Clinical Placement Expansion Programme at Gosh - a Two-Year Review

Background The Clinical Placement Expansion Programme (CPEP) aims to increase clinical placements in the National Health Service (NHS) and to support growth across all Allied Health Professionals (AHPs). At Great Ormond Street Hospital (GOSH), this programme was implemented in 2020, and extended to 2022, with the aim to steadily increase placement capacity across all AHP services. Additionally, the Fair Share model was introduced in London to help benchmark the provision of practice learning relative to workforce capacity, focusing specifically on Speech and Language Therapy, Physiotherapy and Occupational Therapy. The aim is to provide seven weeks of student placement per whole time equivalent. Methods Retrospective data were collected from seven AHP services throughout two academic years: September 2020 to September 2022. This included achieved number of students and placement weeks. Student placement feedback was collected and reported as an average out of five for both academic years. Results From September 2020 to September 2021, a total number of 400 student placements (1153 weeks) were supported at GOSH. From September 2020 to September 2021, 145 students were placed (433 weeks) and from September 2021 to September 2022, a total of 255 students were placed (720 weeks). This shows an increase of 110 students and 283 weeks from 2020/2021 to 2021/2022. The average student feedback for academic years 2020-2022 was 4.75 out of 5. Conclusion Student numbers and weeks increased per academic year from 2020-2022. Barriers to increasing student capacity included clinical capacity and staff pressures, lack of space, changes in service delivery during the COVID-19 pandemic, and staff attitude towards student placements. Placement innovation, such as leadership placements and hybrid working models, positively influenced student numbers at GOSH. There is a continued drive to achieve the Fair Share model, and the sustainability of placement expansion should be reviewed in future.

Collection of real-world evidence of nusinersen treatment for SMA patients through a national registry: description of the paediatric cohort in the UK

Nusinersen is one of three drug treatments currently available to children and adults with spinal muscular atrophy (SMA) in England. The use of nusinersen is conditional to a managed access agreement (MAA) since July 2019. This study aims to describe the characteristics of the paediatric patients in the nusinersen MAA. Retrospective data from July 2019 to March 2022 was extracted from the SMA research and clinical hub UK (SMA REACH UK) registry which includes fifteen centres in England. Patients hold a minimum of 2 data entries yearly for each mandated field after the initial baseline assessment. The data report includes, gender, SMA type, SMN2 copy number, date of nusinersen first dose, and mandatory collection fields for medical and physiotherapy data. Medical data collection includes mortality;respiratory function (FVC and PCF, ventilation type and estimation of hours of ventilation);presence of scoliosis, spinal surgery, and Thoracolumbar support (TLSO) use and presence of fractures. The motor function measures include CHOP-INTEND, HINE, RHS, RULM and summary of contractures. 231 patients (129 males, 102 females) with SMA (Type 1=93, Type 2=85, type 3=50, pre-symptomatic=3) are receiving nusinersen via the MAA. The mean age of enrolment is 5.5 years (age range=10days-17years and 9months). There have been four deaths. During the COVID-19 pandemic structured remote assessments were agreed among the network to ensure continued collection of data, however the pandemic impacted functional outcomes collection. The SMA REACH UK registry provides a robust system to collect information on patients to address clinical uncertainties originally identified by the national institute of clinical excellence (NICE). It serves as a systematic model for longer term real-world data collection to evaluate clinical effectiveness of drugs, as well as potential evaluation of economic impact. The SMA reach UK registry has recently expanded to include adult centres. SMA reach UK is part of the international SMA registry (ISMAR) which includes registries in Italy and the US. [ FROM AUTHOR] Copyright of Neuromuscular Disorders is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Modeling and Evaluation of Clustering Patient Care into Bubbles

COVID-19 has caused an enormous burden on healthcare facilities around the world. Cohorting patients and healthcare professionals (HCPs) into 'bubbles' has been proposed as an infection-control mechanism. In this paper, we present a novel and flexible model for clustering patient care in healthcare facilities into bubbles in order to minimize infection spread. Our model aims to control a variety of costs to patients/residents and HCPs so as to avoid hidden, downstream adverse effects of clustering patient care. This model leads to a discrete optimization problem that we call the BUBBLECLUSTERING problem. This problem takes as input a temporal visit graph, representing HCP mobility, including visits by HCPs to patient/resident rooms. The output of the problem is a rewired visit graph, obtained by partitioning HCPs and patient rooms into bubbles and rewiring HCP visits to patient rooms so that patient-care is largely confined to the constructed bubbles. Even though the BUBBLECLUSTERING problem is intractable in general, we present an integer linear programming (ILP) formulation of the problem that can be solved optimally for problem instances that arise from typical hospital units and long-term-care facilities. We call our overall solution approach Cost-aware Rewiring of Networks (CoRN). We evaluate CoRN using fine-grained-movement data from a hospital-medical-intensive-care unit as well as two long-term-care facilities. These data were obtained using sensor systems we built and deployed. The main takeaway from our experimental results is that it is possible to use CoRN to substantially reduce infection spread by cohorting patients and HCPs without sacrificing patient-care, and with minimal excess costs to HCPs in terms of time and distances traveled during a shift. © 2021 IEEE.