Best Practice Population Health Management:
Achieving The Triple Aim By Moving Decision
Support To Provider Organizations
Dan Chateau, Ph.D., Research Scientist & Assistant Professor, Manitoba Centre for Health
Policy, University of Manitoba
Michael E. Smith, M.D., Chief Medical Officer, East Carolina Behavioral Health
Jackie Fedash Beck, MS, LPCS, NCC, LCASPA, Medicaid Contract Manager, East Carolina
Behavioral Health
Agenda
1. Decision Support In A Value-Based Market
2. Using Data To Manage Prescriptions In Manitoba,
Canada
–
Dan Chateau, Ph.D., Research Scientist & Assistant Professor, Manitoba
Centre for Health Policy, University of Manitoba
3. Using Provider Organization Data To Improve Health
Systems In North Carolina
–
–
Michael E. Smith, M.D., Chief Medical Officer, East Carolina Behavioral
Health
Jackie Fedash Beck, MS, LPCS, NCC, LCASPA, Medicaid Contract
Manager, East Carolina Behavioral Health
4. Integrating Decision Support Into Clinical Workflows
–
Carol Clayton, Ph.D., CEO, Care Management Technologies
5. Questions & Discussion
2
2. Decision Support In
A Value-Based Market
Monica E. Oss, CEO, OPEN MINDS
Environmental Drivers Influencing Health &
Human Services
More managed care
across all payers
Payer preference for
coordinated care –
medical, behavioral,
and social
Blurring of role of
payer and provider
Technology changing
nature of service and
of competition
More value-based
purchasing – risk-based
and P4P
More competition –
with rise of
consolidator companies
4
© 2015. All Rights Reserved.
Transition From Pay-For-Volume To
Pay-For-Value – Across All Payers
Fee-ForService
Case Rates &
Bundled
Payments
Capitation &
Population
Payments
Pay-ForPerformance
(P4P)
About 40% of 2014 commercial health plan reimbursements to provider organizations
linked to value-oriented initiatives; compared to 11% in 2013
Medicare is planning to shift 50% of FFS reimbursement
from volume to value by December 2018
66% of all Medicaid beneficiaries were in some form of managed care in 2014, and
as of June 2014, 1 million+ individuals enrolled in Medicaid health homes
5
© 2015. All Rights Reserved.
More Value-Based Care = More Decision Support
•
•
•
In a value-based market, the competitive edge will go to the
organization that can provide “more” for the same amount of
financial resources
Meaning provider organizations must incorporate better,
faster decision support tools into their operational processes
Because “less than optimal” decisions about consumer care
wastes precious resources – and will decrease the financial
viability under these new value-based arrangements.
“In the next 10 years, data science and software
will do more for medicine than all of the
biological sciences together.”
– Vinod Kholsa
6
© 2015. All Rights Reserved.
The Decision Support Construct
The federal Centers for Medicare & Medicaid Services notes that
successful clinical decision support requires five elements…
The right
information
(evidencebased
guidance,
response to
clinical need)
7
© 2015. All Rights Reserved.
To the right
people (entire
care team –
including the
patient)
Through the
right channels
(e.g., EHR,
mobile
device,
patient
portal)
In the right
intervention
formats (e.g.,
order sets,
flow-sheets,
dashboards,
patient lists)
At the right
points in
workflow (for
decision
making or
action)
2. Using Data To Manage
Prescriptions In
Manitoba, Canada
Dan Chateau, Ph.D., Research Scientist &
Assistant Professor, Manitoba Centre for
Health Policy, University of Manitoba
Manitoba Centre for Health Policy
Evaluation of the Manitoba
IMPRxOVE Program
OPEN MINDS, New Orleans, 2015
Dan Chateau
9
Manitoba?
10
Manitoba?
11
Manitoba?
100
80
60
40
20
0
-20
-40
-60
-80
-100
12
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Winnipeg
New Orleans
Manitoba?
