Development of a Decision Support Model for Scheduling Clinical Studies and Assigning Medical Personnel

Grunow, Martin; Günther, Hans‐Otto; Yang, Gang

doi:10.1007/s10729-004-7539-x

Cited by 11 publications

(8 citation statements)

References 29 publications

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“…Gupta and Denton (2008) uses the term "indirect waiting" to describe the wait to get an appointment which we have captured at the daily level as TFAV. A prior model by Grunow et al (2004) determines the best start times of trials (within a day) and the allocation of individual personnel to clinical tasks using an MIP model. But this paper does not consider items (ii), (iii), and (vi)-(x).…”

Section: Literature Surveymentioning

confidence: 99%

Access planning and resource coordination for clinical research operations

et al. 2019

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This research creates an operations engineering and management methodology to optimize a complex operational planning and coordination challenge faced by sites that perform clinical research trials. The time-sensitive and resource-specific treatment sequences for each of the many trial protocols conducted at a site make it very difficult to capture the dynamics of this unusually complex system. Existing approaches for site planning and participant scheduling exhibit both excessively long and highly variable Time to First Available Visit (TFAV) waiting times and high staff overtime costs. We have created a new method, termed CApacity Planning Tool And INformatics (CAPTAIN) that provides decision support to identify the most valuable set of research trials to conduct within available resources and a plan for how to book their participants. Constraints include (i) the staff overtime costs, and/or (ii) the TFAV by trial. To estimate the site's metrics via a Mixed Integer Program, CAPTAIN combines a participant trajectory forecasting with an efficient visit booking reservation plan to allocate the date for the first visit of every participant's treatment sequence. It also plans a daily nursing staff schedule that is optimized together with the booking reservation plan to optimize each nurse's shift assignments in consideration of participants' requirements/needs.

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Section: Literature Surveymentioning

confidence: 99%

Access planning and resource coordination for clinical research operations

et al. 2019

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“…Spyropoulos [46] classified the infrastructure of a hospital into four groups including personnel, intensive care units, surgical operation units and specialized laboratories. The existing studies on hospital scheduling or planning problems can also be categorized into one of these four groups: personnel [1,2,24], intensive care units [30], surgical operation units [21] and specialized laboratories [38]. These groups are coordinated in such a way as to provide timely services for both routine and emergent cases.…”

Section: Literature Review 21 Hospital Schedulingmentioning

confidence: 99%

“…These groups are coordinated in such a way as to provide timely services for both routine and emergent cases. For example, Grunow et al [24] presented a hierarchical modeling approach for scheduling clinical studies in new drug development, and for assigning medical personnel to tasks in a pre-defined chronological order. Chien et al [12,13] also employed data mining and GA in rehabilitation patient scheduling and conducted a comparison of different algorithms.…”

Section: Literature Review 21 Hospital Schedulingmentioning

confidence: 99%

Decision support system for rehabilitation scheduling to enhance the service quality and the effectiveness of hospital resource management

Huang¹,

Zheng²,

Chien³

2012

Journal of the Chinese Institute of Industrial Engineers

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In hospitals, rehabilitation inpatients often complain about long waiting time between the therapeutic processes. Due to the partial precedence constraints of rehabilitation therapies, the rehabilitation scheduling problem is a hybrid shop scheduling problem. This article aims to construct a decision support system for rehabilitation scheduling. Equipped with the developed genetic algorithm, this system can generate the optimal schedules for rehabilitation patients to minimize waiting time and thus enhance service quality and overall resource effectiveness of rehabilitation facilities. The developed system is also equipped with GUI (graphical user interfaces) to provide scheduling information including Gantt charts and scheduling lists to support various users including therapists and inpatients. We conducted an empirical study in a general hospital for validation. The results showed that the waiting time of each inpatient is reduced significantly and thus demonstrated the practical viability of the proposed solution to enhance the effectiveness of hospital resource management. The developed system has been implemented online in the hospital.

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“…However, our work focuses on the here-andnow problem of scheduling a fixed set of projects. Grunow et al (2004) present an integer programming model for personnel scheduling in service companies for execution of clinical trials. However, in this paper we focus on managing the internal resources of the pharmaceutical firm involved in testing and sample manufacturing activities rather than clinical trial execution.…”

Section: Literature Reviewmentioning

confidence: 99%

Lagrangian heuristics for scheduling new product development projects in the pharmaceutical industry

et al. 2007

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To stay ahead of their competition, pharmaceutical firms must make effective use of their new product development (NPD) capabilities by efficiently allocating its analytical, clinical testing and manufacturing resources across various drug development projects. The resulting project scheduling problems involve coordinating hundreds of testing and manufacturing activities over a period of several quarters. Most conventional integer programming approaches are computationally impractical for problems of this size, while priority rule-driven heuristics seldom provide consistent solution quality. We propose a Lagrangian decomposition (LD) heuristic that exploits the special structure of these problems. Some resources (typically manpower) are shared across all on-going projects while others (typically equipment) are specific to individual project categories. Our objective function is a weighted discounted cost expressed in terms of activity completion times. The LD heuristics were subjected to a comprehensive experimental study based on typical operational instances. While the conventional "Reward-Risk" priority rule heuristic generates duality gaps between 47-58%, the best LD heuristic achieves duality gaps between 10-20%. The 404 G. Blau et al.LD heuristics also yield makespan reductions of over 30% over the Reward-Risk priority rule.

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Development of a Decision Support Model for Scheduling Clinical Studies and Assigning Medical Personnel

Cited by 11 publications

References 29 publications

Access planning and resource coordination for clinical research operations

Access planning and resource coordination for clinical research operations

Decision support system for rehabilitation scheduling to enhance the service quality and the effectiveness of hospital resource management

Lagrangian heuristics for scheduling new product development projects in the pharmaceutical industry

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