1 in+care Campaign Webinar December 7, 2011

2 Ground Rules for Webinar Participation Actively participate and write your questions into the chat area during the presentation(s) Do not put us on hold Mute your line if you are not speaking (press *6, to unmute your line press #6) Slides and other resources are available on our website at incareCampaign.org All webinars are being recorded

3 Agenda Welcome & Introductions, 5min Stories from the Field, 10min Data Integrity Maintenance, 15min Review of December Campaign Data, 15min Stories from the Field, 10min Q & A Session, 5min

4 Project CONNECT James Raper, DSN, CRNP, JD, FAANP, FAAN Associate Professor of Medicine & Nursing University of Alabama at Birmingham

5 UAB 1917 Clinic: Linkage to care Problem identified: Scheduled new patient visits often not attended (“no show”) Study of patients calling to establish HIV care at UAB 1917 Clinic, % of patients (160 of 522) failed to attend a clinic visit within 6 mos. of initial call Mugavero et al. Clin Infect Dis 2007;45:

6 “No Show” Phenomenon Characteristic“Show” Group (n=362, 69%) “No Show” Group (n=160, 31%) OR (95%CI) for “No Show” Age (years) ( ) White male Minority male White female Minority female 125 (80%) 154 (67%) 31 (61%) 52 (62%) 32 (20%) 76 (33%) 20 (39%) 32 (38%) 1.0 (Reference) 1.75 ( ) 2.72 ( ) 2.39 ( ) Private insurance Public insurance Uninsured 127 (83%) 77 (69%) 158 (61%) 26 (17%) 34 (31%) 100 (39%) 1.0 (Reference) 1.91 ( ) 2.62 ( ) Days from call to appointment ( ) Data presented as mean + SD or n (row %) Age OR per 10 years, Days from call OR per 10 days Mugavero et al. Clin Infect Dis 2007;45:

7 Project CONNECT Program launched January 1, 2007 New patients have orientation visit within 5 days of their initial call to the clinic Semi-structured interview, psychosocial questionnaires & baseline labs Uninsured patients meet with clinic SW Prophylactic antibiotics initiated more quickly Expedited referral for SA / MH services Interview Predisposing factors: Education, Income Enabling factors: Insurance, Transportation, Housing, Social support, Spirituality, Stigma Contextual factors: Dependant care, Recent incarceration, Intimate partner violence Perceived barriers to HIV care

8 CONNECT: Program Evaluation Time Period“No Show”Unadjusted OR (95%CI) Adjusted OR (95%CI) a Pre-CONNECT (n=522) Post-CONNECT (n=361) 30.7% 17.7% ( ) ( ) a Multivariable model controls for age, race, sex, insurance, location of residence and time from call to scheduled visit.

9 Data Integrity Maintenance Anne Rhodes Services Analyst, HIV Care Services Virginia Department of Health Terri Fox, MSW Research Analyst Rutgers University, School of Social Work

10 Anne Rhodes Services Analyst, HIV Care Services Virginia Department of Health

11 Provider Data Systems (CAREWare, other) Client Surveys State Data Systems (Medicaid, eHARS, CTS, Labs, etc.) Chart Reviews Quality Management Tools

12 Barriers to Use

13 Measures Gap Measure Healthy persons? Labs/scripts only? Visit Frequency Requires 2 years of data! Newly Enrolled Issue of those never getting to care Viral Load Suppression Outcome measure, others are process measures Only looks at those with medical visits

14 Retention Global Issues

15 Current Retention Measure: 2 or more visits at least 3 months apart * Source: Virginia Client Reporting System (VACRS)

16 Current Work in Virginia Discussion with DIS and CBOs funded for testing to set up consent process Programming all retention measures into VACRS Matching with state surveillance/ obtain other sources of care markers (Medicaid, Medicare, labs)

