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3/14/2014 1 Data Mining for Cross System Collaboration Claudia Zundel, M.S.W. Diane Fox, Ph.D. Nancy Johnson Nagel, Ph.D. Colorado Department of Human Services The Question… Who are the children and adolescents currently utilizing high-cost intensive behavioral health services that could benefit from a System of Care approach?

Data Mining for Cross System Collaborationcmhtampaconference.com/files/27/presentations/s71.pdfData Mining for Cross System Collaboration Claudia Zundel, M.S.W. Diane Fox, Ph.D. Nancy

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Page 1: Data Mining for Cross System Collaborationcmhtampaconference.com/files/27/presentations/s71.pdfData Mining for Cross System Collaboration Claudia Zundel, M.S.W. Diane Fox, Ph.D. Nancy

3/14/2014

1

Data Mining for Cross

System Collaboration Claudia Zundel, M.S.W.

Diane Fox, Ph.D.

Nancy Johnson Nagel, Ph.D.

Colorado Department of Human Services

The Question…

• Who are the children and adolescents

currently utilizing high-cost intensive

behavioral health services that could

benefit from a System of Care approach?

Page 2: Data Mining for Cross System Collaborationcmhtampaconference.com/files/27/presentations/s71.pdfData Mining for Cross System Collaboration Claudia Zundel, M.S.W. Diane Fox, Ph.D. Nancy

3/14/2014

2

The Problem….

• How do we understand the array of

intensive services provided to children and

youth when

– Intensive services (residential treatment) are

provided through a variety of agencies and

funding sources

– Data systems are siloed and difficult to

integrate

Finding the Answers… • Step 1:

– How much is being spent by child serving agencies

on residential treatment and hospitalization?

• Step 2:

– How many unique children are served with these

intensive services?

– Are costs equally distributed to the children served?

– What happens to costs when children are involved

with multiple systems?

• Step 3:

– How many children are receiving services from

multiple agencies over the course of their lives?

Page 3: Data Mining for Cross System Collaborationcmhtampaconference.com/files/27/presentations/s71.pdfData Mining for Cross System Collaboration Claudia Zundel, M.S.W. Diane Fox, Ph.D. Nancy

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3

Step 1: Method for Compiling

Costs of Residential Treatment

• Contacted all agencies that pay for

residential and inpatient services – Child Welfare

– Office of Behavioral Health

• Non-Medicaid eligible

– Division of Youth Corrections

– Medicaid

• Asked for their total expenditures for a

single FY and the number of clients served

Residential and Inpatient

Expenditures for FY2010-11

Funding Agency Number of Children

Agency Expenditure

Additional Medicaid

Contribution Total

Child Welfare 2063 $51,719,376 $5,922,691 $57,642,068

Medicaid Inpatient 1287 $13,938,398 $13,938,398

DYC 577 $12,960,211 $1,495,839 $14,456,050

Medicaid Residential 462 $3,400,666 $3,400,666

Office of Behavioral Health (non-medicaid) 31 $656,148 $147,845.69 $803,993

Total 4420 $82,674,801 $7,566,376 $90,241,177

Page 4: Data Mining for Cross System Collaborationcmhtampaconference.com/files/27/presentations/s71.pdfData Mining for Cross System Collaboration Claudia Zundel, M.S.W. Diane Fox, Ph.D. Nancy

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Step 2: Determining How Many Unique

Children Were Served In the FY

• Went back and asked the same agencies

to provide lists of clients that they served

to comprise these costs

Barriers:

Confidentiality of data!

Success: Able to put in place business associates

agreements to obtain all the necessary data!

