Strategies for Improving HIV Care in Part A Jurisdictions

2. HIV Care Continuum Collaborative:Strategies for Improving Care in Part A Jurisdictions • Jennifer Carter, MPH • Associate, Abt Associates

3. Disclosures Presenters have no financial interest to disclose. This continuing education activity is managed and accredited by AffinityCE/Professional Education Services Group in cooperation with HRSA and LRG. PESG, HRSA, LRG and all accrediting organization do not support or endorse any product or service mentioned in this activity. PESG, HRSA, and LRG staff as well as planners and reviewers have no relevant financial or nonfinancial interest to disclose. Commercial Support was not received for this activity.

4. Learning Objectives • Share examples of how Part A jurisdictions are applying the HIV care continuum framework to their HIV prevention and care approaches. • Discuss approaches and strategies that can be applied within or across jurisdictions. • List the techniques involved in a learning collaborative model.

5. Acknowledgements Mission Analytics Group (MAG) Peggy O’Brien-Strain Ellie Coombs National Alliance of State and Territorial AIDS Directors (NASTAD) Jennifer Flannangan Debbie Isenberg, Evaluator 52 Part A Recipients • HRSA/HAB • Monique Richards • Luigi Procopio • Steven Young • Gary Cook • Susan Robilotto • Abt Associates • Michael Costa • Jennifer Carter • Diane Fraser • Lauren Christopher • Lisa LeRoy

6. Purpose of the Cooperative Agreement • Affect positive outcomes along the HIV care continuum by providing guidance and technical assistance using a collaborative learning approach and rapid improvement principles and practices • Apply data driven evidence based strategies for improving population level health outcomes • Scale-up interventions to improve HIV outcomes by stimulating action across jurisdictions and among many partners

7. Unique and Critical Role of Part A Jurisdictions • Roughly 72% of PLWH in the 52 Part A jurisdictions • Ever-changing epidemic, clinical & financial paradigm • Not just a set of discrete services but a community-based system of care • Public health focus, data-driven, responsive procurement

8. Why Learning Collaborative? • Recipients learn from each other and from experts • Reliance on distance technology to grow and sustain “cyber teams” of self-selected individuals • Innovation fueled by frequent, non-hierarchical communication patterns • Work patterns characterized by transparency and openness to contributions from all participants

9. ACTION Portal • Accelerating Change Through Interactive Online Networks • Shared workspace for multiple small groups • Track and schedule meetings • Ongoing discussion forum • Bulletin board for sharing important information • Resource page to post and share documents or collaborate on new documents • Hold impromptu virtual meetings (face-to-face using webcams)

10. Identifying Domains • Technical guidance provided by Abt Associates and the National Expert Stakeholder Committee (NESC) members that will assist Part A jurisdictions with: • Identifying areas to address along the HIV Care Continuum • Developing targets and establishing baselines • Identifying specific populations and targeted interventions • Developing action plans and providing ongoing technical assistance • Host virtual consultation meetings • Recipients involved in learning collaborative will determine measures

11. Care Continuum Learning Collaborative (CCLC) Domains Year Two Domain Topics Data to Care Pay for Performance • Year One Domain Topics • Data Access and Coordination • Using Data to Inform the Need for, and Selection of Evidenced-based/informed Approaches • Identifying and Implementing Targeted Evidenced-based/informed Interventions • Linkage to Care • Changing Healthcare Landscape

12. Participating Part A Recipients Philadelphia, PA Phoenix, AZ Portland, OR Riverside/San Bern., CA Sacramento, CA San Antonio, TX San Jose, CA San Juan, PR Seattle, WA Tampa/St. Petersburg, FL Washington, DC West Palm Beach, FL Kansas City, MO Los Angeles, CA Memphis, TN Miami, FL Middlesex-Somerset, NJ Minneapolis/St. Paul, MN Nashville, TN Nassau/Suffolk County New York, NY Norfolk, VA Orange County, CA Orlando, FL • Atlanta, GA • Austin, TX • Baltimore, MD • Baton Rouge, LA • Boston, MA • Charlotte, NC • Chicago, IL • Cleveland, OH • Denver, CO • Detroit, MI • Ft. Lauderdale, FL • Houston, TX • Indianapolis, IN • Jersey City, NJ

