IMPROVING PATIENT SATISFACTION WITH ELECTIVE SURGERY WAITING: AN EMPOWERMENT PERSPECTIVE

Waiting for elective surgery has been causing severe patient dissatisfaction and is becoming a major concern in most countries with publicly funded healthcare systems. While waitlists, which are used to rationalize the balances between healthcare service demand and supply, are almost impossible to avoid currently, healthcare policy makers could try to remove the tension through providing more satisfactory elective surgery waitlist information to patients on the waitlist. This work-in-progress paper seeks to build a framework towards improving elective surgery patients’ information satisfaction. We propose that an effective waitlist information system (which can meet the information needs of waiting patients) empowers patients, creating a sense of autonomy and control for their own health situation, reducing their stress and uncertainty, improving their sense of perceived equity and power (relative to the healthcare authorities who manage the waitlist) and eventually improves patient satisfaction towards waiting

Open process innovation: A multi-method study on the involvement of customers and consultants in public sector BPM

Following calls to enhance risk-sensitivity of second generation Operational Risk-Aware Information Systems (ORISs), this paper aims to address the lack of ontological/epistemological grounding for the concept of Operational Risk (OR). Herein, OR is regarded both as a property of a real system and as a representational phenomenon forming part of the core of ORIS in line with Weber’s (2003) view of the core of IS. The paper explores how the ontological/epistemological position of the Critical Realist philosophy of science assists in the Requirements Definition of ORISs by providing an ontology-driven representation of the heterogeneous nature of OR. The retroductive mode of logical inference enabled by Critical Realism supports the discovery of OR causal mechanisms when the historical data about operational loss events is limited. The ontological/epistemological position suggested in the paper contributes to better understanding and representation of OR, informs OR assessment in conditions of a constantly changing socio-economical environment, and so assists in the Requirements Definition of ORISs

An investigation of emergency department overcrowding using data mining and simulation : a patient treatment type perspective

In ongoing efforts to limit the proportion of budgets allocated to healthcare, governments have closed hospitals, reduced bed numbers, and minimised staffing levels. These efficiency drives in Australia and elsewhere have led to the healthcare system being more sensitive to shocks and disruptions because there is little excess capacity to absorb extra demand. Among other impacts, this has resulted in hospital emergency departments (EDs) having to balance patient throughput with patient queues. This balance is often difficult to achieve and EDs become overwhelmed, resulting in long patient waits, overcrowded treatment areas and excessive stress for ED staff. Much research has been done in an effort to limit the frequency and severity of these “overcrowding” incidents. Projects have been driven by preconceived ideas about activities in EDs and often employ parametric methods to identify correlated factors. As such they are limited by the extent of analysts’ knowledge or observations. While these approaches have added to knowledge about ED operations, the problem of patient overcrowding persists. This research questioned whether patient treatment could be implicated in ED overcrowding. Process-based thinking was used in order to derive a simplified model of emergency department operations. This “process-focussed” model of ED operations directed thinking towards the identification of homogenous clusters of treatment with similar activities, so each treatment cluster could be considered to have matching inputs, outputs and resource consumption. Scientific Method was selected as an appropriate methodology for the research. Techniques from the dissociated methods of Data Mining and Management Science were combined within the hypothesis / experimentation framework of Scientific Method. Undirected clustering techniques from Data Mining were used to identify definitive treatment clusters. Discrete event simulation techniques from Management Science were used to drive the study towards the overcrowding problem. The treatment clusters were verified and validated through a number of studies. Process perspectives were employed together with the treatment clusters to simulate patient flows through the ED at an aggregated level. The clusters were combined with patient urgency and disposition to create “patient treatment types” that were tracked through the ED. Analysis of the simulated ED indicated that simultaneous occupation of the ED by certain patient types made the ED unable to accept any new patients for treatment. This thesis contributes to the understanding of ED overcrowding by confirming that exit block is the most likely direct cause of ED overcrowding, and by suggesting that the mix of patients types in, and arriving at, the ED are the most likely precursors of ED overcrowding. It concludes that there will always be a finite chance that a mix of patient types will occur who require admittance to hospital or have long ED treatment times, and consequently, are likely to block the ED. This suggests that it will never be possible to completely eliminate ED overcrowding. Rather, acceptable levels of risk of overcrowding need to be determined. The capacity of EDs and of hospitals to admit ED patients may then be determined based on how risk adverse the hospital is to ED overcrowding

