Presented by Nicole Bartecki Jack Jasper Rishi Shah amp Emily Sweet December 13 2016 SWAT Patient Flow and Personnel Workload Final Presentation 1500 E Medical Center Drive Ann Arbor MI 48109 ID: 637444
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Industrial and Operations Engineering 481 Project Team #10Presented by: Nicole Bartecki, Jack Jasper, Rishi Shah, & Emily SweetDecember 13, 2016
SWAT Patient Flow and Personnel Workload - Final Presentation
1500 E. Medical Center Drive
Ann Arbor, MI 48109
1Slide2
OVERVIEWIntroductionKey Issues
Goals and Objectives
MethodsFindings & Conclusions
RecommendationsExpected Impact
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
2Slide3
INTRODUCTION SWAT: Specialized Workforce for Acute Transport
Transport high/moderate-risk patients
Policy change June 1, 2016
Broadened criteria for high/moderate-risk patients
Increased
“
turf
”
rate
3
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected ImpactSlide4
Key IssuesGoals and Objectives
Methods
KEY ISSUES
Increased turf rate
Increased number of patient transportation and procedure requests
Inefficient scheduling procedure
Perceived shortage of SWAT personnel
4
Introduction
Findings and Conclusions
Recommendations
Expected ImpactSlide5
GOALS AND OBJECTIVESGoalsIncrease the efficiency of the SWAT team and address SWAT employee workload
Objectives
Decrease turf rate
Streamline scheduling process to reduce workload on schedulersIncrease visibility in scheduling to simplify the scheduling process
Determine optimal SWAT staffing levels in order to achieve an acceptable turf rate, a maximum of 8%
5
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected ImpactSlide6
METHODS6
On-
Site Observations
Time Studies
183
transport timesheets over 13 days
79 scheduling timesheets over 16 day
s
Statistical Analysis
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected ImpactSlide7
METHODS7
Value Stream Mapping
Literature Search
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected ImpactSlide8
Findings & ConclusionsHISTORICAL DATAKey Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
Month
Number Turfed Calls
Total Calls
Turf Rate
June
269
1273
0.211
July
281
1298
0.216
August
196
1131
0.173
October
92
674
0.136
November
210
1342
0.156
Average
209.6
1143.6
0.178
1 Full-Time-Employee goes on 43 Transports/month
Need 3.5 additional FTEs to reduce turf rate from ~18% to 8%
8Slide9
Findings & ConclusionsON-SITE OBSERVATIONS Inefficient Current Scheduling Tool
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
9Slide10
Findings & ConclusionsON-SITE OBSERVATIONS Visual difficulties in scheduling Scheduler uses intuition for transport run estimation Turf calls that otherwise could have been picked up
Busiest time period 0730 - 0930
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
10
Need for a new, more user-friendly scheduling tool
Conduct time studies between 0730 - 0930Slide11
Patient Risk LevelMean Transportation Time
Std. Dev. of Transportation Time
Moderate
63.56
112.85
High
71.41
87.97
11
Stratifying by Patient Risk Level
Findings & Conclusions
TIME STUDIES
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
183 Samples
Data Collected from 10/17/16 - 11/1/16Slide12
Patient Risk LevelMean Transportation Time
Std. Dev. of Transportation Time
Moderate
63.56
112.85
High
71.41
87.97
Stratifying by Risk Level
leads to unreliable estimates
12
Stratifying by Patient Risk Level
Findings & Conclusions
TIME STUDIES
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
183 Samples
Data Collected from 10/17/16 - 11/1/16Slide13
Procedure
Mean Time (min)
Std. Dev. of Time (min)
Bedside Sedation
96.67
16.82
CT
47.65
12.57
CPU Return
32.83
6.08
Echo
PET Scan
78.80
141.80
19.35
15.20
MRI
119.00
34.30
Radiology
47.23
16.80
Scheduling Deliverable: Estimated Time Per Procedure
13
183 Samples
Data Collected from 10/17/16 - 11/1/16
Stratifying by Procedure Type
Findings & Conclusions
TIME STUDIES
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected ImpactSlide14
Findings & ConclusionsSTATISTICAL ANALYSISKey Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
Difficult to fit distribution to each run type
Capture at least 75% for estimated time
183 Samples
Data Collected from 10/17/16 - 11/1/16
Distributions of Run Length
14Slide15
Findings & ConclusionsSTATISTICAL ANALYSISKey Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
7 Procedure Types Account for 71.10% of all transports
183 Samples
Data Collected from
10/17/16 - 11/1/16
15Slide16
Findings & ConclusionsSTATISTICAL ANALYSISKey Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
Procedure
Estimated Time (min.)
