1. Adapting Decision Analytic
Models to Meet the Needs of the
Health System
Joe Gricar, MS
Prakash Navaratnam, RPh, MPH, PhD
Steve Duff, MS

2. Disclosures
The presenters do not have conflicts that
would jeopardize the objectivity or integrity
of this presentation

3. Mr. Gricar has 15 years of experience in health economics / outcomes research with over 21
years in pharmaceutical research and consulting. This includes the design and development of
interactive models (budget impact, cost-effectiveness) as well as the design and execution of
retrospective database studies. Joe was one of the original authors of the AMCP Format for
Formulary Guidelines and served on the executive committee responsible for revision and
dissemination from 2000 to 2008. He has also served as a peer-reviewer for Value in Health
Regional Issues (Latin and Asia) and the Journal of Medical Economics
In addition to his consulting experience, Mr. Gricar spent 11 years at Parke-Davis, Pfizer,
Pharmacia and Express Scripts, including both internal and field-based positions.
Joe received a Bachelor of Chemistry from Eastern Michigan University and a Master’s in
Evaluative Clinical Studies from Dartmouth College
3
Joe Gricar, MS

4. Mr. Duff has spent over 15 years providing health economic and reimbursement consulting
services to pharmaceutical, biotechnology, medical device, and diagnostic companies. Prior to
founding his current firm, Mr. Duff spent eight years as a consultant with Covance Health
Economics and Outcomes Services where he focused on medical technology assessment,
economic modeling, and development of dossiers, manuscripts, and strategic plans. His clients
ranged from small start-ups to Fortune 500 companies with technologies in various stages of
development and marketing.
In addition to his consulting experience, Mr. Duff also has held various positions in
pharmaceutical research and clinical development. He spent seven years in research and
development at Kendall McGaw and Allergan, primarily in the field of pharmacokinetics.
Mr. Duff received a Bachelor’s Degree in Biology from the University of California, San Diego and
a Master’s Degree in Health Policy and Management from the Harvard University School of
Public Health.
4
Steve Duff, MS

5. Dr. Navaratnam has over 25 years of experience in healthcare, first as a clinical pharmacy
practitioner and then as a health services researcher and consultant.
His primary research interests have been in the area of pharmaceutical policy, physician decision
making, patient reported outcomes and pharmacoeconomic evaluations of therapeutic
interventions in various therapeutic areas. He is an adjunct Clinical Assistant Professor at The
Ohio State University College of Pharmacy. Dr Navaratnam has authored or co-authored
numerous abstracts, posters and manuscripts and currently serves as a senior advisor on HEOR
issues for a number of companies.
He is currently a senior partner and Director of Business Development for DataMed Solutions,
LLC.
.
Dr. Navaratnam completed his undergraduate pharmacy training at the University of Wisconsin-
Madison and received a Masters in Public Health (MPH) and a Ph.D. in Health Services
Administration from The Ohio State University
5
Prakash Navaratnam, PhD

6. Objectives
• Current Modeling Approaches and Issues
• Adapting Common Models
–Tier Placement Model
–Facility Model
–Portfolio Model
• Discussion

7. • Used when desired information is not available
• Synthesizes information from multiple sources
– RCTs, observational studies, claims data, expert opinion,
preference studies
• Most commonly estimates clinical and
economic outcomes of interest
• Acts as a conceptual framework to aggregate
different data elements
7
What is a Decision Analysis Model?

8. When to Use Decision-Analytic Modeling?
Criteria Description / Definition
Treatment selection Examine numerous potential treatment options
Patient selection Extrapolate results to a broader patient population
Time periods Vary time horizon and/or extrapolate to longer horizon
Evaluate uncertainty Measure impact of variation in effect size, inadequate
power, confounding variables, or data sources
Flexibility Develop analyses to simulate alternative care settings
Timing and cost Produce information more efficiently than primary data
collection

9. Common Economic Models/Analyses
Analysis Type Costs Effectiveness Effectiveness
Measure
Cost Minimization Differ between
alternatives
Assumed equal
between alternatives
None included in
analysis
• Two patients diagnosed with heart disease
• Total costs: Treatment A = $20,000; Treatment B = $10,000
• Same outcomes are achieved with Treatment A and B
Treatment B preferred given its lower cost and equivalent outcomes

