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Team-Based Decision Support in DiabetesOutcomes and Costs
Session 89, 8:30 a.m. February 13, 2019
Gary Ozanich, Ph.D. - College of Informatics, Northern Kentucky University
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Gary Ozanich, Ph.D.
– Has no real or apparent conflicts of interest to report
Conflict of Interest
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Acknowledgement
Research reported in this presentation was supported, in part, by the Kentucky Cabinet for Health and Family Services, Department of Medicaid Services under the Agreement titled “A Study on Poorly Controlled Diabetes Mellitus for Patients Among Medicaid Beneficiaries in Kentucky”
The content is solely the responsibility of the authors and does not necessarily represent the official views of the Cabinet for Health and Family Services, Department of Medicaid Services
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• Learning Objectives
• Type 2 Diabetes care in Kentucky Medicaid
– Overview
– Why teams?
– How to share decision-making
– Building decision support software
– Lessons learned
• Questions
Agenda
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• Describe how shared decision-making tools help patients and providers
recognize barriers and support solutions to adherence in diabetes treatment
Learning Objectives
• Evaluate customized regimens from a decision support tool within the
context of the current literature for clinical decision support and patient
engagement
• Explain the need for a broad interdisciplinary team and each team
member’s role in the shared decision-making processes and decision
support tools
• Appraise the unique problems in treating the diabetes mellitus population
that can be addressed through clinical and financial decision support at the
point of care
• Create strategies for avoiding clinical inertia through new treatment
algorithms
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Project Overview
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Bringing Stakeholders Together
Kentucky Department of Medicaid Services
– Logistics and data support
– Funding conceptualization, processing & management
St. Elizabeth Physicians/St. Elizabeth Healthcare
– Facilities, provider and staff engagement
– Generous in-kind contributions for the funding match
Northern Kentucky University, College of Informatics
– Facilities, faculty and staff engagement
– Contributions for the funding match
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Project Funding
• Initial pilot funded internally by Saint Elizabeth Healthcare
• Based upon pilot success our stakeholder group extended the model to KY Medicaid patients
• Funding secured through the Public University Medicaid Partnership Program www.universitypartnerships.org/content/about
• Federal Medicaid rules sanction the formal participation of state universities in the administration of Medicaid
• Subject to very particular requirements, some state university Medicaid activities may be eligible for Federal Financial Participation (FFP) through matching funding
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Chronic Condition1
Total
Spend
Member
Count
Claim
Count
Ranking
by Total
Spend
Ranking
by Claim
Count
Hypertension $ 392MM 260,419 1,382,703 1 1
Substance Use Disorder $ 285MM 221,716 830,004 2 4
Diabetes $ 284MM 117,706 1,041,257 3 2
Type 2 Diabetes in KY Medicaid
Sources: (1) 2017 Kentucky Diabetes Report, Table 22: Kentucky Medicaid Chronic Condition Summary, Medical Claims Only; Dates of Service in SFY 15
(2) 2017 Kentucky Diabetes Fact Sheet, Page 1
60% higher per-member spend on diabetes vs hypertension
Adult diagnoses rate doubled since 20002
37% of adults have pre-diabetes2
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Type 2 Diabetes in KY Medicaid
Sources: (1) 2017 Kentucky Diabetes Report, Chart 2, page 1
23.6%
15.3%
13.4% 13.1%
9.7%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
< $15K $15K to < $25K $25K to < $35K $35K to < $50K $50K or more
Diabetes Prevalence by Income1
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Goals– Increase adherence through shared decision-making
– Reduce complexity and improve treatment via point-of-care
decision support tool
– Member and system medication cost transparency and
financial decision support
Measures of Impact– Member HbA1c
– System medication cost
– Member medication cost
– Claims for diabetes-related unplanned hospital treatments
A Quality Improvement Project
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Quality Improvement Initiative– 200+ Kentucky Medicaid members
– Adult
– Currently under provider care at Saint Elizabeth Healthcare
– 8.0+ HbA1c
Offices in three Northern Kentucky counties– Grant, Campbell, Kenton counties
Project Term– July 2018 to June 2020
Project Details
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Team-Based Approach
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Diverse skills– Clinical investigation
– Endocrinology
– Pharmacy
– Biostatistics
– Informatics / Computer science
Faster for providers– Project already explained
– Member conversations about priorities take time
• Cost
• Daily routine
Minimize workflow disruptions
Why Teams?
