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H Liu - One of the best experts on this subject based on the ideXlab platform.
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SU-E-T-198: Patient Scheduling Monitor (PSM)-A New Tool for Radiation Therapy Patient Scheduling and Workflow Management in an Increasingly Digital Environment.
Medical physics, 2012Co-Authors: H Liu, J Kim, Z ChenAbstract:To develop an easy-to-use and customizable Patient Scheduling Monitor for 1) active monitoring of radiation therapy workflow from CT simulation to the start of treatment and 2) for optimizing the workflow based on treatment complexity. Microsoft Access database and Visual Basic language were used to create an in-house software application, Patient Scheduling Monitor (PSM). The PSM was designed with three functional modules: a Patient schedule calculator, a workflow progress tracker, and a workflow evaluator. The PSM divides the radiation treatment workflow into 6 tasks including image acquisition/fusion, target delineation, dosimetry planning, MD review, physics QA and RTT QA. On the day of CT simulation, the Scheduling calculator generates a planned timeline for each task based on the CT-simulation date and the default standard established for each given task and treatment type. Each task within the PSM can also be individualized as needed. After simulation, the progress tracker enables staff to actively monitor the workflow. The workflow evaluator will query the database and analyze the planned versus actual timeline and provide data for future workflow analysis. We have used the PSM since Nov, 2011 for 186 Patients. The PSM has allowed us to provide Patient start times at the completion of simulation. It has helped to improve Patient satisfaction. The workflow progress tracker enabled us to actively manage the workflow. Currently, for Patients managed using the PSM, no reScheduling has been required. The use of PSM has reduced the average CT simulation to treatment start times. It also has improved intradepartmental communications. An easy-to-use Patient Scheduling monitor has been developed. The PSM has been shown to be an efficient and effective tool in managing, assessing and improving the radiation therapy workflow and will be useful in managing the timelines in an increasingly dynamic working environment. © 2012 American Association of Physicists in Medicine.
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SU‐E‐T‐198: Patient Scheduling Monitor (PSM)‐A New Tool for Radiation Therapy Patient Scheduling and Workflow Management in an Increasingly Digital Environment
Medical Physics, 2012Co-Authors: H Liu, John J. Kim, Zhe Jay ChenAbstract:Purpose: To develop an easy‐to‐use and customizable Patient Scheduling Monitor for 1) active monitoring of radiation therapy workflow from CT simulation to the start of treatment and 2) for optimizing the workflow based on treatment complexity. Methods: Microsoft Access database and Visual Basic language were used to create an in‐house software application, Patient Scheduling Monitor (PSM). The PSM was designed with three functional modules: a Patient schedule calculator, a workflow progress tracker, and a workflow evaluator. The PSM divides the radiation treatment workflow into 6 tasks including image acquisition/fusion, target delineation, dosimetry planning, MD review, physics QA and RTT QA. On the day of CT simulation, the Scheduling calculator generates a planned timeline for each task based on the CT‐simulation date and the default standard established for each given task and treatment type. Each task within the PSM can also be individualized as needed. After simulation, the progress tracker enables staff to actively monitor the workflow. The workflow evaluator will query the database and analyze the planned versus actual timeline and provide data for future workflow analysis.Results: We have used the PSM since Nov, 2011 for 186 Patients. The PSM has allowed us to provide Patient start times at the completion of simulation. It has helped to improve Patient satisfaction. The workflow progress tracker enabled us to actively manage the workflow. Currently, for Patients managed using the PSM, no reScheduling has been required. The use of PSM has reduced the average CT simulation to treatment start times. It also has improved intradepartmental communications. Conclusions: An easy‐to‐use Patient Scheduling monitor has been developed. The PSM has been shown to be an efficient and effective tool in managing, assessing and improving the radiation therapy workflow and will be useful in managing the timelines in an increasingly dynamic working environment.
Franklin Dexter - One of the best experts on this subject based on the ideXlab platform.
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changing allocations of operating room time from a system based on historical utilization to one where the aim is to schedule as many surgical cases as possible
Anesthesia & Analgesia, 2002Co-Authors: Franklin Dexter, Alex MacarioAbstract:UNLABELLED: Many facilities allocate operating room (OR) time based on historical utilization of OR time. This assumes that there is a fixed amount of regularly scheduled OR time, called "block time". This "Fixed Hours" system does not apply to many surgical suites in the US. Most facilities make OR time available for all its surgeons' Patients, even if cases are expected to finish after the end of block time. In this setting, OR time should be allocated to maximize OR efficiency, not historical utilization. Then, cases are scheduled either on "Any Workday" (i.e., date chosen by Patient and surgeon) or within a reasonable time (e.g., "Four Weeks"). In this study, we used anesthesia billing data from two facilities to study statistical challenges in converting from a Fixed Hours to an Any Workday or Four Weeks Patient Scheduling system. We report relationships among the number of staffed ORs (i.e., first case of the day starts), length of the regularly scheduled OR workday, OR efficiency, OR staffing cost, and changes in services' OR allocations. These relationships determine the expected changes in each service's OR allocation, when a facility using Fixed Hours considers converting to the Any Workday or Four Weeks systems. IMPLICATIONS: We investigated the complex relationships among the number of surgical services, number of staffed operating rooms (ORs), length of the regularly scheduled OR workday, efficiency of use of OR time, OR staffing cost, and changes in each services' allocated OR time.
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how to schedule elective surgical cases into specific operating rooms to maximize the efficiency of use of operating room time
Anesthesia & Analgesia, 2002Co-Authors: Franklin Dexter, Rodney D TraubAbstract:We considered elective case Scheduling at hospitals and surgical centers at which surgeons and Patients choose the day of surgery, cases are not turned away, and anesthesia and nursing staffing are adjusted to maximize the efficiency of use of operating room (OR) time. We investigated Scheduling a new case into an OR by using two Patient-Scheduling rules: Earliest Start Time or Latest Start Time. By using several scenarios, we showed that the use of Earliest Start Time is rational economically at such facilities. Specifically, it maximizes OR efficiency when a service has nearly filled its regularly scheduled hours of OR time. However, Latest Start Time will perform better at balancing workload among services’ OR time. We then used historical case duration data from two facilities in computer simulations to investigate the effect of errors in predicting case durations on the performance of these two heuristics. The achievable incremental reduction in overtime by having perfect information on case duration versus using historical case durations was only a few minutes per OR. The differences between Earliest Start Time and Latest Start Time were also only a few minutes per OR. We conclude that for facilities at which the goals are, in order of importance, safety, Patient and surgeon access to OR time, and then efficiency, few restrictions need to be placed on Patient Scheduling to achieve an efficient use of OR time. (Anesth Analg 2002;94:933–42)
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statistical power analysis to estimate how many months of data are required to identify operating room staffing solutions to reduce labor costs and increase productivity
Anesthesia & Analgesia, 2002Co-Authors: Richard H Epstein, Franklin DexterAbstract:UNLABELLED We performed a statistical power analysis to determine how many historical data are needed for optimal operating room (OR) management decision making. The work applies to hospitals that provide service for all of its surgeons' elective cases on whatever workday the surgeons and Patients choose. The hospital and anesthesia group adjust OR staffing and Patient Scheduling to care for the Patients while minimizing OR staffing costs and maximizing labor productivity. Two years of data were obtained from a seven-OR surgical suite. The data were repeatedly split into training and testing datasets. The optimal staffing solution was calculated for each training dataset to maximize the efficiency of OR time usage and was then applied to the corresponding testing dataset. Training datasets ranged in size from 30 to 270 consecutive workdays. With 30 workdays of data, the statistical method identified staffing solutions that had an average of 35% decreased costs and 27% increased productivity as compared to the existing staffing plan. There was no significant improvement in performance with more than 210 workdays (10 mo) of data. With 30 workdays of OR or anesthesia group data, the optimization method can significantly reduce staffing costs and increase productivity compared with existing staffing. When applied routinely for adjusting staffing (e.g., on a quarterly basis), 9 to 12 mo of data should be used. IMPLICATIONS With 30 workdays of operating room or anesthesia group data, the optimization method can propose staffing solutions that significantly decrease costs and increase productivity compared with existing staffing solutions. We recommend that, when the statistical method is applied routinely for adjusting staffing (e.g., on a quarterly basis), 9 to 12 mo of data be used.
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effect of compensation and Patient Scheduling on or labor costs
AORN Journal, 2000Co-Authors: Alex Macario, Franklin DexterAbstract:To determine whether to accept a contract to provide additional surgical cases, OR managers must determine the incremental costs of caring for the new Patients. The expected profitability of the contract can be computed by subtracting the incremental costs from the revenue. For surgical procedures, the incremental costs of OR labor significantly depend on how employees are paid (e.g., part-time versus full-time). If a surgical suite employs full-time staff members, incremental labor costs also are affected by how the day and time of Patients' cases are selected (e.g., whether new cases are scheduled weeks in advance by the surgeon and the Patient, or are performed on short notice based on the discretion of the surgical suite). This article explains how to estimate the incremental costs of staffing an OR for a case and discusses the use of internet-based online exchanges to match demand for OR time for additional cases to available unused OR capacity in variety of surgical suites.
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an operating room Scheduling strategy to maximize the use of operating room block time computer simulation of Patient Scheduling and survey of Patients preferences for surgical waiting time
Anesthesia & Analgesia, 1999Co-Authors: Franklin Dexter, Alex Macario, Rodney D Traub, Margaret Hopwood, David A LubarskyAbstract:Determining the appropriate amount of block time to allocate to surgeons and selecting the days on which to schedule elective cases can maximize operating room (OR) use.We used computer simulation to model OR Scheduling. Inputs in the computer model included different methods to determine when a pat
E. Grace Mary Kanaga - One of the best experts on this subject based on the ideXlab platform.
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A Distributed Optimized Patient Scheduling using Partial Information
International Journal of Artificial Intelligence & Applications, 2012Co-Authors: G. Mageshwari, E. Grace Mary KanagaAbstract:A software agent may be a member of a Multi-Agent System (MAS) which is collectively performing a range of complex and intelligent tasks. In the hospital, Scheduling decisions are finding difficult to schedule because of the dynamic changes and distribution. In order to face this problem with dynamic changes in the hospital, a new method, Distributed Optimized Patient Scheduling with Grouping (DOPSG) has been proposed. The goal of this method is that there is no necessity for knowing Patient agents information globally. With minimal information this method works effectively. Scheduling problem can be solved for multiple departments in the hospital. Patient agents have been scheduled to the resource agent based on the Patient priority to reduce the waiting time of Patient agent and to reduce idle time of resources.
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Literature Review on Patient Scheduling Techniques
2012Co-Authors: G. Mageshwari, E. Grace Mary KanagaAbstract:Patients need to undergo several checkups, tests, surgery and treatments according to their illness. This paper describes the challenges of Patient Scheduling and Patient Scheduling techniques. An efficient Scheduling technique is needed to minimize the waiting time of Patients and to improve the resource utilization. The Patient treatment processes are not completely decided at the beginning of treatment. The goal of the Patient Scheduling is to develop a Scheduling system that manages Patient-focused schedules and treat the Patients immediately. Patient Scheduling is difficult to deal because of the complexity involved in the problem. Software agent based-Scheduling system is a good option to schedule the Patients efficiently in the hospital.
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Multi-agent based Patient Scheduling Using Particle Swarm Optimization
Procedia Engineering, 2012Co-Authors: E. Grace Mary Kanaga, M. L. ValarmathiAbstract:Abstract Patient Scheduling is the process of Scheduling and sequencing the Patients for various multiple resources in health care domain. The multiple constraints and multiple goals to be achieved in minimal time makes this problem highly complex. Computational complexity is high in using exact methods for solving optimization problem. Motivated by the real needs in hospital environments, this paper focuses on finding an optimal schedule using the meta-heuristic technique Particle Swarm Optimization (PSO) and coordinating the hospital environment using multi-agents. Agents have been proved to be an effective approach to resource allocation because of its coordination and social abilities. For solving, various agents like Patient Agent (PA), Resource Agent (RA) and Common Agent (CA) are used. In addition to these agents a PSO Agent is used to perform the PSO optimization. On arrival of the Patients, this PSO agent is called to perform PSO optimization dynamically and an optimized schedule is generated. The objective is to reduce the Patient waiting time in the hospital. This agent based approach is implemented in JADE (Java Agent Development Environment) and tested for different data sets. The results are compared with the traditional dispatching rules like First Come First Serve(FCFS), Minimum Slack(MS),Shortest Processing Time(SPT) and Longest Processing Time (LPT). The performance improvement of total weighted waiting time in Agent-based Patient Scheduling using PSO (APS-PSO) for 10 resources and 50 Patients is 4.13% when compared to the best performing dispatching rule MS and 52% with respect to LPT. Similarly, there is an average of 8.69% improvement in terms of total weighted completion time. When the number of late Patient is considered, the performance of the PSO based approach increased by 17%.
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A Novel 3D Approach for Patient Schedule Using Multi-Agent Coordination
Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition, 2012Co-Authors: E. Grace Mary Kanaga, M. L. Valarmathi, Preethi S. H. DariusAbstract:This chapter presents a novel 3D approach for Patient Scheduling (3D-PS) using multi-agents. Here the 3Ds refers to the Distributed, Dynamic and Decentralized nature of the Patient Scheduling. As in many other Scheduling problems, in the hospital domain, a major problem is the efficient allocation of resources to the Patients. The resources here mean the doctor, diagnosing equipments, lab tests, et cetera. Commonly, Patient Scheduling is performed manually by human schedulers with no automated support. Human Scheduling is not efficient, because the nature of the problem is very complex; it is inherently distributed, dynamic, and decentralized. Since agents are known to represent distributed environment well and also being capable of handling dynamism, an agent based approach is chosen. The objectives are to reduce Patient waiting times, minimize the Patient stay in the hospital, and to improve resource utilization in hospitals. The comparison of several agent-based approaches is also reviewed, and the auction-based approach is chosen. The complete multi-agent framework given in literature is adapted to suit the Patient Scheduling scenario. The Patient Scheduling system is implemented in the JADE platform where Patients and resources are represented as agents. The chief performance metric is the weighted tardiness which has to be minimized in order to obtain an effective schedule. The experiment is carried out using constant number of resources and varying number of Patients. The simulation results are presented and analyzed. 3D-PS produces up to 30% reduction in total weighted tardiness in a distributed environment, as compared to other traditional algorithms. A further enhancement to this approach aims to reduce the communication overhead by reducing the number of messages passed and hence resulting in a better coordination mechanism. This auction based mechanism aims to provide the basic framework for future enhancements on Patient Scheduling.
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Synergy of Multi-agent Coordination Technique and Optimization Techniques for Patient Scheduling
Information Technology and Mobile Communication, 2011Co-Authors: E. Grace Mary Kanaga, M. L. ValarmathiAbstract:This paper discusses about the synergy of multi-agent coordination technique with different optimization techniques viz. integer programming optimization with Lagrangian Relaxation and a simple heuristic approach with experience based learning effect in case of Patient Scheduling problem. The objective is to achieve good reduction in waiting time of the Patients in hospital while achieving better resource utilization. The proposed methods have been implemented in JADE and the results are compared with the traditional Scheduling techniques. The performance of the proposed methods based on total weighted waiting time of the Patient increases by 15% to 52% when compared to traditional Scheduling techniques. The waiting time of the Patient is further reduced by 5%, when experience based learning effect is incorporated. Optimization based on simple heuristic method shows that it gives near optimal solution with drastic reduction in the execution time ie. 90.84% when compared to integer programming approach.
Zhe Jay Chen - One of the best experts on this subject based on the ideXlab platform.
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SU‐E‐T‐198: Patient Scheduling Monitor (PSM)‐A New Tool for Radiation Therapy Patient Scheduling and Workflow Management in an Increasingly Digital Environment
Medical Physics, 2012Co-Authors: H Liu, John J. Kim, Zhe Jay ChenAbstract:Purpose: To develop an easy‐to‐use and customizable Patient Scheduling Monitor for 1) active monitoring of radiation therapy workflow from CT simulation to the start of treatment and 2) for optimizing the workflow based on treatment complexity. Methods: Microsoft Access database and Visual Basic language were used to create an in‐house software application, Patient Scheduling Monitor (PSM). The PSM was designed with three functional modules: a Patient schedule calculator, a workflow progress tracker, and a workflow evaluator. The PSM divides the radiation treatment workflow into 6 tasks including image acquisition/fusion, target delineation, dosimetry planning, MD review, physics QA and RTT QA. On the day of CT simulation, the Scheduling calculator generates a planned timeline for each task based on the CT‐simulation date and the default standard established for each given task and treatment type. Each task within the PSM can also be individualized as needed. After simulation, the progress tracker enables staff to actively monitor the workflow. The workflow evaluator will query the database and analyze the planned versus actual timeline and provide data for future workflow analysis.Results: We have used the PSM since Nov, 2011 for 186 Patients. The PSM has allowed us to provide Patient start times at the completion of simulation. It has helped to improve Patient satisfaction. The workflow progress tracker enabled us to actively manage the workflow. Currently, for Patients managed using the PSM, no reScheduling has been required. The use of PSM has reduced the average CT simulation to treatment start times. It also has improved intradepartmental communications. Conclusions: An easy‐to‐use Patient Scheduling monitor has been developed. The PSM has been shown to be an efficient and effective tool in managing, assessing and improving the radiation therapy workflow and will be useful in managing the timelines in an increasingly dynamic working environment.
Z Chen - One of the best experts on this subject based on the ideXlab platform.
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SU-E-T-198: Patient Scheduling Monitor (PSM)-A New Tool for Radiation Therapy Patient Scheduling and Workflow Management in an Increasingly Digital Environment.
Medical physics, 2012Co-Authors: H Liu, J Kim, Z ChenAbstract:To develop an easy-to-use and customizable Patient Scheduling Monitor for 1) active monitoring of radiation therapy workflow from CT simulation to the start of treatment and 2) for optimizing the workflow based on treatment complexity. Microsoft Access database and Visual Basic language were used to create an in-house software application, Patient Scheduling Monitor (PSM). The PSM was designed with three functional modules: a Patient schedule calculator, a workflow progress tracker, and a workflow evaluator. The PSM divides the radiation treatment workflow into 6 tasks including image acquisition/fusion, target delineation, dosimetry planning, MD review, physics QA and RTT QA. On the day of CT simulation, the Scheduling calculator generates a planned timeline for each task based on the CT-simulation date and the default standard established for each given task and treatment type. Each task within the PSM can also be individualized as needed. After simulation, the progress tracker enables staff to actively monitor the workflow. The workflow evaluator will query the database and analyze the planned versus actual timeline and provide data for future workflow analysis. We have used the PSM since Nov, 2011 for 186 Patients. The PSM has allowed us to provide Patient start times at the completion of simulation. It has helped to improve Patient satisfaction. The workflow progress tracker enabled us to actively manage the workflow. Currently, for Patients managed using the PSM, no reScheduling has been required. The use of PSM has reduced the average CT simulation to treatment start times. It also has improved intradepartmental communications. An easy-to-use Patient Scheduling monitor has been developed. The PSM has been shown to be an efficient and effective tool in managing, assessing and improving the radiation therapy workflow and will be useful in managing the timelines in an increasingly dynamic working environment. © 2012 American Association of Physicists in Medicine.