10. Common Economic Models/Analyses
Analysis Type Costs Effectiveness Effectiveness
Measure
Cost Effectiveness May differ between
alternatives
May differ between
alternatives
Any: blood pressure;
cases cured; death
• Two patients diagnosed with heart disease
• Total costs: Treatment A = $20,000; Treatment B = $10,000
• Life expectancy after Treatment A is 5 years but only 4 years after Treatment B
• Incremental costs of A vs. B = $10,000
• Incremental life-years of A vs. B = 1 year
• Cost-effectiveness ratio (A vs. B) = $10,000/life-year gained
Treatment A may be preferred to B if CE ratio is less than a threshold

11. Common Economic Models/Analyses
Analysis Type Costs Effectiveness Effectiveness
Measure
Cost Utility May differ between
alternatives
May differ between
alternatives
Quality-adjusted life-
years (QALYs)
• Two patients diagnosed with heart disease
• Total costs: Treatment A = $20,000; Treatment B = $10,000
• Life expectancy after both Treatment A and B is 5 years
• Quality of life (utility) after Treatment A is 0.85 and is 0.80 after Treatment B
• Incremental costs of A vs. B = $10,000
• Incremental QALYs of A vs. B = 0.25 QALYs
• Cost-effectiveness ratio (A vs. B) = $40,000/QALY gained
Treatment A may be preferred to B if CE ratio is less than a threshold

12. Key Issues Related to Models
ModelComplexity
Transparency
Bias
Uncertainty
Perspective
Interpretability
Relevance

13. 23
Model Perspective
Health Plan Perspective
• Cost to plan (Rx, medical)
• Benefits to plan
↓ hospitalization rates
↓ ER visits
↓ physician visits Facility Perspective
• Cost to facility(technology $)
• Benefits to facility
↑ procedure volume
↑ reimbursement
↓ expenses
Patient Perspective
• Cost to patient (co-pays)
• Indirect costs (lost work days)
• Health benefits to patient
↓ symptoms / sick days
↓ need for outside care
↑ in patient/caregiver QoL Clinician Perspective
• Cost to MD (time/opportunity)
• Benefits to MD
↑ reimbursement
↑ health for patients
Societal Perspective
• All direct & indirect costs
• All benefits

14. Tier Placement Models

15. Tier Placement Model Description
• Similar in many ways to normative models, Tier
Placement models focus on finding the optimal
product placement within the clinical pathway
– Provides alternative to restricting access to new, high
cost products to avoid high upfront acquisition costs
– A Tier Placement model seeks to evaluate the impact
of product placement on the overall pharmacy and
medical costs as well as the impact to patient
outcomes

16. Tier Placement Model Business Rationale
• Appropriate tier placement can maximize the
cost effectiveness of an individual product’s use
within the available product category
– This will have an economic and clinical impact to the
health plan and provider
– This approach may create an environment in which
patients are treated more aggressively initially to
avoid creating medical issues downstream, perhaps
improving the patient’s experience

17. Tier Placement Model Conceptualization
Variable Description
Patient Population Patients diagnosed with disease of interest that are eligible for treatment with new therapy
Comparators Current clinical pathway vs. 1-3 additional approaches (1st line, 2nd line, 3rd line use)
Perspective Health plan
Time Horizon 1 year or more (dependent on disease state)
Type of Analysis
Economic and clinical impact upon introducing new product at various alternative clinical
pathway points
Unit of Analysis /
Results
1. Overall costs and outcomes of interest (by Scenario)
2. Detail on AE's (Total, Lead to Switch)
3. Cost ratios as deemed useful
4. Graphs to demonstrate the impact over time
Sensitivity Analysis One-way and two-way sensitivity analyses
Data Sources Internal information (i.e., market share estimates, cost, etc), literature, data analytics
Software Platform Microsoft Excel

18. 33
Tier Placement Model
Inputs
• Costs (Med / Rx)
• Clinical efficacy
and AE rates
• Resources
required for
patient switch
• Market share
Drivers
• Medical resource
use (hospital, ER)
• Pharmacy costs
• Disease prevalence
• At risk sub-
population
• Event Rates
Outputs
• Medical costs
(total / sub-totals)
• Pharmacy costs
• Outcome
measures (events
avoided, etc)
• Results in Total,
PMPM, etc

19. Tier Placement Model Assumptions
• The tier model should allow for maximum flexibility and
allow the user to customize the exact placement of
each therapy in the clinical pathway
• Data exists that measures the incremental costs
required to enforce restricted formulary access
• Substantial data exists to show that the new data is
highly efficacious relative to existing products
• The impact of products is measurable for both clinical
outcomes and direct cost incurred by the health plan
• Decision maker is interested in the total impact of
product and not just pharmacy costs

20. Tier Placement Model Pros
• Detailed model that that can be used to
determine “optimal” product placement in
clinical pathway
• Allows decision-makers to make choices about
expanding or contracting access using
information that aligns with clinical pathways
• This approach uses the EXACT same underlying
modeling structure for each product (more
consistency in the modeling programming
allowing for ease of QA and revisions)

21. Tier Placement Model Cons
• Model requires more flexibility making this
approach more complicated
• Assumptions regarding the impact of treating
naïve patients vs. “failed” patients may need to
be made
• How to address patients that fail due to AE’s—do
you deal with these patients differently (patient
memory in the model)?

