Intensive case management for high-risk patients with first-episode psychosis: service model and outcomes

Background : The first episode of psychosis is a crucial period when early intervention can alter the trajectory of the young person's ongoing mental health and general functioning. After an investigation into completed suicides in the Early Psychosis Prevention and Intervention Centre (EPPIC) programme, the intensive case management subprogramme was developed in 2003 to provide assertive outreach to young people having a first episode of psychosis who are at high risk owing to risk to self or others, disengagement, or suboptimal recovery. We report intensive case management model development, characterise the target cohort, and report on outcomes compared with EPPIC treatment as usual.

Methods : Inclusion criteria, staff support, referral pathways, clinical review processes, models of engagement and care, and risk management protocols are described. We compared 120 consecutive referrals with 50 EPPIC treatment as usual patients (age 15–24 years) in a naturalistic stratified quasi-experimental real-world design. Key performance indicators of service use plus engagement and suicide attempts were compared between EPPIC treatment as usual and intensive case management, and psychosocial and clinical measures were compared between intensive case management referral and discharge.

Findings : Referrals were predominately unemployed males with low levels of functioning and educational attainment. They were characterised by a family history of mental illness, migration and early separation, with substantial trauma, history of violence, and forensic attention. Intensive case management improved psychopathology and psychosocial outcomes in high-risk patients and reduced risk ratings, admissions, bed days, and crisis contacts.

Interpretation : Characterisation of intensive case management patients validated the clinical research focus and identified a first episode of psychosis high-risk subgroup. In a real-world study, implementation of an intensive case management stream within a well-established first episode of psychosis service showed significant improvement in key service outcomes. Further analysis is needed to determine cost savings and effects on psychosocial outcomes. Targeting intensive case management services to high-risk patients with unmet needs should reduce the distress associated with pathways to care for patients, their families, and the community.

Modelling the results of health promotion activities in Switzerland: development of the Swiss Model for Outcome Classification in Health Promotion and Prevention

This paper describes the Model for Outcome Classification in Health Promotion and Prevention adopted by Health Promotion Switzerland (SMOC, Swiss Model for Outcome Classification) and the process of its development. The context and method of model development, and the aim and objectives of the model are outlined. Preliminary experience with application of the model in evaluation planning and situation analysis is reported. On the basis of an extensive literature search, the model is situated within the wider international context of similar efforts to meet the challenge of developing tools to assess systematically the activities of health promotion and prevention.

Predição da criatividade e saúde mental

A criatividade tem sido mencionada desde há muito tempo pela literatura. A forma de a conceptualizar tem dependido em muito dos investigadores e da sua visão sobre o que é criatividade, porém as suas potencialidades têm tomado cada vez mais interesse pela comunidade científica. Os seus benefícios estendem-se a diversas áreas da vida humana, como sentimentos de satisfação, bem-estar e saúde mental. A investigação aqui apresentada procura encontrar modelos preditivos de comportamentos criativos, pressupondo que estes poderão ser de grande auxílio para o desenvolvimento de um melhor bem-estar psicológico. Assim a investigação comportou o uso de quatro variáveis: a pessoa, o processo, o produto e o ambiente criativo. A amostra contou com 215 estudantes universitários e os resultados revelaram o encontro de quatro modelos preditivos de comportamentos criativos, com uma maior ênfase nas artes visuais e manuais. Conclui-se com a ideia de que a predição de produtos criativos é possível e que tal poderá ser preponderante para um bom desenvolvimento da saúde mental.

Predição da criatividade e saúde mental

A criatividade tem sido mencionada desde há muito tempo pela literatura. A forma de a conceptualizar tem dependido em muito dos investigadores e da sua visão sobre o que é criatividade, porém as suas potencialidades têm tomado cada vez mais interesse pela comunidade científica. Os seus benefícios estendem-se a diversas áreas da vida humana, como sentimentos de satisfação, bem-estar e saúde mental. A investigação aqui apresentada procura encontrar modelos preditivos de comportamentos criativos, pressupondo que estes poderão ser de grande auxílio para o desenvolvimento de um melhor bem-estar psicológico. Assim a investigação comportou o uso de quatro variáveis: a pessoa, o processo, o produto e o ambiente criativo. A amostra contou com 215 estudantes universitários e os resultados revelaram o encontro de quatro modelos preditivos de comportamentos criativos, com uma maior ênfase nas artes visuais e manuais. Conclui-se com a ideia de que a predição de produtos criativos é possível e que tal poderá ser preponderante para um bom desenvolvimento da saúde mental.

Understanding the Main Phases of Developing a Maturity Assessment Model

Practitioners and academics have developed numerous maturity models for many domains in order to measure competency. These initiatives have often been influenced by the Capability Maturity Model. However, an accumulative effort has not been made to generalize the phases of developing a maturity model in any domain. This paper proposes such a methodology and outlines the main phases of generic model development. The proposed methodology is illustrated with the help of examples from two advanced maturity models in the domains of Business Process Management and Knowledge Management.

Towards a Business Process Management Maturity Model

Business Process Management (BPM) has been identified as the number one business priority by a recent Gartner study (Gartner, 2005). However, BPM has a plethora of facets as its origins are in Business Process Reengineering, Process Innovation, Process Modelling, and Workflow Management to name a few. Organisations increasingly recognize the requirement for an increased process orientation and require appropriate comprehensive frameworks, which help to scope and evaluate their BPM initiative. This research project aims toward the development of a holistic and widely accepted BPM maturity model, which facilitates the assessment of BPM capabilities. This paper provides an overview about the current model with a focus on the actual model development utilizing a series of Delphi studies. The development process includes separate studies that focus on further defining and expanding the six core factors within the model, i.e. strategic alignment, governance, method, Information Technology, people and culture.

Seeing is understanding : the effect of visualisation in understanding programming concepts

How and why visualisations support learning was the subject of this qualitative instrumental collective case study. Five computer programming languages (PHP, Visual Basic, Alice, GameMaker, and RoboLab) supporting differing degrees of visualisation were used as cases to explore the effectiveness of software visualisation to develop fundamental computer programming concepts (sequence, iteration, selection, and modularity). Cognitive theories of visual and auditory processing, cognitive load, and mental models provided a framework in which student cognitive development was tracked and measured by thirty-one 15-17 year old students drawn from a Queensland metropolitan secondary private girls’ school, as active participants in the research. Seventeen findings in three sections increase our understanding of the effects of visualisation on the learning process. The study extended the use of mental model theory to track the learning process, and demonstrated application of student research based metacognitive analysis on individual and peer cognitive development as a means to support research and as an approach to teaching. The findings also forward an explanation for failures in previous software visualisation studies, in particular the study has demonstrated that for the cases examined, where complex concepts are being developed, the mixing of auditory (or text) and visual elements can result in excessive cognitive load and impede learning. This finding provides a framework for selecting the most appropriate instructional programming language based on the cognitive complexity of the concepts under study.

Re-engineering project planning processes : a best practice model for managing project phase integration issues

Competitive markets are increasingly driving new initiatives for shorter cycle times resulting in increased overlapping of project phases. This, in turn, necessitates improving the interfaces between the different phases to be overlapped (integrated), thus allowing transfer of processes, information and knowledge from one individual or team to another. This transfer between phases, within and between projects, is one of the basic challenges to the philosophy of project management. To make the process transfer more transparent with minimal loss of momentum and project knowledge, this paper draws upon Total Quality Management (TQM) and Business Process Re-engineering (BPR) philosophies to develop a Best Practice Model for managing project phase integration. The paper presents the rationale behind the model development and outlines its two key parts; (1) Strategic Framework and (2) Implementation Plan. Key components of both the Strategic Framework and the Implementation Plan are presented and discussed.

Impact of a data warehouse model for improved decision-making process in healthcare

The health system is one sector dealing with a deluge of complex data. Many healthcare organisations struggle to utilise these volumes of health data effectively and efficiently. Also, there are many healthcare organisations, which still have stand-alone systems, not integrated for management of information and decision-making. This shows, there is a need for an effective system to capture, collate and distribute this health data. Therefore, implementing the data warehouse concept in healthcare is potentially one of the solutions to integrate health data. Data warehousing has been used to support business intelligence and decision-making in many other sectors such as the engineering, defence and retail sectors. The research problem that is going to be addressed is, "how can data warehousing assist the decision-making process in healthcare". To address this problem the researcher has narrowed an investigation focusing on a cardiac surgery unit. This research used the cardiac surgery unit at the Prince Charles Hospital (TPCH) as the case study. The cardiac surgery unit at TPCH uses a stand-alone database of patient clinical data, which supports clinical audit, service management and research functions. However, much of the time, the interaction between the cardiac surgery unit information system with other units is minimal. There is a limited and basic two-way interaction with other clinical and administrative databases at TPCH which support decision-making processes. The aims of this research are to investigate what decision-making issues are faced by the healthcare professionals with the current information systems and how decision-making might be improved within this healthcare setting by implementing an aligned data warehouse model or models. As a part of the research the researcher will propose and develop a suitable data warehouse prototype based on the cardiac surgery unit needs and integrating the Intensive Care Unit database, Clinical Costing unit database (Transition II) and Quality and Safety unit database [electronic discharge summary (e-DS)]. The goal is to improve the current decision-making processes. The main objectives of this research are to improve access to integrated clinical and financial data, providing potentially better information for decision-making for both improved from the questionnaire and by referring to the literature, the results indicate a centralised data warehouse model for the cardiac surgery unit at this stage. A centralised data warehouse model addresses current needs and can also be upgraded to an enterprise wide warehouse model or federated data warehouse model as discussed in the many consulted publications. The data warehouse prototype was able to be developed using SAS enterprise data integration studio 4.2 and the data was analysed using SAS enterprise edition 4.3. In the final stage, the data warehouse prototype was evaluated by collecting feedback from the end users. This was achieved by using output created from the data warehouse prototype as examples of the data desired and possible in a data warehouse environment. According to the feedback collected from the end users, implementation of a data warehouse was seen to be a useful tool to inform management options, provide a more complete representation of factors related to a decision scenario and potentially reduce information product development time. However, there are many constraints exist in this research. For example the technical issues such as data incompatibilities, integration of the cardiac surgery database and e-DS database servers and also, Queensland Health information restrictions (Queensland Health information related policies, patient data confidentiality and ethics requirements), limited availability of support from IT technical staff and time restrictions. These factors have influenced the process for the warehouse model development, necessitating an incremental approach. This highlights the presence of many practical barriers to data warehousing and integration at the clinical service level. Limitations included the use of a small convenience sample of survey respondents, and a single site case report study design. As mentioned previously, the proposed data warehouse is a prototype and was developed using only four database repositories. Despite this constraint, the research demonstrates that by implementing a data warehouse at the service level, decision-making is supported and data quality issues related to access and availability can be reduced, providing many benefits. Output reports produced from the data warehouse prototype demonstrated usefulness for the improvement of decision-making in the management of clinical services, and quality and safety monitoring for better clinical care. However, in the future, the centralised model selected can be upgraded to an enterprise wide architecture by integrating with additional hospital units’ databases.

