Analysis of the Joint Distribution of Disproportionate Share Hospital Payments

8: Three State Analysis of Alternative DSH Policies

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Contents

• Overview of Medicaid DSH Programs
• Financial Data
• Variables
• Methods
• Results
• Conclusions

In this chapter, we discuss the results of our analyses using the HCUP SID and detailed financial data for hospitals located in California, New York, and Wisconsin. We chose these states because they participate in HCUP, require uniform financial reporting systems, have different types of Medicaid DSH programs and, perhaps most importantly, hospital-specific information is available on intergovernmental transfers and other DSH contributions made by the hospitals. By linking the claims and financial data for the hospitals in these states, we are able to:

• Trace the actual distribution of "new" Medicaid DSH funds.
• Estimate funds retained by the states.
• Assess how well the claims-generated measures of low-income patients correspond to measures generated from financial data (e.g. proportion of self-pay and no-charge patients relative to bad debt and uncompensated care); and,
• Compare how well the current and alternative DSH allocation policies target financially vulnerable safety net hospitals. In this regard, we were able to examine how funds might be redistributed across classes of hospitals, but because only three states were involved, we could not estimate the redistributions that might occur across states under the different alternatives.

Overview of Medicaid DSH Programs

California

The California's DSHprogram is funded entirely through federal match of intergovernmental transfers (IGTs) from public entities (county hospitals and the University of California clinics) to the Medi-Cal Inpatient Adjustment Fund. The amount of IGTs paid by a public entity is based on the ratio of the hospital's projected DSHpayments to that of all public hospitals and is increased by their pro-rata obligation for all DSHpayments to private hospitals. The IGTs, less a state administrative fee, is subject to federal matching.

DSH funding is restricted to hospitals that meet the minimum federal requirements for DSH payments, i.e., either have a Medi-Cal inpatient hospital utilization rate at least one standard deviation above the statewide mean or a low-income utilization rate in excess of 25 percent (with at least one percent Medi-Cal utilization). The low-income utilization rate is defined as the proportion of revenue attributable to Medi-Cal and charity care.

The DSH funds are distributed to eligible hospitals using a per diem formula that takes into account the type of hospital (public, private, and those converted from public to private) and the hospital's low- income utilization rate. The per diem amount rewards Medi-Cal days more heavily than charity care and increases with the hospital's low-income utilization rate. The hospital's projected DSH payment is capped by the OBRA 93 limits (see Chapter 1) and any funds that are not expended in the base payments are distributed through supplemental payments to the remaining DSH hospitals.

New York

The New York Health Care Reform Act (HCRA) substantially deregulated the state's inpatient hospital rate system effective January 1, 1997 but continued to require non-Medicare payers to make surcharge payments to subsidize indigent care and other health care initiatives⁽¹⁾. The surcharges vary by payer and are lower if the payer makes payments directly to the indigent care pool instead of the hospital remitting the surcharge amounts. The indigent care pool is also funded by provider taxes of 1% of inpatient gross receipts from all hospitals and a 0.7% assessment on gross receipts from all patient services from distressed hospitals. The payer and hospital contributions to the fund qualify for federal DSH matching funds.

1998 marked the mid-way transition from the pre-HCRA indigent pool to HCRA. The anticipated funding level for the indigent care pool was $738 million but full funding did not occur. Most hospitals received 25% of their 1996 distribution from the old indigent care pool and 75 percent of a new payment scale. The formula for distributing the indigent care pool under the new scale is based on a hospital's uncompensated care needs. Need for general hospitals is defined as losses from bad debts (reduced to costs) and the costs of charity care expressed as a percentage of reported costs. Hospitals must have at least 0.5% need to qualify for funds. Funds are distributed using a sliding scale:

Some funds were reserved from the pool for adjustments to non-public hospitals whose need was greater than 4% of reported costs and were distributed based on the hospital's share of aggregate need over 4%. Also, special phase-in provisions applied to non-public financially distressed hospitals and major public hospitals.⁽²⁾ Taxes for distressed hospitals were phased-in with 25% owed in 1998.