22. • Case Study introduction
– New product launched into a market with
several existing therapy options
– New product
• More expensive acquisition cost
• Clinical trials show improved patient outcomes
• Current options have proven safety profiles in real world
setting
– Typical plan approach might be to add new product
at 2nd/3rd tier with restrictions
37
Tier Placement Case Study

23. • Questions
– Is this the best economic and clinical approach for
the plan?
• Do restrictions to the product actually decrease the overall
costs to the plan or just lower acquisition costs?
• How do the economics of using the product on 1st tier
compare to 2nd, 3rd or off-formulary?
– What is the impact on patient outcomes? (Does
providing restrictive access to superior products
result in increasing medical resource use?)
– Are there additional burdens to the plan?
38
Tier Placement Case Study

24. Tier Placement Model Case Study
Current Scenario
Health plan
spends
$300M to
treat HTN
annually
30,000
patients
treated with
a 1st line Tx
New agent
has superior
efficacy but
is $0.30 more
per day

25. Tier Placement Model Case Study
Scenario (Desired vs. Actual)
DESIRED RESULT: Pharmacy costs are maintained close to
current levels (~0.7% increase)
ACTUAL RESULT: Overall costs are decreased by 4% vs. using
new agent as first line therapy. Event rates are also lower
(7%)

26. Facility Models

27. Facility Model General Description
• Similar to normative models in many ways but
emphasizes the facility perspective
• Can be simple accounting of facility revenues
and expenses before and after introduction of a
new technology
• More complex versions can integrate other
perspectives and interactions

28. Facility Model Business Rationale
• As organizations evolve to take on greater
financial risk, a clearer understanding of the
impact of facility economics and provider
behavior on health plans will be crucial for
decisionmaking

29. Facility Model Pros
• More granular understanding of facility resource
use and economics
• Evaluation of how financial drivers of clinician
decisionmaking may impact health plan
• Exploration of how new technologies can impact
(enhance/detract) facility efficiency

30. Facility Model Cons
• Largely excludes non-financial domains (QOL,
value, patient satisfaction, etc.)
• May require extensive data collection/analysis
• Behaviors (clinicians, patients, etc.) are
multifactorial and may not adhere closely to
model assumptions

31. 47
Facility Model
Inputs
• Practice patterns and
resource use
• Expenses and
reimbursement
• Impact of new
technology or policy
• Optional: health plan
and clinician
perspectives
Drivers
• Reimbursement policy
• Importance and
magnitude of
disruption points
• Changes in reaction to
disruption
Outputs
• Profit/loss
• Efficiency/throughput
• Budget impact

32. Facility Model Case Study
• Case Study introduction
– An episodic infectious disease may eventually
require outpatient surgery
– Most surgical cases are conducted in the hospital
outpatient department (HOPD); occasionally in an
ambulatory surgery center (ASC)
– A new device/procedure allows treatment in a
physician office setting (OFFICE)
– Procedure tends to be safer and requires less work
by the clinician than current technology

33. Facility Model Case Study
• Assumptions
– Health plan likely will cover technology/procedure
but payment levels have yet to be set by plan
– All else being equal, health plans prefer that surgery
is performed in the least intensive/costly setting
– Clinicians take into account both clinical AND
financial factors when making a decision to adopt a
new technology/procedure

34. Facility Model Case Study
• Questions
– What is the economic impact on the plan of current
practice patterns and reimbursement?
– What resources and expenses are incurred in
different settings; how do patients flow through the
facility and in what volume; what is the revenue?
– What are likely disruption points with the new
approach—shorter OR/recovery room times, less
nurse time required for monitoring—and how might
clinicians and facilities respond to these changes?

35. Facility Model Case Study
**KEY QUESTIONS**
What should the health plan pay for the new
device/procedure?
Are there reimbursement levels that can balance
needs of all stakeholders (plan, facility, clinician,
and patient)?

