opportunities to use electronic behavioral health records and national treatment data standards to...
TRANSCRIPT
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]
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
Goals of this Presentation are to
More in BZ, CA, CN, JP, MX
ID
ILMO
ND
VI
ME
OK
PR
SD
AR
KS
MS
MT
NM
WVIN
AL
AK
IA
MN
NJNV
RI
SC
UT
HI
LA
DENE
TN
PA
VT
VADC
MI
COKY
GA
OH
OR
MD
AZ
TX
NY
NH
WI
CA
NC
CT
FL
MA
WA
WY
No of GAIN Sites
None (Yet)
1 to 14
15 to 30
31 to 165
Will be using data from the Global Appraisal of Individual Needs (GAIN) Collaborators
State or Regional System
GAIN-Short Screener
GAIN-Quick
GAIN-Full
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 GAIN-related 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
* 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
NQ
F
WC
G
CS
AT
NO
MS
NIA
TX P
FP
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 X X X X
Within Cost Bands: see French et al 2009 X X
Evaluation of Existing Measures
Strengths:– Easy to collect/ calculate in electronic health records– Give broad overview of where problems– Useful for program evaluation and pay for performance
Weaknesses:– 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 careSource: www.tresearch.org/centers/nqf_docs/NQF_Crosswalk.pdf
8.9%
21.2%
7.3%
0.6%1.0%0.5%0%
5%
10%
15%
20%
25%
12 to 17 18 to 25 26 or older
Abuse or Dependence in past yearTreatment in past year
Why we need to be expand beyond specialty care into health care..
Source: OAS, 2006 – 2003, 2004, and 2005 NSDUH
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
Few Get Treatment: 1 in 17 adolescents,
1 in 22 young adults, 1 in 12 adults
Inclusion of the whole behavioral health system doubles the coverage, but
still misses over 90%
Comorbidity is Common in Household Population
Source: Dennis, Scott, Funk & Chan forthcoming; National Co morbidity Study Replication
Lifetime Number of Disorders
Lifetime Pattern of Disorders
None54%
1 Disorder18%
2 Disorders10%
3 to 16 Disorders
18%
Substance Only3%
None48%
Sub.+Int4%
Ext.+Int.10%
Sub. + Ext. + Int. 8%
Sub.+Ext1%
Internalizing Only21%
Externalizing Only5%
(28%/46% Any)=61% Co-occurring
(13%/16% SUD)=81% Co-occurring
Lifetime Treatment Participation is related to the to Number of Dis. and Pattern of Multimorbidity
Source: Dennis, Scott, Funk & Chan forthcoming; National Co morbidity Study Replication
Number of Disorders Pattern of Disorders
5%
39%
54%
75%
4%
29%
19%
50%
49%
64%
60%
79%
0%10%20%30%40%50%60%70%80%90%
100%
Non
e
1 D
isor
der
2 D
isor
ders
3 to
16
Dis
orde
rs
Non
e
Sub
stan
ce O
nly
Ext
erna
lizi
ng O
nly
Inte
rnal
izin
g O
nly
Sub
stan
ce+
Ext
erna
lizi
ng
Sub
stan
ce+
Inte
rnal
izin
g
Ext
erna
lizi
ng+
Inte
rnal
izin
g
Sub
. + E
xt.
+ I
nt.
Any Behavioral Health TxAny Mental Health TxAny Substance Disorder Tx
The problem is the higher the comorbidity, the less likely people are to reach Recovery (no past year symptoms)
Source: Dennis, Scott, Funk & Chan forthcoming; National Co morbidity Study Replication
Number of Disorders Pattern of Disorders
64%
50%
19%
68%
65%
41% 51
%
26%
24%
16%
0%10%20%30%40%50%60%70%80%90%
100%
Non
e
1 D
isor
der
2 D
isor
ders
3 to
16
Dis
orde
rs
Non
e
Subs
tanc
e O
nly
Ext
erna
lizi
ng O
nly
Inte
rnal
izin
g O
nly
Subs
tanc
e+E
xter
nali
zing
Subs
tanc
e+In
tern
aliz
ing
Ext
erna
lizi
ng+
Inte
rnal
izin
g
Sub.
