Australian and New Zealand Academy of Management Conference. Auckland, New Zealand. December 2008

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1 Australian and New Zealand Academy of Management Conference Auckland, New Zealand. December 2008 VISUAL WORKFLOW AND PROCESS OPTIMISATION: A METHOD OF ANALYSIS FOR PATIENT FLOW IN THE HOSPITAL EMERGENCY DEPARTMENT Anneke Fitzgerald* Terry Sloan* Simeon Simoff Premaratne Samaranayake Mark Johnston Centre for Industry and Innovation Studies, University of Western Sydney, NSW a.fitzgerald@uws.edu.au Postal Address: Building EHa, North Parramatta Campus Cnr Victoria Rd & Pemberton St Parramatta NSW 2150 Preferred Stream: Stream 16 Technology, Innovation and Supply Chain Management 1

2 VISUAL WORKFLOW AND PROCESS OPTIMISATION OF PATIENT FLOW IN THE HOSPITAL EMERGENCY DEPARTMENT ABSTRACT Recent policy announcements by State and Federal Governments have centred on the need to explore the use of process management principles, normally applied to manufacturing industry, to health services management. The work presented in this paper is part of a broader project that will provide methods to reduce patient waiting times in emergency departments through making these processes visual, and plan for work process improvements. This paper discusses the development of an innovative method to analyse patient flow in an emergency department and provide solutions for health services. The expected outcomes include improvements in the efficiency and effectiveness of emergency departments through the visual optimisation methodologies for identifying and improving process inefficiencies that can be readily adapted to other contexts. DEMAND AND SUPPLY OF HOSPITAL SERVICES The management of an emergency department is complex because of unpredictability of demands for service, the nature of the service provided, the resources required and the outcomes to be achieved. The Emergency Department capacity is one of the most pressing concerns of both government and health department managers. In NSW Emergency Department demand since 2004/05 grew 6.9% annually. More than 30% of the population visited NSW Emergency Departments in 2006/07, and 26% visited on more than one occasion (NSW Government Department of Health, 2007). In addition to increasing demands, hospital bed capacity is strained and these compounding factors have caused hospital occupancy to exceed 95% (ACEM, 2004). Further, NSW has one of the lowest ratios of emergency specialists to the number of patients treated in Emergency Departments in Australia (Joint Committee on the Royal North Shore Hospital, 2007). The increase in demand, reduction of bed capacity and the low specialist-to-patient ratio lead to access block, and has flow-on consequences for overall hospital management. Inpatient bed access block occurs when a patient who has completed Emergency Department care waits for an inpatient bed. The Emergency admission performance indicator, measuring the percentage of patients admitted from the Emergency Department in less than eight hours, measures access block as the remaining percentage of patients (Joint Committee on the Royal North Shore Hospital, 2007). Access block results from both increased demand for Emergency Department services, and inadequate systems or processes to ensure occupied beds are vacated on time. According to the Booz Allen Hamilton report (2007), the key drivers for an increased demand include changing patient 2

3 demographics, patient morbidity, patient expectations, referral patterns and a change in usage of ambulances. In addition, improved population health campaigns have resulted in better informed patients who seek help earlier. Further to the these key drivers identified by Booz Allen Hamilton (2007), the demands for Emergency Department care may have been caused by hospital initiatives to improve access to hospital care. These include the higher skill levels of emergency department staff and being seen by a specialist physician in the same visit. Furthermore, some patient flow improvement activities and different models of care such as fast track and the setting up of patient flow units etc have reduced waiting times for patients, and as such may have helped eliminate one of the last remaining disincentives for patients to visit public hospital Emergency Departments (NSW Government Department of Health, 2007, page 126). An additional driver is the decreasing General Practitioner-to-population ratio, leading to many patients not having a choice but to visit the Emergency Department. In combination, these factors have resulted in an increase in primary care patients visiting the Emergency Department up from 22% in 2004/05 to 28% in 2006/07. In addition to governmental concerns, the Australasian College of Emergency Medicine (ACEM, 2004) identified bed access block as the single biggest threat to the provision of emergency care. The problem is that in Emergency Departments across New South Wales, as much as 30-40% of patients occupy beds that should be available for new Emergency Department patients. The functionality and performance of Emergency Departments is known to degrade once access block exceeds 10% of patients awaiting admission. However, access block is not just a result of pressures external to the Emergency Department. Whilst, undeniably, demand pressures are difficult to control, an improved patient flow and more efficient work processes through waste reduction, simplification of steps/forms, integration of process steps with data and automated approvals/workflows may assist to reduce overcrowding, reduce ambulance diversion, better utilisation of resources and improve Emergency Department waiting times. INTERRUPTIONS TO PATIENT FLOW NSW Health has suggested that current systems do not focus on assessing patients and moving them through the Emergency Department efficiently into a hospital ward (NSW Health, 2003, 2005). 3