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Workflow
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Shared Decision-Making
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Clinical Inertia in Diabetes Treatment
Source: Clinical Inertia in Individualizing Care for Diabetes, Strain, et al. www.ncbi.nlm.nih.gov/pmc/articles/PMC4269638/
Failure to establish appropriate targets and
escalate treatment to achieve treatment goals.
Failure to establish appropriate targets and
escalate treatment to achieve treatment goals.
Recent studies show that clinical inertia may result up to 80
percent of heart attack and strokes related to
management of chronic conditions like
hypertension, diabetes, and lipid
disorders.
Recent studies show that clinical inertia may result up to 80
percent of heart attack and strokes related to
management of chronic conditions like
hypertension, diabetes, and lipid
disorders.
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Factors Affecting Clinical Inertia
Source: Nonadherence, Clinical Inertia, or Therapeutic Inertia? Allen, et al, 2009. www.jmcp.org/doi/pdf/10.18553/jmcp.2009.15.8.690
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Decision-Making Today
Source: “Giving Voice - Developing a medication decision aid for patients with type 2 diabetes”, Mayo Clinic Center for Innovation
Patient and clinician begin consultation
Patient and clinician discuss medication
Patient leaves with a prescription
Patient makes decision about medication
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Implementing Shared Decisions
Ask patient about things that affect adherence
Cost
– Monthly budget for medication
– Cost sensitivity (“What if you had to spend entire amount?”)
Adherence
– Intentional and unintentional non-adherence
– Work-related hypoglycemia risk
Review regimen pros and cons
Prompt patient to express values and preferences
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Decision Support Tool
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Complexity is a Problem
Regimen Selection– 60 treatments for Type 2 Diabetes
– 1 to 5 typically prescribed
– ~6 million possible regimens
Insurance coverage and prices– 6 plans for KY Medicaid
– Unique formularies
– Different member, system costs
This is too much for providers
5,461,635
487,635
34,220
1,770
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Unique Regimens
Possible
1 to 5 Treatments
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How Complexity Is Handled Now
Simplify by omission– Only consider 5 to 7 medicines
Pros– Familiar with typical behavior on patients
– Fast decisions
Cons– Patient data may eliminate options
• Comorbidities
• Preferences e.g., no injections
– Pleiotropic benefits considered?
– Fit with patient budget and insurance?
– Clinical inertia
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How Complexity Is Handled Now
Use professional treatment algorithms– American Association of Clinical Endocrinologists (AACE)
– American Diabetes Association (ADA)
Pros– Consistent logic
– Considers all medication classes
– Addresses many concerns• Avoid hypoglycemia, encourage weight loss
Cons– Many pages for an office setting
– Classes, not medicines
– Hard to address cost, patient values and preferences
– Clinical inertia
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Decision Support Tool Goals
Evaluate all 6 million regimens
Standard data and rules
Web- and cloud-based for access anywhere + scale
Known (or directional) system cost
Directed by patient:– Formulary and out-of-pocket cost
– Budget and lifestyle goals
– Discuss options
– Use patient feedback in new recommendations
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Decision Support - Scoring SystemAll 6 million regimens get composite 0 to 100 score
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Decision Support Inputs
* For display purposes, this example shows only a subset of available decision support inputs
EMR Data
Hasn’t / Won’t Work
Insurance Coverage
Patient Budget
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Decision Support Outputs
* For display purposes, this example shows only a subset of available decision support outputs
Patient Out-of-Pocket Cost
Regimen
Estimated A1c
Weight Change
Side Effects & Reminders
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Shared Decision-Makingwith Support Tool
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Typical Office Visit
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Example
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Lessons Learned
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Pilot Results
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Lessons Learned – Team Approach
Software recommendations challenge providers to:– Think outside their traditional prescribing habits
– Explore full spectrum of medications available
Recruit providers willing to:– Look critically at their own prescribing patterns
– Trade a measure of time for potentially better outcomes
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Lessons Learned – Shared Decisions
Discussions take time– Prepare before office visit
– Read body language in office
Some conversations need prompting
– “Let’s see what happens if…” is a good ice-breaker
Incremental change is better than no change
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Lessons Learned – Decision Tool
Usability and UI Design– Workflow, workflow, workflow
– Be easy for providers to navigate
• Minimize clicks, mouse use, data entry
– Analysis must be fast (results in seconds)
– Be clear about what prompts mean
• “Already Taking” means taking not prescribed
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Next Steps
EHR integration
Expand geography
Telehealth
Population health & drug price modeling
Predictive analytics and machine learning
