1. Use of a real-time alert system to identify and re-engage lost-to-care HIV patients
Melanie L. Fritza
Ronald J. Lubelchek, MD a, b, c*
Katelynne J. Finnegan, MPHa
William E. Trick,c, d
a. Ruth M. Rothstein CORE Center
b. Division of Infectious Diseases, John H. Stroger, Jr. Hospital of Cook County
c. Department of Internal Medicine, John H. Stroger, Jr. Hospital of Cook County
d. Collaborative Research Unit, John H. Stroger, Jr. Hospital of Cook County

2. Presenter Disclosures
Melanie Fritz
(1) The following personal financial relationships with commercial interests relevant to this presentation existed during the past 12 months:
No relationships to disclose

3. HIV new infection rate holding steady since ~2009
Introduction
HIV new infection rate holding steady since ~2009
MSM
12%
( )
Young MSM
22%
( )
Young, Black MSM
43%
( )

4. linkage to care and retention in care predict better outcomes
Diagnosed
Linked to Care
Retained in Care
Prescribed antiretroviral therapy
Virally Suppressed
Outcomes
Improved patient health
Reduced transmission in community

5. Patient-navigation based techniques
An EMR-Based Alert Intervention
Patient-navigation based techniques
Health information technology

6. Methodology

7. Setting and Patients Included
Ruth M Rothstein CORE CENTER
John H Stroger Hospital
Patients lost to care for over 7 months

8. Alert Design
Staff log onto password- protected website to access patient and registration event information.
Clerk registers patient at CCHHS clinical site
Registration information transmitted to Collaborative Research Unit server in real time
Query cross references registration information with list of RMR CORE Center lost-to- care patients 1 2 3
Real time alert notifies program staff member 4 5

9. Outreach Intervention
Response to Alert
Receive Alert
Chart Review
Outreach Intervention

10. Results

11. Appointment already in system 60 (52%) No appointment in the system
398 Alerts Received
Unique patients
198 (50%)
Included in analysis
115 (58%)
Repeat alerts
Appointment already in system
60 (52%)
No appointment in the system
55 (48%)
In-person outreach
22 (40%)
Phone outreach
33 (60%)
Contacted
13 (39%)
Unable to contact
20 (61%)
No Outreach Attempted
Excluded from analysis
83 (42%)
Figure2: Summary of lost-to-care patients living with HIV/AIDS triggering our real-time alert system between April 1, 2014 to September 30, 2014

12. Appt in system (no intervention)
Table 1: Demographic/clinical characteristics and rate of return-to-care for alert-triggering lost-to-care HIV+ patients
Appt in system (no intervention)
N=60
Successful contact
N=35
No successful contact
N=20
Total
N=115
p-valuea
Mean age (95% CI)
46 (43 – 48)
45 (42 -49)
44 (40 – 47)
45 (43 – 47)
p = 0.53
Gender
Males (%)
43/60 (72)
28/35 (80)
12/20 (60)
83/115 (72)
p = 0.28
Race
African American (%)
47/60 (78)
25/35 (71)
18/20 (90)
90/115 (78)
Ethnicity
Hispanic (%)
7/60 (12)
6/35 (17)
1/20 (5)
14/115 (12)
p = 0.47
HIV risk factor
Men who have sex with men (%)
14/44a (31)
10/24 (42)
7/17 (23)
31/85 (36)
p = 0.65

13. Appt in system (no intervention)
Table 1: Demographic/clinical characteristics cont’d
Appt in system (no intervention)
N=60
Successful contact
N=35
No successful contact
N=20
Total
N=115
p-valuea
Mean time lost to care, days (95% CI)
387 (343 – 431)
316 (270 – 362)
393 (301 – 485)
366 (335 – 398)
p = 0.11
Time lost to care < 1 year (%)
39/60 (65)
26/35 (74)
12/20 (60)
77/115 (67)
p = 0.49
Median CD4, cells/ml3 (25-75%)
325 (162 – 462)
352 (212 – 517)
321 (157 – 714)
331 (192 – 512)
p = 0.71
HIV viral load undetectable (%)
26/54 (48)
23/33 (69)
6/17 (35)
55/104 (53)
p = 0.04
Patient attended primary care appt. within 3 months of prompt (%)
44/60 (73)
29/35 (83)
9/20 (45)
82/115 (71)
p = 0.01

14. Table 2: Logistic regression for associations with not following up for care within three months for lost-to-care HIV+ patients
Odd’s ratioa
95% confidence interval
p-value
Age
≥ 45 years
< 45
ref
0.90
0.25 – 3.923
p = 0.87
Gender
Male
Female
1.61
0.36– 7.19
p = 0.53
Race
Non-African American
African American
4.18
0.56 – 31.26
P = 0.16
Ethnicity
Hispanic
Non-Hispanic
0.09
0.01 – 1.62
p = 0.10
HIV risk factor
MSM
Non-MSM
0.64
0.14 – 2.82
p = 0.56
Time lost-to-care < 1 year
< 1 year
> 1 year
1.09
0.29 – 4.11
p = 0.90

15. In-Person Contact: 86% follow-up
Table 2: Logistic regression cont’d
Odd’s ratioa
95% confidence interval
p-value
CD4 count closest to index visit
≥ 200 cells/ml3
< 200 cells/ml3
ref
0.25
0.06 – 0.99
p = 0.05
HIV viral load
Undetectable
> 40 copies/ml3
1.38
0.35 – 5.40
p = 0.65
Contact success
Contacted alert patient
Appt. already in system
Unable to contact alert patient
1.13
0.15
0.25 – 5.11
0.03 – 0.89
P = 0.87
P = 0.04
On method of contact
In-Person Contact: 86% follow-up
Attempted Phone Contact: 58% follow-up

16. Discussion Scheduling an appointment = more likely to return to care
In-person contact success rate
Phone contact success rate
Conclusion: This is one example of a successful intervention.

17. Limitations No randomized control group Moderate number of patients
Located within a single health system

18. Future Opportunities
Utilize alert system as less-intensive intervention
Explore more intensive targeted interventions for harder-to-engage patients
Regional Health Information Exchange with other health systems

19. acknowledgements
HIV Treatment Cascade administrative supplemental funding from the Chicago Developmental Center for AIDS Research (P30 AI , Alan Landay, Principal Investigator).
Anna Hotton, PhD for input on our analysis.
Alex Patino, George Markovski, and Francisco Angulo for building the text messaging and secure website alert system.

20. Thank you!
Any questions?
Melanie Fritz