Risk factors for prolonged length of hospital stay following elective hip replacement surgery: a retrospective longitudinal observational study.

OBJECTIVES: Our aim was to identify which patients are likely to stay in hospital longer following total hip replacement surgery. DESIGN: Longitudinal, observational study used routinely collected data. SETTING: Data were collected from an NHS Trust in South-West England between 2016 and 2019. PARTICIPANTS: 2352 hip replacement patients had complete data and were included in analysis. PRIMARY AND SECONDARY OUTCOME MEASURES: Three measures of length of stay were used: a count measure of number of days spent in hospital, a binary measure of ≤7 days/>7 days in hospital and a binary measure of remaining in hospital when medically fit for discharge. RESULTS: The mean length of stay was 5.4 days following surgery, with 18% in hospital for more than 7 days, and 11% staying in hospital when medically fit for discharge. Longer hospital stay was associated with older age (OR=1.06, 95% CI 1.05 to 1.08), being female (OR=1.42, 95% CI 1.12 to 1.81) and more comorbidities (OR=3.52, 95% CI 1.45 to 8.55) and shorter length of stay with not having had a recent hospital admission (OR=0.44, 95% CI 0.32 to 0.60). Results were similar for remaining in hospital when medically fit for discharge, with the addition of an association with highest socioeconomic deprivation (OR=2.08, 95% CI 1.37 to 3.16). CONCLUSIONS: Older, female patients with more comorbidities and from more socioeconomically deprived areas are likely to remain in hospital for longer following surgery. This study produced regression models demonstrating consistent results across three measures of prolonged hospital stay following hip replacement surgery. These findings could be used to inform surgery planning and when supporting patient discharge following surgery.

Development and application of simulation modelling for orthopaedic elective resource planning in England.

OBJECTIVES: This study aimed to develop a simulation model to support orthopaedic elective capacity planning. METHODS: An open-source, generalisable discrete-event simulation was developed, including a web-based application. The model used anonymised patient records between 2016 and 2019 of elective orthopaedic procedures from a National Health Service (NHS) Trust in England. In this paper, it is used to investigate scenarios including resourcing (beds and theatres) and productivity (lengths of stay, delayed discharges and theatre activity) to support planning for meeting new NHS targets aimed at reducing elective orthopaedic surgical backlogs in a proposed ring-fenced orthopaedic surgical facility. The simulation is interactive and intended for use by health service planners and clinicians. RESULTS: A higher number of beds (65-70) than the proposed number (40 beds) will be required if lengths of stay and delayed discharge rates remain unchanged. Reducing lengths of stay in line with national benchmarks reduces bed utilisation to an estimated 60%, allowing for additional theatre activity such as weekend working. Further, reducing the proportion of patients with a delayed discharge by 75% reduces bed utilisation to below 40%, even with weekend working. A range of other scenarios can also be investigated directly by NHS planners using the interactive web app. CONCLUSIONS: The simulation model is intended to support capacity planning of orthopaedic elective services by identifying a balance of capacity across theatres and beds and predicting the impact of productivity measures on capacity requirements. It is applicable beyond the study site and can be adapted for other specialties.

Participatory design research for the development of real-time simulation models in healthcare.

The implementation challenges for modelling and simulation in health and social care are well-known and understood. Yet increasing availability of data and a better understanding of the value of Operational Research (OR) applications are strengthening opportunities to support healthcare delivery. Participative approaches in healthcare modelling have shown value through stakeholder engagement and commitment towards co-creation of models and knowledge but are limited in focus on model design and development. For simulation modelling, a participative design research methodology can support development for sustained use, emphasising model usefulness and usability using iterative cycles of development and evaluation. Within a structured methodology, measures of success are built into the design process, focusing on factors which contribute to success, with implicit goals of implementation and improvement. We illustrate this through a participative case study which demonstrates development of the component parts of a real-time simulation model aimed at reducing emergency department crowding.