creating briefing notes for managers – which collectively enabled experts to gain control and re-establish operational order. Once the operational order was re-established, managers regained control by applying a distinct set of managerial control practices – formalizing structures, allocating roles, and creating policies – which enabled managers to take the response to scale. This process of negotiating order also led to a renegotiation of the organizational order because which individuals were in positions of control varied with every shift of the locus of control. However, such opportunities for positional shifts were not equally distributed across the organization. We discuss the implications for studies on the bureaucratization of expertise, upward influence, and intra-occupational inequality in organizations.

Contrasting patterns of longitudinal population dynamics and antimicrobial resistance mechanisms in two priority bacterial pathogens over 7\xa0years in a single center

BackgroundTwo of the most important pathogens contributing to the global rise in antimicrobial resistance (AMR) are Klebsiella pneumoniae and Enterobacter cloacae. Despite this, most of our knowledge about the changing patterns of disease caused by these two pathogens is based on studies with limited timeframes that provide few insights into their population dynamics or the dynamics in AMR elements that they can carry.ResultsWe investigate the population dynamics of two priority AMR pathogens over 7 years between 2007 and 2012 in a major UK hospital, spanning changes made to UK national antimicrobial prescribing policy in 2007. Between 2006 and 2012, K. pneumoniae showed epidemiological cycles of multi-drug-resistant (MDR) lineages being replaced approximately every 2 years. This contrasted E. cloacae where there was no temporally changing pattern, but a continuous presence of the mixed population.ConclusionsThe differing patterns of clonal replacement and acquisition of mobile elements shows that the flux in the K. pneumoniae population was linked to the introduction of globally recognized MDR clones carrying drug resistance markers on mobile elements. However, E. cloacae carries a chromosomally encoded ampC conferring resistance to front-line treatments and shows that MDR plasmid acquisition in E. cloacae was not indicative of success in the hospital. This led to markedly different dynamics in the AMR populations of these two pathogens and shows that the mechanism of the resistance and its location in the genome or mobile elements is crucial to predict population dynamics of opportunistic pathogens in clinical settings.

Improving the patient discharge process: implementing actions derived from a soft systems methodology study

ABSTRACTIn the period from January 2013 to July 2014, three process change initiatives were undertaken at a major UK hospital to improve the patient discharge process. These initiatives were inspired by the findings of a study of the discharge process using Soft Systems Methodology. The first initiative simplified time-consuming paperwork and the second introduced more regular reviews of patient progress through daily multi-disciplinary “Situation Reports”. These two initiatives were undertaken in parallel across the hospital, and for the average patient they jointly led to a 41% reduction between a patient being declared medically stable and their being discharged from the hospital. The third initiative implemented more proactive alerting of Social Care Practitioners to patients with probable social care needs at the front door, and simplified capture of important patient information (using a “SPRING” form). This initiative saw a 20% reduction in total length of stay for 88 patients on three wards where the SPRING form was used, whilst 248 patients on five control wards saw no significant change in total length of stay in the same period. Taken together, these initiatives have reduced total length of stay by 67% from 55.8 days to 18.6 days for the patients studied.

Modelling patient flow in an emergency department to better understand demand management strategies

This paper presents a simulation model of the emergency department (ED) at a major UK hospital, which like most other EDs, is struggling to meet the key ED performance target to admit or discharge 95% of patients within 4 h of arrival. Only marginal improvements have been evidenced from recent investigations piloted as attempts to improve cost, quality and throughput in the ED, leading to increased enthusiasm from management to explore the potential of simulation to expedite patient flow. The objective of this project was to create a discrete event simulation model of the ED to improve understanding of the current system, identify shortcomings of previously piloted interventions and assess the impact of alternative initiatives. The project has served as an excellent means to promote simulation as an effective tool to design ED services. Its success to generate outputs above those anticipated by management has accelerated the development of a multidisciplinary Flow Team, which meets regularly at the hospital to review improvements and explore further proposals to improve patient flow.

Nurse Rostering with Genetic Algorithms

In recent years genetic algorithms have emerged as a useful tool for the heuristic solution of complex discrete optimisation problems. In particular there has been considerable interest in their use in tackling problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle constraints and successful implementations usually require some sort of modification to enable the search to exploit problem specific knowledge in order to overcome this shortcoming. This paper is concerned with the development of a family of genetic algorithms for the solution of a nurse rostering problem at a major UK hospital.

A Component Based Heuristic Search Method with Evolutionary Eliminations

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and natural mutation process on these components respectively to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs an evaluation function which evaluates how well each component contributes towards the final objective. Two elimination steps are then applied: the first elimination eliminates a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.

