AUDIENCE THEORY -CULTIVATION THEORY - GERBNER.pptx
Session 12 - Introduction to Information Tools
1. Session 12: Performance
Improvement Tools: Gathering the
Evidence
R.S. Crawford, III, MD, MBA
rcrawford@usuhs.mil
William Hirst, BSN, MPH, CPHQ
MHirst@SouthcentralFoundation.com
April 2012
2. Setting the Stage
Analyst Goal with Dr Crawford
• Establish a working relationship, trust and listen
What insight does he already have?
• Assist in decreasing his level of uncertainty
This isn’t a research study
• Provide useful information for decision support
• Demonstrate actionable information tools for all staff
Executives
Managers
Front-end Staff
3. Big Picture
Population at
Prevention
Risk
Recovery
Incidence of
Disability or Disease
Case Management Level 1
Level 2
Prevalence of Level 3
Disability or Disease Level 4
Level 5
Death
4. d
l
e
n
a
p
m
P
0
1
/
t
s
i
V
R
E
60
65
70
75
80
9
0
-
g
u
A
9
0
-
p
e
S
LCL 62.4
UCL 73.9
9
0
-
t
c
O
Mean 68.1
9
0
-
v
o
N
9
0
-
c
e
D
0
1
-
n
a
J
0
1
-
b
e
F
0
1
-
r
a
M
0
1
-
r
p
A
0
1
-
y
a
M
0
1
-
n
u
J
0
1
-
l
u
J
0
1
-
g
u
A
0
1
-
p
e
S
0
1
-
t
c
O
0
1
-
v
o
N
0
1
-
c
e
D
1
-
n
a
J
1
-
b
e
F
ER Visits / 1000 Pt. Empanelled
1
-
r
a
M
1
-
r
p
A
1
-
y
a
M
1
-
n
u
J
1
-
l
u
J
1
-
g
u
A
1
-
p
e
S
1
-
t
c
O
75.2
1
-
v
o
N
77.5
1
-
c
e
D
5. %
55
60
65
70
75
u
A
9
0
-
g
9
0
-
p
e
S
LCL 60.6
UCL 67.0
9
0
-
t
c
O
Mean 63.8
9
0
-
v
o
N
c
9
0
-
e
D
0
1
-
n
a
J
0
1
-
b
e
F
0
1
-
r
a
M
p
A
0
1
-
r
0
1
-
y
a
M
0
1
-
n
u
J
0
1
-
l
u
J
u
A
0
1
-
g
0
1
-
p
e
S
0
1
-
t
c
O
0
1
-
v
o
N
c
0
1
-
e
D
1
-
n
a
J
1
-
b
e
F
1
-
r
a
M
% ER Visits Between 0800 - 1700
p
A
1
-
r
1
-
y
a
M
1
-
n
u
J
1
-
l
u
J
u
A
1
-
g
1
-
p
e
S
1
-
t
c
O
70.3
1
-
v
o
N
c
1
-
e
D
69.8
6. c
a
m
h
A
0
1
/
t
s
i
V
R
E
10
15
20
25
30
35
40
45
9
0
-
g
u
A
9
0
-
p
e
S
LCL 16.5
UCL 37.2
9
0
-
t
c
O
Mean 26.8
v
o
N
9
0
-
9
0
-
c
e
D
0
1
-
n
a
J
0
1
-
b
e
F
0
1
-
r
a
M
0
1
-
r
p
A
0
1
-
y
a
M
0
1
-
n
u
J
0
1
-
l
u
J
0
1
-
g
u
A
0
1
-
p
e
S
0
1
-
t
c
O
v
o
N
0
1
-
0
1
-
c
e
D
1
-
n
a
J
1
-
b
e
F
ER Visits / 1000 Asthmatics
1
-
r
a
M
1
-
r
p
A
1
-
y
a
M
1
-
n
u
J
1
-
l
u
J
1
-
g
u
A
1
-
p
e
S
1
-
t
c
O
v
o
N
1
-
40.2
1
-
c
e
D
7. Dec 11
Clinic ER Visits Total ER Visits per 1000 % of Total
Empanelled Empanelled Visits
Family Practice 1,844 17,500 105.37 79.31%
Internal Med 97 2,500 38.80 4.17%
Peds 384 10,000 38.40 16.52%
Total 2,325 30,000 77.50 100.00%
Dec 10
Clinic ER Visits Total ER Visits per 1000 % of Total
Empanelled Empanelled Visits
Family Practice 1,498 17,500 85.60 74.94%
