Digital methods to enhance the usefulness of patient experience data in services for long-term conditions: the DEPEND mixed-methods study

Background Collecting NHS patient experience data is critical to ensure the delivery of high-quality services. Data are obtained from multiple sources, including service-specific surveys and widely used generic surveys. There are concerns about the timeliness of feedback, that some groups of patients and carers do not give feedback and that free-text feedback may be useful but is difficult to analyse. Objective To understand how to improve the collection and usefulness of patient experience data in services for people with long-term conditions using digital data capture and improved analysis of comments. Design The DEPEND study is a mixed-methods study with four parts: qualitative research to explore the perspectives of patients, carers and staff; use of computer science text-analytics methods to analyse comments; co-design of new tools to improve data collection and usefulness; and implementation and process evaluation to assess use of the tools and any impacts. Setting Services for people with severe mental illness and musculoskeletal conditions at four sites as exemplars to reflect both mental health and physical long-terms conditions: an acute trust (site A), a mental health trust (site B) and two general practices (sites C1 and C2). Participants A total of 100 staff members with diverse roles in patient experience management, clinical practice and information technology; 59 patients and 21 carers participated in the qualitative research components. Interventions The tools comprised a digital survey completed using a tablet device (kiosk) or a pen and paper/online version; guidance and information for patients, carers and staff; text-mining programs; reporting templates; and a process for eliciting and recording verbal feedback in community mental health services. Results We found a lack of understanding and experience of the process of giving feedback. People wanted more meaningful and informal feedback to suit local contexts. Text mining enabled systematic analysis, although challenges remained, and qualitative analysis provided additional insights. All sites managed to collect feedback digitally; however, there was a perceived need for additional resources, and engagement varied. Observation indicated that patients were apprehensive about using kiosks but often would participate with support. The process for collecting and recording verbal feedback in mental health services made sense to participants, but was not successfully adopted, with staff workload and technical problems often highlighted as barriers. Staff thought that new methods were insightful, but observation did not reveal changes in services during the testing period. Conclusions The use of digital methods can produce some improvements in the collection and usefulness of feedback. Context and flexibility are important, and digital methods need to be complemented with alternative methods. Text mining can provide useful analysis for reporting on large data sets within large organisations, but qualitative analysis may be more useful for small data sets and in small organisations. Limitations New practices need time and support to be adopted and this study had limited resources and a limited testing time. Future work Further research is needed to improve text-analysis methods for routine use in services and to evaluate the impact of methods (digital and non-digital) on service improvement in varied contexts and among diverse patients and carers. Funding This project was funded by the NIHR Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 28. See the NIHR Journals Library website for further project information

Co-designing new tools for collecting, analysing and presenting patient experience data in NHS services: working in partnership with patients and carers

Background The way we collect and use patient experience data is vital to optimise the quality and safety of health services. Yet, some patients and carers do not give feedback because of the limited ways data is collected, analysed and presented. In this study, we worked together with researchers, staff, patient and carer participants, and patient and public involvement and engagement (PPIE) contributors, to co-design new tools for the collection and use of patient experience data in multiple health settings. This paper outlines how the range of PPIE and research activities enabled the co-design of new tools to collect patient experience data. Methods Eight public contributors represented a range of relevant patient and carer experiences in specialist services with varied levels of PPIE experience, and eleven members of Patient and Participation Groups (PPGs) from two general practices formed our PPIE group at the start of the study. Slide sets were used to trigger co-design discussions with staff, patient and carer research participants, and PPIE contributors. Feedback from PPIE contributors alongside verbatim quotes from staff, patient and carer research participants is presented in relation to the themes from the research data. Results PPIE insights from four themes: capturing experience data; adopting digital or non-digital tools; ensuring privacy and confidentiality; and co-design of a suite of new tools with guidance, informed joint decisions on the shaping of the tools and how these were implemented. Our PPIE contributors took different roles during co-design and testing of the new tools, which supported co-production of the study. Conclusions Our experiences of developing multiple components of PPIE work for this complex study demonstrates the importance of tailoring PPIE to suit different settings, and to maximise individual strengths and capacity. Our study shows the value of bringing diverse experiences together, putting patients and carers at the heart of improving NHS services, and a shared approach to managing involvement in co-design, with the effects shown through the research process, outcomes and the partnership. We reflect on how we worked together to create a supportive environment when unforeseen challenges emerged (such as, sudden bereavement)

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images

10.1016/j.eswa.2012.02.095Expert Systems with Applications39109661-9668ESAP

Integrating FCM and Level Sets for Liver Tumor Segmentation

10.1007/978-3-540-92841-6_49IFMBE Proceedings23202-20

Exergy analysis for day lighting, electric lighting and space cooling systems for a room space in a tropical climate

Turning off the electric lamp during available daylight will save electricity, while at the same time thermal energy from solar radiation transmitted through the window will increase the space-cooling load. Therefore, it is necessary to evaluate the whole system that includes not only the room space with the windows and the electric lighting systems, but also the air conditioning system. For analysis of the whole system using different types of energy (i.e. electricity, solar radiation, light emitted by lamps and thermal energy), it is important to take into account the quality of these different types of energy. The concept of entropy and exergy were applied in this analysis. The purpose of this study is to show the energy use for daylighting, electric lighting, and space cooling systems as a series of exergy input, output, and consumption and reveal how a daylighting system consumes solar exergy and how electric lighting and space cooling systems consume exergy from fossil fuel. The methodology to calculate the exergy consumption of the system during a given time was developed first. This method was then applied to the lighting and cooling for a typical room. The study found that electric lighting consumes the lowest amount of exergy while the space cooling consumes the highest amount of exergy for the system

Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation

10.1016/j.compbiomed.2010.10.007Computers in Biology and Medicine4111-10CBMD

Level set diffusion for MRE image enhancement

10.1007/978-3-642-15699-1_32Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)6326 LNCS305-31

Thymidylate synthase: A novel genetic determinant of plasma homocysteine and folate levels

10.1007/s00439-002-0779-2Human Genetics1113299-302HUGE

Image processing and modeling for active needle steering in liver surgery

10.1109/CAR.2009.65Proceedings - 2009 International Asia Conference on Informatics in Control, Automation, and Robotics, CAR 2009306-31