Comparative study of analytical rental model and statistical models for predicting house rental levels

The need for a house rental model in Townsville, Australia is addressed. Models developed for predicting house rental levels are described. An analytical model is built upon a priori selected variables and parameters of rental levels. Regression models are generated to provide a comparison to the analytical model. Issues in model development and performance evaluation are discussed. A comparison of the models indicates that the analytical model performs better than the regression models.

The structured and practical approach in development of decision support system for consultant selection in public sector infrastructure project

Nowadays, most of the infrastructure development projects undertaken are complex in nature. Practically, public clients who do not have a good understanding of the design and management may suffer severe losses, especially for infrastructure projects. There is a need for luring the right consultant to secure client's investment in infrastructure developments. Throughout the project life cycle, consultants play vital role from the inception to completion stage of a project. A few studies in Malaysia show that infrastructure projects involving irrigation and drainage have experience problems such as poor workmanship, delay and cost overrun due to the consultant's inability or the client incompetence of recruiting consultants in time. This highlights the need of aided decision making and an efficient system to select the best consultant by using Decision Support System (DSS). On the other hand, recent trends reveal that most DSS in construction only concentrate on decision model development. These models are impractical and unused as they are complicated or difficult for laymen such as project managers to utilize. Thus, this research attempts to develop an efficient DSS for consultant selection namely consultDeSS. Driven by the motivation and research aims, this study deployed Design Science Research Methodology (DSRM) dominant with a combination of case studies at the Malaysian Department of Irrigation and Drainage (DID). Two real projects involving irrigation and drainage infrastructure were used to design, implement and evaluate the artefact. The 3-tier consultDeSS was revised after the evaluation and the design was significantly improved based on user feedback. By developing desirable tools that fit client's needs will enhance the productivity and minimize conflict within groups and organisations. The tool is more usable and efficient compared to previous studies in construction. Thus, this research has demonstrated a purposeful artefact with a practical and valid structured development approach that is applicable in a variety of problems in construction discipline.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Evaluation of a patient-specific finite-element model to simulate conservative treatment in adolescent idiopathic scoliosis

Study design Retrospective validation study. Objectives To propose a method to evaluate, from a clinical standpoint, the ability of a finite-element model (FEM) of the trunk to simulate orthotic correction of spinal deformity and to apply it to validate a previously described FEM. Summary of background data Several FEMs of the scoliotic spine have been described in the literature. These models can prove useful in understanding the mechanisms of scoliosis progression and in optimizing its treatment, but their validation has often been lacking or incomplete. Methods Three-dimensional (3D) geometries of 10 patients before and during conservative treatment were reconstructed from biplanar radiographs. The effect of bracing was simulated by modeling displacements induced by the brace pads. Simulated clinical indices (Cobb angle, T1–T12 and T4–T12 kyphosis, L1–L5 lordosis, apical vertebral rotation, torsion, rib hump) and vertebral orientations and positions were compared to those measured in the patients' 3D geometries. Results Errors in clinical indices were of the same order of magnitude as the uncertainties due to 3D reconstruction; for instance, Cobb angle was simulated with a root mean square error of 5.7°, and rib hump error was 5.6°. Vertebral orientation was simulated with a root mean square error of 4.8° and vertebral position with an error of 2.5 mm. Conclusions The methodology proposed here allowed in-depth evaluation of subject-specific simulations, confirming that FEMs of the trunk have the potential to accurately simulate brace action. These promising results provide a basis for ongoing 3D model development, toward the design of more efficient orthoses.