Wisconsin

Wisconsin supports its Medicaid DSH program through general revenues so that, unlike the California and New York DSH programs, all DSH funds are "new" money to the hospitals. Hospitals qualify for DSH payments if they meet the federal minimum requirements for DSH payments. The DSH payment is incorporated into a hospital-specific diagnosis-related per discharge payment for inpatient services provided to Medicaid beneficiaries.

• For community hospitals with a high Medicaid utilization rate, the DSH formula ranges from a minimum 3% adjustment to a 5.5% adjustment to the hospital with the highest Medicaid utilization rate.⁽³⁾
• The low-utilization rate is the sum of the hospital's total payments from the Medicaid program and local indigent care subsidies as a percentage of net revenues plus the percentage of the hospital's gross revenues attributable to charity care. Courtesy care and bad debt are excluded from the definition of charity care. The DSH adjustment factor is:

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Financial Data

California

California hospitals are required to submit Annual Disclosure Reports containing financial and statistical information within four months of the close of the hospital's fiscal year. The Office of Statewide Health Planning and Development produces an electronic data file that contains selected information from the annual reports. We used the data submitted by the hospitals for fiscal years ending in 1998. The file contains 455 hospital reports, of which 383 are for hospitals classified as general acute care by the state. This includes cancer hospitals and children's hospitals that are excluded from the Medicare prospective payment system. The most common fiscal year ends are June 30 (39%) and December 31 (30%).

The report uses five payer categories: Medicare, Medi-Cal, County Indigent Programs, Other Third Parties (includes contractual purchasers and indemnity payers) and Other Payers. Medi-Cal patients enrolled in Medi-Cal HMOs are included in the Other Third Parties category. The County Indigent Programs category includes all patients for whom the county is responsible. The Other Payer category includes all patients not covered by a third party payer and includes self-pay patients and indigent patients who are not a county responsibility (CA OSHPD, 2000). While gross revenues by payer are reported separately for inpatient and outpatient services, information on contractual allowances and bad debt by payer is combined for inpatient and outpatient services. Key data that we used included the following:

• Gross revenues. Revenues by payer are collected separately for inpatient and outpatient services. Since Medi-Cal patients enrolled in HMOs are in the Other Third Parties category, Medicaid revenues are understated. About 51 percent of Medicaid enrollees are in managed care (KFF, 2002).
• Medicaid shortfalls. These can be determined only for fee-for-service enrollees only. The report provides information on Medicaid gross patient revenues, Medi-Cal contractual adjustments, and net revenues (gross revenues less contractual allowances and other deductions from revenue, including charity care and bad debt related to Medi-Cal non-managed care enrollees).
• Bad debt and uncompensated care. Bad debt is reported as a deduction from revenue and is not included in operating expense. A single line item is reported for bad debt.
• Medicaid DSH payments. Medicaid DSH payments are reported as a credit to Medicaid contractual adjustments. DSH funds transferred from the hospitals to a related public entity (i.e. intergovernmental transfers) are reported as a non-operating expense.

The California Association of Public Hospitals provided us with additional information on federal fiscal year 1998 DSH payments and IGTs.

New York

New York hospitals file an annual institutional cost report with the New York State Department of Health. The cost report collects utilization and revenue information for 14 payer categories, including separate categories for Medicaid fee-for-service enrollees, Medicaid managed care enrollees, self-pay, charity care and courtesy care patients. Not all categories (including Medicaid HMO) are used consistently. Gross revenues, net revenues and bad debt by payer are reported by type of service. We used data from the reports filed for calendar year 1998 (the fiscal year for all hospitals is 1/1/98-12/31/98). The cost report also collects information on the hospital's contributions to the indigent care pool, the DSH payments that it received, and its DSH cap. The Greater New York Hospital Association provided us with an electronic file with selected data from the institutional cost report that also had supplemental information from the Department of Health on indigent care pool distributions. Key data that we used included the following:

