Team-Based Decision Support in Diabetes Outcomes and Costs

Transcription

1 Team-Based Decision Support in Diabetes Outcomes and Costs Session 89, 8:30 a.m. February 13, 2019 Gary Ozanich, Ph.D. - College of Informatics, Northern Kentucky University 1

2 Conflict of Interest Gary Ozanich, Ph.D. Has no real or apparent conflicts of interest to report 2

3 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 3

4 Agenda Learning Objectives Type 2 Diabetes care in Kentucky Medicaid Overview Why teams? How to share decision-making Building decision support software Lessons learned Questions 4

5 Learning Objectives Describe how shared decision-making tools help patients and providers recognize barriers and support solutions to adherence in diabetes treatment 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 5

6 Project Overview 6

7 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 7

8 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 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 8

9 Type 2 Diabetes in KY Medicaid Chronic Condition 1 Total Spend Member Count Claim Count Ranking by Total Spend Ranking by Claim Count Hypertension $ 392MM 260,419 1,382, Substance Use Disorder $ 285MM 221, , Diabetes $ 284MM 117,706 1,041, % higher per-member spend on diabetes vs hypertension Adult diagnoses rate doubled since % of adults have pre-diabetes 2 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 9

10 Type 2 Diabetes in KY Medicaid 25.0% 23.6% Diabetes Prevalence by Income % 15.0% 15.3% 13.4% 13.1% 10.0% 9.7% 5.0% 0.0% < $15K $15K to < $25K $25K to < $35K $35K to < $50K $50K or more Sources: (1) 2017 Kentucky Diabetes Report, Chart 2, page 1 10

11 A Quality Improvement Project 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 11

12 Project Details 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

13 Team-Based Approach 13

14 Why Teams? 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 14

15 Workflow 15

16 Shared Decision-Making 16

17 Clinical Inertia in Diabetes Treatment 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. Source: Clinical Inertia in Individualizing Care for Diabetes, Strain, et al. 17

18 Factors Affecting Clinical Inertia Source: Nonadherence, Clinical Inertia, or Therapeutic Inertia? Allen, et al,

19 Decision-Making Today Patient and clinician begin consultation Patient and clinician discuss medication Patient leaves with a prescription Patient makes decision about medication Source: Giving Voice - Developing a medication decision aid for patients with type 2 diabetes, Mayo Clinic Center for Innovation 19

20 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 20

21 Decision Support Tool 21

22 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 Unique Regimens Possible 1 to 5 Treatments 60 1,770 34, ,635 5,461,635 22

23 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 23

24 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 24

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26 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 26

27 Decision Support - Scoring System All 6 million regimens get composite 0 to 100 score 27

28 Decision Support Inputs EMR Data Hasn t / Won t Work Insurance Coverage Patient Budget 28 * For display purposes, this example shows only a subset of available decision support inputs

29 Decision Support Outputs Patient Out-of-Pocket Cost Regimen Estimated A1c Weight Change Side Effects & Reminders 29 * For display purposes, this example shows only a subset of available decision support outputs

30 Shared Decision-Making with Support Tool 30

31 Typical Office Visit 31

32 Example 32

33 Lessons Learned 33

34 Pilot Results 34

35 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 35

36 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 36

37 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 37

38 Next Steps EHR integration Expand geography Telehealth Population health & drug price modeling Predictive analytics and machine learning 38

39 Questions Gary Ozanich, Ph.D. Please complete online session evaluation 39