Participatory system dynamics modelling approach to safe and efficient staffing level management within hospital pharmacies

With increasingly complex safety-critical systems like healthcare being developed and managed, there is a need for a tool that allows us to understand their complexity, design better strategies and guide effective change. System dynamics (SD) has been widely used in modelling across a range of applications from socio-economic to engineering systems, but its potential has not yet been fully realised as a tool for understanding trade-off dynamics between safety and efficiency in healthcare. SD has the potential to provide balanced and trustworthy insights into strategic decision making. Participatory SD modelling and learning is particularly important in healthcare since problems in healthcare are difficult to comprehend due to complexity, involvement of multiple stakeholders in decision making and fragmented structure of delivery systems. Participatory SD modelling triangulates stakeholder expertise, data and simulation of implementation plans prior to attempting change. It provides decision-makers with an evaluation and learning tool to analyse impacts of changes and determine which input data is most likely to achieve desired outcomes. This thesis aims to examine the feasibility of applying participatory SD modelling approach to safe and efficient staffing level management within hospital pharmacies and to evaluate the utility and usability of participatory SD modelling approach as a learning method. A case study was conducted looking at trade-offs between dispensing backlog (efficiency) and dispensing errors (safety) in a hospital pharmacy dispensary in an English teaching hospital. A participatory modelling approach was employed where the stakeholders from the hospital pharmacy dispensary were engaged in developing an integrated qualitative conceptual model. The model was constructed using focus group sessions with 16 practitioners consisting of labelling and checking practitioners, the literature and hospital pharmacy databases. Based on the conceptual model, a formal quantitative simulation model was then developed using an SD simulation approach, allowing different scenarios and strategies to be identified and tested. Besides the baseline or business as usual scenario, two additional scenarios (hospital winter pressures and various staffing arrangements, interruptions and fatigue) identified by the pharmacist team were simulated and tested using a custom simulation platform (Forio: user-friendly GUI) to enable stakeholders to play out the likely consequences of the intervention scenarios. We carried out focus group-based survey of 21 participants working in the hospital pharmacy dispensaries to evaluate the applicability, utility and usability of how participatory SD enhanced group learning and building of shared vision for problems within the hospital dispensaries. Findings from the simulation illustrate the knock-on impact rework has on dispensing errors, which is often missing from the traditional linear model-based approaches. This potentially downward-spiral knock-on effect makes it more challenging to deal with demand variability, for example, due to hospital winter pressures. The results provide pharmacy management in-depth insights into potential downward-spiral knock-on effects of high workload and potential challenges in dealing with demand variability. Results and simulated scenarios reveal that it is better to have a fixed adequate staff number throughout the day to keep backlog and dispensing errors to a minimum than calling additional staff to combat growing backlog; and that whilst having a significant amount of trainees might be cost efficient, it has a detrimental effect on dispensing errors (safety) as number of rework done to correct the errors increases and contributes to the growing backlog. Finally, capacity depletion initiated by high workload (over 85% of total workload), even in short bursts, has a significant effect on the amount of rework. Evaluative feedback revealed that participatory SD modelling can help support consensus agreement, thus gaining a deeper understanding of the complex interactions in the systems they strive to manage. The model introduced an intervention to pharmacy management by changing their mental models on how hospital winter pressures, various staffing arrangements, interruptions and fatigue affect productivity and safety. Although the outcome of the process is the model as an artefact, we concluded that the main benefit is the significant mental model change on how hospital winter pressures, various staffing arrangements, interruptions and fatigue are interconnected, as derived from participants involvement and their interactions with the GUI scenarios. The research contributes to the advancement of participatory SD modelling approach within healthcare by evaluating its utility and usability as a learning method, which until recently, has been dominated by the linear reductionist approaches. Methodologically, this is one of the few studies to apply participatory SD approach as a modelling tool for understanding trade-offs dynamics between safety and efficiency in healthcare. Practically, this research provides stakeholders and managers, from pharmacists to managers the decision support tools in the form of a GUI-based platform showcasing the integrated conceptual and simulation model for staffing level management in hospital pharmacy

