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This study compares two different methodologies of scheduling cases in the operating room.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Historical means method | Active Comparator | Operative time will be predicted using historical service means. Schedule will be constructed using this time |
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| Predictive Modeling System (PMS) | Experimental | Operative time will be predicted using a regression model. Schedule will be constructed using this time |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Scheduling using historical means | Other | Scheduling will be performed taking into account historical means only for anesthetic, operative, and turn around time |
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| 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 |
| 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). |
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Inclusion Criteria:
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):
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| Name | Affiliation | Role |
|---|---|---|
| Panagiotis Kougias, MD MSc | Michael E. DeBakey VA Medical Center, Houston, TX | Principal Investigator |
| David H. Berger, MD | Michael E. DeBakey VA Medical Center, Houston, TX | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Michael E. DeBakey VA Medical Center, Houston, TX | Houston | Texas | 77030 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 29697450 | Derived | 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. |
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Operative days that were on holidays, or when staff surgeons were out of town were excluded. Similarly, did not schedule any cases during the re-calibration of the predictive models
Calendar days during which vascular surgery operations were performed were randomly scheduled using either the Historical Means or the Predictive Modeling System methodologies. Please, note that unit of randomization was operative days, not patients
| ID | Title | Description |
|---|---|---|
| FG000 | Historical Means Method | Scheduling using historical means: Scheduling will be performed taking into account historical means only for anesthetic, operative, and turn around time |
| FG001 | Predictive Modeling System (PMS) | 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 |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
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| ID | Title | Description |
|---|---|---|
| BG000 | Historical Means Method | Scheduling using historical means: Scheduling will be performed taking into account historical means only for anesthetic, operative, and turn around time |
| BG001 | Predictive Modeling System (PMS) |
| Units | Counts |
|---|---|
| Participants |
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| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Customized | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | 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. | We analyzed data from 107 operative days in the HM arm, and 100 days in the PMS arm | Posted | Mean | Standard Deviation | Minutes | Three years |
|
Randomization unit in this study was operative days; thus, no adverse events were expected.
All-Cause Mortality, Serious, and Other [Not Including Serious] Adverse Events were not monitored/assessed.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Historical Means Method | Scheduling using historical means: Scheduling will be performed taking into account historical means only for anesthetic, operative, and turn around time |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Panos Kougias MD MSc | Michael E. DeBakey VAMC | 713.794.7700 | panagiotis.kougias@va.gov |
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| Scheduling using regression modeling system | Other | A regression model that uses predictor of operative length will be used to predict operative, anesthetic, and turn around time length |
|
| 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 |
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
| BG002 | Total | Total of all reporting groups |
| Operative Days |
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| Years |
| Participants |
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| Sex/Gender, Customized | Count of Participants | Participants | Participants |
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| Available operative days for randomization | Count of Units | Operative Days | Operative Days |
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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
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| Secondary | Difference in Throughput | Difference in total number of cases scheduled per unit of time analyzed between the two study arms | Operative days | Posted | Number | Operations/day analyzed | Three years | Operative Days | Operative Days |
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| Secondary | 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). | Health care providers | Posted | Mean | Full Range | units on a scale | Three years | Responses | Responses |
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| Secondary | 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 | Patients | Posted | Count of Participants | Participants | No | Three years |
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| 0 |
| 0 |
| 0 |
| 0 |
| EG001 | Predictive Modeling System (PMS) | 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 | 0 | 0 | 0 | 0 |
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| Personal Accomplishment |
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