Transcript Insert Workshop Title

Opportunities to use electronic behavioral
health records and national treatment data
standards to improve the quality, effectiveness
and cost-effectives of care
Michael Dennis, Ph.D.
Chestnut Health Systems, Normal, IL
Presentation at the ninth State Systems Development Program (SSDP IX)
conference sponsored by the Substance Abuse and Mental Health Services
Administration’s (SAMHSA) Center for Substance Abuse Treatment (CSAT),
Baltimore, MD, August 24-26, 2010.. This presentation reports on treatment
& research funded by the SAMHSA contract 270-07-0191, as well as several
individual CSAT, NIAAA, NIDA and private foundation grants. The opinions
are those of the author and do not reflect official positions of the consortium
or government. Available on line at www.chestnut.org/LI/Posters or by
contacting Joan Unsicker at 448 Wylie Drive, Normal, IL 61761, phone:
(309) 451-7801, Fax: (309) 451-7763, e-mail: [email protected]
Goals of this Presentation are to
1. Examine the limits of existing performance
measures and shift focus from structure to
clinical utility, and quality
2. Demonstrate the need to connect with general
health care and value of even short common
measures
3. Explore the value to clinical care of electronic
behavioral health record (EBHR) systems that
incorporate support for clinical decision making
4. Link back to why this makes embracing the
more detailed requirements (e.g., CCR, LOINC,
SNOMED) desirable for our field and clients
Will be using data from the Global Appraisal of
Individual Needs (GAIN) Collaborators
NH
WA
MT
VT
MN
ND
ME
MA
OR
ID
WY
NV
CA
UT
WI
SD
MI
NE
CO
KS
AZ
OK
NM
TX
AK
NY
PA
IA
NJ
OH
DE
WV
MO
VA
MD
KY
DC
NC
TN
State or
No of
AR
SC
GAIN Sites Regional System
GA
GAIN-Short
None (Yet)
MS AL
Screener
1 to 14
GAIN-Quick
LA
15 to 30
IL IN
FL
HI
RI
CT
More in BZ, CA,
CN, JP, MX
31 to 165
GAIN-Full
VI
PR
3/10 3
Some numbers as of June 2010

1,501 Licensed GAIN administrative units from 49
states (all by ND) and 7 countries

3,270 users in 396 Agencies using GAIN ABS

60,380 intake assessments (largest in field)

22,045 (88% w 1+ follow-up) from 278 CSAT
grantees

22 states, 12 Federal, 6 Canadian provinces, 6 other
countries, and 3 foundations mandate or strongly
encourage its use

4 dozen researchers have published 179 GAINrelated research publications to date
4
The GAIN is ..





A family of instruments ranging from screening,
to quick assessment to a full Biopsychosocial and
monitoring tools
Designed to integrate clinical and research
assessment
Designed to support clinical decision making at
the individual client level
Designed to support evaluation and planning at
program level
Designed to support secondary analyses and
comparisons across individuals and programs
The GAIN is NOT an electronic health record (EHR), but a
component that can interface with and support EHRs.
Some Common Record Based
Performance Measures
PFP
NIATX
NOMS
CSAT
WCG
NQF
Initiation: Treatment within 2 weeks of diagnosis
X X
X X X
Engagement: 2 additional sessions within 30 days
X X
X X X
Continuing Care: Any treatment 90-180 days out
X
X
X
Detox Transfer: Starting treatment within 2 weeks
X
X
Residential Step Down: Starting OP Tx w/in 2wks
X
Evidenced Based Practice: From NREP/Other lists
Within Cost Bands: see French et al 2009
X
X X
X
X X
* NQF: National Quality Forum; WCG: Washington Circle Group; CSAT: Center for
Substance Abuse Treatment evaluations; NOMS: National Outcome Monitoring
System; NIATX: Network for the Improvement of Addiction Treatment; PFP: Pay for
Performance evaluations
Evaluation of Existing Measures

Strengths:
–
–
–

Weaknesses:
–
–
–
–

Easy to collect/ calculate in electronic health records
Give broad overview of where problems
Useful for program evaluation and pay for performance
Doesn’t lead to specific changes or intervention with
individuals
Doesn’t address case mix or context issues
Doesn’t easily lead to specific improvement at the
program level
Doesn’t address relationships with other gaps in the
macro system
Linkage to other behavioral health record systems
is efficient, but limited by the coverage, content and
quality of those systems
Additional NQF Standards of Care






