Elective Surgery PDF Download

The national shutdown in response to the COVID-19 has severely compromised the financial health of the healthcare industry, in great part because shutdown of elective surgical procedures. Finally, after two months elective surgeries are beginning again but returning the system to its pre-COVID state will be difficult. There is a backlog of patients waiting for elective surgery. There are a number of complicating factors. Surgical staff has been furloughed and will take time to reactivate. Additional short-term staff may be needed. Hospital beds reserved for potential surges in COVID-19 may limit inpatient surgical throughput. Specialist practices that refer patients for elective procedures have been shut down as well, creating a backlog of waiting patients to manage. To create insight on how to the ramp-up elective surgery might unfold, we have created a computer simulation based on a mid-size hospital. Our model provides many lessons. In the best case, the number of patients waiting for surgery triples compared with pre-COVID and returning elective surgical rates to pre-COVID-19 levels will take five months. if hospitals can double their surgical capacity over pre COVID levels in the short term. Hence, reintroducing furloughed elective surgical personnel will be insufficient. If the build-up of patients waiting for referrals by specialists and surgical bed constraints are also considered, the peak number of patients will almost quadruple and take over a year-and-a-half to resolve. The “upside” is that less additional surgical workforce is required because bed constraints put a cap on the maximum rate of surgeries that can be performed per week, no matter what the size of the workforce. The downside is that reduced surgery rates created by these constraints create yet more pent-up demand for elective surgeries. This results in excessive patient waiting time, causing many patients waiting for surgery to ultimately decide to cancel their procedures. Importantly, the model structure is generic. Hence all its parameters and values can be changed for “what-if” testing or to tailor the model for other hospitals. We conclude by discussing sensitivity analyses and a number of lessons from the analysis that will help the medical system cope with these issues during ramp-up.