Transcript Document

PATIENT TRANSFER
REPORTS
ZEN SIGMA TEAM – PROGRESS REPORT
Kawa Shwaish, Hayzel Criollio, Kulbhusian Sinha, Fatih Yilmazer
Devin Leibert, Amy Nguyen
BACKGROUND
• UPMC’s Patient Transfer call center
facilitates provider-to provider
communication in order to assure bed
assignment for patients.
• UPMC determined that this was a problem
when mgmt system was producing unuseful or incomplete reports.
D M A
I
C
• Define
• Measure
• Analyze
• Improve (make recommendations)
• Control (suggestion for how to control)
D
PROBLEM AND DEFECT
• Problem Statement: The reports
generated by the patient transfer
system include significant
inconsistencies in the data which
reduces the reliability and usefulness of
the report.
• The Defect: Missing or inaccurate data
in daily report to case managers
DEFINE
D
• Objective
• Understand the extent of defect and the transfer center
business processes contribute to the defect.
• Identify possible changes within the transfer center that can
reduce the defect.
• Projected Benefits
• Metric
• Area of Focus
D
LEVEL 1 PROCESS MAP
MEASURE
M
• Data Collection
• Observation
• Meetings and phone
conferencing
• Files (csv, pdf, excel)
• Email
• Paper forms and templates
• QA of two weeks of reports
• Given vs. Calculated
Shift data
(pdf &
excel)
Annotated
reports
(email &
excel)
System
data
dump
(csv)
Consolidate into
Database
M
MEASUREMENT SYSTEM
ANALYSIS
• Where is the data coming from
• How reliable is the data
• Assumption and Concerns
• The QA process performed by the case managers is fairly
accurate
• The data is not sufficient to run statistical analysis such as
regression and control charts
• The four hospitals selected are representative of the 24 hospitals
in the network
• Data related to # of calls per coordinator does not include calls
they might have processed to other hospitals
M
SHIFT DATA
M
DATA COLLECTION
• Transform to unique
shifts
• i.e. If shift end is after
midnight add a day
• Cross reference to
identify coordinators
• Coordinator
• Start time
• End time
M
DATA COLLECTION
• Linking the different
data sources
• Annotated report
• Data dump
• Unique shifts
M
DEFECT DETECTION
• If not marked ok by
coordinator
• If coordinator
missed the fact that
there is no time
stamp
• If elapsed time was
negative
• IIf(([Sheet1].[cs_status_id]=2)
• And
(([sheet2].[comments]<>"OK
")
• Or ([sheet2].[Bed Assigned]
Is Null)
• Or ([Calculated Elapsed
Time]<0)),1,0)
• AS [Defect of completed]
ANALYZE
A
• Fishbone Diagram (start from the flow chart)
•
•
•
•
•
•
Receiving Call
Identifying Availability
Conferencing Dr.s
Conferencing Nrs.
Data entry
Report generation
FISHBONE DIAGRAM
Plant/Technology
Procedures
Computers Systems
e
s
g
min
tat
iv
pe
, re
es
tim
Wa
it
s
a rd
nd
sta
Missing or incorrect data
Aging workforce
etc
.
all
gic
en
try
olo
hn
ata
nD
ow
People
sd
Wo
rk
for
c
en
ot
va
by
ed
ue
ca
us
Policies
Lack of typing skills
tec
ryin
tio
n
gs
be
hif
ts
twe
en
Varying shifts
ntia
dif
fer
e
No
y in
skil
ep
lse
t
ts
Ou
td
Everyemploye’s treated the same
Slo
w
ate
dt
im
ec
ly
on
tive
su
No
on
n-c
No
c
ffe
es
se
on
all
ph
c
te
ny
ibu
ma
istr
Fax Machines
Fa
tig
yst
em
es
dS
yst
em
cte
fra
m
ne
o
To
td
ain
Data Entry
no
dM
es
ate
Doctor Conference
Phone system
Do
Ou
td
A
A
ANALYZE
• Metrics to measure dependent variable
• Defect rate (actual counts of defect)
• Number of defects per call per hour
• Metrics to measure independent variables
• Call traffic
• Average number of coordinators per hour
• Number of coordinators on duty per call
A
COORDINATOR EFFECTIVENESS
• Out of 701 Sample calls analyzed over two weeks, the
number of defects ASTRONOMICAL:
55.9 %
• The correlation between the number of calls received and
defects occurring:
99.9 %
TREND OF DEFECT VS. NO. OF
COORDINATORS
A
180
160
140
120
100
Count of Call ID
80
Sum of Defect
60
40
20
0
0
2
4
6
8
10
No. of Coordinators on duty
12
14
16
TREND OF DEFECT RATES BY HOUR
A
1.000
0.900
0.800
0.700
0.600
0.500
0.400
0.300
0.200
0.100
0.000
0
5
10
15
Hour of the day 0-23
Defect Rate/ Call/ Hour
Defect Rate/ Call/ Hour/Coordinator
20
25
A
VARIATION OF DEFECT WITH
AVAILABLE COORDINATORS
1.000
0.900
0.800
0.700
0.600
0.500
0.400
0.300
0.200
0.100
0.000
0
5
10
15
20
Hours 0-23
Defect Rate/ Call/ Hour
Defect Rate/ Call/ Hour/Coordinator
Average of available coordinators/10 to scale
25
TIME OF DAY
A
7
90%
80%
6
70%
5
60%
4
50%
3
40%
30%
2
20%
1
10%
0
0%
0
5
10
15
Average # calls per coordinator
20
Defect Rate
25
30
A
ANALYZE
• Correlation levels for:
• Defect Rate/call/hour vs. Average number of coordinators
per hour= -0.72
• Correlation between number of calls per hour and the
average number of coordinators per hour= 0.865
ANALYZE
A
Average of available coordinators
10.000
9.000
Available Coordinators
8.000
7.000
6.000
5.000
4.000
3.000
y = -12.047x + 12.66
R² = 0.5214
2.000
1.000
0.000
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
Defect rate/Call/hour
Average of available coordinators
Linear (Average of available coordinators)
0.900
1.000
A
ANALYZE
• So, almost 52 percent variability can be explained
by the model.
• So, the regression equation line,
y = -12.04x + 12.66
Example: To contain the defect rate/call/ hour at 0.1, the
average number of coordinators required= 11.456
Decide between 11 and 12 coordinators!
A
COORDINATOR CORRELATION
• We analyzed, whether number of coordinators available
during a call has an effect on defect and we found a weak
correlation of -28.87 percent between available
coordinators and number of defect.
• The negative sign re-emphasizes intuition that with the
increase in number of coordinators on duty, defects will go
down.
A
COORDINATOR EFFECTIVENESS
100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
Percentage
Cumulative Percentage
DEFECT PERCENTAGE PER
OPERATOR
A
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
11
12
19
23
24
25
27
28
28
28
29
30
30
31
31
33
34
37
AB CMS DNB MXK KJH TLM MAF KKH MAS AM1 EJK TCG GDC RLO MDG DAH PEC RWM WJS
38
40
43
49
SJM MES PJE
Percentage
Cumulative Percentage
Windber Medical Center
Summersville Memorial Hospital
Other West Virginia
Mercy Medical Center
Charleston Area Medical Center
UPMC Bedford Memorial
Ohio Valley General Hospital Pittsburgh
Home
Community Hospital of Kane
Punxsutawney Area Hospital
Magee Women's Hospital
Other Ohio
East Liverpool City Hospital
Wheeling Hospital
United Community Hospital (Grove City)
Canonsburg General Hospital
V A Pittsburgh Health Care System
Medical Center of Beaver
Allegheny Valley Hospital
Hamot Medical Center
Trinity Medical Center West
Washington Hospital
UPMC Horizon - Greenville Campus
UPMC Shadyside Hospital
Indiana Hospital
Altoona General Hospital
UPMC St Margaret Hospital
UPMC Passavant Cranberry
A
REFERRING FACILITY
100.00%
90.00%
80.00%
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
A
REFERRING UNIT TYPE
Pareto Chart of Defect & Source Medical Center
100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
ED
Tel
MED
ICU
(blank)
Percentage
Labor &
Delivery
Other
Home
Cumulative Percentage
MD Office
Rehab
Surg
Percentage
Cumulative Percentage
TOXICOLOGY
UROLOGY
TRANSPLANT
RADIATION ONCOLOGY
PEDIATRICS
ORAL SURGERY
OPHTHALMOLOGY
GYNECOLOGY
GERIATRIC MEDICINE
GASTROENTEROLOGY
(blank)
SURGICAL ONCOLOGY
PLASTIC SURGERY
OTOLARYNGOLOGY
CRITICAL CARE MED
VASCULAR SURGERY
FAMILY MEDICINE
TRAUMATIC SURGERY
HEMATOLOGY/ONCOLOGY
CARDIOTHORACIC
ORTHOPAEDIC SURGERY
PULMONARY MEDICINE
EMERGENCY MEDICINE
OBSTETRICS&GYNECOLOG
GENERAL MEDICINE
NEUROSURGERY
NEUROLOGY
CARDIOLOGY
GENERAL SURGERY
A
ADMIT SERVICE
100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
ADMIT SERVICE RATIO
A
100%
100%
100%
88%
84%
80%
70%
67%
59%
50%
46%
59%
53%
43%
41%
38% 38%
57%
50%
53%
44%
38%
33%
29%
20%
14%
14%
0%
0%
DAY OF THE WEEK
A
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
Wednesday
Tuesday
Friday
Saturday
Monday
Sunday
Thursday
A
BED TYPE
Pareto for Bed Type
100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
Percentage
Cumulative Percentage
DEFECT RATE / BED TYPE
A
0.9
0.80
0.8
0.69
0.7
0.6
0.5
0.57
0.56
0.53
0.53
0.45
0.43
0.43
0.37
0.4
0.3
0.2
0.1
0
0.25
0.24
0.40
0.20
A
DEFECT BASED ON TIME OF DAY
80%
70%
60%
50%
Day Shift
40%
30%
20%
Night Shift
VARIATION OF DEFECT WITH
AVAILABLE COORDINATORS
A
1.000
0.900
0.800
0.700
0.600
0.500
0.400
0.300
0.200
0.100
0.000
0
5
10
15
20
Hours 0-23
Defect Rate/ Call/ Hour
Average of available coordinators/10 to scale
25
Night Time
Day Time
Night Time
Day Time
Night Time
Day Time
Night Time
Day Time
Night Time
Day Time
Night Time
Day Time
Night Time
Day Time
Night Time
Day Time
Night Time
Day Time
Night Time
Day Time
Night Time
Day Time
Night Time
Day Time
Night Time
Day Time
A
TRENDS (10/15–10/27)
Defect Rate / Time of Day
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
10/15/10 10/16/10 10/17/10 10/18/10 10/19/10 10/20/10 10/21/10 10/22/10 10/23/10 10/24/10 10/25/10 10/26/10 10/27/10
DEFECTS PER RECEIVING
HOSPITAL
A
60%
55%
56%
58%
50%
50%
40%
30%
20%
10%
0%
Magee
Passavant
Presby
Shadyside
A
TIME OF DAY DEFECT RATIO
0.8
71%
0.7
58%
0.6
0.5
45%
0.4
0.3
0.2
0.1
0
Day Time
Evening Time
Night Time
TIME OF DAY DEFECTS
A
p Chart for defects/shifts
p Chart for defects/shifts
0.8
p Chart for defects/shifts
1
1
1.0
0.937019
0.743287
0.8
0.8
0.6
0.707547
0.6
0.580357
p
p
0.4
0.4
0.4
0.2
0.15033
0
0.223695
0.2
0
0
10
20
Sample
Day
30
40
0.6
p
0.446809
0.229638
0.2
0
0
10
20
30
Sample
Evening
40
0
10
20
Sample
Night
30
40
DAILY DEFECTS
A
P-Chart for Daily Defects
p Chart for defects/shifts
1
0.898197
0.8
0.6
p
0.534143
0.4
0.2
0.170089
0
0
10
20
Sample
30
40
I
RECOMMENDATIONS
• Night shift’s performance should be improved
• Training
• Performance based evaluation for their
Human Capital Management Process
• Reduce number of simultaneous calls
handled by coordinators
• Rework the phone call routing
• Headset
• Merge phone lines
• Review process of ED transfers
• Optimize the scheduling of the coordinators
• Improve information infrastructure
C
CONTROL