+ E
xt.
+ I
nt.
Past YearRecovery Rate
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
81%
78%
65%
64% 69
%
18%
68% 73
%
43%
44%
69%
17%
69%
51%
53%
51%
17%
4%
56%
46%
31%
31%
17%
3%
0%10%20%30%40%50%60%70%80%90%
100%
SubstanceAbuse
Treatment(n=75,208)
Eastern StateHospital(n=422)
Corrections:Community(n=2,723)
Corrections:Prison
(n=7,881)
Mental HealthTreatment(55,847)
ChildrensAdministration
(n=1,238)
Either High on Mental Health High on Substance 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/
Problems could be easily identified & Comorbidity common
Washington State Validation of Co-occurring: GAIN Short Screener vs Clinical Records
17%
3%
59%
39%
22%
56%
0%
10%20%
30%40%
50%
60%70%
80%90%
100%
Substance Abuse Treatment(n=75,208)
Mental Health Treatment(55,847)
Childrens Administration(n=1,238)
GAIN Short Screener 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/
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
0 20,0
00
40,0
00
60,0
00
80,0
00
100,
000
120,
000
Any Behavioral Health (n=106,818)
Mental Health (n=94,832)
Substance Abuse (n=67,115)
Co-Occurring (n=55,128)
Substance Abuse Treatment Eastern State HospitalCorrections: Community Corrections: PrisonMental Health Treatment Childrens Administration
Where in the System are the Adults with Mental Health, Substance Abuse and Co-occurring?
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/
Substance Abuse Treatment is over half of treatment system for substance disorders, other mental disorders, and co-occurring
77% 86
%
73%
75%
61%67
%
83%
62%
75%
60%
57%
40% 46
%
12%
12%
47%
37%
35%
12%
11%
0%10%20%30%40%50%60%70%80%90%
100%
Substance AbuseTreatment(n=8,213)
Student AssistancePrograms(n=8,777)
Juvenile Justice(n=2,024)
Mental HealthTreatment (10,937)
Children'sAdministration
(n=239)
Either High on Mental Health High on Substance 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/
Washington State Results with GAIN Short Screener: Adolescent
Problems could be easily identified & Comorbidity common
35%
12%
11%
56%
34%
15%
9%
47%
0%10%20%30%40%50%60%70%80%90%
100%
Substance AbuseTreatment (n=8,213)
Juvenile Justice(n=2,024)
Mental HealthTreatment (10,937)
Children'sAdministration
(n=239)
GAIN Short Screener 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/
Adolescent Client Validation of Hi Co-occurring from GAIN Short Screener vs Clinical Records
by Setting in Washington State
Two page measure closely approximated all found in the clinical record after the next two years
0 5,000 10,000 15,000 20,000 25,000
Any BehavioralHealth (n=22,879)
Mental Health(21,568)
Substance AbuseNeed (10,464)
Co-occurring(9,155)
Substance Abuse Treatment Student Assistance ProgramJuvenile Justice Mental Health TreatmentChildren's Administration
Where in the System are the Adolescents with Mental Health, Substance Abuse and Co-occurring?
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/
School Assistance Programs (SAP) largest part of BH/MH system; 2nd largest of SA & Co-
occurring systemsSAP+ SA Treatment
Over half of system
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)
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
Screener Quick C
omprehensive S
pecial
More E
xtensive / Longer/ E
xpensive
Longer assessments identify more areas to address in treatment planning
40%
69%
94%98%
22%
13%
3% 0%
22%
8%
1% 0%
9%8%
1% 1%3% 1% 1%7%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
GAIN SS GAIN Q(v2)
GAIN Q(v3 -Beta)
