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CUSP teams and 7 CUSP working groups by surgical subspeci- alty, intervention, and campus to initiate surgical best practices to reduce surgical complications, including SSIs. These teams and working groups have implemented 29 major perioperative quality improvement interventions. In this study, we estimated the hos- pital costs and savings associated with TOH’s quality improve- ment program, namely NSQIP and CUSP, and we performed a return on investment (ROI) analysis based on total hospital investment in SSI prevention compared to savings from averted SSI cases.
Methods Study setting and population
This retrospective study used aggregated records for all TOH patients who underwent surgical procedures between April 2010 and January 2015. No individual patient data were used. The Ottawa Hospital is a tertiary-care teaching institution containing 1,118 beds.
Data source and costing
Data used for this study are obtained from TOH Data Warehouse, a relational database containing the operational information of each of TOH’s campuses. In this study, hospital costs for each inpatient encounter were identified within the case-costing sys- tem of TOH Data Warehouse, a standardized case-costing methodology developed by the Ontario Case Costing Initiative14 based on the Canadian Institute for Health Information Man- agement Information Systems guidelines.15 The case-costing system links financial, clinical, and patient activity information stored within the Data Warehouse to define intermediate pro- ducts, such as nursing time, medications, and laboratory tests. The total hospital costs were equal to the sum of the direct and indirect costs for each intermediate product used during an encounter for each patient. The Data Warehouse houses administrative data dating back
to 1996; however, SSI cases have only been reliably collected through NSQIP since March 2010. We used monthly incidence rate of SSI, incurred hospitalization costs, and average hospital length of stay from April 2010 to January 2015 from the NSQIP system. Surgical patient information and hospital costs were aggregated per month by surgical specialty (ie, department or division) in 3 broadly planned admissions categories: inpatients, overnight patients (only 1 night hospitalization) and day surgery patients, and SSI infection status. Attributable cost of SSI was calculated independently for each of the 3 surgical categories and then combined using a weighted average based on the proportion of surgical patients in each category. The cost of NSQIP is based on an annual fee to the hospital,
which includes licensing, information technology and main- tenance, and designated staff to input health records information into the data repository system. Although NSQIP monitors between 250 and 500 complications,7 guidance from the hospital quality improvement teams led us to conclude that the decision to invest in NSQIP is based on the target outcomes of 19 major postoperative complications. SSI is one of these outcomes. Our model attributes 1 of 19 of the total annual cost of NSQIP (5.3%) to SSI prevention, as shown in the annual cost column reported in Table 2 hospital costs.
Sasha van Katwyk et al The cost of CUSP is based on the annual average cost to the
hospital of managing the surgical complications prevention initiatives, which includes costs of materials introduced to the surgical protocols and postsurgical care, staff training and mon- itoring time, and salaries of the designated quality improvement coordinators.
Analysis
In the primary analysis for the return on investment model, we calculated the annual savings from averted SSI cases (Table 1). Savings were calculated as a product of the cost per SSI case, and the change in surgical specialty incidence of SSI from the previous year. Surgical department level costs are the share of total annual costs to the hospital to manage NSQIP and CUSP as a proportion of number of surgical patients within that specialty per year. Annual net savings are the difference in total savings from averted SSIs minus the total investment. All costs are adjusted to 2016 Canadian dollars. An annual discount rate of 1.5% was applied to both investment and savings. The return on investment analysis calculates the cumulative net saving over several years and divides the savings over the cumulative spending on NSQIP and CUSP initiatives to provide a cost–benefit ratio as dollar return per dollar spent. We also introduced several scenario analyses to the primary
analysis to determine whether the results indicate the same direction and scale of return on investment if we expanded the analytical perspective. In the first scenario, we asserted the cost of new admissions for
every averted SSI case. The Ottawa Hospital typically operates at full capacity, and consequentially, an averted SSI case represents a now open inpatient space that can be filled. In our first scenario, therefore, we applied the average daily cost of an admitted patient to TOH for the number of added days associated with an SSI case. In the second scenario, we extended the implications of new admissions by adding a fixed payment to the hospital from the health system as added revenue from increasing total patient capacity of the hospital. The manner by which the health system calculates hospital operational reimbursements is complex and is not driven by a single standardized unit of improved patient care or total patients seen. Therefore, we assumed a wide range of revenue received by the hospital per patient based on expert consultation. In the third scenario, we considered potential savings from
averted SSI cases, which expanded the analytical perspective to include both hospital and axillary care institutions (eg, rehabili- tation, long-term care, etc.). An SSI can require additional care following discharge, particularly in the form of home care services that are not captured in the administrative data base search.4 We considered the added costs of postdischarge patient care assuming that this care falls within the cost perspective of the hospital. We performed a one-way sensitivity analysis on all parameters
within the base case model as well as the scenario analyses. The results of the sensitivity analysis are presented in a tornado plot to show what parameters contribute most significantly to model uncertainty. Historical data on SSI incidence were based on limited chart
linkages to administrative costing data, leading to the introduc- tion of NSQIP at TOH. Therefore, we do not have reliable pre- intervention incidence data from which we could calculate the attributable reduction in SSI incidence based on a preintervention incidence trend. We performed a secondary sensitivity analysis that specifically addressed this uncertainty in preintervention
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