6. Alternative DSH Allocation Policies
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Endnote
We are interested in evaluating how different measures of financially vulnerable
safety net hospitals would affect 1) the set of hospitals eligible to receive
federal subsidies and 2) the distribution of funds among those hospitals.
The literature concerning safety net hospitals and the current policies for
Medicare and Medicaid DSH payments suggest a set of analytical policy issues
related to the distribution of DSH funds.
-
How sensitive is the allocation of DSH funds to different measures of vulnerable
populations? Do measures that use only a subset of low-income patients, e.g.
Medicaid patients, target the same hospitals as more inclusive measures?
-
Is it feasible for the allocation formula to take into account both inpatient
and outpatient services? How does this affect the relative distribution of
DSH funds?
-
Do measures based on the proportion of care furnished to low-income patients
target a different set of hospitals than those based on the financial risk
associated with serving low-income patients?
-
Does a strategy such as a minimum threshold or sliding scale strategy improve
the relationship between a hospital's financial risk and the subsidy it would
receive from a DSH fund?
Our analysis is within the context of using a single federal payment mechanism
to distribute DSH funds. Our baseline is current law Medicare payments and
the federal share of DSH payments. The simulations assume funding would be
allocated to hospitals separately from program payments for patient care
services. Using a separate funding stream has implications for the formula
that is used for allocating the funds (e.g. whether Medicare/SSI beneficiaries
need to be taken into consideration in the allocation formula). It also affects
the vehicle that is used to distribute the funds (e.g. Medicare DSH payments
are paid as an add-on to the DRG standard payment rate so that hospitals
with a large uninsured caseload and few Medicare patients receive little
support).
The simulations include:
-
the joint distribution of Medicare and the federal share of Medicaid DSH
payments under current policies to the hospitals in our dataset;
-
a MedPAC-like approach that would distribute funds based on the hospital's
proportion of low-income revenues and adjusted discharges (with further
adjustments for differences in geographic location and case mix);
-
policies focused on the financial risk associated with serving Medicaid and
self-pay patients (i.e., Medicaid shortfalls and uncompensated care costs).
In exploring the impact of alternative allocation policies, the issue of
which hospitals should be eligible to receive DSH payments should be separated
from the issue of how the funds should be distributed to eligible hospitals.
The advantage of this approach is that it allows the possibility of basing
eligibility on the patient population served by the hospital but determining
how much the hospital receives in DSH payments on the financial risk it bears
as a result. The risk borne by a hospital whose patients are covered by Medicare
and Medicaid is less that that borne by a hospital with a substantial uninsured
population.
To minimize issues related to whether higher costs are attributable to hospital
inefficiency or justifiable differences in costs, the financial measures
used in the eligibility and allocation policies do not measure costs directly;
rather, they express financial risk associated with serving poor people as
a percentage of revenues or costs. In some allocation policies, adjustments
are made for cost differences attributable to case mix and hospital wage
levels.
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In this section, we describe potential measures associated with serving
low-income patients that could be used either to establish eligibility to
receive DSH funding or allocate the funds to eligible hospitals. Some of
these measures can be generated from inpatient claims data and others require
financial data. We provide definitions of the policies associated with specific
measures in Table 6.1.
Claim-based Measures
Measures 1.1 and 1.2 are based on the amount of care a hospital furnishes
to low-income patients as measured through claims data, i.e. the proportion
of days or discharges attributable to low-income patients. Inpatient claims
data can also be used to measure the hospital's percentage of gross inpatient
revenues attributable to low-income patients (1.3). These inpatient claim-based
measures involve several assumptions:
-
All utilization on the claim is attributed to the primary payer. For example,
the measure is not sensitive to situations where Medicaid is a secondary
payer for part of an inpatient stay.
-
The measures assume that low-income patients utilize outpatient services
in the same proportion as inpatient services. Low-income patients tend to
have a relatively higher outpatient utilization rates than inpatient (because
they have less access to community physicians). This is an issue examined
in greater detail in our three-state analyses discussed in
Chapter 8.
-
The measure implicitly assumes that no-charge patients are charity care and
that self-pay patients represent low-income patients that are unable to pay
for their care. We know that no-charge patients include those receiving courtesy
and employee allowances and that the percentage of no-charge patients is
likely to overestimate the percentage of charity care patients. Similarly,
some self-pay patients are able to pay for some or all of their care. We
examine the relationship between the proportion of self-pay and no-charge
patients and bad debts and charity care in our three-state analyses.
