Nurses' experiences of pain management for people with advanced dementia approaching the end of life: a qualitative study

AIMS AND OBJECTIVES: To explore hospice, acute care and nursing home nurses' experiences of pain management for people with advanced dementia in the final month of life. To identify the challenges, facilitators and practice areas requiring further support.

BACKGROUND: Pain management in end-stage dementia is a fundamental aspect of end of life care; however, it is unclear what challenges and facilitators nurses experience in practice, whether these differ across care settings, and whether training needs to be tailored to the context of care.

DESIGN: A qualitative study using semi-structured interviews and thematic analysis to examine data.

METHODS: 24 registered nurses caring for people dying with advanced dementia were recruited from ten nursing homes, three hospices, and two acute hospitals across a region of the United Kingdom. Interviews were conducted between June 2014 and September 2015.

RESULTS: Three core themes were identified: challenges administering analgesia, the nurse-physician relationship, and interactive learning and practice development. Patient-related challenges to pain management were universal across care settings; nurse- and organisation-related barriers differed between settings. A need for interactive learning and practice development, particularly in pharmacology, was identified.

CONCLUSIONS: Achieving pain management in practice was highly challenging. A number of barriers were identified; however, the manner and extent to which these impacted on nurses differed across hospice, nursing home and acute care settings. Needs-based training to support and promote practice development in pain management in end-stage dementia is required.

RELEVANCE TO CLINICAL PRACTICE: Nurses considered pain management fundamental to end of life care provision; however, nurses working in acute care and nursing home settings may be under-supported and under-resourced to adequately manage pain in people dying with advanced dementia. Nurse-to-nurse mentoring and ongoing needs-assessed interactive case-based learning could help promote practice development in this area. Nurses require continuing professional development in pharmacology. This article is protected by copyright. All rights reserved.

IMPLEMENTING BACK PAIN TREATMENT PATHWAYS IN CLINICAL PRACTICE: QUALITATATIVE ANALYSIS OF STAKEHOLDER PERCEPTIONS

Background
Evidence-based practice advocates utilising best current research evidence, while reflecting patient preference and clinical expertise in decision making. Successfully incorporating this evidence into practice is a complex process. Based on recommendations of existing guidelines and systematic evidence reviews conducted using the GRADE approach, treatment pathways for common spinal pain disorders were developed.

Aims
The aim of this study was to identify important potential facilitators to the integration of these pathways into routine clinical practice.

Methods
A 22 person stakeholder group consisting of patient representatives, clinicians, researchers and members of relevant clinical interest groups took part in a series of moderated focus groups, followed up with individual, semi-structured interviews. Data were analysed using content analysis.

Results
Participants identified a number of issues which were categorized into broad themes. Common facilitators to implementation included continual education and synthesis of research evidence which is reflective of everyday practice; as well as the use of clear, unambiguous messages in recommendations. Meeting additional training needs in new or extended areas of practice was also recognized as an important factor. Different stakeholders identified specific areas which could be associated with successful uptake. Patients frequently defined early involvement in a shared decision making process as important. Clinicians identified case based examples and information on important prognostic indicators as useful tools to aiding decisions.

Conclusion
A number of potential implementation strategies were identified. Further work will examine the impact of these and other important factors on the integration of evidence-based treatment recommendations into clinical practice.

Uses of Agent-Based Modeling for Health Communication: the TELL ME Case Study

Government communication is an important management tool during a public health crisis, but understanding its impact is difficult. Strategies may be adjusted in reaction to developments on the ground and it is challenging to evaluate the impact of communication separately from other crisis management activities. Agent-based modeling is a well-established research tool in social science to respond to similar challenges. However, there have been few such models in public health. We use the example of the TELL ME agent-based model to consider ways in which a non-predictive policy model can assist policy makers. This model concerns individuals' protective behaviors in response to an epidemic, and the communication that influences such behavior. Drawing on findings from stakeholder workshops and the results of the model itself, we suggest such a model can be useful: (i) as a teaching tool, (ii) to test theory, and (iii) to inform data collection. We also plot a path for development of similar models that could assist with communication planning for epidemics.

Hierarchical Task Network Planning with Common-Sense Reasoning for Multiple-People Behaviour Analysis

Safety on public transport is a major concern for the relevant authorities. We
address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A common-sense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from passengers is modelled as a time-series of human activities. Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone.