1. Using EMRs to personalize and prioritize HIV care: Future research directions R. Scott Braithwaite, MD, MS, FACP Chief, Section of Value and Comparative Effectiveness NYU School of Medicine

2. Dream
Clinical decision supports improve care by incorporating patient-level medical information and preferences.
Presentation will focus on two ways in which care may be improved
Personalization
Prioritization

3. Background - personalization
Guidelines may not apply to patient because of individual-level characteristics.
Especially true for patients with substantial morbidity burden
Whether due to HIV- or non-HIV-related factors
Guidelines that have harms in the short-term and benefits only in the long-term
Colorectal cancer screening
Prostate cancer screening
Aneurysm screening in smokers
Intensive glucose control

4. Background - prioritization
Providers have demands to act on multiple simultaneous problems and guidelines
Provider time and/or patient attention limited.
One patient at one visit may benefit from
Colorectal cancer screening
Alcohol screening
Smoking cessation
Antiretroviral adherence counseling,
Hyperlipidemia treatment
Hypertension treatment
Influenza immunization

5. Background- prioritization
Yet all of these can not be addressed simultaneously.
Shared patient/provider decision making may be informed by estimating
Amount of benefit
Rank order of benefit

6. Personalizing guidelines based on comorbidity
Payoff Time = Minimum time until incremental benefits > incremental harms
Applies to any guideline where harms are short-term and benefits are long-term
Colorectal cancer screening (CRC)
Will vary by guideline and by patient population
Payoff time can be compared to life expectancy
If death likely before payoff time, guideline not advised
If death unlikely before payoff time, guideline advised
Our approach involves the concept of a “payoff time”, which we define as the minimum time until the benefits from a guideline exceed its harms. This concept would apply to any guideline where harms occur in the short-term and benefits occur in the long-term, and will vary by guideline and patient population. Guidelines involving procedures with substantial complication risks are likely to have long payoff times.
We used this concept of a payoff time to develop a systematic method for evaluating guidelines, based on the heuristic that if an individual’s death is likely to occur before the payoff time, the guideline is likely not advised for that individual, whereas if death is unlikely to occur before the payoff time, then the guideline may be advised for that individual.

7. Illustrative Cases: Screen for colorectal cancer?
1. 60 year-old HIV+ male on salvage ARV, CD4 count 46, hazardous alcohol
Comorbidities: COPD (severe), hepatitis C
2. 60 year-old HIV+ female on 1st line ARV, CD4 count 392
Comorbidities: diabetes

8. Payoff time applicable? No Stop
Yes
Estimate payoff time
Payoff time needs adjustment? No
Yes
Adjust payoff time
Simulation or prognostic model
Estimate life expectancy
Payoff time > LE?
Yes No
Compare payoff time to life expectancy
Screening likely harmful
Screening may be beneficial

9. Is payoff time applicable?
Case 1: 60 year-old HIV+ male on salvage ARV, CD4 count 46, haz alcohol
Comorbidities of Case 1: COPD (severe), hepatitis C
Is payoff time applicable for CRC screening for case 1?
YES

10. Payoff time needs adjustment?
Payoff time applicable? No
Stop
Yes
Estimate payoff time
Payoff time needs adjustment? No
Yes
Adjust payoff time
Simulation or prognostic model
Estimate life expectancy
Payoff time > LE?
Yes No
Compare payoff time to life expectancy
Screening likely harmful
Screening may be beneficial

11. Does payoff time need adjustment?
Characteristic
Impact on benefits (RR)
Impact on harms (RR)
Smoking17*†
1.8
Unknown‡
Obesity 18,19*
1.5
Heavy alcohol 20§
1.3
Diabetes 21*§
Aspirin (regular use) 22 §
0.8
NSAID (regular use) 22 §
0.7
Hormone replacement therapy23§
0.6
Unknown‡║
Coumadin 14§
Unknown¶
4.0
American Anesthesiology Society Class 1 (“normal healthy patient”) 14§
American Anesthesiology Society Class 3 (“severe systemic disease”) 14§
4.3
1st degree relative with CRC, age unknown24†
2.3
1st degree relative with CRC, age<4524†
3.9
>1 1st degree relative with CRC24†

12. Does payoff time need adjustment?
Case 1: 60 year-old HIV+ male on salvage ARV, CD4 count 46, haz alcohol
Does CRC screening payoff time need adjustment for Case 1?
YES
Relative risk for harms multiplied by 4.3 because Case 1 is ASA Class 1
Relative risk for benefits increased by haz alcohol
Personalized benefit to harm ratio = relative risk for benefit/relative risk for harm = 1.3 / = 0.30