4 This is not only an issue for the Emergency Department. Waste in front line hospital work is common and largely unrecognised. Several observational studies of hospital workers report fragmented workflow and significant time spent on non-patient care activities (Tucker, 2004; Potter et al, 2004; Hollingsworth et al, 1998; Finkler et al, 1993). Wallace and Savitz (2008) observed that just 41% of time of care givers was spent with patients or family performing direct care. The remaining 59% of care givers time is spend on communication, including meetings, perusing medical records and locating information (20%); activities that do not fundamentally change service delivery such as paperwork and preparation time (19%); correcting mistakes or interruptions that required a corrective response, including defects that involves equipment (2%); and stocking supplies, travelling, waiting and locating missing items or people (17%). Hospital wards do not work in isolation and inefficiencies in one area has a flow-on effect in other areas. For example, some preliminary work done by Fitzgerald and Sloan (2008) indicates that bottlenecks in the Emergency Department are often caused by waiting for results from pathology, X-ray department etc. Hence, Emergency Department flow is directly related to other hospital departments (in)efficiencies and their level of concern with Emergency Department flow. This situation can be improved through appropriate process mapping and subsequent modelling of patient flow process, based on simple process principles: waste elimination, simplification, integration and automation. There is some pessimism about the ability of the Health Services to manage the demand issues. However, reducing access block should be the highest priority in allocating resources to reduce ED overcrowding. Improving hospital patient flow, and as such directly reducing access block, is most likely to achieve this. PROCESS MODELLING Generally, research and development in workflow systems has focused on supporting the execution of processes (Van der Aalst & Van Hee, 2002), with less attention to monitoring, analysing and influencing on-the-flight of this execution in organisational reality (Cichocki et al. 1998; Fisher, 2007). Our work focuses on filling this gap in process management and test the approach in hospital environment. In order to do that our methodology includes undertaking an in-depth analysis of the 4

5 Emergency Department processes through the means of interactive computing. Such analysis will enable the virtual evaluation of various resource allocation levels and matching planned to actual processes (extended process mining). The proposed approach for Emergency Department patient flow simulations and optimisation (optimum resource utilisation and reduced waiting times) is through the means of visual computing targeting macro- and micromanagement of an Emergency Department. At the micro level it targets the reduction of wait times for central diagnostics, the reduction of wait times in waiting rooms, better utilisation of recourses, and the reduction of patients Length of Stay (LOS) in the department. At the macro level the visual optimisation techniques developed in the project will assist strategic and operational planning purposes. From a workflow viewpoint, an Emergency Department can be characterised as having a semistructured emergent workflow a combination of deterministic and stochastic processes. Hence, part of it can be modelled based on analysing policies and regulations, but part of it requires actual workflow data in order to develop a model that operates under uncertainty. In this project we plan to incorporate both aspects of the process through appropriate process modelling methods. Analysis of documentation, interviews and close observation will be used to get an understanding of the real-world process in an emergency department, which will be complemented by respective data mining techniques (Han and Kamber, 2006) and process mining (Van der Aalst et al., 2004) of the data available from the information systems in the department. While data mining aims at characterisation and prediction at a macro level (e.g. it could provide predictive models at the strategic level about the days of the week and time range to expect specific type of emergency), process mining operates at micro level of the dynamics of the processes. Process mining addresses the gap between what is prescribed to happen and what actually happened. In our complementary approach, this activity will also assist to refine the models, based on interviews, as the information gained is based on individuals perceptions of reality rather than on unbiased quantitative recordings of that reality. Further, refined models incorporate both process and data elements and form the basis for visual workflows and process optimisation. Formalised extracted knowledge about the processes provides the background for the simulation models, which are essential part of the visual optimisation methodology. 5

