Use of Predictive Modeling to Improve Operating Room Scheduling Efficiency

Study Overview
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Brief Summary
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This study compares two different methodologies of scheduling cases in the operating room.

Detailed Description
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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.

Official Title
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Use of Predictive Modeling to Improve Operating Room Scheduling Efficiency

Conditions
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Operating Room Scheduling

Intervention / Treatment
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* Other: Scheduling using historical means
* Other: Scheduling using regression modeling system


Participation Criteria
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Eligibility Criteria
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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
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Health Services Research
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Quadruple


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: Historical means method<br>Operative time will be predicted using historical service means. Schedule will be constructed using this time | Other: Scheduling using historical means<br>* Scheduling will be performed taking into account historical means only for anesthetic, operative, and turn around time<br>|
| Experimental: Predictive Modeling System (PMS)<br>Operative time will be predicted using a regression model. Schedule will be constructed using this time | Other: Scheduling using regression modeling system<br>* A regression model that uses predictor of operative length will be used to predict operative, anesthetic, and turn around time length<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Difference Between the Actual and Predicted Length of Operative Day (in Minutes) | The scheduling imprecision between the two scheduling approaches will be compared. Scheduling imprecision is defined as the difference between the actual and predicted length of operative day. | Three years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Difference in Throughput | Difference in total number of cases scheduled per unit of time analyzed between the two study arms | Three years | 
| Operative Suite Personnel Job Satisfaction | Comparison of job satisfaction between study arms using three domains of the Maslach Burnout Inventory: Depersonalization (range 0-17, score of 17 indicates worse depersonalization). Emotional Exhaustion (range: 0-36, score of 36 is the worse). Personal accomplishment (range 1-60, score of 60 is best). | Three years | 
| Complications: A Composite Endpoint of Death, Myocardial Infarction, Bleeding, Amputation | Comparison of the perioperative (30-day postoperative) composite endpoint of death, myocardial infarction, bleeding, amputation between the two study groups | Three years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Operating room utilization