36. Facility Model Case Study
**KEY QUESTIONS**
What should the health plan pay for the new device/procedure?
Are there reimbursement levels that can balance needs of all stakeholders (plan,
facility, clinician, and patient)?
Many ways to answer these questions; a facility
model may help inform the decision or the
consequences of the decision

37. Facility Model Case Study
Current Scenario
Health plan
spends $10M
on this
procedure
annually
80% HOPD
20% ASC
0% OFFICE
2,000
procedures
performed
annually in
plan

38. Facility Model Case Study
Scenario 1 (Desired)
Due to a less complicated procedure for clinician, health
plan covers new technology and procedure but at a greatly
reduced payment level to current scenario
DESIRED RESULT: 30%-40% reduction in plan expenses—
savings of $3M-$4M

39. Facility Model Case Study
Scenario 1 (Desired)
Health plan
spends $6M-
$7M on this
procedure
annually
20% HOPD
60% ASC
20% OFFICE
2,000
procedures
performed
annually in plan

40. Facility Model Case Study
Scenario 1 (Actual)
Due to much lower payment, new technology is minimally
adopted with only a minor shift out of the HOPD setting
ACTUAL RESULT: Instead of 30%-40% reduction in
expenses, achieve only a 7% reduction

41. Facility Model Case Study
Scenario 1 (Actual)
Health plan
spends $9.3M
on this
procedure
annually
75% HOPD
25% ASC
0% OFFICE
2,000
procedures
performed
annually in plan

42. Facility Model Case Study
Scenario 2
Although procedure is less complicated, plan adopts only
minimal decrease in procedure payment; facilities enjoy
efficiencies and greater profitability; widespread product
adoption and setting shifts ensue including marketing to
patients that would not have otherwise received the
procedure

43. Facility Model Case Study
Scenario 2
Health plan
experiences 10%
increase; spends
$11M on this
procedure
annually
10% HOPD
50% ASC
40% OFFICE
3,300
procedures
performed
annually in plan

44. Facility Model Case Study
Scenario 3
Although procedure is less complicated, plan adopts only
moderate decrease in procedure payment; facilities enjoy
efficiencies and greater profitability; reasonable product
adoption and setting shifts ensue

45. Facility Model Case Study
Scenario 3
Health plan
experiences 17%
decrease;
spends $8.3M
on this
procedure
annually
30% HOPD
40% ASC
30% OFFICE
2,300
procedures
performed
annually in plan

46. Portfolio Models

47. • Seek to optimize the mix of products and
services offered to meet desired end-points over
a distinct time window
• Conceptually similar to portfolio management
models derived from finance—that is, how do
you optimize your ‘ROI’ of the mix of products or
services for a particular ‘portfolio’?
• ROI for a health plan may be to be more efficient
(minimize costs or improve outcomes or both)
63
Portfolio Model Description

48. • There is increasing pressure on health plans to
ensure that the products and services offered to
their patients yield optimal returns for the
investments made by the health plan and/or
realized value savings for health plan clients
(such as the government)
64
Portfolio Model Business Rationale

49. Portfolio Model Conceptualization
Variable Description
Patient Population Patients within a therapeutic area
Comparators
Depends on health plan definitions of cost/revenue centers. Comparators could be surgical,
medical and pharmaceutical.
Perspective Health plan or providers
Time Horizon
The time horizon will be based on the wishes of the health plan. By definition, a portfolio
model should take a longer time perspective than traditional normative models. As in a
financial portfolio model, the greatest ROIs are realized in a longer time window. Minimum of
1 year, optimally 3-5 years.
Type of Analysis Longitudinal economic and clinical impact over time
Unit of Analysis /
Results
• Financial: Overall portfolio ROI or average ROI per service/procedure/medication
• Outcomes: Mortality/morbidity end-point such as ROI per event averted (based on
established benchmarks)
Sensitivity Analysis Probabilistic sensitivity analyses
Data Sources Internal information (market share estimates, cost, etc), literature, data analytics
Software Platform Microsoft Excel

50. • The portfolio model consists of distinct products
and services which can be priced in a discrete
manner and can be tracked over time
• A detailed understanding of the patterns of care
and the relative impact of competing
interventions on each other (for instance, does a
surgical procedure impact medical and
medications utilization downstream?)
66
Portfolio Model Assumptions

51. • Powerful models that can be useful in planning
and resource allocation over time
• Allows decision-makers to weed out potentially
unnecessary procedures or medications
• Ability to simulate a new technology or service
to determine the impact on the overall portfolio
67
Portfolio Model Pros