17 Terri Fox, MSW Research Analyst Rutgers University, School of Social Work

18 1.Make the data a priority by using it 2.Infuse data expectations in contract language, regular reporting, and outcome evaluation 3.Work with a diverse group of staff/providers/consumers (depending on your ‘program’ unit) to ensure common understanding of definitions of services, units and realistic outcomes 4.Get the input from all parties involved about what goals for service should be 5.Commit to working towards the stated goals NJ Data Integrity Maintenance

19 6.Ensure that the scope isn’t too broad; the focus and amount of data should be narrow—(what do we fix first? ); where there are too many variables, each one gets a little lost to someone, diluting the potential for impact 7.When there is a question of the integrity of the data, there MUST be a follow up to address the data issue. Do NOT accept ‘there is a problem with the data’ as the end of the discussion. Next steps should always be to address said ‘data’ problem NJ Data Integrity Maintenance

20 8.Identify data champions at each site; a data champion understands the local service system and how service information is translated into data points in whatever software/form/chart that you are using 9.Include the data champions in any and all discussions about data, especially where the program manager may not be as data savvy as the nurse, case manager, etc. 10.Check the data on a regular basis- does it look like the levels of service are being met- how are the outcome variables? What is the quality of care as expressed by the data? NJ Data Integrity Maintenance

21 11.Train annually. This never hurts anyone. You can even let your champions participate in training new/newer staff 12.Do NOT make data entry overly complicated. There should be no translation/interpretation needed from contact sheet to database 13.Make realistic goals for care. Do not set providers up to fail. (Meet providers where they are- take steps towards achievable goals) 14.Communicate that data are part of care; not an addition to care NJ Data Integrity Maintenance

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24 Review of December Campaign Data Michael Hager, MPH MA NQC Manager

25 Data Review – Measure 1: Gap Data Points: 85 organizations submitted data 54,256 patients in sample Data Results: 16.16% patients experienced gap in care Top 10%: 2.85%; Top 25%: 4.60%

26 Data Review – Measure 2: Visit Frequency Data Points: 59 organizations submitted data 34,508 patients in sample Data Results: 60.43% patients retained in care for 2 yrs Top 10%: 88.60%; Top 25%: 85.93%

27 Data Review – Measure 3: New Patients Data Points: 76 organizations submitted data 5,021 patients in sample Data Results: 61.5% new patients retained in care for yr Top 10%: 99.98%; Top 25%: 89.96%

28 Data Review – Measure 4: Viral Suppression Data Points: 77 organizations submitted data 56,094 patients in sample Data Results: 70.33% patients virally suppressed at last viral load test Top 10%: 86.90%; Top 25%: 82.58%

29 December Campaign Data What have you learned?

30 Retention and Viral Suppression Theresa Rubin, MA, MPhil Quality Coordinator, AIDS Care Unit North Carolina Department of Health & Human Services

31 NC has 95 counties in Part B Program 8,201 Part B Clients for April 1, 2010 to March 31, 2011 All subgrantees and their contractors required to use CAREWare Viral Load data are for all HIV+ clients 91% had at least 1 Viral Load test Suppression was defined as VL ≤ 100 We will be using VL ≤ 200 for the future Looking at Viral Load

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37 Time for Questions and Answers

38 Introducing Campaign Office Hours Purpose: directly communicate with NQC staff and consultants ask general questions request technical assistance engage in dialogue about the Campaign. Details: Mondays and Wednesdays from 4pm-5pm ET Conference Call #: Participant Code #: #

39 First Data Collection Submission Deadline: December 15, 2011 (Deadline Extended) First Improvement Update Submission Deadline: December 15, 2011 Office Hours: December 7/12/14/19/21 4pm-5pm ET January Webinar: TBA Meet the Author – Dr. Thomas Giordano: January 12, 2012 at 12pm ET Next Steps

40 Campaign Headquarters: National Quality Center (NQC) 90 Church Street, 13 th floor New York, NY Phone incareCampaign.org youtube.com/incareCampaign