100
90
80
70
60
New Orleans
Winnipeg
50
40
30
20
10
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
13
The Manitoba Centre for Health Policy
MCHP is located within the Department of Community Health Sciences,
College of Medicine, Faculty of Health Sciences, University of
Manitoba. It provides accurate and timely information to health care
decision-makers, analysts and providers, so they can offer services
which are effective and efficient in maintaining and improving the health
of Manitobans. Our researchers rely upon the unique Population Health
Research Data Repository to describe and explain patterns of care and
profiles of illness, and to explore other factors that influence health,
including income, education, employment and social status.
14
MCHP Houses the Anonymized
Population Health Research Data Repository
CancerCare
Income
Assistance
Social
Housing
Education
Hospital
Pharmaceuticals
Home Care
PopulationBased
Health
Registry
15
Immunization
Medical
Services
Lab
Emergency
Department
Manitoba Centre for Health Policy
Vital
Statistics
Provider
• Diagnostic
Services
• Cadham
Nursing
Home
Clinical
Health Links
Census
Data at
Area Level
• Family First
• Healthy Baby
• EDI
Healthy
Child MB
Justice
Family
Services
• K to Grade 12
• Post-Secondary
(UofM)
• ICU
• FASD
• Pediatric
Diabetes
Team Members & Advisory Group
• MCHP
–
–
–
–
–
–
–
16
• Advisory Group
Dr. Dan Chateau
Dr. Murray Enns
Oke Ekuma
Chelsey McDougall
Ina Koseva
Elisa Allegro
Christina Kulbaba
–
–
–
–
–
Sandra Boutcher (MHHLS)
Dr. Patricia Caetano (MHHLS)
Kathy McDonald (MHHLS)
Jeff Onyskiw (MHHLS)
Dr. Harold Carmel (Care
Management Technologies)
– Dr. Jack Gorman (Care Management
Technologies)
– Dr. Silvia Alessi-Severini (UofM
Pharmacy)
– Dr. Shawn Bugden (UofM Pharmacy)
The Manitoba IMPRXOVE Program
Improving Medication Prescribing and Outcomes Via Education
•
Launched in June, 2011 (ongoing)
•
Run by MHHLS and Care Management Technologies
•
Design: “audit and feedback” program, Randomized Controlled Trial
– reviews drug prescription behaviours in relation to an intervention target
(using quality indicators)
– targets a decrease in inappropriate prescription behaviours of physicians
in Manitoba (i.e., reduced QI triggers)
– provides feedback of physician performance (i.e., educational mailing
package)
– randomizes all family physicians, pediatricians & psychiatrists in Manitoba
into a control group and an intervention group (i.e., eligible for mailing)
17
Quality Indicators
Continued on next slide
Primary (Launched in June, 2011)
18
Age Group
Description
Youth
Use of 2 or more benzodiazepines for 45 or more days
Adult
Use of 2 or more benzodiazepines for 60 or more days
Older adults
Use of 2 or more benzodiazepines for 45 or more days
Older adults
Use of any long-acting benzodiazepine for 30 or more days
Youth
Use of a benzodiazepine at a higher than recommended dose for 60
or more days
Adult
Use of benzodiazepines at a higher than recommended dose for 60 or
more days
Adult
Use of 2 or more anti-insomnia agents for 60 or more days
Older adults
Use of 2 or more anti-insomnia agents for 60 or more days
Quality Indicators (continued)
Secondary (Launched in January, 2012)
Age Group
Description
Adult
Use of 5 or more psychotropics for 60 or more days
Adult
Use of 2 or more SSRIs for 60 or more days
Older adults
Use of 2 or more SSRIs for 60 or more days
Adult
Multiple prescribers of 1 or more opioids for 30 or more days
Older adults
Multiple prescribers of 1 or more opioids for 30 or more days
Adult
Patient failed to refill newly prescribed antidepressant within 30 days
of prescription ending
Older adults
Patient failed to refill an antipsychotic within 30 days of prescription
ending
SSRI = selective serotonin reuptake inhibitor (antidepressant)
19
The Manitoba IMPRXOVE Program Evaluation
•
June, 2011–Feb, 2013 (primary QIs)
•
Jan, 2012–Feb, 2013 (secondary QIs)
•
Some physicians & QI triggers excluded, as per:
– program design & algorithm rules (i.e., filters)
– our additional criteria for cohort building
•
Final evaluation cohort
– 145,372 QI triggers in 16 months
– 1,147 eligible physicians (control: 571; intervention: 576)
20
Research Questions
1) What is the effect of a mailed educational
intervention on physician prescribing practices in
Manitoba?