Children not identified with a universal identifier

Success: Used a constructed unique identifier in

combination with Medicaid ID to merge data sets

Clients in the Top Third of BHO Medicaid

Spending Accounted for 70% of the Total

Spending

33.3

8.0

33.3

22.0

33.3

70.0

0

10

20

30

40

50

60

70

80

90

100

% of Clients % of Total Medicaid Spending

Per

cen

t

Highest 1/3

Middle 1/3

Lowest 1/3

Cost per client:

$23,398.34

Cost per client:

$7,207.73

Cost per client:

$2,611.57

*Clients were grouped into three equal groups (high, medium, and low utilizers) then their % of total

spending and cost per client were calculated

Page 5: Data Mining for Cross System Collaborationcmhtampaconference.com/files/27/presentations/s71.pdfData Mining for Cross System Collaboration Claudia Zundel, M.S.W. Diane Fox, Ph.D. Nancy

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Unique Clients Served in Residential and

Inpatient Settings in a SINGLE Fiscal Year

• Result: There were 3,888 unduplicated youth

– 488 (12.6%)children/youth were served by multiple systems

87.4

11.5 1.1

0

10

20

30

40

50

60

70

80

90

100

Per

cen

t Three Agencies

Two Agencies

Single Agency

Step 3: Determining how many children are

receiving services in multiple systems

• First looked at a single large urban county

to see the overlap between

– Child Welfare (high utlizers)

– Youth Corrections

– Mental Health Services

– Substance Abuse Services

Page 6: Data Mining for Cross System Collaborationcmhtampaconference.com/files/27/presentations/s71.pdfData Mining for Cross System Collaboration Claudia Zundel, M.S.W. Diane Fox, Ph.D. Nancy

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How this Data was Used

• Provided justification for the creation of a

Care Management Entity Pilot site in the

county.

– Currently serving high needs youth in the

community

Taking That Analyses Statewide

• The Colorado State Division of Child Welfare (DCW) provided data

for the top 20% of children in Colorado who generated the highest

expenditures in Child Welfare in FY2011-2012. The sample was

comprised of 1881 children.

• Historical data that included any case open on July 1, 2006 or later

were obtained from

– Division of Youth Corrections and

– Office of Behavioral Health (mental health and substance data).

• These data were then merged with the data from Child Welfare to

determine the overlap between child welfare, juvenile justice,

substance abuse, and mental health services for these 1881

children.

Page 7: Data Mining for Cross System Collaborationcmhtampaconference.com/files/27/presentations/s71.pdfData Mining for Cross System Collaboration Claudia Zundel, M.S.W. Diane Fox, Ph.D. Nancy

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System Overlap for youth

with high CW costs

CW/MH/DYC, n=854, 45%

of CW has DYC & MH

CW/DYC, n=28, 1.5% of

CW has DYC only

CW/SA, n=6, 0.3% of CW has

SA only

CW/MH/SA, n=100, 5.3%

of CW has MH and SA

CW/MH, n=883, 47%

of CW has MH only

CW/DYC/MH/SA, n=266, 14% of

CW has all

CW/DYC/SA, n=10, 0.5%

of CW has DYC and SA

CW Only n=113, 6.3%

Who are the youth being seen?

Age Distributions by System Involvement

Page 8: Data Mining for Cross System Collaborationcmhtampaconference.com/files/27/presentations/s71.pdfData Mining for Cross System Collaboration Claudia Zundel, M.S.W. Diane Fox, Ph.D. Nancy

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Policy

• IMPLICATIONS

– Need for System Collaboration and Coordinated

Care; System of Care approach

– Streamline systems for efficiency and seamless care

• IMPACTS

– Establishing a Care Management Entity in One

County

– Mental Health Staff housed at Medicaid

– Medicaid Recommendations

Policy

• QUESTIONS!

– Who are the youth served in the various systems and

when is the best time to intervene?

– Is there a common path or trajectory through the

systems ?

– How do changes in one system affect other systems?

Page 9: Data Mining for Cross System Collaborationcmhtampaconference.com/files/27/presentations/s71.pdfData Mining for Cross System Collaboration Claudia Zundel, M.S.W. Diane Fox, Ph.D. Nancy

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Discuss

• What system of care questions might be

addressed with cross system data?

• What systems would be involved?

• What barriers might exist to obtaining

these data?

• Any ideas that you would like to discuss

with the group?

Great Sand Dunes National Park, Colorado