1. Level of Care Decision Support with the CANS in Child Welfare
John S. Lyons, Ph.D.
Chapin Hall at the University of Chicago
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Decision Support Uses of the CANS The CANS is actively used in decision support within child welfare settings in the United States for each of the following key decisions. Many of these applications have been operations for years. Some for more than a decade.
Placement type and intensity
Service Packages
Level of care
Evidence-based Treatments
Case management intensity
Safety and Risk
Case rates
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3. States with Child Welfare Decision Support Applications using the CANS
Illinois
Oregon
Indiana
Tennessee
Maryland
Texas
New Jersey
Wisconsin
(not all child welfare systems using the CANS have implemented decision support algorithms as of this date)
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Difference between CANS algorithms and other measures approaches to decision support
Most existing measures use total scores with cut-offs for decision support applications. These cut- off approaches can be problematic at the margins because very small differences in a total score can lead to very different decisions. Further, in Larry P v. Riles (1984), norm-based decision support using IQ in schools was deemed discriminatory in CA.
Because of the item level reliability and action level format, the CANS uses patterns of action need to general algorithms using boolian (branching logic) models.
These models are far more intuitive clinically and therefore more defensible as they are easy to describe and the differences between youth at different levels are always meaningful.
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5. Structure and Logic of the Algorithms
example decision model for Treatment Foster Care
Criterion 1. At least one ‘2’ or ‘3’ one a Behavioral Health Need
Criterion 2. At least a ‘2’ or ‘3’ on Developmental Need
Criterion 3. At least a ‘2’ or ‘3’ on Medical
Stage one the child needs to meet either Criterion 1 OR Criterion 2 or Criterion 3
These three criterion establish that there is something to ‘treat’ in Treatment Care
However, that these needs could be handled with outpatient care. To justify TFC, a higher level of complexity is required, therefore…..
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6. Treatment Foster Care (continued)
Criterion 4 At least one ‘2’ or ‘3’ on any of the following Functioning items
School Attendance, Family, Living Situation, Social Functioning,
Criterion 5 At least one ‘2’ or ‘3’ on any of the following items in the Medical module.
Impact on Functioning, Life Threat, Organization Complexity
Criterion 6 At least one ‘2’ or ‘3’ on any of the following Risk Behaviors
Suicide, Self Injurious Behavior, Other Self Harm, Danger to Others, Runaway, Delinquency
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7. To be Recommended for Referral to TFC
The child should meet
Criterion 1 OR Criterion 2 OR Criterion 3
And, the child should also meet either
Criterion 4 OR Criterion 5 OR Criterion 6
This algorithm results in identifying a child who both has something to treat (medical, behavioral health or developmental) AND has either a notable functional impairment, a notable medical complication, or a notable risk behavior that requires a supported living environment to ensure the most effective treatment and recovery.
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8. Customization of Algorithms
Unlike most existing measures, the CANS uses a flexible TCOM framework to think about decision support rather than an ‘off-the-shelf’ logic that applies the same standards everywhere.
In reality different systems are different and to generate meaningful change you have to start where the system is currently functioning.
For example, the original algorithm for residential treatment placement in Illinois was VERY low. This still resulted in a 30% decline in residential placements. Once the system was effectively evolved, then the algorithm could be adjusted to reflect the evolving system’s performance.
As such, the approach allows for the customization of decision models by jurisdiction (county) to reflect different cultural contexts.
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9. Evidence of the Utility of CANS Decision Support Algorithms
Each of the following States have successfully used CANS decision support algorithms for LOC decisions in Child Welfare for more than a decade. There have been no problems and notable system improvement:
Indiana
New Jersey
New York
Wisconsin
Tennessee
A number of other states and counties have used and continue to use CANS decision support algorithms for a period shorter than a decade.
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10. New Jersey’s System of Care
First statewide implementation of a cross-systems (comprehensive) version of the CANS
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In New Jersey
Despite the number of children and youth served in their system of care tripling over the past decade [Figure A]
The actual number of youth placed in residential treatment has been reduced by over one third during the same period [Figure B]
CANS algorithms were used to support this process and research using the CANS informed the design of the step down process
At the same time nearly 1/3 of detention centers have been closed along with all state hospitals for children and youth.
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Figure A. New Jersey’s expansion of their Children’s System of Care from 2008 to 2016
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Figure B. New Jersey Children’s System of Care number of youth placed in residential care from 2010 to 2016
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14. Illinois Department of Children and Family Services
State with the first use of a CANS Algorithm applied within a Team Decision Making process
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In a series of published studies, Brian Chor and his colleagues studied the Illinois CANS decision support algorithm as used in their team decision making process. They found…
Following the CANS level of care recommendation predicted greater clinical improvement as compared to not adhering to the recommendation. Chor, et al (2012) Children and Youth Services Review
Serving children below the CANS recommended level of care resulted in less improvement in functioning, and serving them at a higher than recommended level of care resulted in reduced rates of improvement as compared to adhering to the CANS recommendation. Chor, et al (2014) Administration and Policy in Mental Health
The CANS algorithms out-performed the placement decisions made by the child-family team Chor, et al (2013) Child Abuse and Neglect
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16. In my book Communimetrics, the following
Following the placement recommendations of the CANS decision support algorithm within a Team Decision Making process was associated with more stable placements over time. [Figure C]
Placing children in a higher than recommended level of care placement results in the second most stable placement history.
Under-serving children by placing them below the CANS recommended level of care was associated with dramatically less stable placements over time.
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Figure C. Survival analysis of time to placement disruption for children/youth whose placement matches CANS recommendations (Match=0 green), those whose placed is at a lower intensity than recommended (match=1 blue) and those whose placement is more intensive than recommended (match=-1 brown).
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18. Tennessee Child Welfare
First state in which case workers completed the CANS themselves
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Figure D. In an independent replication, Epstein, et al also found that following a CANS algorithm was associated with more stable placements than not following that algorithm on the initial placement in child welfare Epstein, et al (2015) Residential Treatment for Children and Youth
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20. Tennessee implemented the CANS as a result of a law suit
The implementation was the first to train case workers to complete the CANS. Training and support was provided to case workers to help them build the necessary skill set through Vanderbilt University’s Center for Excellence.
State leadership credits the use of the CANS as part of the reason that they are now out from under this lawsuit as they went from being ranked as one of the least effective systems to one of the more effective systems during the decade after the CANS was fully implemented.
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In Summary
The CANS is widely used in decision support applications as described above including Placement and Level of Care (and case rates/service packages).
The CANS has been successfully used by a number of jurisdictions for more than a decade without incident and with sustained successful system change.
No other cross-sector decision support approach has experienced this level of sustained success in child welfare and behavioral health, although many have been tried.
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