Annual screening for tobacco, alcohol and other
drugs using systematic methods
Referral for further multidimensional assessment to
guide patient-centered treatment planning
Brief intervention, referral to treatment and
supportive services where needed
Pharmacotherapy to help manage withdrawal,
tobacco, alcohol and opioid dependence
Provision of empirically validated psychosocial
interventions
Monitoring and the provision of continuing care
Source: www.tresearch.org/centers/nqf_docs/NQF_Crosswalk.pdf
Why we need to be expand beyond specialty
care into health care..
Few Get Treatment:
1 in 17 adolescents,
1 in 22 young adults,
25%
1 in 12 adults
Over 88% of adolescent and
young adult treatment and
over 50% of adult treatment is
publicly funded and expected to
increase under health care reform
21.2%
Inclusion of the whole
behavioral health system
doubles the coverage, but
still misses over 90%
20%
15%
10%
8.9%
7.3%
5%
0.5%
1.0%
0.6%
0%
12 to 17
18 to 25
26 or older
Abuse or Dependence in past year
Treatment in past year
Source: OAS, 2006 – 2003, 2004, and 2005 NSDUH
Comorbidity is Common in Household Population
3 to 16
Disorders
18%
2 Disorders
10%
(28%/46% Any)=
61% Co-occurring
Lifetime
Pattern of Disorders
Substance
Only
3%
None
54%
1 Disorder
18%
Externalizing
Only
5%
Internalizing
Only
21%
None
48%
Sub.+Ext
1%
Lifetime
Number of Disorders
(13%/16% SUD)=
81% Co-occurring
Source: Dennis,
Scott, Funk & Chan forthcoming;
Sub.+Int
4%
Sub. + Ext. +
Int.
8%
Ext.+Int.
10%
National Co morbidity Study Replication
Lifetime Treatment Participation is related to the
to Number of Dis. and Pattern of Multimorbidity
29%
79%
60%
49%
Source: Dennis, Scott, Funk & Chan forthcoming; National Co morbidity Study Replication
Sub. + Ext. + Int.
Substance+Internalizing
Substance+Externalizing
Externalizing Only
Substance Only
Internalizing Only
Pattern of Disorders
Externalizing+Internalizing
Number of Disorders
None
3 to 16 Disorders
2 Disorders
1 Disorder
4%
19%
50%
54%
64%
75%
Any Behavioral Health Tx
Any Mental Health Tx
Any Substance Disorder Tx
39%
5%
None
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Pattern of Disorders
Source: Dennis, Scott, Funk & Chan forthcoming; National Co morbidity Study Replication
16%
Sub. + Ext. + Int.
24%
Externalizing+Internalizing
Substance+Externalizing
26%
41%
Internalizing Only
Substance+Internalizing
65%
Externalizing Only
None
19%
3 to 16 Disorders
Number of Disorders
Past Year
Recovery Rate
51%
68%
Substance Only
50%
2 Disorders
1 Disorder
None
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
64%
The problem is the higher the comorbidity, the less likely
people are to reach Recovery (no past year symptoms)
The Movement to Increase Screening

Screening, Brief Intervention and Referral to Treatment
(SBIRT) has been shown to be effective in identifying people
not currently in treatment, initiating treatment/change and
improving outcomes (see http://sbirt.samhsa.gov/ )

The US Preventive Services Task Force (USPSTF, 2004;
2007), National Quality Forum (NQF, 2007), and Healthy
People 2010 have each recommended SBIRT for tobacco,
alcohol and increasingly drugs

CSAT and NIDA are both funding several demonstration and
research projects to develop and evaluate models for doing this