2) What characteristics of physicians might be
associated with the likelihood to respond to a mailed
educational intervention?
21
Key Findings
•
Trends in drug prescriptions
– 90% of physicians had up to 25,000 prescriptions over the study period
– 4% of physicians had over 40,000 prescriptions over the study period
•
Trends in QI triggers
– 88% of physicians trigger QIs up to 300 times in total
– 4% of physicians trigger QIs over 600 times in total
– 23 physicians had >1,000 triggers for a single QI
•
Some QIs were not triggered enough for monitoring purposes
– Inappropriate prescriptions of benzodiazepines to youth & multiple SSRIs
to adults/older adults were rare
22
Key Findings
•
The intervention was successful for the primary QIs
•
The intervention didn’t have an impact on the secondary QIs
•
The intervention influenced mainly physicians with ‘better’
prescribing practices (i.e., the majority of physicians)
– Fewer physicians engaged in really poor prescribing practices
•
No specific physician or practice characteristic was more likely
to lead to higher QI trigger rates at baseline
•
No specific physician or practice characteristic was more likely
to lead to an intervention effect
– The intervention should be applied universally
23
Quality Indicator Triggers
Quality Indicators
Number of
Triggers
Number of
Physicians
with Triggers
Primary
24
2+ benzodiazepines for youth
101
25
2+ benzodiazepines for adults
35,832
803
2+ benzodiazepines for older adults
10,214
531
Long-acting benzodiazepines for older adults
68,287
897
High-dose benzodiazepines for youth
82
16
High-dose benzodiazepines for adults
2,018
143
2+ anti-insomnia agents for adults
13,100
639
2+ anti-insomnia agents for older adults
7,616
423
Factors Associated with Trigger Rates at Baseline
Quality Indicators
Control
Prescriber
Canadian
Graduate
Male
Physician
Years of
Physician
# of
Practice in
Aged 50+
Prescriptions
Manitoba
% of
Patients
Aged 65+
Primary
+
2+ benzodiazepines for adults
–
+
2+ benzodiazepines
for older adults
+
+
Long-acting benzodiazepines
for older adults
+
+
High-dose benzodiazepines
for adults
+
+
+
2+ anti-insomnia agents
for adults
+
2+ anti-insomnia agents
for older adults
+
+
+ indicates odds ratio > 1
- indicates odds ratio < 1
25
2+ benzodiazepines for adults
Overall trigger rates (35,832 triggers)
Trigger Rates per Physician
2.0
Intervention
1.8
Control
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
2011
16
2012
There is a statistically significant interaction overall (intervention effect)
* indicates a significant difference (p≤0.01) between control and intervention at specified time point
February
January
December
November
October
September
August
July
June
May
April
March
February
January
December
November
October
September
August
July
June
0.0
2013
2+ benzodiazepines for adults
16
Group 1 (4%)* †
Group 2 (15%)* †
Group 3 (34%)* †
Group 4 (47%)* †
14
12
10
8
6
4
2
2011
*
†
indicates statistically significant trend over time (p<0.05)
percent of physicians assigned to each group
2012
February
January
December
November
October
September
August
July
June
May
April
March
February
January
December
November
October
September
August
July
0
June
Average Number of Triggers per Physician
Intervention group trigger patterns (n = 398 physicians)
2013
Factors Associated with Response to the Intervention
Factors
Group 1
Group 2
Group 3
Trained in Canada
Male physician
Physician 50 years and older
+
+
Years of practice in Manitoba
–
Number of drug prescriptions
+
+
+
Percent of patients 65 years and older
–
–
–
+ indicates odds ratio > 1
– indicates odds ratio < 1
28
Long-acting benzodiazepines for older adults
Overall trigger rates (n = 68,287 triggers)
Trigger Rates per Physician
3.0
2.5
2.0
1.5
Intervention
Control
1.0
0.5
2011
16
2012
There is a statistically significant interaction overall (intervention effect)
* indicates a significant difference (p≤0.01) between control and intervention at specified time point
February
January
December
November
October
September
August
July
June
May
April
March
February
January
December
November
October
September
August
July
June