6 SIMULATION MODELS Computer simulation of operational processes is a versatile, powerful tool to gain insight into the operation of systems and assist in optimising these processes. Generally, a model that represents certain key characteristics or behaviours of the system is analysed to show the eventual real effects of alternative conditions and courses of action. The strength of simulation is that it enables precisely this what if analysis, i.e., it allows managers to look into the future under certain assumptions. It also provides sensitivity analysis over several dependent variables. The simulation in our methodology is used for in-flight process optimisation. In order to develop an integrated picture of the dynamics of the emergency department, we explore the combination of formalisms for process modelling at a micro level, such as Petri nets (mathematical representations of the system) derived from the emergency services scenarios (Lorenz and Juhas, 2006), with computational models based on interactions between systems, such as agent-based models, that execute the scenarios, and emulate different decision-making strategies. Various work processes within the Emergency Department are complex in nature and involve a range of entities, including material and human resources. Further, the situation is exacerbated due to rapidly changing environment as a result of changes in policies, procedures and government regulations. Due to the complex nature of those work processes and ever changing conditions, planning and scheduling of various entities including hospital beds, doctors, nurses, various pieces of equipment, and operators demand a systematic approach for better utilisation of resources. Currently planning and scheduling of those entities in the Emergency Department is based on ad-hoc scheduling methods, where most up-to-date information about patients and status of resource availability is not always readily available. In addition, possible alternate responses to contingencies and emergencies can not be evaluated to choose the best plan and schedule due to various limitations within current operating environment. In order to improve current practices of planning and scheduling, work processes associated with the Emergency Department need to be modelled. Once those processes are modelled, their simulation will allow visualisation of the dynamic interactions between entities for the purpose of optimisation of processes, based on historical data and estimated times. Further, modelling of current 6

7 work processes will allow both numerical and system simulation by manipulating parameters, that can be used to quantitatively test various scenarios to improve current processes. Thus, a comprehensive study on existing healthcare processes in the selected NSW Emergency Department will be carried out with a view to identifying areas for improvements. The expected outcomes of the proposed study include a process model using the enhanced Event-driven Process Chain (EPC) methodology, through integration of various entities including functions, resources and events, relationships between components, communication links and information flow. The modelled processes will be tested in a simulation system environment and tested using past data for identifying bottleneck resources. Areas for further process improvements will be identified and recommendations for improvement will be made. Process optimisation methods are proposed for removing current bottleneck situations and for effective and efficient planning and scheduling of bottleneck resources, under dynamic changing and uncertain conditions. Our methodology is utilising (semi-) automatic discovery of simulation models using network mining and process mining techniques. The proposed process model(s) will be numerically tested using historical data drawn from the selected Emergency Department. The applicability of the approach presented here relies on the availability of suitable measurement points. Fortunately, it can be observed that more and more events are being logged in a wide variety of hospital systems. VISUAL WORKFLOW AND PROCESS OPTIMISATION Visual analytics can be described as the science of analytical reasoning facilitated by interactive visual interfaces (Thomas and Cook, 2005). It is an iterative process that combines the strengths of machines and humans. This is central to our work as human capabilities to perceive, relate and conclude make visual analytics an effective way of dealing with the complexity of real world analysis, in particular, the complexity of optimising the patient flow and functional dynamics of an emergency department unit. Visual workflow can be viewed as a new technique in visual analytics. It relies on making patient flow issues in the Emergency Department visible to clinicians in a way that will assist them with the identification of improvement opportunities. The technique relies on the visual 7

8 interaction with the simulation model, the visualised summaries of the workflow data and the visual representations of the current load of the Emergency Department. Through its ability to rapidly vary the parameters effecting patient flow and model the corresponding changes in Emergency Department operations, interactive computing, will allow the visualisation and trial of proposed improvements prior to their implementation. The proposed visual workflow is intuitive and requires no understanding of complex mathematical programming models and optimisation algorithms or parameters it is based on. On the other hand, it provides substantial information in order to understand the process and to explain the decisions made, which in a hospital environment is of crucial importance. As such it presents to management an easily employed and understood decision making tool. Therefore, the project develops an innovative way of optimising workflow in a hospital emergency department through the integration of business process modelling and simulation, process mining and visual analytics techniques. The evaluation mechanisms and the framework that will be developed in the project will have value on their own in enabling, on the one hand, process mining researchers evaluate and compare the performance of their algorithms in an application domain, and on the other hand, domain experts to evaluate the validity of the process mining and simulation results, and interactive visual process optimisation techniques. PROPOSED RESEARCH METHODS Following on from the experience gained in an earlier study (Fitzgerald and Sloan, 2008), the following steps are planned for the modelling and testing of business processes in the Emergency Department: Step 1. Process data collection: This step will develop the strategies and automated procedures for creating event logs of the different processes in the Emergency Department. It will involve the identification of different events and their integration as core business processes and corresponding functions, resources and associated entities including key procedures and people involved. The step will start with collection and examination of all documented emergency department operating procedures, followed by the verification of these through interviews with the personnel involved and direct observation. During this 8

