B1-3: Improving Surgical Case Duration Accuracy with Advanced Predictive Modeling

Dravenstott, Ronald W.; Reich, Eric; Strongwater, Steven; Devapriya, Priyantha

doi:10.3121/cmr.2014.1250.b1-3

Cited by 2 publications

(6 citation statements)

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“…Most published literature that we were able to identify to date, examining case duration accuracy, are limited to retrospective data modeling and theoretical improvements . We found 1 recently published study that performed implementation of a statistical regression model, showing the ability to better predict when the surgical day would end.…”

Section: Discussionmentioning

confidence: 95%

“…In studies focusing on efforts to reduce surgical case duration and turnover, Dexter et al, Strum et al, and Stepaniak et al emphasized that improving the reliable time estimate of surgical cases leads to improved efficiency of OR processes. Their published literature examining accuracy in the estimation of case duration has been limited to retrospective data modeling.…”

Section: Introductionmentioning

confidence: 99%

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Effect of a Predictive Model on Planned Surgical Duration Accuracy, Patient Wait Time, and Use of Presurgical Resources

et al. 2021

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IMPORTANCE Accurate surgical scheduling affects patients, clinical staff, and use of physical resources. Although numerous retrospective analyses have suggested a potential for improvement, the real-world outcome of implementing a machine learning model to predict surgical case duration appears not to have been studied. OBJECTIVESTo assess accuracy and real-world outcome from implementation of a machine learning model that predicts surgical case duration. DESIGN, SETTING, AND PARTICIPANTSThis randomized clinical trial was conducted on 2 surgical campuses of a cancer specialty center. Patients undergoing colorectal and gynecology surgery at Memorial Sloan Kettering Cancer Center who were scheduled more than 1 day before surgery between April 7, 2018, and June 25, 2018, were included. The randomization process included 29 strata (11 gynecological surgeons at 2 campuses and 7 colorectal surgeons at a single campus) to ensure equal chance of selection for each surgeon and each campus. Patients undergoing more than 1 surgery during the study's timeframe were enrolled only once. Data analyses took place from July 2018 to November 2018. INTERVENTIONSCases were assigned to machine learning-assisted surgical predictions 1 day before surgery and compared with a control group. MAIN OUTCOMES AND MEASURESThe primary outcome measure was accurate prediction of the duration of each scheduled surgery, measured by (arithmetic) mean (SD) error and mean absolute error. Effects on patients and systems were measured by start time delay of following cases, the time between cases, and the time patients spent in presurgical area.RESULTS A total of 683 patients were included (mean [SD] age, 55.8 [13.8] years; 566 women [82.9%]); 72 were excluded. Of the 683 patients included, those assigned to the machine learning algorithm had significantly lower mean (SD) absolute error (control group, 59.3 [72] minutes; intervention group, 49.5 [66] minutes; difference, −9.8 minutes; P = .03) compared with the control group. Mean start-time delay for following cases (patient wait time in a presurgical area), dropped significantly: 62.4 minutes (from 70.2 minutes to 7.8 minutes) and 16.7 minutes (from 36.9 minutes to 20.2 minutes) for patients receiving colorectal and gynecology surgery, respectively. The overall mean (SD) reduction of wait time was 33.1 minutes per patient (from 49.4 minutes to 16.3 minutes per patient). Improved accuracy did not adversely inflate time between cases (surgeon wait time). There was marginal improvement (1.5 minutes, from a mean of 70.6 to 69.1 minutes) in time between the end of cases and start of to-follow cases using the predictive model, compared with the control group. Patients spent a mean of 25.2 fewer minutes in the facility before surgery (173.3 minutes vs 148.1 minutes), indicating a potential benefit vis-à-vis available resources for other patients before and after surgery.CONCLUSIONS AND RELEVANCE Implementing machine learning-generated predictions for surgical case durations may improve case duration accura...

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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Effect of a Predictive Model on Planned Surgical Duration Accuracy, Patient Wait Time, and Use of Presurgical Resources

et al. 2021

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“…Chen et al [29] also reported that the predictive capabilities of the model were much lower for new patient appointments in comparison to follow-up visits. Of note, the nal predictive power and precision of a model have also been shown to depend on the type of medical institution and patient characteristics [30]. These two factors were also considered when training our AI model.…”

Section: Discussionmentioning

confidence: 99%

“…For the NHS England, the percentage of no-shows dropped from 8.3% in 2007-2008 to 6.7% in 2017-2018 [31], even after the introduction of a wide range of interventions, highlighting the need for additional, more re ned solutions based on advanced technologies, such as AI. One of the rst AI models to predict noshows was developed by Dravenstott et al [30]; this model used arti cial neural networks to predict the no-show risk separately for primary care and endocrinology clinics. To teach the model, historical data on 3 million visits over the previous two years were included, which covered clinic, provider and patientspeci c variables.…”

Section: Discussionmentioning

confidence: 99%

“…To teach the model, historical data on 3 million visits over the previous two years were included, which covered clinic, provider and patientspeci c variables. The predictive performance of the model was below the attendance rate with an AUC of 0.78 and 0.81 for the primary care and endocrinology clinics, respectively [30]. Another model, which also used deep neural networks, was proposed by Dashtban and Li [24,32]; this model was based on sparse stacked denoising autoencoders (SDAEs).…”

Section: Discussionmentioning

confidence: 99%

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An artificial intelligence-based model to reduce the no-show rate in outpatient clinics of an academic hospital

Aij,

Knoester,

Werkhoven

2024

Preprint

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Purpose non-attendance of patients for outpatient appointments, known as "no-shows," poses a persistent challenge for healthcare facilities, with significant repercussions for both patients and healthcare systems. This study aimed to investigate whether targeting high-risk individuals with interventions could effectively reduce the rate of no-shows within reasonable resource allocation. Methods we developed an artificial intelligence (AI) algorithm-based prediction model to estimate the likelihood of an appointment resulting in a no-show. Utilizing retrospective data from 24 outpatient clinics, a machine learning (ML) model was constructed and trained to identify patients at high risk of no-show. Subsequently, over a 6-month period, 35% of the highest-risk patients were randomly assigned to either the intervention group (receiving a reminder phone call three workdays before their appointment) or the control group (no reminder call). Results following the intervention, the intervention group experienced a notable 26.2% reduction in no-shows. This reduction translates to a 14.3% decrease in the overall number of no-shows, demonstrating the efficacy of the reminder service. Moreover, this intervention led to additional benefits, including the ability to schedule new patients on previously avoided no-show slots, enhanced patient experience, reduced staff preparation time for missed appointments, and a decrease in administrative burden associated with rescheduling no-shows. Conclusions Our AI-powered model proved to be an effective tool for identifying high-risk patients prone to missing their outpatient appointments. This allowed for targeted interventions, such as reminder phone calls, to be implemented. The substantial reduction in no-show rates underscores the potential impact of this approach on optimizing healthcare resource allocation and improving patient attendance.

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B1-3: Improving Surgical Case Duration Accuracy with Advanced Predictive Modeling

Cited by 2 publications

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Effect of a Predictive Model on Planned Surgical Duration Accuracy, Patient Wait Time, and Use of Presurgical Resources

Effect of a Predictive Model on Planned Surgical Duration Accuracy, Patient Wait Time, and Use of Presurgical Resources

An artificial intelligence-based model to reduce the no-show rate in outpatient clinics of an academic hospital

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