0.0
2013
Long-acting benzodiazepines for older adults
Average Number of Triggers per Physician
Intervention group trigger patterns (n = 461 physicians)
16
14
12
Group 1 (8%)* †
10
Group 2 (23%)†
8
Group 3 (33%)* †
Group 4 (36%)* †
6
4
2
2011
20
*
†
indicates statistically significant trend over time (p<0.05)
percent of physicians assigned to each group
2012
February
January
December
November
October
September
August
July
June
May
April
March
February
January
December
November
October
September
August
July
June
0
2013
2+ anti-insomnia agents for older adults
Overall trigger rates (n = 7,616 triggers)
Trigger Rates per Physician
1.0
0.9
Intervention
0.8
Control
0.7
0.6
0.5
0.4
0.3
0.2
0.1
2011
16
2012
There is a statistically significant interaction overall (intervention effect)
* indicates a significant difference (p≤0.01) between control and intervention at specified time point
February
January
December
November
October
September
August
July
June
May
April
March
February
January
December
November
October
September
August
July
June
0.0
2013
Factors Associated with Response to the Intervention
Factors
Group 1
Trained in Canada
Male physician
Physician 50 years and older
Years of practice in Manitoba
+
Number of drug prescriptions
Percent of patients 65 years and older
+ indicates odds ratio > 1
- indicates odds ratio < 1
32
28
2011
2012
February
January
December
November
October
September
August
July
June
May
April
March
February
January
December
November
October
September
August
July
June
Trigger Rates per Physician
High-dose benzodiazepines for adults
Overall trigger rates (n = 2,018 triggers)
1.0
0.9
Intervention
0.8
Control
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
2013
Multiple prescribers of 1+ opioids for adults
Overall trigger rates ( n = 3,620 triggers)
1.0
Intervention
Trigger Rates per Physician
0.9
Control
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
2012
30
*
February
January
December
November
October
September
August
July
June
May
April
March
February
January
0.0
2013
indicates a statistically significant difference (p≤0.01) between control and intervention at specified time point
Intervention Effect
Trigger
Frequency
Intervention
Effect
2+ benzodiazepines for youth
Low
Insufficient data
2+ benzodiazepines for adults
High
Significant
Moderate
Significant
Long-acting benzodiazepines for older adults
High
Significant
High-dose benzodiazepines for youth
Low
Insufficient data
High-dose benzodiazepines for adults
Moderate
No change
High
Significant
Moderate
Significant
Quality Indicators
Primary
2+ benzodiazepines for older adults
2+ anti-insomnia agents for adults
2+ anti-insomnia agents for older adults
35
Why Was the Intervention Successful?
•
A high base rate of the targeted behaviour
– particularly true for three of the QIs that showed a significant impact
•
Feedback provided by a senior colleague
– the letter accompanying all packages was signed by the head of Psychiatry
and the head of Family Medicine
•
Feedback provided both verbally and in writing
– the intervention was successful with only written feedback
•
The intervention should target a decrease in behaviours, rather
than an increase
– true for most QIs
36
Conclusions
•
IMPRXOVE was very successful for the primary quality indicators
– Use of 2 or more benzodiazepines for 60 or more days (Adult)
– Use of 2 or more benzodiazepines for 45 or more days (Elderly)
– Use of any long-acting benzodiazepine for 30 or more days (Elderly)
– Use of 2 or more insomnia agents for 60 or more days (Adult)
– Use of 2 or more insomnia agents for 60 or more days (Elderly)
•
The intervention influenced mainly physicians with better
prescribing practices (i.e., the majority of physicians)
– Fewer physicians engaged in really poor prescribing practices
•
No specific physician or practice characteristic was more likely
to lead to an intervention effect
– The intervention should be applied universally
37
Thank You/Questions
• Dan Chateau: [email protected]
• umanitoba.ca/centres/mchp
• facebook.com/mchp.umanitoba
• twitter.com/mchp_umanitoba (@mchp_umanitoba)
• DOWNLOAD the full report.
• www.mchp.ca
• http://mchpappserv.cpe.umanitoba.ca/reference//ImproveRx_report_website.pdf
38
39
3. Using Provider Organization Data
To Improve Health Systems In North
Carolina
Michael E. Smith, M.D., Chief Medical Officer, East
Carolina Behavioral Health
Jackie Fedash Beck, MS, LPCS, NCC, LCASPA,
Medicaid Contract Manager, East Carolina
Behavioral Health
Michael Smith, MD
Jackie Beck, MS, LPCS, NCC, LCASA
East Carolina Behavioral Health MCO/LME
•
•
•
•
•
•
Local Management Entity (LME) and Managed Care Organization (MCO) responsible for:
• publicly funded behavioral health (mental health and substance use) and
• intellectual/ developmental disability services and supports for people living in or whose
Medicaid eligibility was established in the counties served.
Political sub-division of the State created under the authority of NC GS §122C.
Public authority governed by a board.
• Board of Directors is a policy-making body, which focuses on establishing and monitoring
goals as well as the development of public policy.
Service area consists of 19 geographically, socioeconomically, and culturally diverse counties.
• Rolling farmland, sparsely populated areas, and a coastline that entices millions of beachgoing tourists each year present very different opportunities and challenges.
Second largest LME/MCO in terms of geography covered and numbers of counties served.
Total population of catchment 610,722, with nearly 105,000 being Medicaid-eligible over age
3 according to the 2012 Census.
East Carolina Behavioral Health
How MCOs Use Data to Manage Care
Gaps and Needs
Quality Improvement and Quality Management
Dashboards – forecasting and predictive
management
Network/Provider Monitoring
Compliance with DMA contract
Consumer care
Strategic Planning for agency
Gaps and Needs Assessment
 ECBH performs annual survey to assess the gaps in services
and needs in catchment area
 The data is complied in narrative and graphic formats for
interpretation
 Maps demonstrate concentrations of service availability as
well as gaps in service availability
 This annual survey allows planning for future needed services
based on documented needs of our consumers
 This permits responsible use of public funds
 This allows the MCO to create productive change by putting
means and ends into useful perspective
Quality Improvement
 Data is best utilized by the provider in conjunction with
the MCO.
 Identify quality improvement needs for our network
providers.
 Monitor network trends.
 One initiative in process allows providers access to
data to enhance consumer care through integrated
care. This will assist in shaping our network of
providers through improving quality of care.
Prescriber Data in ProAct
DM - Measures
Providers whose patients have been flagged
Dashboards
Allow the MCO to utilize forecasting and predictive
measures for agency planning.
Monitoring of Evidence-Based Practices.
Monitoring of special projects and initiatives.
Monitor fiduciary investments to insure appropriate
utilization of Medicaid monies.
Monitor internal benchmarks and productivity.
Network/Provider Monitoring
Standardized monitoring tool for providers as
set by contract with DMA/DMH.
Development of Network QIPs – first responder
services. Reduce ED admissions by 10%.
Quarterly measurement of ED admissions for
providers with first responder services.
Provider satisfaction surveys – implemented
internal POC.
High Prescriber* Ranking Report
Provider Info
N Patients Patients Flagged
for Any DM
Per
Provider
Patients
w/ 1+ BH
Hospitalization
Patients
w/ 1+ BH ER
Patients
w/ 1+ BH
Outpatient
Service
N
N
%
N
%
N
%
% of
Outpatient
Total
Services
Behavioral
Rendered by
Service Cost
other Provider
Groups
%
Provider A
2,904
2,697
93%
274
9.4%
361
12.4% 2839
98% $1,385,743
42%
Provider B
1,699
1,618
95%
264
15.5%
244
14.4% 1522
90%
$400,300
42%
Provider C
1,346
1,251
93%
358
26.6%
580
43.1%
54%
$312,873
57%
727
* Criteria – had to have at least 10 patients who have been seen at least 2 or more times to be included in the
Integrated Care
Access to integrated health care data
for care coordinators, call center, care
coordinators.