An investigation of emergency department overcrowding using data mining and simulation : a patient treatment type perspective

In ongoing efforts to limit the proportion of budgets allocated to healthcare, governments have closed hospitals, reduced bed numbers, and minimised staffing levels. These efficiency drives in Australia and elsewhere have led to the healthcare system being more sensitive to shocks and disruptions because there is little excess capacity to absorb extra demand. Among other impacts, this has resulted in hospital emergency departments (EDs) having to balance patient throughput with patient queues. This balance is often difficult to achieve and EDs become overwhelmed, resulting in long patient waits, overcrowded treatment areas and excessive stress for ED staff. Much research has been done in an effort to limit the frequency and severity of these “overcrowding” incidents. Projects have been driven by preconceived ideas about activities in EDs and often employ parametric methods to identify correlated factors. As such they are limited by the extent of analysts’ knowledge or observations. While these approaches have added to knowledge about ED operations, the problem of patient overcrowding persists. This research questioned whether patient treatment could be implicated in ED overcrowding. Process-based thinking was used in order to derive a simplified model of emergency department operations. This “process-focussed” model of ED operations directed thinking towards the identification of homogenous clusters of treatment with similar activities, so each treatment cluster could be considered to have matching inputs, outputs and resource consumption. Scientific Method was selected as an appropriate methodology for the research. Techniques from the dissociated methods of Data Mining and Management Science were combined within the hypothesis / experimentation framework of Scientific Method. Undirected clustering techniques from Data Mining were used to identify definitive treatment clusters. Discrete event simulation techniques from Management Science were used to drive the study towards the overcrowding problem. The treatment clusters were verified and validated through a number of studies. Process perspectives were employed together with the treatment clusters to simulate patient flows through the ED at an aggregated level. The clusters were combined with patient urgency and disposition to create “patient treatment types” that were tracked through the ED. Analysis of the simulated ED indicated that simultaneous occupation of the ED by certain patient types made the ED unable to accept any new patients for treatment. This thesis contributes to the understanding of ED overcrowding by confirming that exit block is the most likely direct cause of ED overcrowding, and by suggesting that the mix of patients types in, and arriving at, the ED are the most likely precursors of ED overcrowding. It concludes that there will always be a finite chance that a mix of patient types will occur who require admittance to hospital or have long ED treatment times, and consequently, are likely to block the ED. This suggests that it will never be possible to completely eliminate ED overcrowding. Rather, acceptable levels of risk of overcrowding need to be determined. The capacity of EDs and of hospitals to admit ED patients may then be determined based on how risk adverse the hospital is to ED overcrowding

Abstract Decision Support for Strategy Control

This paper presents a decision support approach that combines multiple criteria concepts with optimisation methods to assist with exploration of alternatives in strategic control. The contributions of this paper are the novel way in which different strategies may be characterised and objectives quantified, and the generation of “good ” combinations of alternatives, rather than mere ranking of alternatives that is standard practice in most decision support methods. An illustrative example is provided. The paper describes how the approach can be used in the comparison of different strategies, identification of effective and efficient courses of action and recognition of emergent opportunities

Prototyping an online elective surgery waitlist management system

Excessive waiting time for elective surgery and a lack of information regarding patient's status in the waiting list have left the patients upset and unsatisfied while waiting for their turn to receive the treatment. The situation was made worse by inefficient waitlist management that possibly results in unexpected delay and negative impacts on the health condition of the patients. In this paper, we investigate current waitlist management systems implemented in Australia's five states and suggest a design prototype that could better address patient needs and empower them by providing personalised information about their waitlist status and decision support on implications of changing their preferences.10 page(s

Application of Domain Ontology for Decision Support in Medical Emergency Coordination

Due to the complex and constantly evolving nature of emergency management (EM), there is little consensus regarding many concepts used to describe informational structure of EM. This limits the efficiency and effectiveness of the decision making processes and can potentially lead to challenges in communication among disaster stakeholders and delays in the execution of emergency responses. This paper presented domain ontology for EM that can be useful to be shared across different emergency agencies and systems. The potential benefits of the proposed domain ontology include enabling better and faster decision making through explicit and shared structure of EM concepts and their relationships. We illustrate how domain ontology can facilitate more effective decision making processes in EM in the context of medical emergency coordination