-
The HCUP claims data do not include a separate classification for indigent
care programs. We used other government days (which is a residual after excluding
CHAMPUS and workmen's compensation) as our proxy for local indigent care
programs.
Table 6.1
Potential Measures of Care Provided to Low-Income Patients
| Measure |
Formula |
| 1. Utilization measures from claims data based
on proportion on low-income inpatients |
1.1
% Low-Income Days |
Medicare days*SSI ratio + Medicaid days + self-pay days
+ no-charge days + Title V days + other government days
————————————————————————
Total inpatient days |
1.2
% Low-Income Discharges |
Medicare discharges*SSI ratio + Medicaid discharges + self-pay
discharges
+no-charge discharges + Title V discharges + other government discharges
——————————————————————————————
Total inpatient discharges |
1.3
% Low-income inpatient revenue |
Medicare charges*SSI ratio + Medicaid charges
+ self-pay charges +no-charge charges + Title V charges
+ other government charges
——————————————————————
Total inpatient charges |
| 2. Gross revenue measures based on percentage
of revenue attributable to low-income patients |
2.1
% gross revenue attributable to low-income Medicare and Medicaid patients,
local indigent care programs, bad debt and uncompensated care (MedPAC Model) |
Medicare revenue* SSI ratio + Medicaid revenue
+ local indigent care revenue + bad debt expense
+ charity care revenue foregone
———————————————————
Total gross patient revenue |
2.2
% gross revenues attributable to charity and no-charge patients |
Gross revenues for charity and no-charge patients
___________________________________________
Total gross revenues |
| 3. Financial risk measures based on losses
attributable to low-income patients |
3.1
% total cost attributable to shortfalls from Medicaid and indigent care programs,
bad debt & uncompensated care |
(Medicaid patient care gross revenue + other government gross revenue + bad
debt + uncompensated care) X cost-to-charge ratio - Medicaid payments (exclusive
of "new DSH) - payments from other government programs)
————————————————————————————————————————
Total operating cost |
3.2
Bad debt and uncompensated care as % of total operating cost |
(Charity care revenue + bad debt expense) X cost-to-charge ratio
——————————————————————————
Total operating cost |
| 4. Market share model for urban hospitals based
on proportion of financial risk in the community assumed by the hospital |
4.1.
Financial risk measure from 3.2 |
(Charity care revenue + bad debt expense) X cost-to-charge ratio
——————————————————————————
 (Charity
care revenue + bad debt) X cost-to-charge ratio for the MSA |
Measures Derived From Financial Data
Measures 2.1 and 2.2 use financial data to measure the percentage of gross
revenue attributable to low-income patients. Measure 2.1 is similar to MedPAC
model's definition of a hospital's low-income
share.(1) Gross revenues derived from financial
data have several advantages over those derived from inpatient claims data.
-
Secondary payers are accounted for (assuming the reporting is accurate).
-
Both inpatient and outpatient volume are directly measured,
-
Implicit recognition is given to differences in severity across the hospital's
patient population.
There are issues, however, regarding uniform reporting of financial data
generally, and uncompensated care and bad debt in particular. We decided
to use both bad debt and uncompensated care costs in the models derived from
financial data because of reporting inconsistencies (see
Chapter 2). Basing a policy on uncompensated care only
(with uniform definitions) or uncompensated care and bad debt attributable
to self-pay patients might be more appropriate policies to target financially
vulnerable safety net hospitals than including bad debt associated with care
provided to insured patients. Nevertheless, given current reporting
inconsistencies, we have included all bad debt and uncompensated care in
our models that use financial data.
Measures 3.1 and 3.2 focus on the financial risk associated with serving
low-income patients. Measure 3.1 defines financial risk in terms of shortfalls
from Medicaid and local indigent care programs, bad debt, and uncompensated
care. Medicare SSI patients and Medicaid patients to the extent the Medicaid
payment covers the cost of their care are not taken into consideration. The
Medicaid shortfall could be attributable to either low-payment rates or to
hospital inefficiency. In computing the Medicaid shortfall, we exclude DSH
funds so that the measure is financial risk in the absence of DSH funding
(see more detailed discussion of methodology in Chapter
8). There is some danger that including the Medicaid shortfall could
provide a perverse incentive to reduce payment rates. The actual incentive
will depend on the relationship between the state's FMAP and the generosity
of the DSH payments. An alternative to including the Medicaid shortfall would
be to count only a portion (e.g. 50%) of gross Medicaid revenues in constructing
a revenue measure.
