Optimization of Orthopedic Surgical Instrument Trays: Lean Principles to Reduce Fixed Operating Room Expenses

Cichos, Kyle H.; Hyde, Zane; Mabry, Scott E.; Ghanem, Elie; Brabston, Eugene W.; Hayes, Leslie; McGwin, Gerald; Ponce, Brent A.

doi:10.1016/j.arth.2019.07.040

Cited by 41 publications

(29 citation statements)

References 42 publications

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“…Since there is great pressure to reduce waste and cost in healthcare [ 24 ], the reprocessing of reusable instruments has grown, resulting in increased workload at the SPDs [ 2 ]. Some researchers have focused on surgical tray optimization by reducing the instruments present on surgical trays or by creating trays for specific procedures [ 2 , 3 , 7 , 23 , 25 , 26 ]. Nevertheless, little emphasis has been brought to surgical instrument processing research in the medical literature [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Kaizen Event in a Sterile Processing Department of a Rural Hospital: A Case Study

Nino

Chipperfield

Valladares

et al. 2020

IJERPH

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Operating Rooms (ORs) generate the largest revenues and losses in a hospital. Without the prompt supply of sterile surgical trays from the Sterile Processing Department (SPD), the OR would not be able to perform surgeries to its busy schedule. Nevertheless, little emphasis has been brought in the medical literature to research on surgical instrument processing in the medical literature. The present study was done applies an Enhanced Kaizen Event (EKE) in the SPD of a rural hospital to identify sources of waste and minimize non-value-added steps in the SPD processes. The EKE consisted of three successive Plan-Do-Check-Act (PDCA) cycles, which focused on improvements at the departmental level first, then at an area level, and finally at the station level. The EKE yielded an improved streamlined workflow and a new design for the SPD layout, one of its areas, and a workstation. This paper aims at building a methodology, including identified steps. Results exhibited a 35% reduction in travel distance by the staff, eliminating non-value-added processes, reducing errors in the sterilization process, and eliminating cross-contamination for sterilized materials.

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Section: Introductionmentioning

confidence: 99%

“…The main idea behind LT is to identify value in any process and eliminate waste [ 6 ]. Approximately 40% of the total healthcare cost is attributed to waste [ 25 ]. Additionally, LT helps to overcome barriers between departments, encouraging them to work together, which results in better service for patients [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Kaizen Event in a Sterile Processing Department of a Rural Hospital: A Case Study

Nino

Chipperfield

Valladares

et al. 2020

IJERPH

View full text Add to dashboard Cite

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“…The accurate prediction of implant sizes used in TKA may allow for consolidation of surgical trays, more efficient operating room processes and streamlined efficiency, and lower hospital costs [4,7]. To this end, the present study is a step in this direction.…”

Section: Discussionmentioning

confidence: 97%

“…Although templating has become a useful tool for some knee surgeons in preoperative planning because of these benefits, mismatches between planned and final component sizes still occur. In community settings where additional trays may not be readily available, inaccurate preoperative templating may result in longer operative times and an inability to accommodate appropriate implant selection [3,4]. Furthermore, adequate planning may allow for better inventory management and aid the workflow in community hospitals and surgery centers.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Predicts Femoral and Tibial Implant Size Mismatch for Total Knee Arthroplasty

et al. 2021

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Background: Despite reasonable accuracy with preoperative templating, the search for an optimal planning tool remains an unsolved dilemma. The purpose of the present study was to apply machine learning (ML) using preoperative demographic variables to predict mismatch between templating and final component size in primary total knee arthroplasty (TKA) cases. Methods: This was a retrospective case-control study of primary TKA patients between September 2012 and April 2018. The primary outcome was mismatch between the templated and final implanted component sizes extracted from the operative database. The secondary outcome was mismatch categorized as undersized and oversized. Five supervised ML algorithms were trained using 6 demographic features. Prediction accuracies were obtained as a metric of performance for binary mismatch (yes/no) and multilevel (undersized/correct/oversized) classifications. Results: A total of 1801 patients were included. For binary classification, the best-performing algorithm for predicting femoral and tibial mismatch was the stochastic gradient boosting model (area under the curve: 0.76/0.72, calibration intercepts: 0.05/0.05, calibration slopes: 0.55/0.7, and Brier scores: 0.20/0.21). For multiclass classification, the best-performing algorithms had accuracies of 83.9% and 82.9% for predicting the concordance/mismatch of the femoral and tibial implant, respectively. Model predictions of greater than 51.0% and 47.9% represented high-risk thresholds for femoral and tibial sizing mismatch, respectively. Conclusions: ML algorithms predicted templating mismatch with good accuracy. External validation is necessary to confirm the performance and reliability of these algorithms. Predicting sizing mismatch is the first step in using ML to aid in the prediction of final TKA component sizes. Further studies to optimize parameters and predictions for the algorithms are ongoing.

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“…Cost savings were determined by evaluating the cost of processing the instruments (e.g., detergents, equipment maintenance, employee salary, utilities). After optimization of the trays, the mean time to clean each tray decreased by 3.2 minutes, resulting in a cost savings of $15,533 per tray 5 …”

Section: Optimizing Processing Efficiencymentioning

confidence: 99%

Key considerations for processing surgical instruments

Croke¹

2021

AORN Journal

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Optimization of Orthopedic Surgical Instrument Trays: Lean Principles to Reduce Fixed Operating Room Expenses

Cited by 41 publications

References 42 publications

An Enhanced Kaizen Event in a Sterile Processing Department of a Rural Hospital: A Case Study

An Enhanced Kaizen Event in a Sterile Processing Department of a Rural Hospital: A Case Study

Machine Learning Predicts Femoral and Tibial Implant Size Mismatch for Total Knee Arthroplasty

Key considerations for processing surgical instruments

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