Percentage Captured
Radiology
50
75
CT
50
75.68
Echo
90
80
MRI
125
80
Bedside Sedation
110
100
CPU Return
35
100
PET Scan
155
75
Times Used in Scheduling Tool
183 Samples
Data Collected from 10/17/16 - 11/1/16
16Slide17
Findings & ConclusionsVALUE STREAM MAPPING
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
Total Time:
21.07 min
Value-Add Time:
7.40 min -
35.12%
Non-Value Add Time:
13.67 min - 64.88%
79 scheduling timesheets collected over 16 days
17Slide18
Findings & ConclusionsVALUE STREAM MAPPING
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
Total Time:
8.40 min
Value-Add Time:
7.40 min - 88.10%
Non-Value Add Time:
1 min - 11.90%
18Slide19
Findings & ConclusionsVALUE STREAM MAPPING6.64 minutes
Additional calls
12.67 minutes saved per call
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
~ 60% Time Reduction
Paperwork
~ 6.03 minutes
19Slide20
Findings & ConclusionsLITERATURE SEARCHRules for schedulers: augment intuition with standardization [1]Objectives for intra-hospital transport [1]Recommends additional staffing during exceptionally busy hours [2]
Staffing can be altered throughout a shift based on need [3]
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
[1] T. Hanne, T, Melo, S. Nickel, (2009) Bringing Robustness to Patient Flow Management Through Optimized Patient Transports in Hospitals. Interfaces 39(3):241-255. http://dx.doi.org/10.1287/inte.1080.0379
[2] M. Blasco, M. Brennan, and L. Soderstrom, (2006) Nursing SWAT Patient Transport Analysis Regarding Workload and Tasks. Ann Arbor, MI: University of Michigan, Industrial and Operations Engineering Department
[3] K. Wilson, (2009) Fire Department Staffing: A Need, Not a Want. Fire Engineering.
http://www.fireengineering.com/articles/print/volume-162/issue-8/features/fire-department-staffing-a-need-not-a-want.html
20Slide21
Findings & ConclusionsSUMMARYThe scheduling tool needs to be updated to assist schedulers in optimally scheduling transports
The paperwork portion of the scheduling process can and should be automated to increase efficiency
The scheduling process should be streamlined to increase efficiency
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
21Slide22
RecommendationsNEW SCHEDULING TOOL
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
22Slide23
RecommendationsNEW SCHEDULING TOOL
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
23Slide24
RecommendationsNEW SCHEDULING TOOL
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
Other
Button Form:
When Overlapping Transports:
24Slide25
RecommendationsSTREAMLINE SCHEDULING PROCESSUtilize Standard Operating Procedures Eliminate Additional CallsUtilize MiChart Printing Feature
Eliminate Handwriting Paperwork
Free up one scheduler to join transporters after 0930
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
25Slide26
RecommendationsADD PERSONNELNew scheduling tool allows ~40 additional runs/ monthStreamlined scheduling process provides additional transporterHire 1.5 additional FTE
Meet acceptable turf rate of 8%
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
26Slide27
EXPECTED IMPACT
Recommendation
Decrease in Turf Rate
Cumulative Decrease in Turf Rate
Expected Turf Rate
Utilize Scheduling Tool
2.85%
2.85%
15.03%
Move Scheduler to Transporter
2.30%
5.15%
12.73%
Add 1.5 Full-Time Employees
4.60%
9.75%
8.13%
Key Issues
Goals and Objectives
Methods
Introduction
Findings and Conclusions
Recommendations
Expected Impact
27Slide28
QUESTIONS?28
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