The Responsiveness, Content Validity, and Convergent Validity of the Measure Yourself Concerns and Wellbeing (MYCaW) Patient-Reported Outcome Measure

Objective Measure Yourself Concerns and Wellbeing (MYCaW) is a patient-centred questionnaire that allows cancer patients to identify and quantify the severity of their ‘Concerns’ and Wellbeing, as opposed to using a pre-determined list. MYCaW administration is brief and aids in prioritising treatment approaches. Our goal was to assess the convergent validity and responsiveness of MYCaW scores over time, the generalisability of the existing qualitative coding framework in different complementary and integrative healthcare settings and content validity. Methods Baseline and 6-week follow-up data (n=82) from MYCaW and FACIT-SpEx questionnaires were collected for a service evaluation of the ‘Living Well With The Impact of Cancer’ course at Penny Brohn Cancer Care. MYCaW construct validity was determined using Spearman's Rank Correlation test, and responsiveness indices assessed score changes over time. The existing qualitative coding framework was reviewed using a new dataset (n=158) and coverage of concern categories compared to items of existing outcome measures. Results Good correlation between MYCaW and FACIT-SpEx score changes were achieved (r= -0.57, p≥0.01). MYCaW Profile and Concern scores were highly responsive to change: SRM=1.02 and 1.08; effect size=1.26 and 1.22. MYCaW change scores showed the anticipated gradient of change according to clinically relevant degrees of change. Categories including ‘Spirituality’, ‘weight change’ and ‘practical concerns’ were added to the coding framework to improve generalisability. Conclusions MYCaW scores were highly responsive to change, allowing personalized patient outcomes to be quantified; the qualitative coding framework is generalisable across different oncology settings and has broader coverage of patient-identified concerns compared with existing cancer-related patient-reported outcome measures.

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

Examining Chinese Tourists’ Nature-based Tourism Participantion Behavior: Incorporating Environmental Concern into a Constraint-Negotiation Modela

Finding the balance between economic development and preservation of the natural environment is a challenging yet important task. This is a particularly pressing issue in the case of China, as it is the largest and fastest-growing market for tourism. The purpose of this research is to examine Chinese tourists’ participation in nature-based, tourism activities by incorporating tourists’ environmental concern, measured by a revised New Environmental Paradigm (NEP) scale, into a tourism constraint-negotiation model. The responses of 409 Chinese tourists show environmental concern will positively affect tourists’ motivation, which, in turn, will affect their negotiation strategy and ultimately their participation behavior. The theoretical and managerial implications of this study are discussed in the context of the tourism literature.

Interaction Rituals and Jumbled Emotions Among “Relative Strangers”: Simulated Patient Work on a Trainee Complementary Therapy Practitioner Program

Learning games such as role-play (which we refer to as “simulated interaction rituals”) are commonly used as social tools to develop trainee health practitioners. However, the effect of such rituals on individual and group participant emotions has not been carefully studied. Using a heuristic approach, we explore the experiences of complementary therapy practitioner trainees (and their trainers) participating in a personal development course. Ten trainees and two tutors were interviewed, observational notes taken, and a secondary qualitative analysis undertaken. Participants and tutors described a medley of disparate emotional and moral responses to group rituals, conceptualized in this article as “jumbled emotions.” Such emotions required disentangling, and both trainees and staff perceived participating in unfamiliar rituals “with relative strangers” as challenging. Front of stage effects are frequently processed “backstage,” as rituals threaten social embarrassment and confusion. Concerns around emotional triggers, authenticity, and outcomes of rituals arise at the time, yet trainees can find ways to work through these issues in time.

Business Process Modelling based on formal temporal theory with an application to hospital patient flows

Existing Workflow Management Systems (WFMSs) follow a pragmatic approach. They often use a proprietary modelling language with an intuitive graphical layout. However the underlying semantics lack a formal foundation. As a consequence, analysis issues, such as proving correctness i.e. soundness and completeness, and reliable execution are not supported at design level. This project will be using an applied ontology approach by formally defining key terms such as process, sub-process, action/task based on formal temporal theory. Current business process modelling (BPM) standards such as Business Process Modelling Notation (BPMN) and Unified Modelling Language (UML) Activity Diagram (AD) model their constructs with no logical basis. This investigation will contribute to the research and industry by providing a framework that will provide grounding for BPM to reason and represent a correct business process (BP). This is missing in the current BPM domain, and may result in reduction of the design costs and avert the burden of redundant terms used by the current standards. A graphical tool will be introduced which will implement the formal ontology defined in the framework. This new tool can be used both as a modelling tool and at the same time will serve the purpose of validating the model. This research will also fill the existing gap by providing a unified graphical representation to represent a BP in a logically consistent manner for the mainstream modelling standards in the fields of business and IT. A case study will be conducted to analyse a catalogue of existing ‘patient pathways’ i.e. processes, of King’s College Hospital NHS Trust including current performance statistics. Following the application of the framework, a mapping will be conducted, and new performance statistics will be collected. A cost/benefits analysis report will be produced comparing the results of the two approaches.

A general framework for Business Process Modelling (BPM) based on Formal Temporal Theory with an application to Hospital Patient flows

Existing Workflow Management Systems (WFMSs) follow a pragmatic approach. They often use a proprietary modelling language with an intuitive graphical layout. However the underlying semantics lack a formal foundation. As a consequence, analysis issues, such as proving correctness i.e. soundness and completeness, and reliable execution are not supported at design level. This project will be using an applied ontology approach by formally defining key terms such as process, sub-process, action/task based on formal temporal theory. Current business process modelling (BPM) standards such as Business Process Modelling Notation (BPMN) and Unified Modelling Language (UML) Activity Diagram (AD) model their constructs with no logical basis. This investigation will contribute to the research and industry by providing a framework that will provide grounding for BPM to reason and represent a correct business process (BP). This is missing in the current BPM domain, and may result in reduction of the design costs and avert the burden of redundant terms used by the current standards. A graphical tool will be introduced which will implement the formal ontology defined in the framework. This new tool can be used both as a modelling tool and at the same time will serve the purpose of validating the model. This research will also fill the existing gap by providing a unified graphical representation to represent a BP in a logically consistent manner for the mainstream modelling standards in the fields of business and IT. A case study will be conducted to analyse a catalogue of existing ‘patient pathways’ i.e. processes, of King’s College Hospital NHS Trust including current performance statistics. Following the application of the framework, a mapping will be conducted, and new performance statistics will be collected. A cost/benefits analysis report will be produced comparing the results of the two approaches.