Washington State mandated screening in all adolescent and
adult substance abuse treatment, mental health, justice, and
child welfare programs with the 5 minute Global Appraisal of
Individual Needs (GAIN) short screener
Washington State Results with
GAIN Short Screener: Adults
4%
3%
17%
17%
18%
17%
69%
69%
44%
51%
31%
64%
43%
53%
31%
Eastern State
Hospital
(n=422)
65%
78%
73%
Substance
Abuse
Treatment
(n=75,208)
51%
46%
81%
68%
69%
56%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Problems could be easily identified
& Comorbidity common
Either
Corrections:
Community
(n=2,723)
High on Mental Health
Corrections:
Prison
(n=7,881)
Mental Health
Treatment
(55,847)
High on Substance
Childrens
Administration
(n=1,238)
High on Both
Source: Lucenko et al (2009). Report to the Legislature: Co-Occurring Disorders
Among DSHS Clients. Olympia, WA: Department of Social and Health Services.
Retrieved from http://publications.rda.dshs.wa.gov/1392/
Higher rate in clinical record in Mental Health and
Children’s Administration. But that was based on
-“any use” vs. “week use + abuse/dependence”
- and 2 years vs. past year
3%
17%
22%
39%
59%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
56%
Washington State Validation of Co-occurring:
GAIN Short Screener vs Clinical Records
Substance Abuse Treatment
(n=75,208)
Mental Health Treatment
(55,847)
GAIN Short Screener
Childrens Administration
(n=1,238)
Clinical Indicators
Source: Lucenko et al (2009). Report to the Legislature: Co-Occurring Disorders
Among DSHS Clients. Olympia, WA: Department of Social and Health Services.
Retrieved from http://publications.rda.dshs.wa.gov/1392/
Any Behavioral Health (n=106,818)
Mental Health (n=94,832)
Substance Abuse (n=67,115)
Co-Occurring (n=55,128)
Substance Abuse Treatment
Corrections: Community
Mental Health Treatment
Eastern State Hospital
Corrections: Prison
Childrens Administration
Source: Lucenko et al (2009). Report to the Legislature: Co-Occurring Disorders
Among DSHS Clients. Olympia, WA: Department of Social and Health Services.
Retrieved from http://publications.rda.dshs.wa.gov/1392/
120,000
100,000
80,000
60,000
40,000
20,000
Substance Abuse Treatment is over half of
treatment system for substance disorders,
other mental disorders, and co-occurring
0
Where in the System are the Adults with Mental
Health, Substance Abuse and Co-occurring?
Substance Abuse Student Assistance
Treatment
Programs
(n=8,213)
(n=8,777)
Either
Juvenile Justice
(n=2,024)
High on Mental Health
Mental Health
Treatment (10,937)
High on Substance
12%
11%
12%
12%
40%
37%
46%
35%
61%
60%
73%
62%
75%
75%
Problems could be easily identified
& Comorbidity common
86%
83%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
77%
67%
57%
47%
Washington State Results with
GAIN Short Screener: Adolescent
Children's
Administration
(n=239)
High on Both
Source: Lucenko et al (2009). Report to the Legislature: Co-Occurring Disorders
Among DSHS Clients. Olympia, WA: Department of Social and Health Services.
Retrieved from http://publications.rda.dshs.wa.gov/1392/
Adolescent Client Validation of Hi Co-occurring from
GAIN Short Screener vs Clinical Records
by Setting in Washington State
Substance Abuse
Treatment (n=8,213)
Juvenile Justice
(n=2,024)
GAIN Short Screener
Mental Health
Treatment (10,937)
9%
11%
15%
12%
34%
35%
56%
Two page measure closely approximated all found
in the clinical record after the next two years
47%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Children's
Administration
(n=239)
Clinical Indicators
Source: Lucenko et al (2009). Report to the Legislature: Co-Occurring Disorders
Among DSHS Clients. Olympia, WA: Department of Social and Health Services.
Retrieved from http://publications.rda.dshs.wa.gov/1392/
Where in the System are the Adolescents with Mental
Health, Substance Abuse and Co-occurring?
0
5,000
10,000
15,000
20,000
25,000
Any Behavioral
Health (n=22,879)
Mental Health
(21,568)
Substance Abuse
Need (10,464)
SAP+ SA
Treatment
Over half of
system
Co-occurring
(9,155)
Substance Abuse Treatment
Juvenile Justice
Children's Administration
School Assistance
Programs (SAP) largest
part of BH/MH system;
2nd largest of SA & Cooccurring systems
Student Assistance Program
Mental Health Treatment
Source: Lucenko et al (2009). Report to the Legislature: Co-Occurring Disorders
Among DSHS Clients. Olympia, WA: Department of Social and Health Services.
Retrieved from http://publications.rda.dshs.wa.gov/1392/
Use of a short common screener can

Provide immediate clinical feedback that is a
good approximation of diagnosis and be used to
guide placement and treatment planning

Can be used repeatedly to track change

Support evaluation and planning at program or
state level (e.g., needs, case mix, services needed)

Provide practice based evidence to guide future
clinical decision

Be incorporated into health risk/ wellness
assessments and/or school surveys
In practice we need a Continuum of Measurement
(Common Measures)
Quick
Comprehensive Special
More Extensive / Longer/ Expensive
Screener

Screening to Identify Who Needs to be “Assessed” (5-10 min)
–
–
–
–
–
–
Focus on brevity, simplicity for administration & scoring
Needs to be adequate for triage and referral
GAIN Short Screener for SUD, MH & Crime
ASSIST, AUDIT, CAGE, CRAFT, DAST, MAST for SUD
SCL, HSCL, BSI, CANS for Mental Health
LSI, MAYSI, YLS for Crime

Quick Assessment for Targeted Referral (20-30 min)
– Assessment of who needs a feedback, brief intervention or referral for
more specialized assessment or treatment
– Needs to be adequate for brief intervention
– GAIN Quick
– ADI, ASI, SASSI, T-ASI, MINI

Comprehensive Biopsychosocial (1-2 hours)
– Used to identify common problems and how they are interrelated
– Needs to be adequate for diagnosis, treatment planning and placement
of common problems
– GAIN Initial (Clinical Core and Full)
– CASI, A-CASI, MATE

Specialized Assessment (additional time per area)
–
–
Additional assessment by a specialist (e.g., psychiatrist, MD, nurse,
spec ed) may be needed to rule out a diagnosis or develop a treatment
plan or individual education plan
CIDI, DISC, KSADS, PDI, SCAN
Longer assessments identify more
areas to address in treatment planning
100%
90%
7%
9%
3%
8%
8%
22%
13%
80%
70%
1%
0%
98%
0 Reported
1 Prob.
69%
60%
50%
1%
1%
3%
94%
22%
2 Probs.
40%
30%
40%
3 Probs.
20%
10%
4 Probs.
0%
GAIN SS GAIN Q GAIN Q GAIN I
(v2)
(v3 -Beta)
5 min.
20 min
30 min
1-2 hr
Source: Reclaiming Futures Portland, OR and Santa Cruz, CA sites (n=192)
Most substance
users have
multiple
problems
22
Major Predictors of Bigger Effects
Found in Multiple Meta Analyses
1.
A strong intervention protocol based on
prior evidence
2.
Quality assurance to ensure protocol
adherence and project implementation
3.
Proactive case supervision of individual
4.
Triage to focus on the highest severity
subgroup
Impact of the numbers of these Favorable
features on Recidivism in 509 Juvenile
Justice Studies in Lipsey Meta Analysis
Average
Practice
Source: Adapted from Lipsey, 1997, 2005
The more
features,
the lower
the
recidivism
Evidenced Based Treatment (EBT) that
Typically do Better than Usual Practice in
Reducing Juvenile Recidivism (29% vs. 40%)