Identify needs of individuals via
pharmacy, E/D, and health care data.
Patient Member List
Integrated Health Profile - IHP
IHP - continued
IHP - continued
Lessons Learned
Empowering providers with data
 Sets benchmarks.
 Decreases cost of services by utilizing the
appropriate service.
 Decreases hospital and ED admonitions by utilizing
predictive measures.
 Increases quality of care.
 Data driven care enhances consistency.
4. Integrating Decision
Support Into Clinical
Workflows
Carol Clayton, Ph.D., CEO, Care
Management Technologies
Questions & Discussion
Transition From Patient Specific/FFS
To Population Health
Electronic Record
Population Health Data Solution
Individual Patient Focused
Population Focused
Unstructured Data
Structured Data
Point of Care Usage
Retrospective Data for Prospective Use
Personalized
Aggregated/Deidentified
Subjective (Process Oriented)
Metric Based (Outcomes Oriented)
First 90 Days
Second 180
Days
Third Phase (No
Commitment Of
Time Frame)
Fourth Phase
(Year 2 Or 3)
Vendor-Based Analytics Solutions
• Allows providers to focus on what they do best—
care delivery
• Experienced with:
•
•
•
•
Data sourcing and data aggregation
Data integrity QA
Technical specifications
Project management
CMT Differentiation
Behavioral Health
Expertise
Evidenced Based
Wise, Expertly
Driven
Development
Process
Over 200+
Algorithms
Risk Based
CMT
Massive Data
Warehousing
Experience and
Capabilities
• 19 million consumers,
10 billion data points
Turning Data Into Information Is Our Expertise
Drive Case Rate/Revenue Stream
[ ] Aggregated Data
N
% of Total
N Hospitalizations
per Hundred
Relative Risk of
Hospitalization
No Multimorbidity
12,646
310
97.61%
2.39%
15
45
1.00
3.00
Chronic Renal Failure/ESRD, Coronary Heart Disease,
Dementia, Hypertension, Mental Illness
23
0.18%
91
6.07
Asthma/COPD, Congestive Heart Failure, Coronary Heart
Disease, Dementia, Hypertension, Mental Illness
18
0.14%
89
5.93
Chronic Renal Failure/ESRD, Congestive Heart Failure,
Coronary Heart Disease, Dementia, Hypertension, Mental
Illness
12
0.09%
75
5.00
Chronic Renal Failure/ESRD, Congestive Heart Failure,
Coronary Heart Disease, Dementia, Hypertension
12
0.09%
75
5.00
26
0.20%
73
4.87
17
0.13%
59
3.93
15
0.12%
53
3.53
234
1.81%
42
2.80
21
0.16%
38
2.53
Any Multimorbidity
Cerebrovascular Accident/Stroke, Congestive Heart Failure,
Coronary Heart Disease, Dementia, Hypertension, Mental
Illness
Chronic Renal Failure/ESRD, Congestive Heart Failure,
Dementia, Hypertension
Asthma/COPD, Chronic Pain, Coronary Heart Disease,
Drug/Alcohol Disorders, Hypertension, Mental Illness, Spine
Disorders
Asthma/COPD, Drug/Alcohol Disorders, Mental Illness,
Schizophrenia
Antipsychotic/Mood Stabilizer Drugs, Anxiety Disorders,
Depressive Disorders, Drug/Alcohol Disorders, Schizophrenia
* Deleted rows with < 10 patients.