GAIN I
0 Reported
1 Prob.
2 Probs.
3 Probs.
4 Probs.
Source: Reclaiming Futures Portland, OR and Santa Cruz, CA sites (n=192)
Most substance users have multiple problems
22
5 min. 20 min 30 min 1-2 hr
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
Source: Adapted from Lipsey, 1997, 2005
Average Practice
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
Source: Adapted from Lipsey et al 2001, Waldron et al, 2001, Dennis et al, 2004
NOTE: There is generally little or no differences in mean effect size between these brand names
Implementation is Essential (Reduction in Recidivism from .50 Control Group Rate)
The effect of a well implemented weak program is
as big as a strong program implemented poorly
The best is to have a strong
program implemented
well
Thus one should optimally pick the strongest intervention that one can
implement wellSource: Adapted from Lipsey, 1997, 2005
27
Percentage Change in Abstinence (6 mo-Intake) by level of Adolescent Community Reinforcement Approach (A-CRA) Quality Assurance
4%
24%36%
0%10%20%30%40%50%60%70%80%90%
100%
Training Only Training,Coaching,
Monitoring
Clinical TrialOnsite Protocol
Monitors
% P
oint
Cha
nge
in A
bsti
nenc
e
Source: CSAT 2008 SA Dataset subset to 6 Month Follow up (n=1,961)
Effects associated with intensity of quality
assurance and monitoring (OR=13.5)
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 Systems1. 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
Direct Services
Training and quality assurance on administration, clinical interpretation, data management, follow-up and project coordination
Data management
Evaluation and data available for secondary analysis
Software support
Technical assistance and back up to local trainer/expert
Development
Clinical Product Development
Software Development
Collaboration with IT vendors (e.g., WITS)
Over 36 internal & external scientists and students
Workgroups focused on specific subgroup, problem, or treatment approach
Labor supply (e.g., consultant pool, college courses)
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
Source: 2008 CSAT AAFT Summary Analytic Dataset
553/771=72%unmet need
218/224=97% to targeted
771/982=79% in need
Electronic Health Records can also support more substantive performance measures
Size of the Problem
Extent to which services are currently being targeted
Extent to which services are not reaching those in most need
Treatment Received in the first 3 months
Mental Health Need at Intake
No/Low Mod/High Total
Any Treatment 6 218 224
No Treatment 205 553 758
Total 211 771 982
Mental Health Problem (at intake) vs. Any MH Treatment by 3 months
79%
97%
72%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
% of Clients WithMod/High Need
(n=771/982)*
% w Need but No ServiceAfter 3 months
(n=553/771)
% of Services Going toThose in Need
(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
36%
52%
90%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
% of Clients WithMod/High Need
(n=349/980)*
% w Need but NoService After 3 months
(n=315/349)
% of Services Going toThose in Need (n=34/66)
Opportunity to redirect
existing funds through better
targeting
Source: 2008 CSAT AAFT Summary Analytic Dataset
40
EHR can provide practice based evidence: Lessons from a Decade of GAIN data from CSAT Grants
AK
ALAR
AZ
CACO
CT
DCDE
FL
GA
HI
IA
ID
ILIN
KSKY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PARI
SC
SD
TN
TX
UTVA
VTWA
WI
WV
WY
PR VI
AAFTARTATDCBIRTJTDCEARMARKEATFDCJDCOJJDPORPRCFSACSCANSCYTCEYORP
41
2009 CSAT Data Set by Age
Source: CSAT 2009 Summary Analytic Data Set (n=22,045)
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)
42
Diagnosis Time Period Matters
57%48%
18%
30%
32%
18%
13%19%
63%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Lifetime Past Year Past Month
No Use
Use
Abuse
Dependence
Source: CSAT 2009 Summary Analytic Data Set (n=21,659)
43
Definition of Substance Use Severity Matters
80%
54%
24%
93%
34%
5%
26%
48%
57%
72%
0% 10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Past Year Substance Diagnosis
3 or More Years of Use
Weekly Use
Any Past Year Dependence
Any Withdrawal Symptoms in the Past Week
Severe Withdrawal (11+ Symptoms)
Can Give 1+ Reasons to Quit*
Client Believes Need ANY Treatment
Acknowledges Having an AOD Problem
Any Prior Substance Abuse Treatment
Source: CSAT 2009 Summary Analytic Data Set (n=21,816) *(n=11,066)
44
Multiple Clinical Problems are the NORM!
