Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review

Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.

Metodologia e produzione delle Linee Guida

This article has the aim of informing the reader about methodology and production of Clinical Practice Guidelines (CPG) and the instruments in order to be able to approach the whole process, from the production to the publication and distribution, but also to updated CPG. The importance of the issue is given by: 1) the need to avoid the CPG duplication, which is the reason of waste of resources and that raises confusion among users; 2) the need to ensure the independence of the experts involved and without conflict of interests; 3) the use of methodologies that respect the best quality standards; 4) the inhomogeneity of healthcare processes in the Italian territory and practices not based on scientific evidences; 5) the consultation and the commentary of stakeholders on the recommendations produced.

Healthcare and Economic Impact of Lung Perfusion Scintigraphy in Patients Affected by Acute Pulmonary Embolism

Acute pulmonary embolism (APE) is a cardiovascular emergency, representing the main cause of mortality, morbidity, and hospitalisation in Europe. We aim to evaluate the economic and healthcare impact of lung perfusion scintigraphy (LPS) used in patients with suspected APE, in the event of non-conclusive or contraindicated computed tomography pulmonary angiography (CTPA). We considered two alternative healthcare processes for APE diagnosis, with and without LPS. We performed a cost analysis with the aim of evaluating the average direct healthcare costs for diagnosis, risk assessment, and treatment of APE. We used data from a monocentric trial. Our economic model showed that the strategy with LPS was preferable in terms of costs. The average per-patient costs for the diagnosis and treatment of the acute phase of PE in low-risk patients with a non-conclusive or not-executable CTPA, with and without LPS, are EUR 2145.25 and EUR 4912.45, respectively. LPS is a simple, quick, and economic examination, useful in this setting of patients not only for an early diagnosis but also to exclude APE, demonstrating an advantage in terms of healthcare resources. To the best of our knowledge, this study is the first to analyse the economic and healthcare impact of the use of LPS in the diagnostic pathway of suspected APE.

Process Mining to Explore Variations in Endometrial Cancer Pathways from GP Referral to First Treatment

The main challenge in the pathway analysis of cancer treatments is the complexity of the process. Process mining is one of the approaches that can be used to visualize and analyze these complex pathways. In this study, our purpose was to use process mining to explore variations in the treatment pathways of endometrial cancer. We extracted patient data from a hospital information system, created the process model, and analyzed the variations of the 62-day pathway from a General Practitioner referral to the first treatment in the hospital. We also analyzed the variations based on three different criteria: the type of the first treatment, the age at diagnosis, and the year of diagnosis. This approach should be of interest to others dealing with complex medical and healthcare processes.

Executing Distributed Healthcare and Research Processes – The HiGHmed Data Sharing Framework

Several standards and frameworks have been described in existing literature and technical manuals that contribute to solving the interoperability problem. Their data models usually focus on clinical data and only support healthcare delivery processes. Research processes including cross organizational cohort size estimation, approvals and reviews of research proposals, consent checks, record linkage and pseudonymization need to be supported within the HiGHmed medical informatics consortium. The open source HiGHmed Data Sharing Framework implements a distributed business process engine for executing arbitrary biomedical research and healthcare processes modeled and executed using BPMN 2.0 while exchanging information using FHIR R4 resources. The proposed reference implementation is currently being rolled out to eight university hospitals in Germany as well as a trusted third party and available open source under the Apache 2.0 license.

BlackLivesMatter in Healthcare: Racism and Implications for Health Inequity among Aboriginal and Torres Strait Islander Peoples in Australia

Despite decades of evidence showing that institutional racism serve as significant barriers to accessible healthcare for Aboriginal and Torres Strait Islander Peoples, attempts to address this systemic problem still fall short. The social determinants of health are particularly poignant given the socio-political-economic history of invasion, colonisation, and subsequent entrenchment of racialised practices in the Australian healthcare landscape. Embedded within Euro-centric, bio-medical discourses, Western dominated healthcare processes can erase significant cultural and historical contexts and unwittingly reproduce unsafe practices. Put simply, if Black lives matter in healthcare, why do Aboriginal and Torres Strait Islander Peoples die younger and experience ‘epidemic’ levels of chronic diseases as compared to white Australians? To answer this, we utilise critical race perspectives to theorise this gap and to de-center whiteness as the normalised position of ‘doing’ healthcare. We draw on our diverse knowledges through a decolonised approach to promote a theoretical discussion that we contend can inform alternative ways of knowing, being, and doing in healthcare practice in Australia.

Approaching Inpatient Glycemic Control Using Six Sigma Methodology

To Err is Human: Building a Safer Health System [1] is a report issued by the U.S. Institute of Medicine (IOM) in November 1999 and has resulted in increasing awareness regarding high medical errors in healthcare industry. The report, which was based on an analysis of adverse health outcomes by a variety of organisations, concluded that between 44,000 and 98,000 people die each year as a result of preventable medical errors. Since its publication; there has been a significant enthusiasm to improve patient safety and to improve healthcare outcome. As a result of that, we have witnessed the introduction of many new innovations and reengineered processes. In spite of that medical error rate remains high, and possibly higher as shown by a more recent medical error analysis [2]. In other industries, any measurement falling outside of industry standards is regarded as a defect. This is how quality is defined, at least in companies that have adopted the Six Sigma approach which is a statistical approach for quality improvement. Processes that operate with "six sigma quality" over the short term are assumed to produce long-term defect levels below 3.4 defects per million opportunities (DPMO) [3]. Six Sigma's implicit goal is to improve all processes, but not to the 3.4 DPMO level necessarily. Its main philosophy it to reduce process variation to minimum level using a well-defined statistical approach. Many large companies use Six Sigma methodology to reduce the defect rate to its lowest possible value. The Six Sigma attempts to reduce the number of defects to below 3.4 per million opportunities; industries such as aviation target and achieve an even lower defect rate (less than 1 defect per 2 million opportunities). Simply, the Six Sigma concept is a statistical approach to improving the quality and performance of a specific process by focusing on the “Critical to Quality Step” as identified by the “Voice of the Customer”. It aims to maintain the mean result within a target range (i.e., between upper and lower specification limits) and focuses on reducing the variation in the outcome to the lowest possible level. The variation in outcome is usually measured as the standard deviation around the mean (i.e., Sigma). The Six Sigma method aims to fit six standard deviations around the mean without crossing the lower or upper specification targets. This process yields high performance and high potential [3]. Healthcare processes have usually high defect rate and wide variations (low sigma level). Six Sigma approach can be used in healthcare to improve specific processes using the same methodology used by other industries. The purpose of this analysis is to illustrate how to use Six Sigma approach in healthcare processes to decrease variation, using inpatient glycemic control as an example. Introducing this approach in medical education can help medical students, residents and other professionals to approach healthcare quality and patient safety in standardized way and can allow them to use recent advances in medical technology and artificial intelligence to achieve better healthcare outcomes. Another purpose of this article is to illustrate how to study the process variation using Six Sigma approach and how to use it in teaching and approaching healthcare quality. Education on process variation using six sigma methodology is a valuable way to teach the new generation of physicians the statistical approach to quality.

Evaluating the huddle as a method of improving staff communication and process efficiency on a psychiatric inpatient ward

Background/Aims Variation in healthcare processes is widespread in mental health care and can lead to inefficient processes and unnecessarily long inpatient stays. This study aimed to identify sources of variation and implement a huddle intervention to increase system efficiency on a psychiatric inpatient ward in London. Methods Using a Lean Six Sigma approach, areas for improvement on the authors' ward and processes working well on other wards were identified through process mapping and stakeholder interviews. The huddle intervention was implemented, then evaluated and improved through four Plan Do Study Act cycles based on feedback from staff and green-to-red methodology. Results Inconsistencies in multidisciplinary team communication was identified as the most prominent area of concern and inefficiency. The huddles were found to improve staff communication and increase ward activity. Concerns regarding the huddles' duration and focus on tasks were identified and addressed. Conclusions Huddles are a useful means of improving staff communication and increasing ward efficiency without taking up a significant amount of clinicians' time to care for patients. This could increase ward efficiency and reduce lengths of stay, although further research is required to establish this.

The impact of severe mental illness on healthcare use and health outcomes for people with type 2 diabetes

Background: People with severe mental illnesses (SMI) have reduced life expectancy compared with the general population. Diabetes is a major contributor to this disparity with higher prevalence and poorer outcomes in people with SMI. Aim: To determine the impact of SMI on healthcare processes and outcomes for diabetes. Design and setting: Retrospective observational matched nested case-control study using patient records from the Clinical Practice Research Datalink linked to Hospital Episode Statistics. Methods: We compared a range of healthcare processes (primary care consultations, physical health checks, metabolic measurements) and outcomes (prevalence and hospitalisation for cardiovascular disease (CVD), and mortality risk) for 2,192 people with SMI and type 2 diabetes (cases) with 7,773 people with diabetes alone (controls). Socio-demographics, comorbidity and medication prescription were covariates in regression models. Results: SMI was associated with increased risk of all-cause mortality (Hazard Ratio [HR]: 1.92; 95% CI: 1.60 to 2.30) and CVD-specific mortality (HR: 2.24; 1.55 to 3.25); higher physician consultation rates (Incidence Rate Ratio [IRR]: 1.15; 1.11 to 1.19); more frequent checks of blood pressure (IRR: 1.02; 1.00 to 1.05) and cholesterol (IRR: 1.04; 1.02 to 1.06); lower prevalence of angina (Odds Ratio [OR]: 0.67; 0.45 to 1.00); higher emergency admissions for angina (IRR: 1.53; 1.07 to 2.20) and lower elective admissions for ischaemic heart disease (IRR: 0.68; 0.51 to 0.92). Conclusion: Monitoring of metabolic measurements was comparable for people with diabetes with and without SMI. Increased mortality rates observed in SMI may be attributable to under-diagnosis of CVD and delays in treatment.