Operational strategies to manage non-elective orthopaedic surgical flows: a simulation modelling study

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.

Leveraging machine learning and prescriptive analytics to improve operating room throughput

Successful days are defined as days when four cases were completed before 3:45pm, and overtime hours are defined as time spent after 3:45pm. Based on these definitions and the 460 unsuccessful days isolated from the dataset, 465 hours, 22 minutes, and 30 seconds total overtime hours were calculated. To reduce the increasing wait lists for hip and knee surgeries, we aim to verify whether it is possible to add a 5th surgery, to the typical 4 arthroplasty surgery per day schedule, without adding extra overtime hours and cost at our clinical institution. To predict 5th cases, 301 successful days were isolated and used to fit linear regression models for each individual day. After using the models' predictions, it was determined that increasing performance to a 77% success rate can lead to approximately 35 extra cases per year, while performing optimally at a 100% success rate can translate to 56 extra cases per year at no extra cost. Overall, this shows the extent of resources wasted by overtime costs, and the potential for their use in reducing long wait times. Future work can explore optimal staffing procedures to account for these extra cases.

Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review

Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.

Change in Policy Allowing Overlapping Surgery Decreases Length of Stay in an Academic, Safety-Net Hospital

BACKGROUND

The practice of surgeons running overlapping operating rooms has recently come under scrutiny.

OBJECTIVE

To examine the impact of hospital policy allowing overlapping rooms in the case of patients admitted to a tertiary care, safety-net hospital for urgent neurosurgical procedures.

METHODS

The neurosurgery service at the hospital being studied transitioned from routinely allowing 1 room per day (period 1) to overlapping rooms (period 2), with the second room being staffed by the same attending surgeon. Patients undergoing neurosurgical intervention in each period were retrospectively compared. Demographics, indication, case type, complications, outcomes, and total charges were tracked.

RESULTS

There were 59 urgent cases in period 1 and 63 in period 2. In the case of these patients, the length of stay was significantly decreased in period 2 (13.09 d vs 19.52; P = .006). The time from admission to surgery (wait time) was also significantly decreased in period 2 (5.12 d vs 7.00; P = .04). Total charges also trended towards less in period 2 (${\$}$150 942 vs ${\$}$200 075; P = .05). Surgical complications were no different between the groups (16.9% vs 14.3%; P = .59), but medical complications were significantly decreased in period 2 (14.3% vs 30.5%; P = .009). Significantly more patients were discharged to home in period 2 (69.8% vs 42.4%; P = .003).

CONCLUSION

As a matter of policy, allowing overlapping rooms significantly reduces the length of stay in the case of a vulnerable population in need of urgent surgery at a single safety-net academic institution. This may be due to a reduction in medical complications in these patients.