Platinum-based anticancer drugs in waste waters of a major UK hospital and predicted concentrations in recipient surface waters

Concentrations of the cytotoxic platinum-based anticancer drugs, as total Pt, have been measured over a three week period in one of the main drains and in the effluent of the oncology ward of a major UK hospital (Derriford, Plymouth). Concentrations of Pt were highly variable in both discharges, and ranged from about 0.02 to 140μgL−1 in the oncology effluent and from about 0.03 to 100μgL−1 in the main drain. A comparison of drug administration figures over the study period with an estimate of the quantity of Pt discharged through the drains suggests that about 22% of total Pt is emitted to the environment from the hospital with the remainder being discharged by treated patients in the wider community. Administration figures for the three Pt-based drugs used in the hospital (cisplatin, carboplatin and oxaliplatin) coupled with published measurements on the removal of the drugs by conventional sewage treatment allowed the concentrations of Pt arising from each drug to be predicted in recipient surface waters as a function of water flow rate. For conditions representative of the region under study, concentrations of total Pt between a few tens and in excess of 100pgL−1 are predicted, with the principal form of the metal occurring as carboplatin and its metabolites. Although predicted concentrations are below EMEA guidelines warranting further risk assessment, the presence of substances in surface waters that are potentially carcinogenic, mutagenic and teratogenic and yet whose environmental effects are not understood is cause for concern.

How efficient can an emergency unit be? A perfect world model

Various approaches have been used to identify possible routes for improvement of patient flow within an emergency unit (EU). One such approach is to use simulation to create a ‘real...

A Component-Based Heuristic Search Method with Evolutionary Eliminations for Hospital Personnel Scheduling

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e., the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and the natural mutation process on these components, respectively, to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated for them to remain there. This demonstration employs an evaluation function that evaluates how well each component contributes toward the final objective. Two elimination steps are then applied: the first elimination removes a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.

Improving service delivery in FM: case study of a UK hospital facilities directorate

PurposeThe purpose of this paper is to describe a case study of SPICE FM implementation in the Facilities Directorate of a major UK hospital. SPICE FM is a process improvement framework for FM organisations, which was developed through university research in the UK. SPICE FM identified strengths and weaknesses of the facilities operation and provides specific guidelines for organisational improvements.Design/methodology/approachThe study was limited to four areas of operations, namely: catering; estates; domestics and portering. A brief organisational strategy is developed, through document review, semi‐structured interviews and a small workshop. A vertical section of staff in the Facilities Directorate participated in the case study to determine the operational capabilities of the organisation. The staff ranged from Director of Facilities to line employees. The managers partook in semi‐structured interviews, while the employees participated in workshops.FindingsThe case study highlights some of the operational deficiencies in the organisation, such as health and safety management, and risk management. It identifies specifically how these processes can be improved, and which improvements are effectively linked to strategy.Research limitations/implicationsThe paper only focuses on level 2 of the SPICE FM framework. Level 3 has not been researched yet. Also the SPICE FM approach must be linked to other major organisational development tools, such as EFQM, IIP and ISO.Practical implicationsA very useful approach in linking strategy with operational process improvements.Originality/valueThis case study puts the SPICE FM organisational learning framework in context. Previous papers have not reported on any major case studies, in order to demonstrates how the framework can be implemented.

A Component Based Heuristic Search Method with Adaptive Perturbations for Hospital Personnel Scheduling

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with adaptive perturbations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then mimic a natural evolutionary process on these components to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs a dynamic evaluation function which evaluates how well each component contributes towards the final objective. Two perturbation steps are then applied: the first perturbation eliminates a number of components that are deemed not worthy to stay in the current schedule; the second perturbation may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.

An indirect Genetic Algorithm for a nurse-scheduling problem

This paper describes a Genetic Algorithms (GAs) approach to a manpower-scheduling problem arising at a major UK hospital. Although GAs have been successfully used for similar problems in the past, they always had to overcome the limitations of the classical GAs paradigm in handling the conflict between objectives and constraints. The approach taken here is to use an indirect coding based on permutations of the nurses, and a heuristic decoder that builds schedules from these permutations. Computational experiments based on 52 weeks of live data are used to evaluate three different decoders with varying levels of intelligence, and four well-known crossover operators. Results are further enhanced by introducing a hybrid crossover operator and by making use of simple bounds to reduce the size of the solution space. The results reveal that the proposed algorithm is able to find high quality solutions and is both faster and more flexible than a recently published Tabu Search approach.

Solving a nurse scheduling problem with knapsacks, networks and tabu search

This paper illustrates how a modern heuristic and two classical integer programming models have been combined to provide a solution to a nurse rostering problem at a major UK hospital. Neither a heuristic nor an exact approach based on a standard IP package was able to meet all the practical requirements. This was overcome by using a variant of tabu search as the core method, but applying knapsack and network flow models in pre- and post-processing phases. The result is a successful software tool that frees senior nursing staff from a time consuming administrative task.

Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem

There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem