Assessing information requirements for complex decision making in healthcare

User assistance systems can help practitioners making decision for logistical problems, for example those arising in healthcare. Optimisation approaches included in such a system to determine an (optimal) solution often need to address more than one and often conflicting objectives leading to a number of alternative solutions of similar quality. The study proposed in this paper investigates how many alternative solutions should be proposed to a decision maker, which characteristics they should have, and which level of detail the presentation of solutions should have in order to enable the user in making the best decisions for the individual problem

Understanding waste management behaviour in care settings in South West England: a mixed methods study

Introduction - Health and social care sector activities in the United Kingdom have a considerable carbon footprint which impacts on the natural environment. Waste management is one area of focus for the reduction of this environmental impact. Previous research has studied the quantities and compositions of healthcare waste highlighting the potential for recycling. Limited research to date has investigated both health and social care waste management in a holistic study incorporating the behaviour, composition and systems. The current study aimed to investigate waste management behaviour, systems and compositions at four health and social care sites in the South West of England, then derive a framework of health and social care waste management behaviour incorporating points of intervention for the improvement of waste management practices. Methods - A mixed methods multi-strategy concurrent triangulation design was used to investigate the waste management at four health and social care sites in the South West of England. This consisted of a management interview sub-study investigating waste management policy, guidance and training. An observational sub-study was used to investigate health and social care employee waste management behaviour. An audit of the clinical and domestic waste streams provided an overview of the waste composition at each site. Finally a self-report questionnaire sub-study of decision making was conducted to investigate the conscious and habitual aspects of waste management decision making. The findings from these four sub-studies were then synthesised through a data triangulation process. Findings - The domestic waste bins were most commonly used to dispose of waste during the observational sub-study. The waste audit sub-study found the domestic waste stream contained the largest percentage of potentially recyclable waste. The observational sub-study also uncovered twenty unique primary themes influencing the employee waste management behaviour. These themes included aspects internal to the health and social employee such as confusion, and external themes such as equipment. The management interviews highlighted a lack of waste management training and a reliance on the local site waste management policies to guide and monitor employee waste management behaviour. Discussion - The health and social care waste management behaviour improvement framework (HWMBIF) is presented. The HWMBIF is a novel framework, derived from the triangulated data of the current study, for understanding and improving waste management behaviour at the participating sites. Several interventions based in the HWMBIF and on the study findings are presented. Future research will focus on testing and refining the HWMBIF, the suggested interventions and developing further interventions

Riociguat treatment in patients with chronic thromboembolic pulmonary hypertension: Final safety data from the EXPERT registry

Objective: The soluble guanylate cyclase stimulator riociguat is approved for the treatment of adult patients with pulmonary arterial hypertension (PAH) and inoperable or persistent/recurrent chronic thromboembolic pulmonary hypertension (CTEPH) following Phase

Understanding health system operation using networks: A literature review

Network analysis has been identified as an important approach for understanding and improving large scale health system operations. This review sought to understand the existing ways in which network analysis has been applied to health system operations by assessing; the topic areas, entity interaction types, network metrics used to describe system operations, the real world correlates of those network metrics and appraise the current and future trends in this area. The review included 52 articles identified using a snowball sampling method. The analysis showed that ’Care coordination’ was the most common topic topic area. The networks were most often constructed using patient level data describing care collaboration, patient transfers, physician collaboration and patient referrals. Measures of connectedness were most often applied such as density, centrality measures and degree. These measures were used to describe collaboration between clinicians and organisations, assess resource requirements and assess how patients were moving through the health system. The majority of the studies reported in the literature present descriptive studies of health system operations. Future work in this area should focus on using network analysis for predictive purposes e.g. forecasting and simulation parameterisation. There is also great potential in the use of dynamic network analysis to assess trends in system operations over time

#BeeWell survey delivered by Kailo

Delivery of a school-based wellbeing survey (#BeeWell) as part of Kailo, a UK-PRP funded initiative committed to addressing the root causes of young people’s mental health, in the places they live

Balancing improvements to avoid out-of-area placements in acute psychiatric inpatient care

Background Increasing pressures on acute adult NHS mental health services have led to the practice of sending patients out of their home county when there is insufficient local capacity to provide them with an acute psychiatric inpatient bed. Aims This study sought to determine how rates of referrals, bed capacity and length of stay could be improved to avoid sending people out of their local area for care. Method A discrete event simulation approach was applied to a mental health trust in the South West of England. Results The model demonstrated that to achieve zero out-of-area placements the rate of referrals would have to be halved, bed capacity would have to be increased from 78 to 123 beds or length of stay would have to halve. If changes were made to all three elements of the system between 85 and 93 beds would be required in combination with between a 22% and 44% reduction in the rate of referrals and between a 11% and 21% reduction in length of stay. Conclusions Due to non-linear effects in the system, avoiding the need to send patients out-of-area for care can best be achieved by improving multiple elements of the system in combination

An analysis of how people with a personality disorder in Devon United Kingdom use mental health services

Mental health service clients with a personality disorder have been identified as high service users and there is disagreement about the effectiveness of current treatments. This study provides a description of personality disorder client service use in Devon, United Kingdom. Descriptive analysis, probabilities of escalation from community to inpatient services and a novel application of network analysis were conducted. In Devon, people with a personality disorder use a median value of four community services and a median value of one inpatient service within the three years of data (January 2015 to February 2018) used in this study. Personality disorder clients use a wide range of community and inpatient services persistently over time. Network analysis showed that they most commonly rely on psychiatric liaison services and Crisis Resolution and Home Treatment teams. Suggested changes include the provision of alternative services to avoid clients repeatedly seeking high intensity emergency mental health care

Deploying Healthcare Simulation Models Using Containerization and Continuous Integration

Methods or approaches from disciplines outside of OR Modeling and Simulation (M&S) can potentially increase the functionality of simulation models. In healthcare research, where simulation models are commonly used, we see few applications of models that can easily be deployed by other researchers or by healthcare stakeholders. Models are treated as disposable artifacts, developed to deliver a set of results for stakeholders or for publication. By utilising approaches from software engineering, M&S researchers can develop models that are intended to be deployed for re-use. We propose one potential solution to deploying free and open source simulations using containerisation with continuous integration. A container provides a self-contained environment that encapsulates the model and all its required dependencies including the operating system, software, and packages. This overcomes a significant barrier to sharing models developed in open source software, which is dependency management. Isolating the environment in a container ensures that the simulation model behaves the same way across different computing environments. It also means that other users can interact with the model without installing software and packages, supporting both use and re-use, and reproducibility of results. We illustrate the approach using a model developed for orthopaedic elective recovery planning, developed with a user-friendly interface in Python, including a clear set of steps to support M&S researchers to deploy their own models using our hybrid framework