13. Payoff time applicable? No Stop
Yes
Estimate payoff time
Payoff time needs adjustment? No
Yes
Adjust payoff time
Simulation or prognostic model
Estimate life expectancy
Payoff time > LE?
Yes No
Compare payoff time to life expectancy
Screening likely harmful
Screening may be beneficial

14. Adjust payoff time Benefit-to-harm ratio Age 40 Age 50 Age 60 Age 70 M
0.1
>10
9.7
7.5
8.6
0.2
7.3
8.7
6.2
6.7
0.5
7.4
6.0
6.5
5.5
5.8 1
9.0
10.0
6.8
5.4
5.7
5.2
5.3 2
7.0
7.6
5.6
5.9
5.1 4
6.3
5.0 10

15. Adjust payoff time For Case #1, adjusted payoff time is 7.3 years
Case 1: 60 year-old HIV+ male on salvage ARV, CD4 count 46, haz alcohol
For Case #1, adjusted payoff time is 7.3 years
This is the minimum time until the benefits from CRC screening exceed the harms
Should not advise screening if Case #1’s life expectancy is < 7.3 years
Advise screening if Case #1’s life expectancy is ≥ 7.3 years

16. Simulation or prognostic model
Payoff time applicable? No
Stop
Yes
Estimate payoff time
Payoff time needs adjustment? No
Yes
Adjust payoff time
Simulation or prognostic model
Estimate life expectancy
Payoff time > LE?
Yes No
Compare payoff time to life expectancy
Screening likely harmful
Screening may be beneficial

17. Estimate life expectancy
CD4
Age
Round 1
Round 2
Round 3
<50
all
5.25
5.38
5.12
50-200
7.5
7.04
6.96
41-50
12.17
11.79
11.46
51-60
11.5
10.79
10.08
61-70
insuff data
8.75
8.62
>=70
7.08
7.25
15.79
15.5
15.42
15.12
13.75
12.67
11.54
10.96
8.58
8.54
>=500
18.17
17.96
17.75
17.67
16.29
14.96
14.04
12.87
9.54
9.29

18. Estimate Life Expectancy
Case 1: 60 year-old HIV+ male on salvage ARV, CD4 count 46
For Case #1, life expectancy is 5.1 years based on validated computer simulation
Note: also could use prognostic model of Justice et al (HIV Med, 2010)

19. Payoff time applicable? No Stop
Yes
Estimate payoff time
Payoff time needs adjustment? No
Yes
Adjust payoff time
Simulation or prognostic model
Estimate life expectancy
Payoff time > LE?
Yes No
Compare payoff time to life expectancy
Screening likely harmful
Screening may be beneficial

20. Compare payoff time to life expectancy
Case 1: 60 year-old HIV+ male on salvage ARV, CD4 count 46
Payoff time for Case 1 is 7.3 years
Life Expectancy for Case 1 is 5.1 years
Because life expectancy is less than payoff time (minimum time until benefits exceed harms), Case 1 is unlikely to benefit from colorectal cancer screening

21. Screening likely harmful
Payoff time applicable? No
Stop
Yes
Estimate payoff time
Payoff time needs adjustment? No
Yes
Adjust payoff time
Simulation or prognostic model
Estimate life expectancy
Payoff time > LE?
Yes No
Compare payoff time to life expectancy
Screening likely harmful
Screening may be beneficial

22. Illustrative Cases
1. 60 year-old HIV+ male on salvage ARV, CD4 count 46, hazardous alcohol
Comorbidities: COPD (severe), hepatitis C
2. 60 year-old HIV+ female on 1st line ARV, CD4 count 392
Comorbidities: diabetes

23. Compare payoff time to life expectancy
Case 2: 60 year-old HIV+ female on 1st line ARV, CD4 count 392
Payoff time for Case 2 is 5.7 years
Life Expectancy for Case 2 is 15.1 years
Because life expectancy is less than payoff time (minimum time until benefits exceed harms), Case 2 is likely to benefit from colorectal cancer screening
Pilot testing in progress at West Haven VA

24. Another example of personalizing
EMR-based decision support to improve HIV symptom management
Nader C et al, AIDS Pt Care & STD 2009
Pts queried about common HIV symptoms and side effects at time of “check-in”
Use index of Justice AC et al, J Clin Epidemiol 2001
Decision support tool
Filters information
Generates EMR progress note
Facilitates clinician response

