As of March 23, 2020 there have been over 354,000 confirmed cases of coronavirus disease 2019 (COVID-19) in over 180 countries, the World Health Organization characterized COVID-19 as a pandemic, and the United States (US) announced a national state of emergency.1, 2, 3 In parts of China and Italy the demand for intensive care (IC) beds was higher than the number of available beds.4, 5 We sought to build an accessible interactive model that could facilitate hospital capacity planning in the presence of significant uncertainty about the proportion of the population that is COVID-19+ and the rate at which COVID-19 is spreading in the population. Our approach was to design a tool with parameters that hospital leaders could adjust to reflect their local data and easily modify to conduct sensitivity analyses. We developed a model to facilitate hospital planning with estimates of the number of Intensive Care (IC) beds, Acute Care (AC) beds, and ventilators necessary to accommodate patients who require hospitalization for COVID-19 and how these compare to the available resources. Inputs to the model include estimates of the characteristics of the patient population and hospital capacity. We deployed this model as an interactive online tool.6 The model is implemented in R 3.5, RStudio, RShiny 1.4.0 and Python 3.7. The parameters used may be modified as data become available, for use at other institutions, and to generate sensitivity analyses. We illustrate the use of the model by estimating the demand generated by COVID-19+ arrivals for a hypothetical acute care medical center. The model calculated that the number of patients requiring an IC bed would equal the number of IC beds on Day 23, the number of patients requiring a ventilator would equal the number of ventilators available on Day 27, and the number of patients requiring an AC bed and coverage by the Medicine Service would equal the capacity of the Medicine service on Day 21. In response to the COVID-19 epidemic, hospitals must understand their current and future capacity to care for patients with severe illness. While there is significant uncertainty around the parameters used to develop this model, the analysis is based on transparent logic and starts from observed data to provide a robust basis of projections for hospital managers. The model demonstrates the need and provides an approach to address critical questions about staffing patterns for IC and AC, and equipment capacity such as ventilators.

Purpose Patellofemoral instability is a common cause of knee pain and dysfunction in paediatric and adolescent patients. The purpose of the study was to evaluate the frequency of patellar dislocations seen in emergency departments (EDs) and the rates of surgical procedures for patellar instability at paediatric hospitals in the United States between 2004 and 2014. Methods The Pediatric Health Information System database was queried for all paediatric patients who underwent surgery for patellar instability or were seen in the ED for acute patellar dislocation between 2004 and 2014. This was compared with the annual numbers of overall orthopaedic surgical procedures. Results Between 2004 and 2014, there were 3481 patellar instability procedures and 447 285 overall orthopaedic surgical procedures performed at the included institutions, suggesting a rate of 7.8 per 1000 orthopaedic surgeries. An additional 5244 patellar dislocations treated in EDs were identified. Between 2004 and 2014, the number of patellar instability procedures increased 2.1-fold (95% confidence interval (CI) 1.4 to 3.0), while orthopaedic surgical procedures increased 1.7-fold (95% CI 1.3 to 2.0), suggesting a 1.2-fold relative increase in patellar instability procedures, compared with total paediatric orthopaedic surgeries. Conclusion This study shows a significant rise in the rate of acute patellar instability treatment events in paediatric and adolescent patients across the country. Surgery for patellar instability also increased over the study period, though only slightly more than the rate of all paediatric orthopaedic surgical procedures. This may suggest that increasing youth sports participation may be leading to a spectrum of increasing injuries and associated surgeries in children. Level of Evidence IV

Objectives: In many health systems, the costs of surgical implants are one of the largest components for surgical budgets, and economies of scale in purchasing agreements do not always provide increased value due to lack of data transparency and administrative complexity. The purpose of the study was to determine if clinician-informed, well-defined negotiation strategies informed by market-based pricing and volume data from supply chain experts within the health system could achieve lower pricing levels for spinal implants and reduce the number of vendors.Methods: Market data based upon pricing levels for implants were reviewed from an industry implant price database and utilized by surgeon clinicians and supply chain management (SCM) to select benchmark pricing levels for common spine implants used at our institution.Results: Benchmark modeling to the 25 th percentile among comparable institutions was used in the request for proposal (RFP) sent to all vendors. After three rounds of structured negotiation involving SCM and surgeon leaders, 20% savings over the previous year's total spend was achieved, with a total savings upward of one million dollars; 8 of 22 vendors were excluded from the system. Conclusion:Negotiation tactics included utilizing benchmark pricing data, "economies of scale" principles, game theory principles, and strong internal communication strategies between supply chain, physician leadership, and actively practicing surgeons. These findings demonstrate that there is significant opportunity for healthcare SCM to further negotiate contracts and achieve favorable pricing on items such as spinal implants with surgeon collaboration and utilization of benchmark data.