20%
41%
80%
48%
33%
63%
11%
24%
14%
34%
27%0% 10
%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Alcohol
Cannabis
Other drug disorder
Depression
Anxiety
Trauma
ADHD
CD
Suicide
Victimization
Violence/ illegal activity
Source: CSAT 2009 Summary Analytic Data Set (n=20,826)
45
The Number of Clinical Problems is related to Level of Care (over lapping but different mix)
41% 45%53%
65%
80%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
OP IOP CC-OP LTR STR
None
One
Two
Three
Four
Five to Twelve
Source: CSAT 2009 Summary Analytic Data Set (n=21,332)
Significantly more likely to
have 5+ problems (OR=5.8)
46
46%
71%
15%0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Low (0) Moderate (1-3) High (4-15)
None
One
Two
Three
Four
Five to Twelve
The Number of Major Clinical Problemsis highly related to Victimization
Source: CSAT 2009 Summary Analytic Data Set (n=21,784)
Significantly more likely to have 5+
problems (OR=13.9)
But this is the issue staff least
like to ask about!
Overcoming Staff Reluctance with General Victimization Scale
40%
31%
6%10%
1%8%9%
26%
29%7%
57%32%
19%11%
35%
0%
10
%
20
%
30
%
40
%
50
%
60
%
70
%
80
%
90
%
10
0%
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
Source: CSAT 2009 Summary Analytic Data Set (n=19,318) 47
48
B1. Intoxication/Withdrawal Treatment Plan Needs
39%
22%
17%
1%
1%
0%
10
%
20
%
30
%
40
%
50
%
60
%
70
%
80
%
90
%
10
0%
Any Detox or withdrawal services
Ambulatory Detox (Risk/Mild)
Non-opioid Meds
Opiate Meds
Monitoring withdrawal and AOD medscompliance
Source: CSAT 2009 Summary Analytic Data Set (n=17,392)
49
B2. Biomedical Treatment Plan Needs
60%
33%
29%
17%
6%
1%
1%
78%
3%
4%
11%
16%
0%
10
%
20
%
30
%
40
%
50
%
60
%
70
%
80
%
90
%
10
0%
Tobacco cessation
Accom. for medical conditions
Discuss compliance w/ prescribed meds
Compliance with meds for PH probs
Discuss ER/hospitalization history
Currently treated for med problem
Tetanus shot
Eating disorder
Treatment of infectious diseases
Accommodations current pregnancy
Reduce sexual behavior risk
Reduce needle use/risk
Source: CSAT 2009 Summary Analytic Data Set (n=17,392)
50
B3. Psychological Treatment Plan Needs
59%
23%
22%
31%
18%
13%
12%
41%
74%
1%
4%
4%
8%
16%
17%
68%
72%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Any Co-occuring
Consq of behavior control problems
Refer to anger management
Suicidal risk intervention
Problems reading and writing
Compliance with psych meds
Currently treated for psych problem
Self-mutilation
Monitor self-mutilation
Cognitive impairment
Discuss lifetime mh hosp. history
Coordination with justice system
Consq of interpersonal illegal acts
Consq of drug-related illegal acts
Discuss lifetime arrest history
Consq of other illegal acts
Civil court proceedings
Source: CSAT 2009 Summary Analytic Data Set (n=18,733)
51
B4.Readiness Treatment Plan Needs
81%
16%
9%
3%
79%
73%
63%
0%
10
%
20
%
30
%
40
%
50
%
60
%
70
%
80
%
90
%
10
0%
Any Treatment Readiness Issues
Wrap-around or casemanagement services
Any pressure to be in treatment
Required to go to treatment
Reviw expectations for length oftreatment
Review dissatisfaction w/treatment
Partner to understandtreatment process
Source: CSAT 2009 Summary Analytic Data Set (n=9,169)
