Transcript Using health datasets to improve the coordination of medical

Using Healthcare Data Sets to
Improve the Coordination of Medical
and Behavioral Health The Potential Role For Health Homes
Richard Surles, Ph.D.
YAI International Conference
New York Hilton, New York, NY
May 2013
Agenda
 Understanding & Aligning Data Sets to Optimize Care and Control Costs
 Potential Use of Data Sets to Identify Members and Needs
 Leveraging Data Sets to Drive Workflow in Support of Effective Medical
Homes
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Complex Conditions Require New Ideas for
Coordination Beyond Traditional Medical Management

~50% of People Who Have a Severe Mental Illness (SMI) Have Medical Comorbidities
– Higher rates of utilization and costs
– Problems achieving desired treatment outcomes
– Lack of access to integrated services


Major Issues in SMI Overall Care are Medication Management and Suboptimal Care
Delivery combined with the Need for Non-medical Support Services
Proven Interventions
– Communication between mental health and physical health providers to provide
integrated care
– Use of information systems (tracking RX refills, clinical visits) to promote patient
adherence and improved outcomes
– Targeted interventions for both patient self care and provider engagement are critical
– Care Management program engagement goals: decrease isolation, promote access
– Relapse prevention programs contribute to medication maintenance, increased patient
self-monitoring of symptoms
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SMI and Medical Comorbidities in ABD* Population
39% of Population Has a SMI
SMI Participants Account for 58% of Total Costs
Non-SMI
42%
SMI
39%
SMI
58%
Non-SMI
61%
Top 5% of SMI Population Account for ~25% of All Costs
100%
$321 M
80%
$778 M
60%
40%
20%
0%
Number of Participants
118,681
Non SMI
SMI 75% spend
SMI 75-90%
*Aged, Blind & Disabled
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Total Costs
$1.34 B
SMI Top 10%
Levels of Complexity for Aged, Blind & Disabled
ABD Medicaid Spend
Category Differences by Acuity
(10/10-9/11)

PMPM
–
–
–

36,408
1.8
4.9
7.1
Low:
Mod:
High:
1.9
5.2
10.7
Average MDs
–
–
–
$582M
Low:
Mod:
High:
Average Risk Score (CDPS)*
–
–
–

