Delivering Value-Based Cancer Care in the Community J. Russell Hoverman, MD, PhD Vice President,...

Delivering Value-Based Cancer Care in the Community

J. Russell Hoverman, MD, PhD

Vice President, Quality Programs

Exponential Growth of Computing

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Source: www.singularity.com/charts/page70.html

http://www.singularity.com/charts/page70.html

Figure 1. Average Medical Oncology Charge per Patient, Age < 65, 1 Standard Deviation

Physicians

Avera

ge C

harg

e/P

ati

en

t

Figures for Lung Cancer A Retrospective Study

Avera

ge C

harg

e/P

ati

en

t

Department

Chemo Lab Medical Pharmacy OverallAdmin Professional Avg.

Upper Group10 PhysiciansAverage(77 patients)

Composite Average(318 patients)

Lower Group 10 Physicians Average(74 patients)

Figure 3. Segregation of Charges by Department, Age < 65


Figure 7. Survival - Extensive NSCLC, P=.99

SurvivalExtensive Non Small Cell Lung Cancer

Years

Su

rviv

al D

istr

ibu

tion

Fu

ncti

on

STRATA: group = Lower group = Upper censored group = Upper

1.00

0.75

0.50

0.25

0.00

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5



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Taxol/Carboplatin 41% Taxotere/Carbo 32% Gemzar + a taxane 3% Cisplatinum/Navelbine 1% Gemzar/Carbo 19% Other 4%

Source: *Network for Oncology Communication and Research

Oncology Case Series: NOCR*1st Line Stage IV NSCLC

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0

10

20

30

40

50

60

70

80

90

1st line 2nd line 3rd line 4th line

Clinical Trial

Taxol/Carbo

Navelbine

Exception

Alimta

Taxotere

Tarceva

KCCC: From 1/1/06 to 9/1/06

NSCLC Treatment by Stage

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Source: Neubauer, Cost-Effectiveness of Evidence-based Treatment Guidelines for the Treatment of NSCLC in the Community Setting, JOP, 6:1.

12-Month Cumulative Cost by Pathway Status

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Neubauer, Cost-Effectiveness of Evidence-based Treatment Guidelines for the Treatment of NSCLC in the Community Setting, JOP, 6:1.

Overall Survival by Pathway Status

Integrates with physician’s

workflow:

Imports patient and clinical

data from commonly-used

EHRs

Identifies relevant Value

Pathways and NCCN Guidelines at

point of care

Allows providers, payers and

employers to align on quality and

value in patient care

A New Technology Standard: Clear Value Plus software to streamline demonstration of quality


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Innovent Oncology – The SolutionAddressing the Three Major Cancer Care Cost Drivers

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Level I Pathways

• Reduced variability in treatment patterns

• Reduced costs of drugs, Reduced medication errors

• Improved clinical efficiency

• Increased patient and caregiver satisfaction

Expected Results

Quality Reporting and Outcomes

Data Analytics

Patient Support Services

• Reduced hospitalizations

• Reduced ER visits

• Increased adherence to treatment plan

• Encourages patient self-management


Expected Results

Advance Care Planning

• Reduced costs of treatment near end of life

• Increased hospice utilization

• Improved symptom management

• Documentation of patient’s values and goals of treatment


Expected Results

Aetna Innovent Commercial Pilot2010-2012

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Study Design

• Prospective, non-randomized evaluation of patients enrolled in the Innovent Oncology Program

Location • Patients seen by Texas Oncology physicians

Study period

• Innovent Oncology Program 6/1/2010-5/31/2012; Program yr 1: 6/1/2010- 5/31/2011; Program yr 2: 5/1/2011-4/31/2012

Inclusion

• Aetna eligible pts diagnosed with an Innovent diagnosis initiating chemotherapy during program year 1 or year 2

• Drug costs• ER and in-patient admissions and costs

Exclusion

• Patient eligible for the program in the last month of each program year

• Patients with a chemotherapy claim in the month prior to the program year

• Patients without a chemotherapy claim within each program year

Results of Aetna commercial pilot

ER visits reduced by 38% Year 1, 52% Year 2, & 48% overall for the program

In-pt admissions reduced by 16% Year 1, 50% Year 2, & 34% overall for the program

Hospital days reduced by 36% Year 1, 51% year 2, & 44% overall for the program

Pathway adherence was 74% in the Innovent program and 63% prior to initiating the program

Total savings calculated: $506,000

Value

Value FormulaV = O/C

Value

Relative ValueRV1 = O1/C1RV2 = O2/C2RV3 = O3/C3

Subjective Data

Relative value may be determined by subjective value, assessable only by the patient.

True value to the patient cannot be determined unless subjective values are explored.

Patient Support Services

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OCN with outbound calls at chemo start. Reviews meds, side effects, ESAS, Advance Directives.

Scheduled calls thereafter with frequency determined by risk.

Referral to clinic for any concerns based on assessment algorithms.

Notes appear in medical record

Values Assessment and Advance Care Planning

Scanty evidence base for PSS although CCO study demonstrated 35% reduction in hospital days with regular ESAS.

Value Assessment and ACP

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Experience with over 800 Value Assessments Little pushback Transition to decision making Too soon to measure impact

(Sample)

Value Assessment

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181

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Introduced

Not introduced

59

122 Intro but did not participateParticipated

Advance Care Planning*

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ACP Introduced 90% ACP Counseling 60%**

** Of total enrollees (n=202)

Compare to Q3: ACP was 85% enrollees Introduced and 60% enrollees Participating in Counseling

*From one of our currently operational Innovent programs

Critical Communication

Temel JS, Greer JA, Muzikansky A, et al. Early Palliative Care for Patients with Metastatic Non-Small-Cell Lung Cancer N Engl J Med 363:733-742, 2010.

Wright AA, Zhang B, Ray A, et al. Associations Between End-of-Life Discussions, Patient Mental Health, Medical Care Near Death and Caregiver Bereavement Adjustment. JAMA 300:1665-1673. 2008.

Kalisiak A, Hedlund S and Gandara E. Positive Impact: Integrating Palliative Care Education and Support in a Community Oncology Practice. J Pain Symptom Manage 41:289, 2011.

Lundquist G, Rasmussen BH and Axelsson B. Information of Imminent Death or Not: Does It Make a Difference. J Clin Oncol 29:3927-3931, 2011.

Hammes BJ, Rooney BL. Death and End-of-Life Planning in One Midwestern Community Arch Int Med 158:383-390. 1998

Mack JW, Conin A, Keating NL et al Associations Between End-of-Life Discussion Characteristics and Care Received Near Death: A Prospective Cohort Study

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The Medicare Population

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Cautions

Any program will require re-engineering practice

Greater opportunity for savings with expanded program

Costs for personnel and infrastructure regardless of model

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Next Year and the Year After

Collect data Streamline processes Negotiate for more management opportunities Link physician income to performance Understand operational costs including data collection and

analysis Explore new methods of retrieving and organizing

information to develop risk prediction models’

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Delivering Value-Based Cancer Care in the Community J. Russell Hoverman, MD, PhD Vice President,...

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