52
B5. Relapse Potential Treatment Plan Needs
67%
2%
84%
30%
28%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
High Relapse Potential
Recovery coach or mentor
Continuing Care aftercontrolled environment
Significant time in controlledenvironment
Discuss substance abusetreatment history
Source: CSAT 2009 Summary Analytic Data Set (n=21,239)
53
B6. Environment Treatment Plan Needs
63%
32%
29%
26%
32%
47%
54%
56%
70%
85%
0%
10
%
20
%
30
%
40
%
50
%
60
%
70
%
80
%
90
%
10
0%
Attended school in past 90 days
Coping with psycho-socialstressors
Child maltreatment
Recent school problems
Dissatisfaction withenvironment
Family fighting in the home
Vocational or governmentassistance
Substance use in the home
Employed in past 90 days
Housing situation
Source: CSAT 2009 Summary Analytic Data Set (n=14,952)
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. 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)
5. 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. 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
7. 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)
8. 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
9. Build on prior work where you can, collaborate to share costs and anticipate problems where you cannot
10. Keep fields for “other” so that you can “learn” from practice what you missed on the first pass
57
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 270-
2007-00004C 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 1-309-451-
7801; E-mail: [email protected]. More information on the GAIN is available at www.chestnut.org/li/gain or by e-mailing [email protected] .
Additional Slides
The following slides were not used in the presentation, but included in the event of questions
Past Year Recovery “Rates” (Remission/Lifetime) by Disorders in the US
Source: Dennis, Scott, Funk & Chan forthcoming; National Co morbidity Study Replication
44%
66% 83
%
77%
58%
89%
89%
50%
45%
41% 56
%
57%
43%
31% 39
%
71%
48%
48%
44%
42%
41%
30%
0%10%20%30%40%50%60%70%80%90%
100%
Any
Dis
orde
r
Any
Sub
stan
ce D
isor
der
Dru
g D
isor
der
Alc
ohol
Dis
orde
r
Ext
erna
lizi
ng D
isor
der
Con
duct
Dis
orde
r
Opp
osit
iona
l Def
iant
AD
HD
Inte
rmit
tent
Exp
losi
ve
Inte
rnal
izin
g D
isor
der
Any
Moo
d D
isor
der:
Maj
or D
epre
ssiv
e E
pi.
Dys
thym
ia
Bi-
Pola
r I
or I
I
Any
Anx
iety
Dis
orde
r:
Adu
lt S
epar
atio
n A
nxie
ty
Gen
eral
ized
Anx
iety
Dis
.
Post
trau
mat
ic S
tres
s D
is.
Soci
al P
hobi
a
Pani
c D
isor
der
Ago
raph
obia
Oth
er S
peci
fic
Phob
ia
Past Year Recovery Rate
Prevalence of Lifetime Disorders and Past Year Remission in the US
Source: Dennis, Scott, Funk & Chan forthcoming; National Co morbidity Study Replication
47%
15%
8% 13% 25
%
10%
10%
8% 8%
37%
20%
19%
4% 2%
31%
7% 8% 7% 12%
5% 2%
13%
0%10%20%30%40%50%60%70%80%90%
100%
Any
Dis
orde
r
Any
Sub
stan
ce D
isor
der
Dru
g D
isor
der
Alc
ohol
Dis
orde
r
Ext
erna
lizi
ng D
isor
der
Con
duct
Dis
orde
r
Opp
osit
iona
l Def
iant
AD
HD
Inte
rmit
tent
Exp
losi
ve
Inte
rnal
izin
g D
isor
der
Any
Moo
d D
isor
der:
Maj
or D
epre
ssiv
e E
pi.
Dys
thym
ia
Bi-
Pola
r I
or I
I
Any
Anx
iety
Dis
orde
r:
Adu
lt S
epar
atio
n A
nxie
ty
Gen
eral
ized
Anx
iety
Dis
.