25. Schematic of Nader’s decision support

26. Another example of personalizing
Single-site pilot test of decision support (N=56)
4 week intervention vs 4 week control
Results
Well accepted by clinicians and patients
Did not interfere with workflow
Incremental time burden< 5 minutes
Pts who thought clinicians “very aware” of their symptoms: 93% vs 75% (p= 0.07)
Symptoms overall: 3.6 vs 2.7 (p=0.07)
Symptoms with plans: 1.9 vs 1.6 (p=0.22)

27. General Framework of Decision Support Tool in VA EMR
Patient
Records
Clinical
Information
Behavioral
Survey at
check - in
Preferences
Symptoms
Decision
Support
Strategy
level data
Population
Apply
strategy?
Use strategy
for QA?
Adapt strategy?
Use incentives
for strategy?
Veterans Health Administration EMR
Effectiveness,
Efficacy
Tailored
decision making
at point of care

28. Specific Framework of Decision Support Tool in VA EMR: Adherence
Patient
Records
Comorbidities , hx of
alcohol/drug abuse
Adherence with
antiretrovirals
Survey at
check - in
Preferences about
treatment initiation
Side effects, alcohol,
drugs, depression
Clinical
Decision
Support
System
Adherence
intervention
for patients
with HIV
level data
Population
Apply adherence
intervention?
Count adherence
intervention as QA?
Treat risk factors?
(alcohol, etc)
Link intervention to
monetary incentive?
Veterans Health Administration EMR ↑
Adherence associated
with
survival
Tailored
decision making
at point of care

29. Dream
Clinical decision supports improve care by incorporating patient-level medical information and preferences.
Presentation will focus on two ways in which care may be improved
Personalization
Prioritization

30. Prioritizing guidelines based on risk factors and preferences
Illustrative Case: 54 year-old HIV-infected male
Moderate ARV nonadherence
Smokes, not interested in stopping
Binge drinker
Depressed, has diarrhea which pt attributes to meds,
HTN can be better controlled
Hyperlipidemia can be better controlled
Needs CR screening
Needs influenza vaccine
Our approach involves the concept of a “payoff time”, which we define as the minimum time until the benefits from a guideline exceed its harms. This concept would apply to any guideline where harms occur in the short-term and benefits occur in the long-term, and will vary by guideline and patient population. Guidelines involving procedures with substantial complication risks are likely to have long payoff times.
We used this concept of a payoff time to develop a systematic method for evaluating guidelines, based on the heuristic that if an individual’s death is likely to occur before the payoff time, the guideline is likely not advised for that individual, whereas if death is unlikely to occur before the payoff time, then the guideline may be advised for that individual.

31. How can I use this visit to maximize health benefit?
Target
Benefit
(high-quality life-years)
Pt willing to change?
Level of evidence
Smoking
3.8 No A
Depression
1.5
Yes B
Alcohol
1.2
Nonadherence
0.9
Hyperlipidemia
0.8
Hypertension
0.7
Colorectal cancer screen
0.3
Influenza
0.1
Note: Based on evidence synthesis and modeling work performed by Dr. Braithwaite’s research group

32. Now clinician limits to ”Pt willing to change” and “Level A” evidence
Option
Benefit
(high-quality life-years)
Pt willing to change?
Level of evidence
Hyperlipidemia treatment
0.8
Yes A
Hypertension treatment
0.7
Screening colobnoscopy
0.3
Influenza
0.1

33. Now clinician drills down to adherence
Option
Benefit
(improvement in % doses taken as directed)
Feasibility
Level of evidence
Alcohol tx (BMI)
14%
Yes B
Alcohol tx (pharm)
11%
Depression tx 8%
Symptom control (diarrhea)* 6% C
DOT 5% No
Pill organizer 3%
Pill alarm

34. Now limit to “feasible” and “highest evidence”
Option
Benefit
(improvement in % doses taken as directed)
Feasibility
Level of evidence
Alcohol tx (BMI)
14%
Yes B
Alcohol tx (pharm)
11%
Depression tx 8%
Pill organizer 3%
Pill alarm

35. Implications
EMR may permit personalization and prioritization in 2nd gen decision supports
Develop framework
Pilot test
Effectiveness testing
May simultaneously improve quality of care and reduce resource expenditures
May impact quality measures and P4P rules
In conclusion, tailoring guidelines to comorbidities is feasible. This goal is likely to be on the short list of innovations that simultaneously saves money and improves health. Cancer screening guidelines may not be appropriate for HIV+ men if adherence is very poor, or if prognostic indices are poor. Finally, our results suggest that task forces and expert panels should avoid “one size fits all” prescriptions whenever possible in this era of increasing comorbidity prevalence.