9 step key measurement points will be identified. As we are looking for ways to construct a (semi)automatic model, this step will include the adaptation of existing and, where necessary, the development of new data structures for handling detailed logs of the events that constitute the work processes. Step 2. Segmentation and structure identification: From the mapping of Emergency Department procedures these operations will be partitioned into groups of procedures having similar resource demands. In this step process mining will be applied for control-discovery, which facilitates the automatic extraction of a structural representation of the underlying process based on a data set of event sequences, prepared in Step 1. Typically, such logs are composed of time-stamped events. For example, a typical input data set can include time-stamped events, the start and completion of the visit to an emergency unit, the sequence of events during the stay, triage rating. The structure, discovered from such data set, is represented as a directed network model. As a starting point for this project, we consider Petri net models and the algorithms for their automatic construction, implemented in the ProM process mining system. Step 3. Measurement, control analysis and performance analysis: Key measurement points will be monitored for a period of two to six months to establish their patterns of variability within 24 hours; within a week and over longer terms. Measurements will be necessary for the decision point analysis and performance analysis. As there is no explicit information in the different log files of supporting systems about which decision was made at a decision point for a process instance, decision point analysis will require measurements about the execution of an activity and relating these measurements to the respective branch in the structure model and its signature in the events sequence data. Performance analysis provides execution/waiting times of the processes, as well as estimates of probabilities for taking alternative paths and how many new cases arrive per time unit (on average) at the process. These distributions will form the basis of the inputs for the simulation modelling of each group of processes and underlying activities. As a starting point we will consider the 9

10 information pyramid for mining collaborative virtual environments (Simoff and Biuk-Aghai, 2007) and the approach of Hornix (2007). Step 4. Simulation model construction: This step will take the output of Steps 2 and 3 and will develop a virtual model of the Emergency Department. The model will be constructed following the integration of the process elements and data structures and procedures identified in Step 2 with the models of control and models based on performance analysis in Step 3. The integrated model is the basis of the simulation model of the actual processes, informed by enhanced Event-driven Process Chain (EPC) methodology. The simulation logs are utilised for producing statistical output of ED procedure efficiency. Step 5. Visual simulation and visual optimisation: The mathematical simulation model from step 4 will be transformed into an interactive graphical representation of core business processes suitable for real-time visual workflow and process optimisation of the patient flow. The operations that constitute the visual workflow and process optimisation techniques will be developed at two levels: (i) a formal level as a combination of primitive actions for manipulation of a process model, independent of the underlying interactive technology, and; (ii) at a technical level, as a mapping between the formal primitives and the interactive operations supported by Plant Simulation software. Step 6. Validation: Emergency Department personnel will view the model to determine its utility in predicting outcomes. In this step, we will also address the problem of assessing the quality of the models discovered by process mining techniques. Possible approaches to evaluate these portions of our models include: (i) using existing comparison metrics, and (ii) using model validation techniques that are used in data mining. 10

11 Step 7. Predictions: Once face validity has been achieved the simulation model will be used to investigate the effects of varying procedures and resourcing levels on patient bed access block. This step will generate simulated process logs, which then will be analysed with process mining techniques and the derived models from the simulated logs will be compared with the derived models from the data of the actual processes during the respective period of time. This step will develop measures of accuracy of predictions and procedures for model refinement, based on these measures. Step 8. Variation testing: In this step we assess the actual success of the visual workflow and process optimisation techniques and the effect that they have on the overall process. Procedure and scheduling improvements suggested by the simulation may be implemented and effects of these changes assessed at the discretion of hospital management. BENEFITS OF THE STUDY The proposed study has the potential to provide seamless integration of healthcare processes and information flow for better decision making in the Emergency Department. The study has the potential to improve patient flow; optimise utilisation of human resource, rooms, beds and equipment; improve communication between departments; enhance time management for human resources; assure accurate patient tracking throughout each test, procedure or event, whilst allowing for any unplanned emergency, cancellation or other related activity. Further, the process models and associated simulation will enable for real-time information on changes to schedules and capacity loads on emergency department resources, and provide hospital managers with required reports for making operational and strategic decisions. The research proposes the use of innovative technologies through the development of creative applications that will provide methods to improve performance in Emergency Departments of Australian Hospitals. This will both build Australia s strengths in research and innovation and improve data management for existing applications in the targeted area of Health Services. In addition to the 11

