Configuring surgical instrument trays to reduce costs

Dobson, Gregory; Seidmann, Abraham; Tilson, Vera; Froix, Anthony

doi:10.1080/19488300.2015.1094759

Cited by 30 publications

(33 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Outro fato gerador de otimização é a redução do tempo de preparo das bandejas, aproximadamente 28% menor (diferença entre o tempo de preparo e de empacotamento de bandejas revisadas), diante da cronoanálise realizada antes da racionalização e publicada em outro estudo da própria instituição 16 . Tais resultados ratificam os achados da literatura [7][8][9][10]12 .…”

Section: Discussionunclassified

“…A aplicação do algoritmo proposto permitiu identificar novas opções de formação de bandejas que possibilitaram a otimização do processo de esterilização. A literatura evidencia que a aplicação de técnicas de clusterização, que consiste em gerar agrupamento de observações (instrumentais, neste caso) com características similares, e de modelagem, permite propor agrupamentos e soluções ótimas para o processo de racionalização 10,12 .…”

Section: Introductionunclassified

See 1 more Smart Citation

Sistemática para racionalização de instrumentais de bandejas cirúrgicas

et al. 2018

View full text Add to dashboard Cite

Objetivo: Relatar a experiência de desenvolver uma sistemática para racionalização de instrumentais em bandejas cirúrgicas. Método: Estudode desenvolvimento de sistemática para racionalização de instrumentais, realizado em 2015, a partir do método qualitativo, em um centro de materiaise esterilização (CME) de um hospital universitário federal de Porto Alegre, Brasil. Resultados: Houve redução média do quantitativo de instrumentaisem bandejas institucionais em 10,92%; diminuição de bandejas de propriedade das equipes médicas, sendo 84,06% pertencentes à equipe da otorrinolaringologia;e inativação definitiva de 369 instrumentais da cirurgia ortopédica, o que significou 72,84% do total dos instrumentais inativados. Além disso,houve condução de melhorias no gerenciamento de instrumentais, otimização do tempo de preparo e redução da esterilização por expiração do prazode utilização. Conclusão: A realocação de instrumentais e o acréscimo de peças em bandejas específicas permitiu a reavaliação das solicitações de comprasde instrumentais e a melhoria das relações entre as equipes. Essa sistemática contribuiu significativamente para o gerenciamento de instrumentais,otimizando processos e envolvendo as equipes cirúrgicas no trabalho do CME e evidenciou que pode ser aplicada em outras instituições.

show abstract

Section: Discussionunclassified

Section: Introductionunclassified

Sistemática para racionalização de instrumentais de bandejas cirúrgicas

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Each order item in an order set can be defaulted ON or OFF according to clinical relevance and frequency of use (Gartner et al (2017)) With respect to the solution methodology, we use a different heuristic approach. Similarly, Dobson et al (2015) propose a mathematical programming formulation to decide on the composition of instrument trays to minimize the costs of owning, maintaining, and using both the trays and the instruments.…”

Section: Clustering Medical Itemsmentioning

confidence: 99%

Reducing clinical workload in the care prescription process: optimization of order sets

Gärtner

Zhang

Padman

2018

IMA Journal of Management Mathematics

View full text Add to dashboard Cite

Order sets are a critical component in hospital information systems, designed to substantially reduce clinician workload and improve patient safety and health outcomes. Order sets represent clusters of order items, such as medications prescribed at hospital admission, that are administered to patients during their hospital stay. In prior research, we constructed order sets for defined time intervals during inpatient stay based on historical data on items ordered by clinicians across a large number of patients. In this study, we build on our prior work to formulate a mathematical program for optimizing order sets that are applicable across the entire duration of inpatient stay and are independent of the time intervals. Furthermore, due to the intractability of the problem, we develop a Greedy algorithm to tackle real-world test instances. We extract data sets for three clinical scenarios and conduct both cognitive and physical workload analyses. Finally, we extend a software application to facilitate the comparison of order sets by practitioners. Our computational results reveal that the optimization-based physical and cognitive workload models can solve small test instances to optimality. However, for real-world instances, the Greedy heuristic is more competitive, in particular when physical workload instead of cognitive workload is the optimization objective. Overall, the Greedy heuristic can solve the test instances within one minute and outperforms the mathematical program in 2/3 of the test instances within a time limit of ten minutes, demonstrating a feasible and promising approach to develop inpatient order sets that can subsequently be validated by clinical experts.

show abstract

“…One of the first binary programs to model clustering problems is based on Vinod [24] which is related to our modelling approach because we employ binary decision variables to assign order items to clusters, among others. Focusing on healthcare, Cardoen et al [6] and Dobson et al [9] group medical items for surgeries which can be seen as a clustering problem. A major difference is that we have a time interval-dependent demand function which captures the patients' length of stay in the hospital.…”

Section: Or Applied To Clustering and Health Carementioning

confidence: 99%

“…The user then decides through one click with a cognitive workload c off,mult that all over-prescribed items are de-selected automatically in order sets. Constraints (9) ensure that a patient's required order item can only be confirmed on if it is defaulted ON in the corresponding order set. Constraints (10) ensure that a patient's required order item can only be confirmed OFF if it is defaulted OFF in the corresponding order set.…”

Section: Cognitive Workload (Cw)mentioning

confidence: 99%

Cognitive workload reduction in hospital information systems

Gärtner

Zhang

Padman

2017

Health Care Manag Sci

View full text Add to dashboard Cite

Order sets are a critical component in hospital information systems that are expected to substantially reduce physicians' physical and cognitive workload and improve patient safety. Order sets represent time intervalclustered order items, such as medications prescribed at hospital admission, that are administered to patients during their hospital stay. In this paper, we develop a mathematical programming model and an exact and a heuristic solution procedure with the objective of minimizing physicians' cognitive workload associated with prescribing order sets. Furthermore, we provide structural insights into the problem which lead us to a valid lower bound on the order set size. In a case study using order data on Asthma patients with moderate complexity from a major pediatric hospital, we compare the hospital's current solution with the exact and heuristic solutions on a variety of performance metrics. Our computational results confirm our lower bound and reveal that using a time interval decomposition approach substantially reduces computation times for the mathematical program, as does a K−means clustering based decomposition approach which, however, does not guarantee optimality because it violates the lower bound. The results of comparing the mathematical program with the current order set The H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA, USA configuration in the hospital indicates that cognitive workload can be reduced by about 20.2% by allowing 1 to 5 order sets, respectively. The comparison of the K−means based decomposition with the hospital's current configuration reveals a cognitive workload reduction of about 19.5%, also by allowing 1 to 5 order sets, respectively. We finally provide a decision support system to help practitioners analyze the current order set configuration, the results of the mathematical program and the heuristic approach.

show abstract

Configuring surgical instrument trays to reduce costs

Cited by 30 publications

References 8 publications

Sistemática para racionalização de instrumentais de bandejas cirúrgicas

Sistemática para racionalização de instrumentais de bandejas cirúrgicas

Reducing clinical workload in the care prescription process: optimization of order sets

Cognitive workload reduction in hospital information systems

Contact Info

Product

Resources

About