Post
trau
mat
ic S
tres
s D
is.
Soci
al P
hobi
a
Pani
c D
isor
der
Ago
raph
obia
Oth
er S
peci
fic
Phob
ia
Lifetime Disorder
Past Year Remission
61
NOMS: Early Treatment Outcomes
56%
66%
76%
84%
72%
58%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Initiation within 14 days
Evidenced Based Practice
Engagement for at least 6weeks
Any Continuing Care (91-180 days)
Substance Use-Abstinent/Reduced 50% at 3 Months
12 month cost within bandsfor initial type of treatment
Source: CSAT 2009 SA Data Set subset to 1+ Follow ups (n=11,668)
62
NOMS: Post Treatment Outcome (6-12 mo)
Source: CSAT 2009 SA Data Set subset to 1+ Follow ups
41%
90%
71%
12%
89%
80%
66%
17%
44%
99%
76%
68%
47%
44%
0% 10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Use
Abuse/Dependence Sx*
Physical Health
Mental Health
Nights of Psychiatric Inpatient
Illegal Activity
Arrests
Housed in Community**
Family/Home Problems
Vocational Problems
Social Support/Engagement
Recovery Environment Risk
Quarterly Cost to Society
In Work/School**
Reduced 50%or NoProblemNo Problem
*This variable measures the last 30 days. All others measure the past 90 days
**The blue bar represents an increase of 50% or no problem
63
But Need to Control for the lack of Problems at Intake
Source: CSAT 2009 SA Data Set subset to 1+ Follow ups
98%
79%
13%
33%37%
52%
78%
61%
11%37%
42%19%
5%
2%
0%
10
%
20
%
30
%
40
%
50
%
60
%
70
%
80
%
90
%
10
0%
Use
Abuse/Dependence Sx*
Physical Health
Mental Health
Nights of Psychiatric Inpatient
Illegal Activity
Arrests
Housed in Community
Family/Home Problems
Vocational Problems
Social Support/Engagement
Recovery Environment Risk
Quarterly Cost to Society
In Work/School
* Variable measures the last 30 days. All others measure the past 90 days.
64
Change in Number of Positive NOMS Outcomes (Last Follow up – Intake)
Source: CSAT 2009 SA Data Set subset to 1+ Follow ups (n=18,770)
8%6%8%
14%
12%
29%
11%
13%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Total
Five or More
Four
Three
Two
One
None
Negative one
Less than negative one
78% Improved in 1 or more areas (29% in 5 or more)
Outcomes May be Hidden by Subgroups: Example of HIV Risk Outcomes
-0.0
3
-0.1
0 -0.0
2
-0.80
-0.60
-0.40
-0.20
0.00
0.20
0.40
A. Low Risk
B. Mod. RiskW/O Trauma
C. Mod. RiskWith Trauma
D. High Risk
Total
Coh
en's
Eff
ect S
ize
d
Unprotected Sex Acts (f=.14)
Days of Victimization (f=.22)
Days of Needle Use (f=1.19)
-0.3
9
0.20
-0.0
4
-0.0
8
0.00
0.15
-0.2
9
0.01
0.10
0.27
0.00
-0.6
9
Source: Lloyd et al 2007
Any Illegal Activity can be better predicted by using Intake Severity on Crime/Violence and Substance Problem Scales
58%46%
36%53%
33%26%44%
27%20%
0%
20%
40%
60%
An
y I
leg
al
Ac
tiv
ity
(mo
nth
s1
-6)
High Mod Low LowMod
High
Crime/Violence Scale (Intake)
Substance Problem Scale
(Intake)
Source: CSAT 2008 V5 dataset Adolescents aged 12-17 with 3 and/or 6 month follow-up (N=9006)
Intake Crime/ Violence Severity
Predicts Recidivism
Intake Substance Problem Severity
Predicts Recidivism
Knowing both is a better predictor(high –high group is 5.5 times more
likely than low low)
While there is risk, most (42-80%) actually do not commit
additional crime