The COVID-19 pandemic has disrupted all segments of daily life, with the healthcare sector being at the forefront of this upheaval. Unprecedented efforts have been taken worldwide to curb this ongoing global catastrophe that has already resulted in many fatalities. One of the areas that has received little attention amid this turmoil is the disruption to trainee education, particularly in specialties that involve acquisition of procedural skills. Hand surgery in Singapore is a standalone combined programme that relies heavily on dedicated cross-hospital rotations, an extensive didactic curriculum and supervised hands-on training of increasing complexity. All aspects of this training programme have been affected because of the cancellation of elective surgical procedures, suspension of cross-hospital rotations, redeployment of residents, and an unsustainable duty roster. There is a real concern that trainees will not be able to meet their training requirements and suffer serious issues like burnout and depression. The long-term impact of suspending training indefinitely is a severe disruption of essential medical services. This article examines the impact of a global pandemic on trainee education in a demanding surgical speciality. We have outlined strategies to maintain trainee competencies based on the following considerations: 1) the safety and wellbeing of trainees is paramount; 2) resource utilization must be thoroughly rationalized; 3) technology and innovative learning methods must supplant traditional teaching methods; and 4) the changes implemented must be sustainable. We hope that these lessons will be valuable to other training programs struggling to deliver quality education to their trainees, even as we work together to battle this global catastrophe.

Aims

The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments.

Aims

Hip fracture patients are at higher risk of severe COVID-19 illness, and admission into hospital puts them at further risk. We implemented a two-site orthopaedic trauma service, with ‘COVID’ and ‘COVID-free’ hubs, to deliver urgent and infection-controlled trauma care for hip fracture patients, while increasing bed capacity for medical patients during the COVID-19 pandemic.