CRD_DMEstimator_0206 by heku

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									        VOLUME 11               •         NUMBER 2                •    FEBRUARY 2006              •        PAGES 13-24


New options for transferring catastrophic risks
Specialty carve-outs address unique reinsurance issues
      For some years, the managed care industry has embraced the concept of disease management (DM)
for chronic conditions such as asthma, diabetes, chronic obstructive pulmonary disease (COPD), and con-
gestive heart failure (CHF). Similarly, the reinsurance industry is moving toward disease-specific care
management and risk transfer, or “carve-out” programs. Much like HMOs focus on managing diseases
that represent a high percentage of medical dollars, reinsurers are focusing on categories of care that rep-
resent a large proportion of reinsurance dollars, explains Dave Kalb, vice president in the Minneapolis
office of Evergreen Re Incorporated, an HMO reinsurance broker and health care risk consulting firm
based in Stuart, FL. (See Figure 1 for an overview of excess claims per diagnosis.)
      To date, specialty carve-out coverage has focused mainly on transplants, neonatal intensive care (NICU),
and trauma, with cancer carve-out programs considered the next frontier, Kalb says. Combined, these ser-
vices represent the majority of total reinsurance claims costs.
      “The fundamental principle of any carve-out program is that a carve-out company can produce better
outcomes -- both economic and clinical -- than an individual health plan,” Kalb points out. “Keep in mind
that we are talking about low frequency but very high severity cases. It’s difficult for most health plans to
dedicate the resources necessary to manage these very complex cases when they may experience only a few
of them in a year.”
      Most capitated
provider organizations                                Figure 1: Frequency and Severity Risk by Diagnosis
are not heavily involved
in specialty carve-outs
due to eligibility, con-
tracting, and medical
management considera-
tions, Kalb tells Capita-
tion Rates & Data. While
the costs and limitations
for transplant carve-outs
are fairly well under-
stood by capitated
providers that purchase
reinsurance, for
instance, the eligibility,
contracting, and medical
management processes
still are largely con-
trolled by health plans.     ING Re Excess Claims 2000-2002
      “Even when
                             Source: Evergreen Re
provider organizations


     Web-based tool estimates DM costs ➤ 16 • Medicare cap rates for ancillaries ➤ 18 • Physician comp, productivity trends ➤ 19 •
                                      Managed care penetration and cancer screening ➤ 21
sourcing catastrophic risk management to a spe-                      access fees?
cialty carve-out should seek specific information up                      “The bottom line is that the range of costs for
front, Kalb advises. For instance:                                   the carve-out program should be slightly below to
     • How will the specialty carve-out bring a                      slightly above the plan’s or provider’s historical
higher level of quality management to complex pro-                   costs depending on the carve-out company’s
cedures?                                                             expected economic gain,” Kalb says. “But there are
     • How will affected members achieve better                      also some intangibles. For example, a specialty
outcomes at equal or lower costs to the plan?                        carve-out is likely to bring a better quality of care to
     • What is the economic value to the plan in                     members. And if the health plan or capitated orga-
transferring this form of risk for a fixed PMPM fee?                 nization budgets a certain percentage of its IBNR for
     • What are the specialty organization’s histori-                catastrophic claims, a specialty carve-out enables
cal costs for managing a particular type of cata-                    that money to be released, which can help the orga-
strophic care?                                                       nization with its capital planning.”
     • How will the carve-out program reduce the                          Editor’s Note: Contact Dave Kalb at (952) 345-0503
plan’s costs, increase productivity, and reduce                      or dkalb@evergreenre.com.


