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

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

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

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

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

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.

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

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

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

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

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†

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 / 4.3 = 0.30

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

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

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

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

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 6.96 200-350 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 7.25 350-500 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

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)

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

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

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

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

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

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

Schematic of Nader’s decision support

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)

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

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

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

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.

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

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

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

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

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.