Internal Med 103 2,500 41.20 5.15%
Peds 398 10,000 39.80 19.91%
Total 1,999 30,000 66.63 100.00%
16. No Show Appointments by Day
# No-Show – Show Appointments by Day- CYMay 07) Family Med
# No Appointments by Day (1 Jun 06 31 2011 Family Med
n=5720 (11% of 52,000 appts)
5720 100%
91% 90%
5005
81% 80%
4290
70%
66%
3575
60%
Number
2860 50%
2117 40%
2145 37%
1659
30%
1430
858 20%
715 572 514
10%
0 0%
Monday Friday Thursday Wednesday Tuesday
17. No Shows by Day and Hr
# Monday No No-Shows Family Med (1 June 06 - 31 MayFamily Med
Monday – Show Appointments by Day CY 2011 07)
n=2117
100%
98%
2000 96%
93%
89% 90%
84%
80%
78%
1500 70% 70%
1270
60% 60%
Number
50%
1000
40%
30%
500
20%
212
169
127 105 10%
84 64 44 42
0 0%
0800-0859 0900-0959 1700-1759 1300-1359 1500-1559 1400-1459 1000-1059 1600-1659 1100-1159
Time of Day
18. No Shows by Day and Hr
# Friday No – Show Appointments by Day CY 2011 07)
Friday No-Shows Family Med (1 June 06 - 31 May Family Med
n=1659
100%
98%
96%
93%
1500 90% 90%
86%
80% 80%
70% 70%
1000 60%
Number
50%
664
40% 40%
500
500 30%
20%
165
100 10%
66 50 49 34 31
0 0%
1700-1759 0800-0859 0900-0959 1300-1359 1400-1459 1000-1059 1100-1159 1600-1659 1500-1559
Time of Day
19. Tricare Operations Center (TOC)
Many More Reports
• 3rd Next Available – under beta testing
• Appointment Utilization
• Access to Care Reports
• Enrollment & Population Reports
• Booked Management Report
• Length of Stay
20. Valhalla Findings
• Provider availability ~ 50% last 12 mo
• % of Appt with PCM is low (44%)
• % of 3rd Next Available Acute Appointments meeting
Access Standards (24hrs) is low in Family Medicine
• Decrease in team continuity
• Low appointment availability on peak days
• Deployment in 6 months
• No-show rates of 11%
• Training during peak hours
21. Potential Items Affecting Capacity
• Provider availability / schedules / templates
• Team experience and continuity
• Provider specialties & manpower issues
• No-show rates
• Leave, TDY, Holiday’s
• Additional duty, provider call
• Procedures
• Facility layout & Support services
• Technology
• Service level agreements with referrals
• Don’t rely on historic utilization patterns
23. Population Questions
• Who are my patients?
• What are their preventive service needs?
• What conditions do they have?
• Who are my high utilizers that may need case
management?
• How well am I doing in the management of their
care?
HEDIS
Primary Care Medical Home
• How do I forecast & manage the demand for
services?
25. MHSPHP Data Sources
DEERS M2 CHCS
AdHocs
Direct Care Purchased
Care/Network PDTS
Inpt and
Outpt Inpt and Outpt
TRICARE Mammography
Enrollment Pap Smears
Clinical Chemistry
Enrollment
MHS Population
Health Portal
30. MHSPHP Patient Detail
•Last 3 B/P’s (currently from Clinical Data Repository)
•Last 6 Labs (note labs in network have no values)
31. MHSPHP Local Exclusions
•Exclusions automatically expire after 1 year
•Exclusions follow pt (new facility can validate or delete)
•30 day flag before they expire
•Different icon if exclusion was at other facility
•Apply to Medical Home and Action Lists, not HEDIS
32. MHSPHP Local Entered Notes
•Notes are NOT MEDICAL RECORD
•Stay forever unless deleted by user
•System flags not when pt changes facility, new facility can
validate it or delete it
•Default is current list, can choose more than 1
33. MHSPHP HEDIS & Medical Home
•Score = Completed/Total as percentage
•Score as compared to All HEDIS® measured health plans
Green=Score is greater than 90th percentile
Yellow= Score is between 50th and 90th percentile
Red= score less than 50th percentile
•Medical Home Completed/Medical Home Total
•No benchmark for comparing medical home scores
•Can be same, higher or even lower than HEDIS® score
36. Demand Forecasting
• Daily Demand Forecasting:
Team huddles with integrated care team
Same reasons as inpatient report
• Planning, Coordination, Safety
• Demand Forecasting and Planning:
Deployment coming up in 6 months in which 6 of the 20
providers will be deployed.
• If we decided to contract out Well Women Exams to include
pap smears. How many would we need to contract out for?
• School physical demand what can we expect? Do we need to
adjust schedules?
38. Demand Forecasting and Planning
• Women 18 and older = 9231
9231/ @ least one Pap q3 yrs = ~3077/yr
• Women 40 and older
2632/ @ least one Mammogram q 2 yrs = ~1316/yr
• Children 5 years old ready to start school
256 School Physicals
• 1 Jan 11 – 31 Dec 11 ~ 52,000 FMC Appts
42. d
l
e
n
a
p
m
P
0
1
/
t
s
i
V
R
E
60
65
70
75
80
9
0
-
g
u
A
9
0
-
p
e
S
9
0
-
t
c
O
LCL 62.4
UCL 73.9
Mean 68.1
9
0
-
v
o
N
9
0
-
c
e
D
0
1
-
n
a
J
0
1
-
b
e
F
0
1
-
r
a
M
0
1
-
r
p
A
0
1
-
y
a
M
0
1
-
n
u
J
0
1
-
l
u
J
0
1
-
g
u
A
0
1
-
p
e
S
0
1
-
t
c
O
0
1
-
v
o
N
0
1
-
c
e
D
1
-
n
a
J
1
-
b
e
F
1
-
r
a
M
1
-
r
p
A
ER Visits / 1000 Pt. Empanelled
1
-
y
a
M
1
-
n
u
J
1
-
l
u
J
1
-
g
u
A
1
-
p
e
S
1
-
t
c
O
75.2
1
-
v
o
N
77.5
1
-
c
e
D
Mean 65.3
2
1
-
n
a
J
2
1
-
b
e
F
64.0
2
1
-
r
a
M
43. %
55
60
65
70
75
9
0
-
g
u
A
9
0
-
p
e
S
LCL 60.6
9
0
-
t
c
O
UCL 67.0
Mean 63.8
9
0
-
v
o
N
9
0
-
c
e
D
0
1
-
n
a
J
0
1
-
b
e
F
0
1
-
r
a
M
0
1
-
r
p
A
0
1
-
y
a
M
0
1
-
n
u
J
0
1
-
l
u
J
0
1
-
g
u
A
0
1
-
p
e
S
0
1
-
t
c
O
0
1
-
v
o
N
0
1
-
c
e
D
1
-
n
a
J
1
-
b
e
F
1
-
r
a
M
1
-
r
p
A
1
-
y
a
M
% ER Visits Between 0800 - 1700
1
-
n
u
J
1
-
l
u
J
1
-
g
u
A
1
-
p
e
S
1
-
t
c
O
70.3
1
-
v
o
N
1
-
c
e
D
Mean 62.7
69.8
2
1
-
n
a
J
2
1
-
b
e
F
64.1
2
1
-
r
a
M
44. c
a
m
h
A
0
1
/
t
s
i
V
R
E
10
15
20
25
30
35
40
45
9
0
-
g
u
A
9
0
-
p
e
S
UCL 37.2
LCL 16.5
9
0
-
t
c
O
Mean 26.8
9
0
-
v
o
N
9
0
-
c
e
D
0
1
-
n
a
J
0
1
-
b
e
F
0
1
-
r
a
M
0
1
-
r
p
A
0
1
-
y
a
M
0
1
-
n
u
J
0
1
-
l
u
J
0
1
-
g
u
A
0
1
-
p
e
S
0
1
-
t
c
O
0
1
-
v
o
N
0
1
-
c
e
D
1
-
n
a
J
1
-
b
e
F
1
-
r
a
M
ER Visits / 1000 Asthmatics
1
-
r
p
A
1
-
y
a
M
1
-
n
u
J
1
-
l
u
J
1
-
g
u
A
1
-
p
e
S
1
-
t
c
O
1
-
v
o
N
40.2
Mean 24.1
1
-
c
e
D
2
1
-
n
a
J
2
1
-
b
e
F
22.8
2
1
-
r
a
M
45. Mar 12
Clinic ER Visits Total Empanelled ER Visits per 1000 % of Total Visits
Empanelled
Family Practice 1,450 17,500 82.86 75.40%
Internal Med 100 2,500 40.00 5.20%
Peds 373 10,000 37.30 19.40%
Total 1,923 30,000 64.10 100.00%
Dec 11
Clinic ER Visits Total Empanelled ER Visits per 1000 % of Total Visits
Empanelled
Family Practice 1,844 17,500 105.37 79.31%
Internal Med 97 2,500 38.80 4.17%
Peds 384 10,000 38.40 16.52%
Total 2,325 30,000 77.50 100.00%
Dec 10
Clinic ER Visits Total Empanelled ER Visits per 1000 % of Total Visits
Empanelled
Family Practice 1,498 17,500 85.60 74.94%
Internal Med 103 2,500 41.20 5.15%
Peds 398 10,000 39.80 19.91%
Total 1,999 30,000 66.63 100.00%
46. Patient Appointments with PCM
(Goal 70%)
Valhalla Medical Center (Jan – Mar 2012)
Source: Tricare Operations Center
MEPRS Clinic % with PCM
Family Practice Clinic A (BGAB) 70.1
Family Practice Clinic B (BGAC) 67.5
Family Practice Clinic C (BGAD) 68.7
47. Access to Care 3rd Next Available
(Acute) - Goal 80%)
Valhalla Medical Center (Jan – Mar 2012)
Source: Tricare Operations Center
MEPRS Clinic % @ Std
Family Practice Clinic A (BGAB) 83.3
Family Practice Clinic B (BGAC) 87.2
Family Practice Clinic C (BGAD) 91.2
Critical Decision Making for Medical Executives: Keys to Improving Healthcare Delivery June 2010October 2007
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Critical Decision Making for Medical Executives: Keys to Improving Healthcare Delivery June 2010October 2007 Critical Decision Making for Medical Executives: Keys to Improving Healthcare Delivery
Critical Decision Making for Medical Executives: Keys to Improving Healthcare Delivery June 2010October 2007 Also access to appointments and services (TRICARE Online)
Critical Decision Making for Medical Executives: Keys to Improving Healthcare Delivery June 2010October 2007 Mike: You know Dr Crawford the questions you asked, fit perfectly into the MHS Population Health Model. You asked…. Mike: Ok enough about theory. Lets look at a couple of tools the MHS supports to answer your type of questions.
Critical Decision Making for Medical Executives: Keys to Improving Healthcare Delivery June 2010October 2007 Critical Decision Making for Medical Executives: Keys to Improving Healthcare Delivery
Critical Decision Making for Medical Executives: Keys to Improving Healthcare Delivery June 2010October 2007 Critical Decision Making for Medical Executives: Keys to Improving Healthcare Delivery