New tool estimates DM                                                population type, region, managed (HMO) or mod-
                                                                     erately managed (PPO, POS, or indemnity) plan
costs even without                                                   type, and age/gender distribution provided by the
administrative claims data                                           user or the tool’s state or three-digit Zip code
                                                                     defaults -- and marries them with several disease
     The DM E$timator, a new Web-based tool                          management inputs: one of five specific DM pro-
developed by Boston-based DxCG, Inc., is designed                    grams implemented (diabetes, congestive heart fail-
to help health plans and providers estimate illness                  ure, coronary artery disease, asthma, chronic
burden and the potential for disease management                      obstructive pulmonary disease), percent participa-
(DM) savings even when administrative claims data                    tion in DM programs by population risk category
are not available.                                                   (very low, low, moderate, high, or very high risk),
     While designed primarily as a pre-sales tool for                and percent of expected savings by risk category.
health plans, the E$timator is an intriguing tool for                The user can provide actual data for any of the
modeling expected costs under two scenarios: 1)                      demographic or DM inputs or use the DxCG default
when administrative claims data are not available                    values.
but eligibility information is available, and 2) when                     The resulting output reports provide users with
both claims and eligibility data are not available but               data on which conditions are expected to contribute
the number of insured lives and their geographic                     the most to their costs, their expected gross dollar
information are available, according to DxCG’s                       savings after implementing a DM program, and
Anju Joglekar, PhD, senior research associate, and                   some key utilization measures.
Ben McMillan, product analyst, who presented the                          For instance, in Figure 1, the user specifies the
new tool at the company’s 2005 User Conference in                    expected risk distribution and percentage of mem-
November.                                                            ber participation, and the DM E$timator reveals
     “The DM E$timator is a ‘what-if’ scenario calcu-                the expected dollar savings after the program’s
lator,” Joglekar explains. “For instance, what would                 implementation.
be the expected savings for a specific group if we                        “This screen uses a simple sample illustration,”
implemented a disease management program and                         Joglekar explains. “In this example with 2,000 cov-
only 20% of the high-risk members participated?”                     ered lives and an expected number of 68 diabetic
     In general, the E$timator is a low-cost, Web-                   cases, the expected total medical cost for the 68 dia-
based application that does not require
sophisticated training or database soft-                            Figure 1: Expected Savings Assumptions
ware. While not designed to provide             Risk Category of
                                                                          % Distribution        % Participation % Expected Savings
actuarially tested data, it can be used as a Patients
benchmarking tool for a variety of sce-         Very Low Risk                 0                      0               3

narios, such as expected cases for a given Low Risk                           21                     100             3
condition, estimates of key utilization                                       47                     0               0
                                                Moderate Risk
measures, expected cost drivers, condi-
tion prevalence by region, and condition        High Risk                     27                     0               0

prevalence by plan type.                        Very High Risk                5                      0               0

     The DM E$timator takes a variety of
demographic inputs -- number of lives,         Source: DxCG, Inc. www.dxcg.com. Reprinted with permission.



16                                              Capitation Rates & Data                                            February 2006
 betics was $712,626. When 100% of the low risk                              Joglekar points out. “Benchmarking is easy to do,
 members participated, lowering overall costs by 3%,                         but this tool allows users to tailor their estimates
 the expected savings are $2.13 per member per                               to regional age and gender differences.”
 month.”                                                                          So far, DxCG offers only commercial and Med-
                                                                             icaid models of the DM E$timator, but the company
 Better input produces better output                                         plans to add a Medicare platform, as well. While
                                                                             DxCG has not yet tested the DM E$timator against
      Figure 2 shows an expected prevalence of                               actual results, “we have some estimates of how the
 3.4%, or 340 per 10,000, for diabetes, compared to                          benchmark tool performs,” Joglekar says, “and if
 2.1% for coronary artery disease, 2.9% for asthma,                          users are interested in doing a validation study, we
 and 3.6% for depression based on the tool’s                                 would be open to testing the tool on a portion of the
 default values. This screen is designed to illustrate                       data.”
 the greatest cost savings opportunities from                                     Editor’s Note: For more information about the DM
 implementing a DM program. When a user inputs                               E$timator, contact Anju Joglekar or Ben McMillan at
 different age and gender data, the DM E$timator                             (617) 303-3790 or bmcmillan@dxcg.com.
 provides a different scenario of expected cases by
 disease group, Joglekar says.
                                                                         Figure 2: Expected Cases for Selected Disease Groups
      The estimates of key utilization measures by
 region, illustrated in Figure 3, provide invaluable               Covered Lives                         2000