$679.94
$3,471.14
$8,262.26
Average Number of Conditions
–
–
–

Low:
Mod:
High:
Low:
Mod:
High:
3.3
6.6
10.9
* Chronic Illness & Disability Payment System; Index risk score is 1.0
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ABD Population – Prevalent BH + Chronic Medical
Top 20% ABD Population (n = 7,282)
Low
Back
MuscSkel
SCHIZ
Sleep
Sub
Abuse
16.4%
40.8%
50.1%
21.0%
15.1%
33.5%
15.9%
14.1%
38.3%
47.0%
25.8%
14.5%
34.5%
27.9%
18.8%
20.7%
41.3%
49.8%
18.8%
16.7%
32.1%
41.9%
23.7%
15.9%
17.7%
28.3%
38.8%
13.7%
11.2%
28.1%
52.7%
17.5%
14.9%
18.8%
44.6%
47.0%
20.7%
11.4%
18.5%
Depress Diabetes
Hyper- Hyperlipidimia tension
Anxiety Asthma
Bipolar
COPD
Anxiety
28.4%
16.2%
44.8%
16.7%
53.1%
22.2%
15.9%
Bipolar
53.8%
14.4%
23.6%
13.5%
49.2%
20.5%
Depress
49.6%
13.7%
38.2%
17.0%
30.4%
SCHIZ
43.7%
11.7%
44.6%
15.6%
Sub Abuse 51.4%
13.6%
44.1%
20.0%
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Dimensions of Care - Supporting the Whole Person
How Treatment is Delivered
Intensive/Procedural
Medical Treatment
Rehabilitative Treatment
Combined Treatment
Patient Education &
Counseling
Self-Help & Natural Supports
What is
Treated
Marital/Familial
Vocational/Financial
Social/Legal
Intrapsychic
Biomedical
Hospital
Home
Office
Community
Partial Care
Where Treatment is Done
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Medical Services
Community Services
Clinical
Supports
Inpatient Hospital
Outpatient Hospital
Critical Access Hospital
FQHC
Skilled Nursing
Home Health
Rural Health Clinic
Other Medical
Ambulance
Office Visits
Specialists
Lab Tests
Comprehensive Medication Service
Minor Procedures
Anesthesia
Major Procedures
ER Visits
Other Tests
Residential Facility
Intermediate Care
Habilitation
Other Supports
Alcohol/Drug Treatment
Community Wrap-Around services
Community Support Services
Case Management
Personal Care
Non-emergency Transportation
BH Day Treatment
Attendant care
Psychosocial Rehabilitation
Crisis Intervention
Assertive Community Treatment
Other DME
Supported Employment
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Are Integrated SMI Health Homes a possibility?
 Affordable Care Act Encourages the Use of Health Homes for Chronically Ill
and People with SMI via Financial Incentives
 SMI Health Homes Addresses Behavioral Health Needs While Responding to
Other Healthcare Issues
– Individuals with SMI, on average, die 25 years earlier than the general
population
– 60% of premature deaths in persons with schizophrenia are due to medical
conditions such as cardiovascular, pulmonary and infectious diseases
– Second generation anti-psychotic medications are highly associated with weight
gain, diabetes, dyslipidemia (abnormal cholesterol) and metabolic syndrome
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Key Features of the Health Home: All Data Driven
Feature
Purpose
Comprehensive care
management
• Predictive Modeling and Disease Stratification to identify
clients with chronic disease and pinpoint risk
• Technology that integrates settings of care and data sources
• Secure messaging for information sharing and coordination
Monitoring
• Enhances clinical care by alerting team to client events and
changes in client status
• Addresses both clients and providers
• Supports innovative payment systems
Reporting & Quality
• Easily accessible performance reports on key measures at
multiple levels – client, provider, region, and state
• Quality improvement program for structured initiatives
Outcome
Measurement
• Stabilization of acuity and reduction of symptoms
• Clinical performance – engagement, medication adherence,
reduction of ER, Inpatient, Readmissions
• Return on Investment analysis
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A Vision of Provider Data Support Systems

Medical Home Has Current and Complete Information Via the
Integrated Technology Platform
– Data Driven Plan of Care
– Aggregate view of all services/billing/interactions

Provider Tools
– Real time access to data via secure Provider Portal
– Reports highlighting alignment to best practice, gaps in care, services
received outside of Medical Home
• Patient Specific Information
• Provider panel aggregate information

Service Vendor Requirements
– Integrated technology platform
– Technical assistance and training
– Community and telephonic member engagement
– Engage providers for care coordination
– Appointment tracking and follow up
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James R: A Member Case Study of Integration
 47 Year Old Male with CAD, Diabetes, HTN, Asthma, Hyperlipidemia,
GERD, Bipolar Disorder
– New enrollee at program “go live”
– Gaps in care analysis triggered (IP, multiple ER), General Assessment
identified positive PHQ-2 and housing issues
 Issue
– Ineffective medical home
– Unstable diabetes and behavioral health conditions
– Unstable housing
 Model Intervention
– Secured stable housing
– Secured effective medical home
– Transitioned from Telephonic Health Coach to Field Health Coach
intervention
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James R: Member Case Study
Assessments Provide Additional Information
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James R: Member Case Study
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Changing the Dialogue: Data Driven Systems of Care
“Health Care Home”
HCH
IDENTIFY
> Data
Driven,
Predictive
Modeling
>Data
analysis
>Assessments
INTERVENE
EVALUATE
>Facilitate
access to care
& service
supports
>Improve selfmanagement
skills
Measure
goal
progress
>Feedback
on results
MONITOR
Engaged,
educated
member
>Informed
HCH
>Alert
system for
HR/HC
potential
“Provider – Clinical, Service & Community Support and Tools”
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The Finish: Issues for a Data Driven Health System




Access and Quality of Data
Privacy and Consumer Consent
Coordination of Medical and Behavioral Care with Pharmaceuticals
Full integration of traditional Medical Managed Care with Non-traditional
Community Support Services Including:
–
–
–
–
Psychosocial Rehabilitation
Habilitation
Personal Care
Other Home and Community Care Services
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