13. Contact Information Michael Costa, MPH (michael_costa@abtassoc.com) Jennifer Carter, MPH (jennifer_carter@abtassoc.com)

14. Data to Care in Detroit • Leanne F. Savola • HIV/STD Director

15. Detroit HIV Care Continuum, 2015

16. Planning 2013- Initial discussions 2014- Early Identification of Individuals with HIV/AIDS workgroup formed • Part A, Part B, Surveillance • Spun off the Data workgroup 2015- Data to Care workgroup 2016- Community engagement

17. Implementation February 2016- began to work with Care Continuum Learning Collaborative to identify areas of focus May- Detroit Health Department (DHD) hired Data to Care staff September- Collaborative meetings began November- completed protocol February 2017- contacted first not in care individual

18. Partnerships Were Vital Persons living with HIV HIV care and prevention service providers Michigan Department of Health & Human Services (MDHHS) • Division of HIV and STD Programs • RW Part B • Prevention • HIV/STD Surveillance and Epidemiology Section Detroit Health Department (DHD) • RW Part A

19. New Patient Appointment Availability July 2016- used HRSA/HAB systems level performance measure to assess availability of new patient appointments to determine how fast we could link individuals engaged through data to care outreach • 50% of Part A funded clinics had three new patient appointments available within 15 business days September 2016- requested literature review from learning collaborative staff re: retention in care and appointment types • Received November 2016

20. Website, Consumer & Provider Brochures Shared with collaborative for feedback

21. Data Sharing Surveillance creates not in care list in Excel spreadsheet which includes: • Name • DOB • Address & phone # • State HIV # • Date & place of diagnosis • Race & risk category • CAREWare URN (if they have one) Secure file transfer to DHD DHD uses Excel spreadsheet to enter individuals into CAREWare

22. Initiating Outreach Each individual from the NIC spreadsheet is added to CAREWare (CW) • Coordination with physicians and RW providers who have worked with individual in the past two years At time of CW entry, D2C Coordinator runs each name and DOB through TLO and Michigan STD database • TLO- letters are sent to every address listed for past two years and calls/text made to every phone # with 66% or higher likelihood of matching • STD database- addresses and phone numbers used for any records in past two years

23. CAREWare Entry Created Data to Care Tab

24. Benefits of Using CAREWare Experience managing Part A data Access to service data for all Parts within Michigan Surveillance regularly uploads lab results Reports

25. Strengths of Part A driven Data to Care Strengthened relationships Improved partnerships Improved quality of services

26. Improvements in the Detroit Care Continuum

27. Moving Forward

28. Link-Up Rx Sped up Data to Care Uses prescription refill information to identify PLWH who have not picked up their antiretroviral therapy (ART) and supports them in getting their medication

29. Program Model

30. Planning 2017 • April- CDC released funding opportunity for Data to Care Rx • May- Detroit & Michigan Health Departments formed a workgroup • Decided not to apply, Michigan will fund • June-July- Initial discussions with pharmacists • August- Large pharmacists meeting • Shortened the intervention timeline suggested by CDC • September-December- Developed draft protocol 2018 • January-June- collected community feedback and IRB approval and baseline data at pharmacy • July- started Link-Up Rx

31. Experience with Collaborative Positives • Regular contact with colleagues from other jurisdictions • Access to resources (staff) Challenges • Our action plan morphed to be more Data to Care focused, others members focused on different types of linkage to care projects • All members couldn’t/didn’t participate in all meetings so their expertise was missed Based on our experience • Joined Pay for Performance collaborative • Held us accountable so we stayed on track with our action plan

32. Contacts Satrise Tillman Linkage Specialist 313-876-4954 tillmans@detroitmi.gov Jacob Watson (MDHHS) HIV Epidemiologist/Data Manager 248-424-7061 watsonj11@michigan.gov Lindsey Kinsinger Link-Up Detroit Coordinator 313-300-5672 kinsingerL@detroitmi.gov Leanne F. Savola HIV/STD Director 313-870-0073 savolaL@detroitmi.gov