*Clarifying Multimorbidity Patterns to Improve Targeting and Delivery of Clinical Services for Medicaid Populations. Cynthia Boyd, Bruce Leff, Carlos Weiss,
Jennifer Wolff, Allison Hamblin and Lorie Martin. Center for Health Care Strategies, Inc. December 2010
Prove Value: Outcomes | Diabetes
70%
37%
67%[VALUE]
42%
59%
60%
46%
64%
59%
57%
53%
50%
50%
2.5 years
47%
46%
42%
38%
40%
30%
27%
22%
18%
20%
10%
0%
Good Cholesterol
(<100 mg/dL)
Feb'12
Baseline
Normal Blood Pressure
(<140/90 mmHg)
Feb'13
12 Months
June'13
18 Months
Jan'14
2 Years
Normal Blood Sugar
(A1c <8.0%)
June'14
2.5 Years
Outcomes | Hypertension and Cardio
2.5 years
70%
34%
41%
60%
55%
65%
62%
55%
55%
49%
50%
41%
40%
37%
30%
24%
21%
20%
10%
0%
Good Cholesterol for Clients w/ CVD
(<100 mg/dL)
Feb'12
Baseline
Feb'13
12 Months
Normal Blood Pressure for Clients w/ HTN
(<140/90 mmHg)
June'13
18 Months
Jan'14
2 Years
June'14
2.5 Years
Outcomes | Metabolic Syndrome Screening
2.5 years
90%
68%
80%
80%
70%
61%
60%
50%
46%
40%
30%
20%
12%
10%
0%
Metabolic Syndrome Screening
(All HCH Enrollees)
Feb'12
Baseline
Feb'13
12 Months
June'13
18 Months
Jan'14
2 Years
June'14
2.5 Years
80%
Improving Uncontrolled A1c
Baseline to Year 1
1 point drop in A1c!
• Reduced the mean
A1c — 9.9 to 8.9
 21% ↓ in diabetes
related deaths
Baseline to Year 2
 14% ↓ in heart attack
• Reduced the mean
A1c — 9.9 to 8.5
 31% ↓ in
microvascular
complications
Provider Comparators: EBP
[ ]/
Comp
ratio
[ ]/
Comp
ratio
[ ]/
Comp
ratio
[ ]/
Comp
ratio
[ ]/
Comp
ratio
[ ]/
Comp
ratio
Use of 3 or More Psychotropics for 60 or More Days
1.22
1.14
1.27
1.29
1.09
1.57
Use of 2 or More Antipsychotics for 60 or More Days
1.96
1.77
2.33
1.96
1.50
2.86
1.77
1.26
2.51
2.44
1.43
1.18
1.53
1.59
1.54
1.29
1.66
1.21
2.03
2.11
1.96
2.83
1.62
2.89
2.11
2.21
1.30
2.09
2.96
0.87
2.02
2.24
1.86
3.01
1.66
2.64
0.83
1.32
0.71
1.77
0.66
0.65
1.86
1.51
3.10
0.41
1.07
3.25
Use of 5 or More Psychotropics for 60 or More Days
1.24
1.00
1.47
1.08
0.95
1.96
Use of 2 or More Opioids for 60 or More Days
0.78
0.58
0.57
2.05
1.41
4.69
1.49
2.94
1.54
0.00
1.46
1.72
1.10
0.48
0.98
0.78
1.36
1.12
0.58
0.53
0.47
0.22
0.71
0.63
Quality Indicator™ Title
Patient Failed to Refill an Antipsychotic within 30 Days
of Prescription Ending
Use of an Atypical Antipsychotic at a Lower Than
Recommended Dose for 45 or More Days
Use of 2 or More Atypical Antipsychotics for 45 or More
Days
Patient Failed to Refill a Mood Stabilizer within 30 Days
of Prescription Ending
Use of 2 or More Atypical Antipsychotics for 60 or More
Days
Patient Failed to Refill Newly Prescribed Antidepressant
Within 30 Days of Prescription Ending
Use of an Atypical and a Typical Antipsychotic for 60 or
More Days
Use of an Antipsychotic at a Higher Than
Recommended Dose for 45 or More Days
Multiple Prescribers of the Same Class of Psychotropic
Drug for 45 or More Days
Use of 2 or More Benzodiazepines for 60 or More Days
Tiered Interventions
Aggregated data
• Approximately 1/3 of prescribers involved in substandard
prescibing practice
• Top 30 adult prescribers account for 50% of the quality
concerns based on CMT EBP Algorithms
Establish clinical criteria
for outreach
• Focus on the top prescribers account for majority of
triggering rate.
Determine
outreach/intervention
• Educational and targeted messaging
• Peer consultation
• Increased authorization protocol for those in your network
based on response to above
• Write up as a QI initiative for your healthplan or
accreditation review
Questions &
Discussion
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