 projections of hospital admissions, ER visits, office
 visits, and prescription utilization by member popu- Northeast
 lation.                                                           Disease Group                 Expected Prevalence             Rate Per 10,000
      In each case, the precision of the estimates                 Diabetes                                  68                         340
                                                                   Coronary Artery Disease                   46                         213
 depends on the amount of information the user pro-
 vides about the group, with age and gender distrib- Congestive Heart Failure                                12                          60
                                                                   Asthma                                    58                         291
 utions improving the estimates for commercial pop- Depression                                               72                         361
 ulations.                                                         Chronic Obstructive
                                                                                                             20                         100
      “A user’s DM programs may perform differ-                    Pulmonary Disease
 ently than savings estimates used as defaults,
 because these are used for ‘what if’ scenarios,”                  South
                                                                   Disease Group                 Expected Prevalence             Rate Per 10,000
 Joglekar points out.
                                                                   Diabetes                                  72                         340
      The potential for double counting is another
                                                                   Coronary Artery Disease                   38                         213
 limitation of the tool.                                           Congestive Heart Failure                  12                          60
      “When using the tool, you select a disease man- Asthma                                                 48                         291
 agement program for one of five conditions,”                      Depression                                70                         361
 Joglekar explains. “If you are looking at all people              Chronic Obstructive
                                                                                                             19                         100
 with diabetes, some will only have diabetes but oth- Pulmonary Disease
 ers will have diabetes with congestive heart failure             Source: DxCG, Inc. www.dxcg.com. Reprinted with permission.
 or another comorbidity. The program
 will act on all of these.
                                                            Figure 3: Estimates of Key Utilization Measures by Region
      “Based on the risk bands, users
 could model a program for diabetics that Population Type:                  Commercial           Population Type:            Commercial
 includes all comorbid conditions, but we Region:                           Northeast            Region:                     South
                                                Projected Admissions Per                         Projected Admissions Per
 have not yet created a program to take         1000
                                                                                        68
                                                                                                 1000
                                                                                                                                    66


 all patients with diabetes or CHF and          Projected Bed Days Per                  235      Projected Bed Days Per             242
                                                1000                                             1000
 discount the overlap,” she adds. “How-         Projected ER Visits Per                          Projected ER Visits Per
                                                                                        25
 ever, we might look at that area in the        1000                                             1000
                                                                                                                                    27


 future.”                                       Projected Office Visits Per
                                                Person
                                                                                        3.2      Projected Office Visits Per
                                                                                                 Person
                                                                                                                                    2.8

      Despite its limitations, the DM           Projected Prescription Per              0        Projected Prescription Per         0
 E$timator enables users to make credi-         Person                                           Person
                                                Projected Percent Lives                          Projected Percent Lives
 ble estimates of condition prevalence,         with No Claims
                                                                                        25
                                                                                                 with No Claims
                                                                                                                                    23


 key utilization measures, expected             Projected Percent of Lives                       Projected Percent of Lives
                                                with No                                 15       with No                            13
 medical costs, average risk score, and         Diagnoses                                        Diagnoses
 the contribution of cost driver condi-         Projected Percent of Lives                       Projected Percent of Lives
 tions even in the absence of administra- with No
                                                Prescriptions
                                                                                        5        with No
                                                                                                 Prescriptions
                                                                                                                                    7


 tive claims data.
      “The power of the tool is that you       Source: DxCG, Inc. www.dxcg.com. Reprinted with permission.
 can tailor it to a specific population,”

February 2006                                          Capitation Rates & Data                                                                     17

								
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