Use of Predictive Modeling to Improve Operating Room Scheduling Efficiency

NCT ID: NCT01892865

Last Updated: 2018-01-02

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 735 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2013-08-31
• Study Completion Date: 2016-07-31

Brief Summary

This study compares two different methodologies of scheduling cases in the operating room.

Detailed Description

The goal of the proposed study is to address the efficacy of a scheduling methodology that uses a regression-based predictive modeling system (PMS) to calculate operative and anesthetic time length. The investigators hypothesize that compared to the traditional scheduling system (TSS) that calculate operative length using historic means, case allocation in an operating room using the PMS will improve scheduling precision, increase operative volume and increase Operative Suite (OS) personnel satisfaction, without having adverse impact on patient outcomes. The investigators will evaluate this hypothesis using a randomized block design in two operating rooms of a single surgical specialty for a total of 100 operative days per arm.

Conditions

Operating Room Scheduling

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: HEALTH_SERVICES_RESEARCH
• Blinding Strategy: QUADRUPLE

Study Groups

Historical means method

Operative time will be predicted using historical service means. Schedule will be constructed using this time

Group Type: ACTIVE_COMPARATOR

Scheduling using historical means

Intervention Type: OTHER

Scheduling will be performed taking into account historical means only for anesthetic, operative, and turn around time

Predictive Modeling System (PMS)

Operative time will be predicted using a regression model. Schedule will be constructed using this time

Group Type: EXPERIMENTAL

Scheduling using regression modeling system

Intervention Type: OTHER

A regression model that uses predictor of operative length will be used to predict operative, anesthetic, and turn around time length

Interventions

Scheduling using historical means

Scheduling will be performed taking into account historical means only for anesthetic, operative, and turn around time

Intervention Type: OTHER

Scheduling using regression modeling system

A regression model that uses predictor of operative length will be used to predict operative, anesthetic, and turn around time length

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• The only requirement for including a day in the study will be that all the procedures performed in that specific day have been previously performed in our hospital at least 5 times a year for each of the last three years. This rule will encompass the vast majority of the performed vascular procedures in our facility. Setting the threshold at a minimum of 5 cases per year is essential to assure that some data will be available to calculate the expected length of the case with either the traditional or the predictive modeling system. If a case is performed in a day when the scheduling imprecision is supposed to be calculated using the PMS but modeling data do not exist, then the anticipated length of this case will be calculated using the historic means.
• Surgery cancellation after the first case will not disqualify that day from inclusion in the study. If the cancellation occurs in the last case of the sequence for the specific day then no particular intervention will be taken. The anticipated end of the surgical day will reset to the end of the last case that took place, and all the imprecision calculations will be performed as described below. If the cancellation occurs in one of the intermediate cases, then the end of the operative day will reset to reflect the removal of the cancelled case.

Exclusion Criteria

• Only one or no cases have been scheduled for the entire operative day
• An emergency case is added as first case, or in between the scheduled cases.
• The operative day falls during a major holiday week (Thanksgiving, Christmas, New Year). The schedule during these time periods tends to be fragmented, cancellation rates are high, and cases are frequently performed with back-up teams only. All these factors may distort the findings.
• There is an unusual case in the schedule that does not meet the minimum requirement of 5 previous operations on a yearly basis for the last three years.
• The first case of the day is cancelled

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

VA Office of Research and Development

FED

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Panagiotis Kougias, MD MSc

Role: PRINCIPAL_INVESTIGATOR

Michael E. DeBakey VA Medical Center, Houston, TX

David H. Berger, MD

Role: PRINCIPAL_INVESTIGATOR

Michael E. DeBakey VA Medical Center, Houston, TX

Locations

Michael E. DeBakey VA Medical Center, Houston, TX

Houston, Texas, United States

Countries

United States

References

Kougias P, Tiwari V, Sharath SE, Garcia A, Pathak A, Chen M, Ramsey D, Barshes NR, Berger DH. A Statistical Model-driven Surgical Case Scheduling System Improves Multiple Measures of Operative Suite Efficiency: Findings From a Single-center, Randomized Controlled Trial. Ann Surg. 2019 Dec;270(6):1000-1004. doi: 10.1097/SLA.0000000000002763.

Reference Type: DERIVED

PMID: 29697450 (View on PubMed)

Other Identifiers

IIR 12-113

Identifier Type: -

Identifier Source: org_study_id