33. San José TGA Ryan White Care Continuum Learning Collaborative • Supriya Rao • Senior Analyst/QI Coordinator

34. Solution Title • Objective: By March 20, 2017, increase retention rates among Ryan White Clients to 80% or an additional 100 clients will be retained in care • Strategy: Provide comprehensive and easy access to services using a centralized case management approach Using retention in care data to inform need for, and selection of evidence-based approaches Domain 2

35. San José TGA Ryan White Learning Collaborative members CQM Coordinator (grantee) Infectious disease epidemiologist (grantee) Non-medical case management service provider Outpatient ambulatory care service provider Outreach service provider Early intervention service provider STD Controller (advisor)

36. HRSA Technical Support Overall technical guidance: Abbot Associates and National Expert Stakeholder Committee members Domain lead support – Peggy O’Brian-Strain • Readiness assessment • Monthly monitoring call • Guest speakers • Action portal • Resource sharing • Monitoring progress • One-on-one support

37. San José TGA Demographics, 2017 San José TGA

38. San José TGA Demographics, 2017 Population: ~1.94 million Majority-minority: >63% Hispanic and Asian/PI 38% Foreign-born 52% speak language other than English High median household income: $101,173 9% of people live in poverty 5% of people uninsured Data Source: https://www.census.gov/quickfacts/fact/table/santaclaracountycalifornia,US/PST045217

39. San José TGA HIV Demographics, 2017 Higher HIV prevalence rates in northern and central (down town) areas of the county Data Source: Santa Clara County Public Health Department, eHARS(2017)

40. San José TGA HIV Demographics, 2017 People living with HIV: 3,360 Male: 86% 25-64 years: 89% Hispanic/Latinos, 40%; Whites, 34% New cases: 156 • 25-64: 84% • Hispanics/Latinos: 42%, • Overwhelming majority 20-44 year old MSM’s Data Source: Santa Clara County Public Health Department, eHARS(2017)

41. San José TGA Ryan White Profile, 2017 Total number of RW Clients: 1,708 Data Source: Santa Clara County Public Health Department, ARIES(2017)

42. San José TGA Ryan White Demographics, 2017 Total number of RW Clients: 1,708 Data Source: Santa Clara County Public Health Department, ARIES(2017)

43. San José TGA Ryan White Demographics, 2017 Total number of RW Clients: 1,708 Data Source: Santa Clara County Public Health Department, ARIES(2017)

44. Why did we chose this project? Evidence of lower linkage to care and retention in care rates in the TGA and/or among Ryan White clients • In care = 1VL or CD4 lab test or medical visit in a year • Retention in care = >/= 2 VL or CD4 lab tests or medical visits in a year • Phase I : Focus on Ryan White clients (2016-2017) • Phase II: Focus on Surveillance based clients,Data to care(2017-current)

45. Evidence: Community Care Continuum NHAS 2020 Goal    Data Source: Santa Clara County Public Health Department, eHARS(2017)

46. Evidence: Ryan White Care Continuum NHAS 2020 Goal   Data Source: Santa Clara County Public Health Department, ARIES(2017)

47. Our approach – Data Driven In depth review • Analyze HIV surveillance and RW client data • Phased, multi-year approach based on findings • Phase I: Ryan White client Data to Care • Phase II: Surveillance based Data to Care

48. Phase I: Ryan White data analysis for prioritization 979 Ryan White clients had at least 1 medical visit – in care 149 Ryan White Clients did not show up for their second visit (gap) Priority 1: Retained in 2015; gap in 2016 N=76 More recently lost and so potentially easier to find Priority 2: Gap in 2015 and 2016 N=73 More difficult to find Further prioritization: Those not virally suppressed 20 of 76 clients Data: ARIES 2015-2016

49. Our approach – Contractual Changes Introduce mandatory performance measures for all Ryan White Contracts • Develop service category data dashboards to monitor performance measures • Develop internal agency agreements Mandate QM projects for all service categories Update policies, procedures and processes • QI project for non-medical case management service provider

50. Our approach –Reprioritization of Ryan White Services Strengthen case management and early intervention services • Significant increase in funding in 2016 • Make non-medical case management(NMCM)the central hub • Re-haul NMCM standards of care • Shift focus away from a ‘eligibility determination and benefits counseling’ model of case management