Finally, Measure 4.1 measures each urban hospital's market share of uncompensated
care and bad debt. The market is defined by MSA. Conceptually, this measures
hospital's uncompensated care load in relation to its market rather than
the national market.
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Volume
We avoided establishing a direct DSH subsidy for inefficiencies by expressing
the low-income measures as a percentage of revenues or costs rather than
absolute dollar amounts. However, this approach also requires that the allocation
formula include a measure to take into account differences in patient volume
across hospitals (Table 6.2). While inpatient days or
discharges could be used for this purpose, a better policy would be to take
into account outpatient volume as well. Adjusted inpatient days and discharges
convert outpatient volume into equivalent inpatient days or discharges. For
example, total adjusted discharges equal total hospital discharges times
the ratio of total hospital gross revenues to hospital inpatient gross revenues.
The weight assigned to a given hospital would be determined as the product
of its low-income patient measure and either adjusted patient days or discharges.
Table 6.2
Potential Allocation Statistics
| Measure |
Formula |
| Total adjusted days |
Low-income measure X total inpatient days X total gross patient revenues
/ total inpatient gross revenues |
| Total adjusted discharges |
Low-income measure X total inpatient discharges X total gross patient
revenues / total inpatient gross revenues |
| Cost-adjusted days |
Low-income measure X total adjusted days X wage index factor |
| Case-mix and cost-adjusted discharges |
Low-income measure X total adjusted discharges X wage index factor X
CMI |
| Cost-adjusted days and state's relative resources |
Low-income measure X total adjusted days X index of state's resources
to other states |
Cost Differentials
The allocation formula could further adjust the hospital's volume-weighted
low-income patient measure for systematic differences in cost. A case-mix
adjustment should be used when adjusted discharges are used in the allocation
formula. A case-mix adjustment is not needed if adjusted inpatient days are
used as the volume statistic because case mix is correlated with length of
stay. The Medicare hospital wage index can be used to adjust for cost differences
across geographic areas.
Resource Differentials
One issue in allocating DSH funds is the extent to which the state's available
resources to finance health care for low-income persons should be taken into
account in the fund distribution formula. Under current law, Medicare DSH
payments are based on national allocation rules without regard to state resources
while the federal share of Medicaid DSH payments is determined under a matching
formula that varies by state. The Federal Medical Assistance Percentage (FMAP)
is intended to provide more generous Medicaid matching percentages to states
that have relatively fewer resources to finance health care programs and/or
relatively more low-income patients to serve.
The current FMAP formula is based on per capita income and has been criticized
for not taking into account total resources available to finance health care
and cross-state differences in the cost of health care and the number of
people living in poverty. Proposals have been made for an "equitable" FMAP
based on the ratio of the state's share of resources (adjusted for differences
in health care costs) to the state's share of low-income patients (adjusted
for cost-of-living differences and age). Adjusting payments to hospitals
using this type of formula would be consistent with a policy that federal
support for uncompensated care costs should be higher in those states with
limited resources. However, our analyses focus on DSH distributions to individual
hospitals rather than aggregate payments to states. If the DSH distribution
is based on a utilization or gross revenue measure, using a FMAP-like factor
in the allocation formula would be one way to adjust for likely differences
in the actual financial risk associated with serving low-income patients
(i.e. Medicaid shortfalls and uncompensated care). The assumption would be
that hospitals located in states with relatively fewer resources have higher
financial risk than hospitals located in states with relatively high resources.
We do not believe that it would be appropriate to use an FMAP-like factor
if the allocation formula is based on actual financial risk. The purpose
of the DSH funds is to protect hospitals from their financial losses associated
with serving low-income patients. Two hospitals with comparable financial
losses should receive similar levels of protection.
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As discussed in Chapter 3, data were not available
that allowed us to examine the alternatives with a national set of hospitals.
Our exploratory analyses drew on two different sets of hospitals. The first
set consists of the hospitals that are represented in the HCUP national sample.
We were able to explore the sensitivity of DSH allocations to low-income
patient definitions that rely on utilization or gross inpatient revenue data
by payer class. The results of these simulations are discussed in
Chapter 7.
By supplementing these analyses with detailed claim and financial information
from California, New York and Wisconsin hospitals, we examined a broader
set of potential DSH eligibility and allocation policies. We discuss these
simulations in Chapter 8.
1. The AHA data used by MedPAC does not have a separate
category for patients under local indigent care programs. As a result, MedPAC
assumes that the shortfalls in the "other" patient category are attributable
to the local indigent care program. Specific information on local indigent
care program revenues is available in our financial data.
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