Solving Problems of Interruptions and Multitasking in the Pharmacy of a Large Hospital Centre

This paper presents an approach to solving problems of interruptions and multitasking in inpatient pharmacy processes of a large hospital centre, which is based on statistical modelling and simulations. The approach is applied to the process of receiving deliveries (from suppliers) to determine the feasibility of improvements in the organization of work. In the initial phase of research, data on the deliveries from suppliers were collected during the time study and a typical daily load on the pharmacy staff and infrastructure in the current state was simulated. Subsequently, a new organizational model, which included two defined blocks of time for delivery, was suggested and three simulation scenarios were created to examine the effects of new organization of work on daily activities. Finally, a comparison of system constraints and results obtained by the simulation models confirmed the feasibility of the proposed improvements. By implementing the new organization of work, it will be possible to avoid overlapping in pharmacy processes, which will reduce interruptions to work and the need for multitasking and will finally result in fewer errors in work

A modelling and simulation framework for health care systems.

International audienceIn this paper, we propose a new modeling methodology named MedPRO for addressing organization problems of health care systems. It is based on a metamodel with three different views: process view (care pathways of patients), resource view (activities of relevant resources), and organization view (dependence and organization of resources). The resulting metamodel can be instantiated for a specific health care system and be converted into an executable model for simulation by means of a special class of Petri nets (PNs), called Health Care Petri Nets (HCPNs). HCPN models also serve as a basis for short-term planning and scheduling of health care activities. As a result, the MedPRO methodology leads to a fast-prototyping tool for easy and rigorous modeling and simulation of health care systems. A case study is presented to show the benefits of the MedPRO methodology

PROJECTIONS OF DEMAND FOR HEALTHCARE IN IRELAND, 2015-2030: FIRST REPORT FROM THE HIPPOCRATES MODEL. ESRI RESEARCH SERIES NUMBER 67 OCTOBER 2017

This report provides baseline estimates and projections of public and private healthcare demand for Irish health and social care services for the years 2015–2030. This is the first report to be published applying the Hippocrates projection model of Irish healthcare demand and expenditure which has been developed at the ESRI in a programme of research funded by the Department of Health. Development of the model has required a very detailed analysis of the services used in Irish health and social care in 2015. This is the most comprehensive mapping of both public and private activity in the Irish healthcare system to have been published for Ireland

Optimising hospital designs and processes to improve efficiency and enhance the user experience

The health sector is facing increasing pressure to provide effective, efficient, and affordable care to the population it serves. The National Health Service (NHS) of the United Kingdom (UK) has regularly faced scrutiny with NHS England being issued a number of challenges in recent years to improve operational efficiency, reduce wasted space, and cut expenditure. The most recent challenge issued to NHS England has seen a requirement to save £5bn per annum by 2020, while reducing wasted space from 4.4% to 2.5% across the NHS estate. Similarly, satisfaction in the health service is also under scrutiny as staff retention and patient experiences are used in determining the performance of facilities. [Continues.

Ergonomics and human factors in healthcare system design – An introduction to this special issue

Ergonomics and human factors in healthcare system design – An introduction to this special issu

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Coping with demand volatility in retail pharmacies with the aid of big data exploration

Data management tools and analytics have provided managers with the opportunity to contemplate inventory performance as an ongoing activity by no longer examining only data agglomerated from ERP systems, but also, considering internet information derived from customers' online buying behaviour. The realisation of this complex relationship has increased interest in business intelligence through data and text mining of structured, semi-structured and unstructured data, commonly referred to as "big data" to uncover underlying patterns which might explain customer behaviour and improve the response to demand volatility. This paper explores how sales structured data can be used in conjunction with non-structured customer data to improve inventory management either in terms of forecasting or treating some inventory as "top-selling" based on specific customer tendency to acquire more information through the internet. A medical condition is considered - namely pain - by examining 129 weeks of sales data regarding analgesics and information seeking data by customers through Google, online newspapers and YouTube. In order to facilitate our study we consider a VARX model with non-structured data as exogenous to obtain the best estimation and we perform tests against several univariate models in terms of best fit performance and forecasting