52. • Model requires a very detailed understanding of
patterns of care and resource utilization and the
impact of competing technologies on patient flows
and outcomes
• Model can become quite complex, especially if
there are a large number of competing technologies
(products and services) within the portfolio
• There may be a perception that the model is overtly
bottom-line driven, especially if the end-points are
purely financial
68
Portfolio Model Cons

53. 69
Portfolio Model
Inputs
• Costs of services
• Costs of
procedures
• Costs for
medications
• Ancillary costs
• Overhead
allocation
Drivers
• Reimbursement
scheme
• Member attrition
• Prevalence
• Regulations
• Technological
changes
Outputs
• ROI PMPY
• ROI per event
averted

54. Portfolio Model Case Study
• Case Study introduction
– A health plan administrator is concerned about
escalating costs in managing a therapeutic area
where disease prevalence is low but optimal
outcomes are difficult to achieve
– The health plan administrator would like to know
which cost center (surgical, medical or
pharmaceutical) has the highest ROI in terms of
patient outcomes
– He/she hopes that it would be possible to use this
information to prioritize care and to cut costs

55. Portfolio Model Case Study
• Assumptions
– It is possible to track complete financial, clinical and outcome
inputs and outputs over the desired time horizon
– Care pathways and drivers are well delineated and
understood
– Patients have access to all three alternative cost centers and
outcomes are realized for all three alternatives within the
time frame for the model
– There are well established benchmarks to gauge performance
(such as past performance, industry benchmarks or published
regional or national data)

56. Portfolio Model Case Study
• Questions
– What is the acceptable overall ROI per unit outcome
to compare alternative cost centers?
– Is there an ROI threshold for services or procedures
deemed to be optimal vs. sub-par?
– Are there other stakeholder interests not explicitly
modeled which should be taken into consideration?

57. Portfolio Model Case Study
Actinic Keratosis Portfolio
Model
• Calculate ROI: ROI= Revenue - Expenses
Expenses
• ROI can be calculated on an annualized basis
• Outcomes: Cases averted= Benchmark value – Actual cases
normalized to the plan population. Can be annualized as above.
• Alternatives: ROI can also be characterized as a financial value using
net present value calculations (NPV)

58. Portfolio Model Case Study
3 Year Actinic Keratosis
Portfolio Model
Surgical
(Cryotherapy)
ROI-1%/BCC
case averted
Medical
(Phototherapy)
ROI-6%/BCC
case averted
Pharmaceutical
(Topical agents)
ROI-10%/BCC
case averted

59. Normative Models--SWOT
STRENGTHS
--Address broader domains such as
QOL, value
--Has strong foundation in economic
theory
--Adaptable to a variety of healthcare
technologies (services, medications,
devices)
--Provides a simpler representation of
complex issues in healthcare
WEAKNESSES
--Often have limited relevance to
decision at hand
--Transparency can be a problem
--End points may be difficult to
understand (ICER, QALYs)
--Overtly reliant on threshold cut-offs
--Uncertainty sensitivity analyses may
be difficult to understand
OPPORTUNITIES
--Widely used and accepted modeling
approaches
--Can be used to simulate impact of
new technologies
THREATS
--May not be as adaptable to address
newer reimbursement schemes (risk-
sharing)
--Difficult to incorporate social equity
and political considerations

60. Adaptive Models--SWOT
STRENGTHS
--Addresses variety of issues often
neglected by normative models
--Highly adaptable
--Highly salient to the needs of the
organization
--Uncertainty impacts better
understood
WEAKNESSES
--Information needs may be great
--Models can be very complex
--Tend to omit issues such as QOL
--More bottom line focused
OPPORTUNITIES
--Necessary in light of changing
reimbursement/payment climate
--Limited budgets: Shift to more
pragmatic models to show value
--Provides ability to pro-actively engage
manufacturers or providers
--Can simulate impact of new
technologies
THREATS
--Not well established: May have
limited buy-in
--Requires moderate investment
--Models designed for internal decision-
making: May not be as useful to show
value to other stakeholders
--Potential conflict of efficiency versus
patient centric value

61. Summary
• Existing normative models are useful but may
not always address real-world needs of
decisionmakers
• Adaptive models provide new perspective and
may help to inform health plan decisions
• As with any type of model, there are challenges
and limitations and issues that each can address
optimally

62. Summary
Introduction of new
technology
Tier
Portfolio
Facility
Prioritization of
competing technologies
and cost control
Tier
Portfolio
Facility
Environmental
disruption
Portfolio
Facility
Intra-organizational
changes
Portfolio
Scenarios that may benefit from adaptive models

63. Q & A

64. Contact Information
Joe Gricar
joe.gricar@apo-med.com
212-260-0589