Aggression Replacement Training
Reasoning & Rehabilitation
Moral Reconation Therapy
Thinking for a Change
Interpersonal Social Problem Solving
MET/CBT combinations and Other manualized CBT
Multisystemic Therapy (MST)
Functional Family Therapy (FFT)
Multidimensional Family Therapy (MDFT)
Adolescent Community Reinforcement Approach (ACRA)
Assertive Continuing Care
NOTE: There is generally little or no differences in mean
effect size between these brand names
Source: Adapted from Lipsey et al 2001, Waldron et al, 2001, Dennis et al, 2004
Implementation is Essential
(Reduction in Recidivism from .50 Control Group Rate)
The best is to
have a strong
program
implemented
well
Thus one should optimally pick the
strongest intervention that one can
implement well
Source: Adapted from Lipsey, 1997, 2005
The effect of a well
implemented weak program is
as big as a strong program
implemented poorly
% Point Change in Abstinence
Percentage Change in Abstinence (6 mo-Intake) by
level of Adolescent Community Reinforcement
Approach (A-CRA) Quality Assurance
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Effects associated with
intensity of quality
assurance and
monitoring (OR=13.5)
36%
24%
4%
Training Only
Training,
Coaching,
Monitoring
Source: CSAT 2008 SA Dataset subset to 6 Month Follow up (n=1,961)
Clinical Trial
Onsite Protocol
Monitors
27
So what does it mean to move towards
Evidence Based Practice (EBP)?

Introducing explicit intervention protocols that are
– Targeted at specific problems/subgroups and outcomes
– Having explicit quality assurance procedures to cause
adherence at the individual level and implementation at the
program level

Introducing reliable and valid assessment that can be used
– At the individual level to immediately guide clinical judgments
about diagnosis/severity, placement, treatment planning, and
the response to treatment
– At the program level to drive program evaluation, needs
assessment, performance monitoring and long term program
planning

Having the ability to evaluate client and program outcomes
– For the same person or program over time,
– Relative to other people or interventions
Key Challenges to Delivery of Quality
Care in Behavioral Health Systems
1. High turnover workforce with variable education
background related to diagnosis, placement,
treatment planning and referral to other services
2. Heterogeneous needs and severity characterized
by multiple problems, chronic relapse, and multiple
episodes of care over several years
3. Lack of access to or use of data at the program
level to guide immediate clinical decisions, billing
and program planning
4. Missing, bad or misrepresented data that needs to
be minimized and incorporated into interpretations
5. Lack of Infrastructure that is needed to support
implementation and fidelity
1. High Turnover Workforce with Variable Education





Questions spelled out and
simple question format
Lay wording mapped onto
expert standards for given area
Built in definitions, transition
statements, prompts, and
checks for inconsistent and
missing information.
Standardized approach to
asking questions across
domains
Range checks and skip logic
built into electronic
applications




Formal training and certification
protocols on administration,
clinical interpretation, data
management, coordination, local,
regional, and national “trainers”
Above focuses on consistency
across populations, level of care,
staff and time
On-going quality assurance and
data monitoring for the
reoccurrence or problems at the
staff (site or item) level
Availability of training resources,
responses to frequently asked
questions, and technical
assistance
Outcome: Improved Reliability and Efficiency
2. Heterogeneous Needs and Severity






Multiple domains
Focus on most common
problems
Participant self description of
characteristics, problems,
needs, personal strengths and
resources
Behavior problem recency,
breadth , and frequency
Utilization lifetime, recency
and frequency
Dimensional measures to
measure change with
interpretative cut points to
facilitate decisions




Items and cut points mapped
onto DSM for diagnosis,
ASAM for placement, and to
multiple standards and
evidence- based practices for
treatment planning
Computer generated scoring
and reports to guide decisions
Treatment planning
recommendations and links to
evidence-based practice
Basic and advanced clinical
interpretation training and
certification
Outcome: Comprehensive Assessment
3. Lack of Access to or use of Data at the Program Level




Data immediately available to
support clinical decision
making for a case
Data can be transferred to
other clinical information
system to support billing,
progress reports, treatment
planning and on-going
monitoring
Data can be exported and
cleaned to support further
analyses
Data can be pooled with other
sites to facilitate comparison
and evaluation




