Session: The Integration of Care for Persons with Disabilities Using Comparative Effectiveness Research and Creative Orthogonal Designs to Improve Outcomes and Increase Efficiency Randall Brown Session: The Integration of Care for Persons with Disabilities 2010SNP Leadership Forum October 28, 2010

Background HHS-OD interested in funding comparative effectiveness research to improve outcomes for dual eligible adults with disabilities Contracted with Mathematica team, experienced evaluator of care coordination programs SNP Alliance/NHPG interested in improving outcomes Dr. Kieron Dey’s pathbreaking work on orthogonal design

The Problem Many components of effective CC/CM for members with disabilities: Assessment Care planning Monitoring Triaging Patient education Facilitating transitions Improving communication among providers, and between patients and providers Increasing patient adherence Service arrangement Medication management

The Problem (continued) Many ways to implement these components Frequency/mode of CC/CM contacts with patient/provider Composition of care team Frequency and mode of team meetings Thresholds for identifying patients to get more intensive intervention Standardized protocol only vs. CC/CM judgment Assessment tool used Screening tool for problems (e.g., for depression) Motivational interviewing Strengthen component (Rx review, cg support, etc.) Little evidence on how to make these choices More of an intervention isn’t necessarily better

Testing What Works Want rigorous, valid, replicable test of what works best Adequate sample size essential to ensure low likelihood of accepting false theories AND low likelihood of rejecting true ones Usual approach: test one variant per RCT study Takes forever, expensive Doesn’t account for interactive effects Efficient orthogonal designs overcome these problems Allows testing many intervention variants simultaneously

What is Efficient Orthogonal Design? Developed by famous statistician (RA Fisher) long ago Used in many industries, but rarely in health care Ingenious, powerful method of testing relative effectiveness of 2 alternatives for many intervention components simultaneously, without requiring every possible combination Identify interventions to test (n, say) Identify units of intervention (e.g., CC/CM)

What is Efficient Orthogonal Design? Use algorithm to determine number of intervention units required and precise combination of n interventions to assign to each operation unit For each intervention, half of CC/CM implement option A, half option B But different half for different interventions Each CC/CM randomly assigned to particular intervention mix Measure outcomes of interest for each patient Compute difference of mean outcome for each intervention (YA – YB)

Illustrative Example 4 interventions to test (e.g., caseload, assessment tool, pharmacist on care team, minimum # contacts/mo.)—binary choices Testing all combinations of the 4 interventions (i.e., full factorial design) would require 16 CC/CMs More efficient orthogonal designs require fewer CC/CMs Fractional factorial would take only 8 CC/CMs Gains much more dramatic when testing many interventions: (e.g., Plackett-Burman design requires only 20 CC/CMs to test 19 interventions)

Illustrative Example (continued) Interventions to be tested (A=basic; B=enhanced): Caseload: 100 (option A) vs. 70 (option B) Assessment tool: simple form (A) vs. longer form (B) Pharmacist: available for consult (A) vs. on CM team (B) Minimum # contacts: 1/mo. (A) vs. 2/mo (B) Assign 8 nurses randomly to these 8 choices: AAAA, BAAB, ABAB, BBAA, AABA, BABB, ABBB, BBBA So, the nurse assigned to combination ABAB would have high caseload, use a longer assessment tool, have a pharmacist available for consult, and contact patients at least twice per month

Current Study in Planning Stage Study limited to dual eligibles with disabilities Focusing on SNPs with comparable structures Design for SNPs with some CC/CM in-person contact Adapt if possible for telephonic only Goal is 5-9 SNPs, 1500+ patients, 20+ interventions Survey to capture patient-centered outcomes Also measure effects on service use, quality of care, costs Study to be completed before May 2012

What Will SNPs Get from the Study? If intervention options well-chosen and sample size adequate, CAN’T FAIL TO LEARN SOMETHING USEFUL Statistically significant findings reveal better options Insignificant findings can identify less expensive choices that are equally effective Answers to questions SNPs have identified as important re whether/how to enhance interventions Quick, rigorous results on patient well-being, satisfaction, quality of care, service use, costs Enhanced reputation for pursuing high quality care

Study Challenges Agreeing on intervention options to test Some options aren’t acceptable to some plans But creative collaboration between plans and researchers can identify key testable options Overcoming fears of complexity Training CC/CMs on interventions Need enough cases per CC/CM to justify IRB approval required? (operations waiver possible) OMB approval needed for survey

Future Opportunities More efficient orthogonal designs enable CER on many issues of importance to SNPs ACTION grants are a potential funding source MPR and SNP Alliance are partnering (AHRQ contract) Contact me with ideas you would like to pursue rbrown@mathematica-mpr.com