Surgeon gap time, surgical case demand, and patient recovery needs are main factors causing scheduling inefficiencies in operating rooms.
Researchers at the University of Massachusetts Amherst have identified the core factors behind widespread operational inefficiencies in ORs, offering a potential path to reduce surgeon burnout and lower healthcare costs.
The study, published in the Journal of the American Medical Informatics Association, analysed a massive dataset of 86,480 surgeries across 14 medical specialities performed at Baystate Medical Centre between 2020 and 2023.
By utilising electronic health record (EHR) data and advanced data analytics, the research team pinpointed three key drivers that disrupt daily hospital workflows: surgeon gap time, surgical case demand, and post-operative patient recovery needs.
According to lead researcher Muge Capan, an associate professor of industrial engineering at UMass Amherst, the typical scheduling system that allocates operations to rigid time blocks fails to account for the volatility of healthcare and leaves expensive OR resources idle.
The study found that gaps shorter than two-and-a-half hours often become unusable, resulting in wasted resources.
Capan explained: ‘Surgeons are highly skilled, and they perform high-risk tasks. When we think about utilising a resource, we don’t want them to sit idle – but we also don’t want to overutilise them because these are not machines; these are people. Finding that right balance is a challenging problem.’
‘However, to efficiently schedule, hospitals need to predict how long a procedure will take. This includes the surgery itself as well as many other factors that surround an operation – has the surgeon recovered from their previous operation? Is the room clean? Is the proper equipment in place? There is a lot of uncertainty there at the system level.
‘If you’re scheduling tennis lessons, it works because a tennis lesson is exactly one hour. You block the court for one hour, you play, you leave, next group. But blocks don’t make sense for surgeries, because they’re so uncertain. As a result, operating rooms (ORs) can sit empty since any block of time less than two-and-a-half hours is unusable for most surgeries.’
To optimise scheduling, the researchers used data analytics to analyse three primary dynamic factors:
Surgeon gap time: Identifies case type, urgency, and location as major predictors of idle time between operations
Surgical case demand: Uses EHR data to map a surgeon’s true operative and cognitive workload throughout the day
Patient recovery clustering: Groups cases by demand, finding that high-demand cases lead to significantly more bottlenecks in post-surgery recovery than lower-demand tiers.
Beyond lowering costs, the researchers aim to protect staff from burnout by optimising workflows.
Capan said that while maximising efficiency is crucial, the aim is to avoid overwhelming surgeons.
‘Surgeons are highly skilled, and they perform high-risk tasks… we don’t want them to sit idle – but we also don’t want to overutilise them because these are not machines, these are people. Finding that right balance is a challenging problem.’
The UMass Amherst researchers hope that hospitals can move towards predictive scheduling, reducing waste and decreasing cognitive overload for the team.