12 national benefits, this research is strongly aligned with work currently undertaken in The Netherlands and Canada. LIMITATIONS Methodological limitations can occur in multidisciplinary research as a result of misalignment of the ontologies with which researchers from different disciplines operate. Hence, the creation of a common understanding and interpretation of the research problem and the methodology addressing it is essential for the success of the project. The team in this project comprises from scholars in mathematics, computing, operations management, business systems and health management. Such choice has been carefully selected to combine expertise and minimise limitations of multidisciplinarity. In addition, this research is highly contextual and success is dependant on the cooperation of hospital managers and staff in every step of the process. In an already stretched Emergency Department, a presence of external researchers can be taxing. However, emphasizing how the burden of participation outweighs the benefits of this research, and obtaining championing support from emergency department clinicians will minimize these barriers. Further, we are not including patients perceptions of work processes in this study. Ethical considerations prevent us from doing so. However, whilst this may be limiting, the research is about work processes and hospital systems. In this research the patient is seen as the object of the process, the product. CONCLUSION We are proposing to introduce both a novel analysis approach and a new tool for identifying inefficient processes in Emergency Departments. This will allow visualisation of dynamic interactions between entities (such as human and material resources) for the purpose of optimisation of processes and improving their effectiveness and efficiency. By manipulating critical parameters, as determined from historical data, animated simulation can quantitatively test various scenarios to improve processes and display the results of this testing in a form readily interpreted by hospital managers. Taking into account the critical mission of emergency departments, it is essential to develop and provide means that optimise their processes, which is the overall goal of this project. 12

13 REFERENCES Australian College for Emergency Medicine, (2004), Access block and overcrowding in Emergency Departments. Cichocki, A., Rusinkiewicz, M, and Woelk, D (1998). Workflow and Process Automation Concepts and Technology, Kluwer Academic Publishers. Finkler, S.A., Knickman, J.R., Hendrickson, G., Lipkin, M. Jr. and Thomspon, W.G. (1993). A comparison of work-sampling and time-and-motion techniques for studies in health services research. Health Services Research, 28(5), Fisher, L. (ed.) (2007). BPM and Workflow Handbook, Future Strategies Inc. Fitzgerald, J.A. and Sloan, T. (2008). Workflow Issues in Imaging Department. (Industry report) Han, J. and Kamber, M. (2006). Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufmann. Hollingsworth, J.C., Chisholm, C.D., Giles, B.K., Cordell, W.H. and Nelson, D.R. (1998). How do physicians and nurses spend their time in the emergency departments? Annals of Emergency Medicine, 31 (1), Hornix, P.T.G (2007). Performance analysis of business processes through process mining. MSc Thesis, Eindhoven University of Technology, Department, of Computer Science, Eindhoven, The Netherlands, Joint Committee on the Royal North Shore Hospital (2007) The Royal North Shore Hospital. Sydney, Joint Select Committee on the Royal North Shore Hospital. Lorenz, R. and Juhas, G. (2006). Towards Synthesis of Petri nets from scenarios. In S. Donatelli and P. S. Thiagarajan (eds), Applications and Theory of Petri Nets 2006, LNCS 4024, Springer, pp NSW Government Department of Health (2007). Booz Allan Hamilton report: Key drivers of demand in the Emergency Department: a hypothesis driven approach to analyse demand and supply. accessed 14 April 2008 NSW Health (2003) Access Issues at NSW Public Hospitals - key strategies. Sydney, NSW Department of Health. NSW Health (2005) Access Block Improvement Program accessed 18th April 2008 Potter, P., Boxerman, S., Wolf, L., Marshall, J., Grayson, D., Sledge, J. and Evanoff, B. (2004). Mapping the nursing process: a new approach for understanding the work of nursing. Journal of Nursing Administration, 34 (2), Rozinat, A., Mans, R.S., Song, M. and Van der Aalst, W.(2007). Discoverying simulation models. Springer, 1-32 Simoff, S. J. and Biuk-Aghai, R. (2007). Supporting virtual workplace design through media mining and reverse engineering. In Petrushin, V. A. and Khan, L. (eds), Multimedia Data Mining and Knowledge Discovery, Springer, Heidelberg. Sydney South West Area Health Service. (2007). A new direction for Sydney South West. Health Service Strategic Plan. Towards Thomas, J. and Cook, K. (2005). Illuminating the Path: Research and Development Agenda for Visual Analytics. IEEE-Press. Tucker, A. (2004). The impact of operational failures on hospital nurses and their patients. Journal of operations management, 22, Van der Aalst, W. and Van Hee, K. (2002). Workflow Management: Models, Methods, and Systems. The MIT Press Van der Aalst, W., Weijters, A. and Maruster, L. (2004). Workflow mining: Discovering process models from event logs. IEEE Transactons on Knowledge and Data Engineering, 16 (9), pp

14 Wallace, J.C. and Savitz, L.S. (2008). Estimating waste in frontline heath care worker activities. Journal of Evaluation in Clinical Practice. 14: