Minimizing Earliness/Tardiness costs on multiple machines with an application to surgery scheduling

Otten, Maarten; Braaksma, Aleida; Boucherie, Richard J.

doi:10.1016/j.orhc.2019.100194

Cited by 12 publications

(8 citation statements)

References 25 publications

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“…We attempted to model the spinal block times to various distributions that have been used previously to model anesthesia times, including the normal distribution [ 9 ], two-parameter log-normal [ 10 , 11 ], gamma distribution [ 12 ], and Weibull distribution [ 13 ]; we used Systat version 12 (Systat Software, San Jose, CA). If a known distribution showed a good fit to the data, then we would have calculated the prediction intervals using the parameters of the theoretical distribution [ 11 ].…”

Section: Methodsmentioning

confidence: 99%

Briefest Time to Perform a Series of Preoperative Nerve Blocks in Multiple Patients: A Simulation Study

Epstein¹,

Dexter²,

Mojica³

et al. 2021

Cureus

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IntroductionTo mitigate first-case delays in operating rooms, sufficient additional time must be allotted when anesthesiologists perform preoperative nerve blocks in multiple patients who are scheduled as the initial cases of the day. We used spinal anesthetics performed in dedicated block rooms located just outside the operating room suite to estimate the briefest times needed to complete a series of spinal, epidural, peripheral, or other regional nerve blocks. We followed this approach because even though the studied hospital had a busy regional anesthesia service, sample sizes were insufficient and electronic data were not available to directly study the time to perform the many other nerve blocks they perform. MethodsWe studied a historical cohort of 8,462 adult patients undergoing spinal anesthesia between 2005 and 2017. Preoperative evaluation, consent, and holding area tasks were completed before entering the block room; the times to complete these tasks were not available for study. Upon block room entry, the electronic anesthetic record was started, a timeout conducted with patient participation, vital signs taken, and the spinal performed. The interval from entry until intrathecal injection was the spinal block time. Because fits of these times to probability distributions previously used for anesthesia times were poor (p < 0.001), percentiles of times to perform one or more spinal anesthetics were calculated using Monte-Carlo simulation (100,000 samples with replacement) from the empirical distributions. ResultsThe mean spinal block time was 8.8 minutes. The 90% upper prediction limit for one block was 14 minutes, with progressively decreasing times for each subsequent block for a 90% chance of finishing on time. For example, for three first-case regional or neuraxial blocks performed outside the operating room by one anesthesiologist, the first patient needs to arrive at least 38 minutes earlier than non-block patients to mitigate operating room start delays. ConclusionsThese minimum time estimates can help nursing leadership ensure that sufficient time will be available after patients are ready for anesthesia to avoid first-case delays when preoperative regional anesthesia is performed outside the operating room. Given that inadequate sample size and documentation issues likely exist universally for the various non-neuraxial preoperative nerve blocks, we recommend that hospitals use our estimates as a minimum starting point rather than try to calculate times using their own data. Then, as a systems-based metric to assess all steps in the process, track the percentages of days for which all blocks were completed in sufficient time to avoid a first-case delay for those patients. Adjustments to the arrival times would then be implemented, if needed, to meet hospital objectives for on-time starts.

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Section: Methodsmentioning

confidence: 99%

Briefest Time to Perform a Series of Preoperative Nerve Blocks in Multiple Patients: A Simulation Study

Epstein¹,

Dexter²,

Mojica³

et al. 2021

Cureus

View full text Add to dashboard Cite

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“…Otten and Boucherie [17] consider an optimal schedule for the multiple OR version of the E/T problem under some technical assumptions on the distribution of the surgery duration, which they show to hold for at least the normal distribution. For the multiple OR E/T problem, they extend the SVF rule, i.e., they show that it is optimal to not only apply the SVF rule to the local sequence of surgeries at each OR but also to the global sequence of all surgeries at all ORs.…”

Section: Deviation From the Schedulementioning

confidence: 99%

Robust Surgery Scheduling: A Model-Based Overview

Otten

Bos

Braaksma

et al. 2021

International Series in Operations Research &Amp; Management Science

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In this chapter we give a model-based overview of robust surgery scheduling literature. A robust schedule maintains to perform well in case of disturbances affecting the schedule. We distinguish three types of disturbances that affect the surgery schedule. First, internal disturbances, such as variations in surgical time. Second, external disturbances, such as non-elective surgeries. Third, disturbances due to artificial variability, such as unavailable operating rooms. For each of these disturbances, we provide an overview of models, described in literature, which reduce the effect of the disturbance on the schedule by making it robust. Furthermore, we identify relevant open problems.

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“…Artificial intelligence (AI) and machine learning (ML) are transforming the optimization of clinical and patient workflows in healthcare. The adoption of AI and ML technologies in care pathway planning and scheduling systems can enable early risk assessment [1], provide more accurate schedules [2][3][4][5][6][7], reduce blocking [8], and thus, maximize efficiency [9], minimize unnecessary costs [10], and tackle excessive waiting times [11] throughout the care pathway. However, the current care pathway planning and scheduling systems are mostly manual, time-consuming, and resource intensive [8].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-enhanced care pathway planning and scheduling system: content validity assessment of required functionalities

Jansson

Ohtonen

Alalääkkölä

et al. 2022

BMC Health Serv Res

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Background Artificial intelligence (AI) and machine learning are transforming the optimization of clinical and patient workflows in healthcare. There is a need for research to specify clinical requirements for AI-enhanced care pathway planning and scheduling systems to improve human–AI interaction in machine learning applications. The aim of this study was to assess content validity and prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system. Methods A prospective content validity assessment was conducted in five university hospitals in three different countries using an electronic survey. The content of the survey was formed from clinical requirements, which were formulated into generic statements of required AI functionalities. The relevancy of each statement was evaluated using a content validity index. In addition, weighted ranking points were calculated to prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system. Results A total of 50 responses were received from clinical professionals from three European countries. An item-level content validity index ranged from 0.42 to 0.96. 45% of the generic statements were considered good. The highest ranked functionalities for an AI-enhanced care pathway planning and scheduling system were related to risk assessment, patient profiling, and resources. The highest ranked functionalities for the user interface were related to the explainability of machine learning models. Conclusion This study provided a comprehensive list of functionalities that can be used to design future AI-enhanced solutions and evaluate the designed solutions against requirements. The relevance of statements concerning the AI functionalities were considered somewhat relevant, which might be due to the low level or organizational readiness for AI in healthcare.

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Minimizing Earliness/Tardiness costs on multiple machines with an application to surgery scheduling

Cited by 12 publications

References 25 publications

Briefest Time to Perform a Series of Preoperative Nerve Blocks in Multiple Patients: A Simulation Study

Briefest Time to Perform a Series of Preoperative Nerve Blocks in Multiple Patients: A Simulation Study

Robust Surgery Scheduling: A Model-Based Overview

Artificial intelligence-enhanced care pathway planning and scheduling system: content validity assessment of required functionalities

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