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Pain management is often considered lower priority than many other aspects of health management in hospitals. However, there is potential for Quality Improvement (QI) teams to improve pain management by visualising and exploring pain data sets. Although dashboards are already used by QI teams in hospitals, there is limited evidence of teams accessing visualisations to support their decision making. This study aims to identify the needs of the QI team in a UK Critical Care Unit (CCU) and develop dashboards that visualise longitudinal data on the efficacy of patient pain management to assist the team in making informed decisions to improve pain management within the CCU. This research is based on an analysis of transcripts of interviews with healthcare professionals with a variety of roles in the CCU and their evaluation of probes. We identified two key uses of pain data: direct patient care (focusing on individual patient data) and QI (aggregating data across the CCU and over time); in this paper, we focus on the QI role. We have identified how CCU staff currently interpret information and determine what supplementary information can better inform their decision making and support sensemaking. From these, a set of data visualisations has been proposed, for integration with the hospital electronic health record. These visualisations are being iteratively refined in collaboration with CCU staff and technical staff responsible for maintaining the electronic health record. The paper presents user requirements for QI in pain management and a set of visualisations, including the design rationale behind the various methods proposed for visualising and exploring pain data using dashboards.

Understanding the relationship between fiducial registration error (FRE) and target registration error (TRE) is important for the correct use of interventional guidance systems. Whilst it is well established that TRE is statistically independent of FRE, system users still struggle against the intuitive assumption that a low FRE indicates a low TRE. We present the SciKit-Surgery Fiducial Registration Educational Demonstrator and describe its use. SciKit-SurgeryFRED was developed to enable remote teaching of key concepts in image registration. SciKit-SurgeryFRED also supports research into user interface design for image registration systems. SciKit-SurgeryFRED can be used to enable remote tutorials covering the statistics relevant to image guided interventions. Students are able to place fiducial markers on pre and intra-operative images and observe the effects of changes in marker geometry, marker count, and fiducial localisation error on TRE and FRE. SciKit- SurgeryFRED also calculates statistical measures for the expected values of TRE and FRE. Because many registrations can be performed quickly the students can then explore potential correlations between the different statistics. SciKit-SurgeryFRED also implements a registration based game, where participants are rewarded for complete treatment of a clinical target, whilst minimising the treatment margin. We used this game to perform a remote study on registration and simulated ablation, measuring how user performance changes depending on what error statistics are made available. The results support the assumption that knowing the exact value of target registration error leads to better treatment. Display of other statistics did not have a significant impact on the treatment performance.

Knee arthroscopy is a very common elective surgery, which is performed globally. Research has stated that there are currently several issues surrounding this surgery which reveal surgeons are frustrated with their instruments, cause unintended damage, and have high levels of cognitive and physical demands. The aim of this research is to explore in greater detail what is actually occurring during surgery and gain insights from the knee surgeons to illuminate the current frailties entangled with knee arthroscopy. This research consists of a field study observing four consultant orthopaedic knee surgeons performing knee arthroscopies, of which a total of 15 surgeries were video-recorded. Cards were created based on the analysis of the videos, which were then used to prompt further reflection in interviews with two of the four knee surgeons. From this study we endeavour to explore how knee surgeons currently employ instruments during knee surgery and the challenges that they experience. Our findings focus on a surgeon's perspective on the following challenges: (1) instrument limitations, (2) limited visibility, and (3) physical and cognitive pressures. Based on these limitations, we explore how current robotic surgical platforms could be introduced as a first step towards designing surgical robots for knee arthroscopy, that will result in more desirable patient outcomes as well as greater satisfaction for the knee surgeons.