PC and web based software
applications and support
Formal training and
certification on using data at
the individual level and data
management at the program
level
Data routinely pooled to
support comparisons across
programs and secondary
analysis
Over three dozen scientists
already working with data to
link to evidence-based practice
Outcome: Improved Program Planning and Outcomes
4. Missing, Bad or Misrepresented Data





Assurances, time anchoring,
definitions, transition, and
question order to reduce
confusion and increase valid
responses
Cognitive impairment check
Validity checks on missing,
bad, inconsistency and
unlikely responses
Validity checks for atypical
and overly random symptom
presentations
Validity ratings by staff





Training on optimizing
clinical rapport
Training on time anchoring
Training answering questions,
resolving vague or
inconsistent responses,
following assessment protocol
and accurate documentation.
Utilization and documentation
of other sources of
information
Post hoc checks for on-going
site, staff or item problems
Outcome: Improved Validity
5. Lack of Infrastructure
Development
Direct Services
Training and quality assurance
on administration, clinical
interpretation, data
management, follow-up and
project coordination

Clinical Product Development

Software Development

Collaboration with IT vendors
(e.g., WITS)

Data management


Evaluation and data available
for secondary analysis
Over 36 internal & external
scientists and students

Workgroups focused on
specific subgroup, problem, or
treatment approach

Labor supply (e.g., consultant
pool, college courses)


Software support

Technical assistance and back
up to local trainer/expert
Outcome: Implementation with Fidelity
Whether getting a paper or electronic referral:




These issues go across the continuum of
measurement and specific measures
While there are things that can be done with the
measure, getting good data is as much about the
human factors on the right
The degree to which you are willing to trust the
data at the individual or program level depends on
how well you believe these issues are addressed
Thus rather than just pass on generic/ collapsed
information (like current performance measures)
it is better to include more information on how
things were measured, who measured them and
basic information on how to interpret them
Electronic Health Records can also support
more substantive performance measures
Mental Health Need
at Intake
No/Low
Mod/High
Treatment Received
in the first 3 months
Any Treatment
6
218
Total
224
218/224=97% to targeted
No Treatment
205
553
758
Total
211
771
982
553/771=72%
unmet need
771/982=79% in need
Size of the Problem
Extent to which services are not reaching those in most need
Extent to which services are currently being targeted
Source: 2008 CSAT AAFT Summary Analytic Dataset
Mental Health Problem (at intake) vs.
Any MH Treatment by 3 months
97%
100%
90%
80%
79%
72%
70%
60%
50%
40%
30%
20%
10%
0%
% of Clients With
Mod/High Need
(n=771/982)*
% w Need but No Service % of Services Going to
After 3 months
Those in Need
(n=553/771)
(n=218/224)
Source: 2008 CSAT AAFT Summary Analytic Dataset
Why Do We Care About Unmet Need?

If we subset to those in need, getting mental
health services predicts reduced mental health
problems

Both psychosocial and medication interventions
are associated with reduced problems

If we subset to those NOT in need, getting mental
health services does NOT predict change in
mental health problems
Conversely, we also care about services being
poorly targeted to those in need.
Residential Treatment need (at intake) vs.
7+ Residential days at 3 months
100%
90%
80%
70%
60%
50%
40%
30%
90%
Opportunity to
redirect
existing funds
through better
targeting
52%
36%
20%
10%
0%
% of Clients With
Mod/High Need
(n=349/980)*
% w Need but No
% of Services Going to
Service After 3 months Those in Need (n=34/66)
(n=315/349)
Source: 2008 CSAT AAFT Summary Analytic Dataset
EHR can provide practice based evidence:
Lessons from a Decade of GAIN data from CSAT Grants
NH
WA
MT
VT
ND
MN
OR
MA
ID
WY
CA
NV
ME
SD
WI
IA
NE
PA
DC
VA
IN OH
UT
CO
NY
MI
KS
MO
IL
NM
SC
AR
MS
TX
NC
TN
OK
NJ
DE
MD
KY
AZ
CT
AL
GA
LA
FL
AK
HI
PR VI
RI
AAFT
ART
ATDC
BIRT
JTDC
EARMARK
EAT
FDC
JDC
OJJDP
ORP
RCF
SAC
SCAN
SCY
TCE
YORP40
2009 CSAT Data Set by Age
18 Years or
Older (18+)
12.7%,
(n=2,793)
Under 15 Years
Old (<15) 16.1%,
(n=3,547)
15-17 Years
Old 71.2%,
(n=15,705)
Source: CSAT 2009 Summary Analytic Data Set (n=22,045)
41
Diagnosis Time Period Matters
100%
90%
13%
No Use
19%
80%
70%
30%
32%
60%
63%
50%
Use
Abuse
40%
30%
57%
20%
48%
10%
18%
Dependence
18%
0%
Lifetime
Past Year
Source: CSAT 2009 Summary Analytic Data Set (n=21,659)
Past Month
42
100%
90%
80%
Past Year Substance Diagnosis
3 or More Years of Use
57%
54%
Weekly Use
Any Past Year Dependence
48%
24%
Any Withdrawal Symptoms in the Past Week
Severe Withdrawal (11+ Symptoms)
80%
70%
60%
50%
40%
30%
20%
10%
0%
Definition of Substance Use Severity Matters
5%
93%
Can Give 1+ Reasons to Quit*
72%
Client Believes Need ANY Treatment
Acknowledges Having an AOD Problem
26%
Any Prior Substance Abuse Treatment
34%
Source: CSAT 2009 Summary Analytic Data Set (n=21,816)
*(n=11,066)
43
Alcohol
33%
Other drug disorder
27%
34%
Depression
100%
90%
80%
14%
24%
Trauma
ADHD
41%
CD
Suicide
70%
20%
Cannabis
Anxiety
60%
50%
40%
30%
20%
10%
0%
Multiple Clinical Problems are the NORM!
48%
11%
Victimization
Violence/ illegal activity
Source: CSAT 2009 Summary Analytic Data Set (n=20,826)
63%
80%
44
The Number of Clinical Problems is related to
Level of Care (over lapping but different mix)
100%
90%
None
80%
One
70%
Two
60%
Three
50%
80%
40%
65%
30%
20%
Four
41%
45%
53%
Five to Twelve
10%
0%
OP
IOP
CC-OP
LTR
Source: CSAT 2009 Summary Analytic Data Set (n=21,332)
STR
Significantly
more likely to
have 5+ problems
(OR=5.8)
45
The Number of Major Clinical Problems
But this is the
is highly related to Victimization
issue staff least
like to ask about!
100%
None
90%
80%
One
70%
Two
60%
Three
50%
40%
71%
30%
10%
Five to Twelve
46%
20%
15%
0%
Low (0)
Moderate (1-3)
Four
High (4-15)
Source: CSAT 2009 Summary Analytic Data Set (n=21,784)
Significantly more
likely to have 5+
problems
(OR=13.9)
46
Ever attacked w/ gun, knife, other weapon
Ever hurt by striking/beating
Abused emotionally
Ever forced sex acts against your will/anyone
Age of 1st abuse < 18
Any with more than one person involved
Any several times or for long time
Was person family member/trusted one
Were you afraid for your life/injury
People you told not believe you/help you
Result in oral, vaginal, anal sex
Currently worried someone attack
Currently worried someone beat/hurt
Currently worried someone abuse emotionally
Currently worried someone force sex acts
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Overcoming Staff Reluctance with
General Victimization Scale
40%
35%
29%
7%
57%
32%
31%
26%
19%
11%
6%
10%
9%
8%
1%
Source: CSAT 2009 Summary Analytic Data Set (n=19,318)
47
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
B1. Intoxication/Withdrawal Treatment Plan Needs
39%
Any Detox or withdrawal services
22%
Ambulatory Detox (Risk/Mild)
17%
Non-opioid Meds
Opiate Meds
Monitoring withdrawal and AOD meds
compliance
1%
1%
Source: CSAT 2009 Summary Analytic Data Set (n=17,392)
48
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
B2. Biomedical Treatment Plan Needs
Tobacco cessation
60%
Accom. for medical conditions
33%
Discuss compliance w/ prescribed meds
29%
Compliance with meds for PH probs
17%
Discuss ER/hospitalization history
16%
Currently treated for med problem
11%
Tetanus shot
6%
Eating disorder
4%
Treatment of infectious diseases
Accommodations current pregnancy
1%
1%
Reduce sexual behavior risk
Reduce needle use/risk
78%
3%
Source: CSAT 2009 Summary Analytic Data Set (n=17,392)
49
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
B3. Psychological Treatment Plan Needs
Any Co-occuring
72%
Consq of behavior control problems
68%
Refer to anger management
59%
Suicidal risk intervention
23%
Problems reading and writing
22%
Compliance with psych meds
17%
Currently treated for psych problem
16%
8%
Self-mutilation
Monitor self-mutilation
4%
Cognitive impairment
4%
Discuss lifetime mh hosp. history
1%
74%
Coordination with justice system
41%
Consq of interpersonal illegal acts
Consq of drug-related illegal acts
Discuss lifetime arrest history
31%
18%
Consq of other illegal acts
13%
Civil court proceedings
12%
Source: CSAT 2009 Summary Analytic Data Set (n=18,733)
50
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
B4.Readiness Treatment Plan Needs
Any Treatment Readiness Issues
81%
Wrap-around or case
management services
79%
Any pressure to be in treatment
73%
Required to go to treatment
63%
Reviw expectations for length of
treatment
Review dissatisfaction w/
treatment
Partner to understand
treatment process
16%
9%
3%
Source: CSAT 2009 Summary Analytic Data Set (n=9,169)
51
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
B5. Relapse Potential Treatment Plan Needs
High Relapse Potential
84%
Recovery coach or mentor
67%
Continuing Care after
controlled environment
30%
Significant time in controlled
environment
Discuss substance abuse
treatment history
28%
2%
Source: CSAT 2009 Summary Analytic Data Set (n=21,239)
52
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
B6. Environment Treatment Plan Needs
Attended school in past 90 days
85%
Coping with psycho-social
stressors
70%
Child maltreatment
63%
Recent school problems
56%
Dissatisfaction with
environment
54%
Family fighting in the home
Vocational or government
assistance
Substance use in the home
Employed in past 90 days
Housing situation
Source: CSAT 2009 Summary Analytic Data Set (n=14,952)
47%
32%
32%
29%
26%
53
Recommendations
1.
Build on existing performance measures using the
current period as a baseline against which to judge
progress
2.
Identify useful standardized assessment tools and
electronic behavioral health record systems already
in use and evaluate the extent to which they address
the 5 big issues in the field
3.
Identify core information currently reported out
and create an export file in XML that can be read
into any other electronic health record where both
are mapped on the Continuity of Care Record
(CCR) standard at
http://www.astm.org/Standards/E2369.htm
Recommendations (Continued)
4.
5.
Where a more detailed assessment or report is
available and used across multiple
programs/systems - file the Logical Observation
Identifiers Names and Codes (LOINC) of their full
export files at http://loinc.org/ so that others can pull
or receive part or all them (e.g., pulling GAIN
treatment planning statements into WITS treatment
planning module)
Code the content of the short and/or long export files
using Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT
http://www.ihtsdo.org/snomed-ct/ ) so that other
systems can interpret the content; in so doing,
include information on type of assessment or record,
who did it, any certification, time period, created
scale/variables, cut point, and interpretation,
Recommendations (Continued)
6.
7.
8.
9.
10.
Review and as necessary work on standardizing cut
points for interpreting measures, linkage between
assessment and treatment / evidenced based practices,
and automate the linkage to increase clinical support
Move away from open ended text which is time
consuming to create, not readily usable electronically,
and has little impact on care (relative to checklists)
Allow for multiple diagnoses, treatment plans, etc and
keep them filed separately in the data base so that you
can track need, unmet need and service targeting
Build on prior work where you can, collaborate to
share costs and anticipate problems where you cannot
Keep fields for “other” so that you can “learn” from
practice what you missed on the first pass
Acknowledgments and Contact Information
Available at www.chestnut.org/li/posters.
This presentation was supported by analytic runs provided by Chestnut Health Systems for the
Substance Abuse and Mental Health Services Administration's (SAMHSA's) Center for Substance
Abuse Treatment (CSAT) under Contracts 207-98-7047, 277-00-6500, 270-2003-00006 and 2702007-00004C using data provided by the following 152 grantees: TI11317 TI11321 TI11323 TI11324
TI11422 TI11423 TI11424 TI11432 TI11433 TI11871 TI11874 TI11888 TI11892 TI11894
TI13190TI13305 TI13308 TI13313 TI13322 TI13323 TI13344 TI13345 TI13354 TI13356 TI13601
TI14090 TI14188 TI14189 TI14196 TI14252 TI14261 TI14267 TI14271 TI14272 TI14283 TI14311
TI14315 TI14376 TI15413 TI15415 TI15421 TI15433 TI15438 TI15446 TI15447 TI15458 TI15461
TI15466 TI15467 TI15469 TI15475 TI15478 TI15479 TI15481 TI15483 TI15485 TI15486 TI15489
TI15511 TI15514 TI15524 TI15524 TI15527 TI15545 TI15562 TI15577 TI15584 TI15586 TI15670
TI15671 TI15672 TI15674 TI15677 TI15678 TI15682 TI15686 TI16386 TI16400 TI16414 TI16904
TI16928 TI16939 TI16961 TI16984 TI16992 TI17046 TI17070 TI17071 TI17334 TI17433 TI17434
TI17446 TI17475 TI17476 TI17484 TI17486 TI17490 TI17517 TI17523 TI17535 TI17547 TI17589
TI17604 TI17605 TI17638 TI17646 TI17648 TI17673 TI17702 TI17719 TI17724 TI17728 TI17742
TI17744 TI17751 TI17755 TI17761 TI17763 TI17765 TI17769 TI17775 TI17779 TI17786 TI17788
TI17812 TI17817 TI17825 TI17830 TI17831 TI17864 TI18406 TI18587 TI18671 TI18723 TI19313
TI19323 TI655374. Any opinions about this data are those of the authors and do not reflect official
positions of the government or individual grantees. Comments or questions can be addressed to
Michael Dennis, Chestnut Health Systems, 448 Wylie Drive, Normal, IL 61761. Phone 1-309-4517801; E-mail: [email protected]. More information on the GAIN is available at
www.chestnut.org/li/gain or by e-mailing [email protected] .
57
Additional Slides

The following slides were not used in the
presentation, but included in the event of
questions
42%
41%
Panic Disorder
Agoraphobia
Other Specific Phobia
30%
44%
48%
48%
39%
Social Phobia
Posttraumatic Stress Dis.
Generalized Anxiety Dis.
Adult Separation Anxiety
Any Anxiety Disorder:
31%
43%
71%
57%
Major Depressive Epi.
Dysthymia
Bi-Polar I or II
41%
Internalizing Disorder
56%
45%
Intermittent Explosive
Any Mood Disorder:
50%
58%
89%
89%
77%
83%
66%
ADHD
Oppositional Defiant
Conduct Disorder
Externalizing Disorder
Drug Disorder
Alcohol Disorder
Any Substance Disorder
44%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Any Disorder
Past Year Recovery “Rates” (Remission/Lifetime)
by Disorders in the US
Past Year
Recovery Rate
Source: Dennis, Scott, Funk & Chan forthcoming; National Co morbidity Study Replication
8%
7%
Generalized Anxiety Dis.
Posttraumatic Stress Dis.
Agoraphobia
Other Specific Phobia
13%
2%
5%
12%
7%
Adult Separation Anxiety
Social Phobia
Panic Disorder
2%
31%
19%
Major Depressive Epi.
Dysthymia
Bi-Polar I or II
Any Anxiety Disorder:
20%
37%
Any Mood Disorder:
4%
8%
Intermittent Explosive
Internalizing Disorder
8%
10%
Oppositional Defiant
ADHD
10%
25%
13%
8%
15%
Conduct Disorder
Externalizing Disorder
Drug Disorder
Alcohol Disorder
Any Substance Disorder
47%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Any Disorder
Prevalence of Lifetime Disorders and
Past Year Remission in the US
Lifetime Disorder
Past Year Remission
NOMS: Early Treatment Outcomes
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
84%
Initiation within 14 days
76%
Evidenced Based Practice
Engagement for at least 6
weeks
72%
Any Continuing Care (91180 days)
58%
Substance Use-Abstinent/
Reduced 50% at 3 Months
66%
12 month cost within bands
for initial type of treatment
Source: CSAT 2009 SA Data Set subset to 1+ Follow ups (n=11,668)
100%
56%
61
76%
41%
Physical Health
44%
Mental Health
99%
Nights of Psychiatric Inpatient
80%
Illegal Activity
90%
Arrests
Housed in Community**
68%
Family/Home Problems
71%
47%
Vocational Problems
Social Support/Engagement
**The blue
bar represents Recovery Environment Risk
an increase
Quarterly Cost to Society
of 50% or no
problem
In Work/School**
100%
66%
Use
Abuse/Dependence Sx*
*This
variable
measures the
last 30 days.
All others
measure the
past 90 days
90%
80%
70%
60%
50%
40%
30%
20%
10%
Reduced 50%
or No
Problem
No Problem
0%
NOMS: Post Treatment Outcome (6-12 mo)
12%
17%
Source: CSAT 2009 SA Data Set subset to 1+ Follow ups
44%
89%
62
Use
19%
42%
Abuse/Dependence Sx*
Physical Health
37%
11%
Mental Health
98%
Nights of Psychiatric Inpatient
61%
Illegal Activity
78%
Arrests
Housed in Community
52%
Family/Home Problems
37%
33%
Vocational Problems
Social Support/Engagement
2%
Recovery Environment Risk
Quarterly Cost to Society
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
But Need to Control for the lack of Problems
at Intake
13%
5%
In Work/School
79%
* Variable measures the last 30 days. All others measure the past 90 days.
Source: CSAT 2009 SA Data Set subset to 1+ Follow ups
63
Change in Number of Positive NOMS Outcomes
(Last Follow up – Intake) 78% Improved in 1 or more areas
(29% in 5 or more)
100%
90%
Five or More
29%
80%
70%
12%
60%
50%
14%
40%
13%
30%
11%
20%
8%
6%
8%
10%
0%
Four
Three
Two
One
None
Negative one
Less than negative one
Total
Source: CSAT 2009 SA Data Set subset to 1+ Follow ups (n=18,770)
64
Outcomes May be Hidden by Subgroups:
Example of HIV Risk Outcomes
0.01
0.00
0.00
0.20
-0.40
Unprotected Sex Acts (f=.14)
-0.60
-0.39
-0.29
-0.08
-0.20
-0.03
-0.10
-0.02
0.00
-0.04
Cohen's Effect Size d
0.15
0.10
0.20
0.27
0.40
Days of Victimization (f=.22)
-0.80
-0.69
Days of Needle Use (f=1.19)
A.
Low Risk
Source: Lloyd et al 2007
B.
C.
Mod. Risk
Mod. Risk
W/O Trauma With Trauma
D.
High Risk
Total
Any Illegal Activity can be better predicted by using Intake
Severity on Crime/Violence and Substance Problem Scales
Knowing both is a better predictor
(high –high group is 5.5 times more
likely than low low)
Any Ilegal Activity
(months1-6)
Intake Crime/
Violence Severity
Predicts Recidivism
60%
58%
40%
20%
46%
53%
33%
44%
27%
36%
26%
20%
High
0%
Intake Substance
Problem Severity
Predicts
Recidivism
While there is
risk, most (4280%) actually
do not commit
additional crime
Mod
High
Mod
Low
Crime/Violence Scale
(Intake)
Low
Substance
Problem Scale
(Intake)
Source: CSAT 2008 V5 dataset Adolescents aged 12-17 with 3 and/or 6 month follow-up (N=9006)