Use of Predictive Modeling to Improve Operating Room Scheduling Efficiency

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VA Office of Research and Development

Status

Completed

Conditions

Operating Room Scheduling

Treatments

Other: Scheduling using historical means
Other: Scheduling using regression modeling system

Study type

Interventional

Funder types

Other U.S. Federal agency

Identifiers

NCT01892865
IIR 12-113

Details and patient eligibility

About

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

Full 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.

Enrollment

735 patients

Sex

All

Volunteers

No Healthy Volunteers

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

A day will be excluded from the study when any of the following occur (based on historical data the investigators anticipate 10-15% of the operative days to meet the 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

Trial design

Primary purpose

Health Services Research

Allocation

Randomized

Interventional model

Parallel Assignment

Masking

Quadruple Blind

735 participants in 2 patient groups

Historical means method
Active Comparator group
Description:
Operative time will be predicted using historical service means. Schedule will be constructed using this time
Treatment:
Other: Scheduling using historical means
Predictive Modeling System (PMS)
Experimental group
Description:
Operative time will be predicted using a regression model. Schedule will be constructed using this time
Treatment:
Other: Scheduling using regression modeling system

Trial contacts and locations

1

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Data sourced from clinicaltrials.gov

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