Trial Outcomes & Findings for Use of Predictive Modeling to Improve Operating Room Scheduling Efficiency (NCT NCT01892865)
NCT ID: NCT01892865
Last Updated: 2018-01-02
Results Overview
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.
COMPLETED
NA
735 participants
Three years
2018-01-02
Participant Flow
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
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
Unit of analysis: Operative Days
Participant milestones
| Measure |
Historical Means Method
Scheduling using historical means: Scheduling will be performed taking into account historical means only for anesthetic, operative, and turn around time
|
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
|
|---|---|---|
|
Overall Study
STARTED
|
356 107
|
379 100
|
|
Overall Study
COMPLETED
|
356 107
|
379 100
|
|
Overall Study
NOT COMPLETED
|
0 0
|
0 0
|
Reasons for withdrawal
Withdrawal data not reported
Baseline Characteristics
Use of Predictive Modeling to Improve Operating Room Scheduling Efficiency
Baseline characteristics by cohort
| Measure |
Historical Means Method
n=107 Operative Days
Scheduling using historical means: Scheduling will be performed taking into account historical means only for anesthetic, operative, and turn around time
|
Predictive Modeling System (PMS)
n=100 Operative Days
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
|
Total
n=207 Operative Days
Total of all reporting groups
|
|---|---|---|---|
|
Age, Customized
|
62 Years
STANDARD_DEVIATION 8 • n=99 Participants
|
62.5 Years
STANDARD_DEVIATION 10 • n=107 Participants
|
62.5 Years
STANDARD_DEVIATION 10 • n=206 Participants
|
|
Sex/Gender, Customized
Sex · Males
|
351 Participants
n=99 Participants
|
373 Participants
n=107 Participants
|
724 Participants
n=206 Participants
|
|
Sex/Gender, Customized
Sex · Females
|
5 Participants
n=99 Participants
|
6 Participants
n=107 Participants
|
11 Participants
n=206 Participants
|
|
Available operative days for randomization
|
107 Operative Days
n=107 Operative Days
|
100 Operative Days
n=100 Operative Days
|
207 Operative Days
n=207 Operative Days
|
PRIMARY outcome
Timeframe: Three yearsPopulation: We analyzed data from 107 operative days in the HM arm, and 100 days in the PMS arm
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.
Outcome measures
| Measure |
Historical Means Method
n=107 Participants
Scheduling using historical means: Scheduling will be performed taking into account historical means only for anesthetic, operative, and turn around time
|
Predictive Modeling System (PMS)
n=100 Participants
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
|
|---|---|---|
|
Difference Between the Actual and Predicted Length of Operative Day (in Minutes)
|
30.8 Minutes
Standard Deviation 99
|
7.2 Minutes
Standard Deviation 67
|
SECONDARY outcome
Timeframe: Three yearsPopulation: Operative days
Difference in total number of cases scheduled per unit of time analyzed between the two study arms
Outcome measures
| Measure |
Historical Means Method
n=107 Operative Days
Scheduling using historical means: Scheduling will be performed taking into account historical means only for anesthetic, operative, and turn around time
|
Predictive Modeling System (PMS)
n=100 Operative Days
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
|
|---|---|---|
|
Difference in Throughput
|
3.33 Operations/day analyzed
|
3.79 Operations/day analyzed
|
SECONDARY outcome
Timeframe: Three yearsPopulation: Health care providers
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).
Outcome measures
| Measure |
Historical Means Method
n=61 Responses
Scheduling using historical means: Scheduling will be performed taking into account historical means only for anesthetic, operative, and turn around time
|
Predictive Modeling System (PMS)
n=53 Responses
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
|
|---|---|---|
|
Operative Suite Personnel Job Satisfaction
Depersonalization
|
3.23 units on a scale
Interval 0.0 to 17.0
|
2.04 units on a scale
Interval 0.0 to 10.0
|
|
Operative Suite Personnel Job Satisfaction
Emotional Exhaustion
|
11.82 units on a scale
Interval 0.0 to 29.0
|
10.03 units on a scale
Interval 0.0 to 36.0
|
|
Operative Suite Personnel Job Satisfaction
Personal Accomplishment
|
37.51 units on a scale
Interval 15.0 to 48.0
|
40.47 units on a scale
Interval 21.0 to 48.0
|
SECONDARY outcome
Timeframe: Three yearsPopulation: Patients
Comparison of the perioperative (30-day postoperative) composite endpoint of death, myocardial infarction, bleeding, amputation between the two study groups
Outcome measures
| Measure |
Historical Means Method
n=356 Participants
Scheduling using historical means: Scheduling will be performed taking into account historical means only for anesthetic, operative, and turn around time
|
Predictive Modeling System (PMS)
n=379 Participants
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
|
|---|---|---|
|
Complications: A Composite Endpoint of Death, Myocardial Infarction, Bleeding, Amputation
|
8 Participants
|
12 Participants
|
Adverse Events
Historical Means Method
Predictive Modeling System (PMS)
Serious adverse events
Adverse event data not reported
Other adverse events
Adverse event data not reported
Additional Information
Results disclosure agreements
- Principal investigator is a sponsor employee
- Publication restrictions are in place