Transcript PIP Improvement Strategies for Name of Plan

PMHP Subgroup Analysis Activity
for the Seven Day Follow-up
Collaborative PIP
ACCESS BEHAVIORAL HEALTH
January 11, 2012
Intervention
 Peer Transition Liaison telephone calls
 Effectiveness:
– 2009 improvement in the PMHP’s rate of seven
day follow-up was associated with
implementation of this intervention
– However 2010 rate has dropped
– Dramatic impact on aftercare group attendance
is a direct result of the intervention
Barriers
 Barriers identified on the peer transition
liaison call logs:
–
–
–
–
–
Transportation issues
Member needs to reschedule appointment
Member refuses to keep appointment
Follow-up appointment not clear on form
Follow-up appointment not within 7 days
Barriers
 Other barriers identified in the overall
population for the seven day follow-up
collaborative PIP include:
– Homeless
– No telephone
– Members whom we do not reach by phone
Subgroups
 Subgroups identified in the overall
population for the seven day follow-up
collaborative PIP are:
– Children vs Adults
– Inpatient Facilities
Subgroups
 Subgroups identified in the overall
population for the seven day follow-up
collaborative PIP are:
– Children vs Adults
• Follow-up within 7 days
– 38% of adult members
– 42% of child members
Subgroups
 Subgroups identified in the overall
population for the seven day follow-up
collaborative PIP are:
– Inpatient Facility
•
•
•
•
•
37% of Baptist Hospital discharges
68% of Bridgeway CSU discharges
35% of Emerald Coast Behavioral discharges
41% of Lakeview CSU discharges
16% of West Florida Hospital discharges
Availability of Data
 Were the data for the identified subgroups
available, complete, and accurate?
– Encounter data
– Peer Transition Liaison Activity Log data
– ABH PTL Follow-up Form us being used only
by the two CSUs (Bridgeway & Lakeview).
– Hospitals are using other formats.
Availability of Data
Approximate proportion of the subgroups to
the overall population for the PIP:
–
–
–
–
–
–
–
Baptist Hosp 71%
Bridgeway CSU 10%
Emerald Coast Behavioral 2%
Lakeview CSU 15%
West Florida Hosp 2%
Adult 57%
Child 43%
Analysis Results
 disparities in the subgroups that were
identified:
– Only adults are admitted to the CSUs
– All area children’s psych beds are in one
hospital
– Out of area children’s psych hospital used when
necessary and appropriate
– Most adults first appointment is a group
– Most children’s first appointment is an
individual appointment
Improvement Strategies
 Do your identified subgroups have unique
causes/barriers?
– Children vs. Adults
• More adults were given appointment within 7 days
• PTL higher success rate reaching parents of children
 Interventions that could be implemented to
address the causes/barriers:
– Bridgeway’s Psychiatric Aftercare day model
– Impact children’s first appointment date
PMHP Subgroup Analysis Activity
for the Seven Day Follow-up
Collaborative PIP
Jackson Public Health Trust /
University of Miami Behavioral
Health
January 11, 2012
Subgroups
 The subgroups identified in the overall
population for the seven day follow-up
collaborative PIP were:
– Facilities with 50+ discharges
– Length of Stay
– Gender and Age Range
Availability of Data
 Were the data for the identified subgroups
available, complete, and accurate?
– Yes
 What was the approximate proportion of the
subgroups to the overall population for the PIP?
– Facilities with 50+ d/c: 92.4%
– Age/Sex: 100%
– Length of Stay: 100%
 Was it adequate for analysis?
– Yes
Analysis Results
 Why did the subgroups make sense for your plan?
– High SSI membership (longer LOS)
– High number of contracted facilities
– Representation of all age groups
 Did the subgroups you originally selected require
additional grouping?
– Yes, original subgroups did not show disparities (i.e.
DSM diagnostic categories)
 Summarize the disparities in the subgroups that were
identified.
– See next 3 slides for charts
Gender and Age Range
SEX
F
F
F
F
Age Range
0-12
13-17
18-64
65+
M
M
M
M
0-12
13-17
18-64
65+
Total
25
96
595
19
43
77
756
25
1636
7-day F/U Compliant?
Yes
% Yes
No
40%
10
15
48%
46
50
43%
255
340
58%
11
8
23
28
285
7
665
53%
36%
38%
28%
40.65%
20
49
471
18
971
Length of Stay
7-day F/U Compliant?
Length of Stay
Total
Yes
%Yes
No
3 or less days
485
158
33%
327
4-6 days
846
375
44%
471
7 or more days
305
132
43%
173
1636
665
40.65%
971
Facilities
7-day F/U Compliant?
Facility (only
those with 50+
discharges)
C
J
L
MC
MS
N
PA
PU
S
U
W
Total
Yes
119
54
191
106
72
64
105
316
158
221
106
1512
28
23
71
51
10
18
37
248
60
57
35
638
% Yes
24%
43%
37%
48%
14%
28%
35%
78%
38%
26%
33%
42.20%
No
91
31
120
55
62
46
68
68
98
164
71
874
Improvement Strategies
 Do your identified subgroups have unique causes/barriers?
–
–
–
–
Discharge planners at low-performing facilities
Lack of facility “buy-in” for the importance of discharge planning
Perception that short MH hospital stays do not require follow-up
Men 65+ resistant/reluctant to attend f/u appointment
 What interventions have been implemented or could be
implemented to address the causes/barriers?
– Additional education/training for discharge planners at lowperforming facilities
– Targeted intervention to emphasize f/u for men who are 65 and
older
 If applicable, discuss how the subgroups and/or barriers
were or will be prioritized.
Lessons Learned
 Has this exercise provided insight into your
plan’s seven day follow-up PIP rates? Why
or why not?
– Yes
 Future plans
– Further drill-down to low-performing facilities
and issues with discharge planning
– Interviewing non-compliant men over 65 to
determine specific reasons for non-compliance
PMHP Subgroup Analysis Activity
for the Seven Day Follow-up
Collaborative PIP
Magellan Behavioral Services
January 11, 2012
Subgroups
 The subgroups identified in the overall population for
the seven day follow-up collaborative PIP were:
 We identified subgroups for the PIP using the
top five axis 1 diagnosis codes found in followup detail for the time period of 1/1/2011 to
9/30/2011 by quarter. We decided on this time
period to see if there was a correlation between
a change in medical necessity criteria that was
implemented over the summer and follow-up
rates.
The report is a combination of PMHP
areas 2, 4, 9, & 11.
The five diagnoses used were
295.34
295.70
296.90
311, and 314.01.
Availability of Data
 Were the data for the identified subgroups
available, complete, and accurate?
Yes, the data is accurate and complete
according to HEDIS requirements
 Was it adequate for analysis?
Yes
 What was the approximate proportion of the
subgroups to the overall population for the
PIP?
• The total % of the combined subgroups
from the total population is 42%.
• The 5 subgroup percentages are detailed in
the results table.
• The total population denominator was
considered anyone who was discharged
from inpatient that fit the HEDIS
requirements.
• The total population numerator was anyone
who had a follow-up appointment within 7
days of discharge that fit the HEDIS
requirement.
• To determine the subgroup % of the
population, the number of enrollees
discharged with each diagnosis code was
divided by the overall number of enrollees
discharged (total population denominator).
Analysis Results
 Why did the subgroups make sense for your
plan?
The Subgroups identified are plausible for
this plan as there is a sufficient sample is
each diagnosis subgroup.
• Did the subgroups you originally
selected require additional grouping?
We do not currently believe the
subgroups require additional grouping.
However, further research into the
highest and lowest scoring subgroups
may be needed as determined by the
clinical team.
• Summarize the disparities in the subgroups
that were identified.
Count of
Member ID
QTR
Q1
Q2
DX1
7 Day
FAH
N
Y
N
Y
OVERALL
(all DXs)
616
682
53%
656
678
51%
Q3
N
559
Y
605
52%
Grand Total
52%
Subgroup % of total population
FAH rate excluding subgroup
295.34 295.70 296.90
73
44
62
62
35
78
46%
44%
56%
72
41
67
44
30
89
38%
65
56
46%
372
10%
53%
42%
34
31
48%
215
6%
52%
57%
62
67
52%
425
11%
51%
311
53
60
53%
63
57
314.01
24
43
64%
28
60
Grand
Total
256
278
52%
271
280
48%
55
60
52%
348
9%
52%
68%
22
41
65%
218
6%
51%
51%
238
255
52%
1578
42%
52%
FAH rate excluding OVERALL
subgroup
(all DX) 295.34
295.70
296.90
311
314.01
Grand
Total
N
1831
210
119
191
171
74
765
Y
1965
162
96
234
177
144
813
N
1621
1712
1640
1660
1757
1066
Y
1803
1869
1731
1788
1821
1152
53%
52%
51%
52%
51%
52%
Improvement Strategies
 Do your identified subgroups have unique
causes/barriers?
 What interventions have been implemented or
could be implemented to address the
causes/barriers?
Causes/Barriers and Interventions by
Diagnosis
Members with thought disorders (295.34 and
295.70) have lower follow up rates. This is
most likely due to the nature of these disorders.
These members tend to be more disorganized in
their thinking, have a lack of insight into their
illness and therefore not acknowledging that
follow up treatment is needed, and have less
stable or no natural support systems.
Interventions for these subgroups (295.34 and 295.70)
would be to involve any and all support systems in the
discharge planning process, prior the member being
discharged from the inpatient facility to better assist
the member in complying with the discharge plan.
Additionally, Magellan staff could provide added
follow up assistance by care management staff, follow
up specialists in the form of welcome home calls and
appointment reminder calls. In some areas of the State
of Florida, a field care worker or peer specialist may
be available to assist in helping the member in
reinforcing the discharge plan and linking with an
outpatient provider.
Causes/Barriers and Interventions by
Diagnosis
 Members with mood disorders (296.9 and
311) had higher follow up rates than those
with thought disorders. These members
tend to have more insight into their illness,
may have a support system in place, though
may have apathy toward complying with
follow up treatment.
Interventions for this subgroup (296.9 and 311)
would entail more outreach efforts by care
management staff with training in motivational
interviewing to engage the member and
encourage/support compliance with the
discharge plan. Additionally, in areas of the
State, a field care worker or peer specialist is
available to provider added support and
encouragement to comply with the discharge
plan.
Causes/Barriers and Interventions by
Diagnosis
 Members with diagnosis code 314.01 had
the highest follow up rate of the subgroups.
This may be attributed to the fact that
members with this diagnosis are mostly
children and have a legal guardian/caretaker
who can assist them in complying with the
discharge plan. These support systems also
have a vested interest in treatment.
Interventions for this subgroup, 314.01,
would entail assistance in complying with the
outpatient appointment. Reminder calls and
assistance in rescheduling the follow up
appointment due to schedule conflicts would
yield the best results.
Improvement Strategies
• If applicable, discuss how the subgroups
and/or barriers were or will be prioritized.
The subgroups are ranked in order of priority in
#8 as are barriers for each subgroup.
Lessons Learned
 Has this exercise provided insight into your
plan’s seven day follow-up PIP rates? Why
or why not? As expected, thought disorders
have the lowest follow up rates. This
exercise has allowed Magellan to focus on
this (and other subgroups) upon admission
to ensure discharge planning incorporates
extra outreach attempts.
 Future plans: Increase outreach and
reminder calls to these populations.
Interventions (Optional)
 Provide information on an intervention that
your PMHP has implemented for the seven
day follow-up collaborative PIP.
 Why do you think that this intervention has
been effective?
 Has the effectiveness of this intervention
been tested?
 Was improvement in the PMHP’s rate of
seven day follow-up attributed to this
intervention?
Provide information on an intervention
that your PMHP has implemented for the
seven day follow-up collaborative PIP.
During the years there has been many
interventions to improve the follow up
appointment rates in all areas. One of the
most effective at the moment is the
implementation of Bridge appointments
which we started to work on in February
2011. We started to receive authorizations
requests in March with the majority of them
from Jackson Memorial Hospital.
Has the effectiveness of this intervention been
tested?
 In July 2011 Emerald Coast, from PMHP
Area 2, agreed to participate in the Bridge
Program and by August it has facilitated 29
appointments. This is more than all other
providers year to date.
 Area 2 increased the follow up rate from
43% in 2010
46% by June 30th, 2011
73% in August
70% in September
and 84% in October.
Area 2 has the biggest increase in rates
Why do you think that this intervention has
been effective?
 One of our biggest barriers is the lack of
follow up from discharging providers. By
implementing the Bridge program we have
been able to increase the follow up rates in
the areas where providers are participating
by improving coordination of follow up
appointments.
Was improvement in the PMHP’s rate of seven day
follow-up attributed to this intervention?
 It is clear that for PMHP Area 2 the Bridge
Program has contributed to the increase in
follow up rates. We are working to increase
providers “enrollment" in this program in
all areas, and to encourage providers that
have agreed to participate to increase their
activities.
Other Interventions
 In 2011 we have instituted other
interventions that have directly contributed
to the increase in follow up rates by 14%: - Welcome Calls to each discharged patient
to ensure they have a follow up appointment
and to encourage attendance to the
appointment.
- Outreach to transportation services in the
areas of highest utilization of inpatient and
crisis unit services.
- Follow up specialists are calling the CBC
points of contacts regarding every child
welfare admission to improve coordination
of care and follow through with
appointments.
PMHP Subgroup Analysis Activity
for the Seven Day Follow-up
Collaborative PIP
NFBHP & FHP
January 11, 2012
Subgroups
 The subgroups identified in the overall population for the
seven day follow-up collaborative PIP were:
 The five (5) distinct Contract Areas that make up NFBHP (A3) and
FHP (A5, A6, A7, A8)
 Adult vs. Children
 Length of Stay (LOS) - <3 vs. >8
 Diagnosis (Dx) – the top three most common diagnoses associated
with the discharge (Depressive Disorder NOS, Mood Disorder
NOS and Schizoaffective Disorder)
Initial Study Population
 Members assigned to the Medicaid PMHP for NFBHP (Contract Area
3) and FHP (Contract Areas 5, 6, 7 & 8)
 All Members discharged from inpatient care during the time period
under evaluation (CY 2010), and who were Medicaid PMHP enrolled
at the time of their inpatient discharge
 All age groups and PMHP covered diagnoses
 Enrollment criteria included Client enrolled in PMHP at any time
during the hospital stay and enrolled in PMHP at the time of discharge
 Individuals continuously enrolled seven (7) days following discharge
Decision for
Subgroups
 Opportunity to present data across ValueOptions five (5) Contract
Areas and complete a comparison as sub-groups
 To identify if Children played a role in the outcome of 7-day followup
as they make up the majority of ValueOptions current PMHP
Membership
 To identify if length of stay plays a role in compliance with outpatient
follow-up (Average Length of Stay for discharges during the
timeframe evaluated was 4.4)
 To determine if the most common diagnoses identified with discharge
affects Member follow-up
Availability of Data
 Data pulled for the timeframe of Jan-Dec 2010 from claims data for
paid inpatient treatment with a d/c date that fell within the timeframe
analyzed
 Examination of encounter and claims data to determine if a qualified
follow-up service occurred within seven (7) days.
 Data was available as it was pulled and analyzed well after the 90 days
claims run to ensure appropriate claims lag.
 ValueOptions monitors encounter data reported by Network Providers
using Monthly Provider Reports that confirms the number of
encounters/records received.
 Providers are required to submit Encounter data within specific
timeframes. Two performance standards are established for timely
submission:
 76% of Encounters submitted within 14 days from date of services and/or
 90% of Encounters submitted within 30 days from date of services
Availability of Data
 Network Providers submit Encounter data to ValueOptions Connects
platform, FileConnect.
 Once Encounters hit ValueOptions system, encounters are examined
with a variety of VEDS edits for completeness and accuracy.
 One of the many VEDS process checks includes ensuring the Dx and
procedure codes submitted meet Medicaid contract specifications.
 For those Encounters that ‘fail’ the examination process, a detailed
error report is generated and sent to the Network Provider for
correction and resubmission.
 Analysis of encounter data submissions reveals that encounter data on
average is 97.5% complete.
Proportion of Subgroup
to overall population
Contract Areas

30% of the total number of
D/C’s is Contract Area 3

29% of the total # of 7-day
F/U’s was in Contract Area 3

Area 7 made up 22% of the
total discharges in the initial
population with 19% of
those total discharges
following up within 7 days
Proportion of Subgroup
to overall population
Adult vs. Child

61% of the total # of
discharges were Adults

39% of the total # of
discharges were Children with
55% of them following up
within 7-days
Proportion of Subgroup
to overall population
Length of Stay

43% of the total # of
discharges had a length of
stay less than three (3)
days
Proportion of Subgroup
to overall population
Diagnoses

The top three most common
diagnoses associated with the
total # of discharges were:



Depressive Disorder NOS
Mood Disorder NOS
Schizoaffective Disorder

Depressive and Mood D/O
NOS each made up 14% of
the total # of discharges

Mood D/O NOS had 18% of
the total # of 7-day follow up
Outcome Rates
with and without sub-groups

Reviewed and compared 7day follow-up with and
without the identified subgroups

Outcomes rates without subgroups demonstrated a small
improvement from the initial
population for 7-day followup

Adult sub-group had the
biggest impact when
removing the sub-group
from the initial population
increasing the 7-day followup from 29% to 40%
Analysis Results
Why this makes sense to ValueOptions
 VO covers 34 counties across Florida
 Wanted to consider Contract Areas as a ‘sub-group’ to identify
potential discrepancies
 VO monitors and manages the five distinct Contract Areas the same
 Majority of VO’s population served is children
 Medical Management Program that encompasses outlier management
Analysis Results
Disparities
 In comparing sub-groups, we identified the greatest disparity in Adults
with a discharge that did not have a 7-day follow-up compared to the
other sub-groups analyzed.
 Contract Area 7 showed a significant disparity in comparison to the
other Contract Areas as they had the second highest number of total
discharges with a disproportionate amount of discharges that did not
have a 7-day follow-up.
Analysis Results
Additional Grouping
 Area 7 had the most statistically significant impact among the five
contract areas on the overall percentage for 7-day follow up
 Sub-grouped Area 7 to identify other potential patterns/trends




Adult vs. Child
Length of Stay
Diagnosis
Provider
 910 Area 7 individuals did not follow through with 7-day follow up
 Of those that did not follow through with 7-day follow-up
 40 Unique Hospitals discharged Area 7 individuals where there was no 7-day
follow-up;
 One specific Network Provider had 435 or 48% out of the 910 discharges where
there was no follow-up within 7-days;
 And of those 435, 75% or 325 were Adults; 40% or 176 had a length of stay of less
than three (3) days and 83 or 19% had a diagnosis of Mood Disorder NOS
Analysis Results
Additional Grouping con’t
 Adults (vs. Children) were identified as being the majority of
discharges but also the majority who did not follow-up within 7 days
 Of the 3412 total Adult Discharges, 2685 or 67% did not have a 7-day
follow-up
 Sub-grouped the Adult population to look at possible causes
 Contract Area
 Provider
 Diagnosis
 Area 3 had the most discharges at 840 (31%) where there was no 7-day
follow up
 One specific Network Provider had the majority of discharges with no
7-day follow up (431or 51% out of the 840)
 Most common diagnosis Depressive Disorder NOS
Analysis Results
So What if………
 All of Contract Area 7 Members followed up within 7days
Analysis Results
So What if……
 All Adults followed up within 7-days
Improvement Strategies
Unique causes/barriers?
 Appears that similarities were found in each of the sub-group analyses
 Although Area 3 had the most discharges with 7-day follow-up out of
the five Contract Areas, it was also identified as having a significant
impact with Adult discharges with no 7-day follow-up
 This is likely due to the fact that Contract Area 3 has the largest
Membership out of all Contract Areas for VO
 Adults, LOS and Diagnosis all kept trending out in the sub-group
analysis
 Difficult to effectively analyze or determine unique causes/barriers as
the primary source of information is administrative data (claims or
encounter data)
 Clinical Record Reviews may be able to provide more insight to
unique causes or barriers
Improvement Strategies
Possible Interventions
 Member Outreach to those identified sub-groups
 Educational Opportunities for Clinical Care Managers (CCMs) in
VO’s Call Center about sub-groups identified for care management
purposes
 Providing Outlier Reports to CCMs for ongoing monitoring
 Re-educating Network Providers about their monthly Discharge Detail
and Outlier Reports
 Individual Network Provider education about sub-group analysis
results
 Develop sampling group from 2010 discharges for Provider Outreach
to determine which discharges might have been result of Baker Act or
substance abuse component
 Targeted Clinical Record Reviews on sub-groups
Lessons Learned
 Still requires a more in-depth look outside of administrative data to
determine a possible root-cause
 Insightful in that often times you are aware of many of the identified
sub-groups and their potential issues but not always able to put the
‘pieces of the puzzle’ together
 Identified a need to bring these ‘pieces of the puzzle’ together to obtain
a more accurate picture
 Recognize that sub-group analysis helps you to identify over ‘avenues’
or possible interventions to improve the outcome for 7-day follow up
Lessons Learned
Plans……
 Review and discussion of Sub-group Analysis in Senior Quality
Committee (SQC) for possible recommendations
 Inform Network Providers of Sub-Group Analysis