• Gross revenues by payer. While the cost report provides for separate reporting of Medicaid fee-for-service and managed care revenues, the file we obtained did not include Medicaid managed care revenues in the Medicaid revenue data because of inconsistent reporting. Information on Medicaid managed care enrollees is often combined with data for other HMO enrollees. About 25 percent of Medicaid enrollees in New York are in managed care (KFF, 2002).
• Medicaid shortfalls. We could only determine the shortfalls for Medicaid fee-for-service enrollees.
• DSH payments. DSH distributions are reported as separate line items and are not included in Medicaid patient revenues. DSH provider assessments and contributions on behalf of third-party payer are also reported as separate line items. We did not include the latter in determining "new" DSH since the funds are essentially a "pass-through" from third-party payers that do not contribute directly to the indigent care pool.
• Bad debt and uncompensated care. Bad debt net of recoveries is reported by payer class by type of service. There are separate revenue lines for self-pay, charity care and courtesy care.

Wisconsin

All non-federal hospitals in Wisconsin are required to submit audited financial data to the Bureau of Health Information within 120 days after the end of their fiscal year. The information is available electronically in a public use file. We combined the data submitted for hospital fiscal years ending in calendar year 1997 and 1998 to develop FY1998 financial information for each acute care hospital (based on the number of months covered by the respective reports that occurred during FY1998). Key data that we used included the following:

• Gross revenues by payer. Medicare and Medicaid managed care gross revenues are reported as specific line items. Gross charges billed to county general relief and self-pay patients are separately reported. Gross revenues billed to non-Wisconsin Medicaid programs are combined with billings to other government programs such as CHAMPUS and VA and are not included in our measure of low-income patient revenues.
• Medicaid shortfalls. Total Medicaid payments are separately reported for Medicaid fee-for-service and managed care enrollees. No breakdown is provided between inpatient and outpatient services. Disproportionate share payments are not separately identified from other Medicaid patient revenue.
• Uncompensated care and bad debt. Charity care is measured on the basis of revenue foregone at full-established rates. Courtesy allowances and employee discounts are separately reported. Charges written off as bad debts are reported as an expense.

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Variables

Low-Income Patient Utilization

We used the HCUP 100% SID files for calendar year 1997 to construct measures on the percentage of inpatient days and discharges attributable to low-income patients. We were concerned that the claims data might under-report the number of inpatient days and that our measure of Medicaid patients in the CA and New York financial data did not include Medicaid managed care enrollees. To adjust for this in our estimate of low-income utilization, we applied the HCUP percentages to the total inpatient days reported in the financial data.

Medicaid shortfall

To define the Medicaid shortfall, we first estimated Medicaid costs by applying an overall cost-to-charge ratio to Medicaid gross charges. We then estimated the amount received or receivable from the Medicaid program (gross charges less contractual allowances) net of any DSH amounts reported as Medicaid patient revenue (or, in the case of WI, the amount reported by the state for FY1998). By subtracting only the contractual allowances, bad debts attributable to Medicaid patients are included with other bad debts.

Uncompensated care and bad debt

We estimated the costs of uncompensated care and bad debts by applying the hospital's cost-to-charge ratio to the gross charges reported for charity care and the amounts reported as bad debt.

Financial risk

We defined the hospital's financial risk as the sum of its Medicaid (fee-for-service) shortfalls, bad debt, and uncompensated care costs. We measured the hospital's financial risk per adjusted day and as a percentage of operating costs.

Total margin net of "new" DSH

We were interested in estimating what the hospital's margin would have been in the absence of DSH payments. We subtracted actual Medicare DSH payments and, for Wisconsin hospitals, the amounts reported by the state in FY1998 Medicaid DSH payments from net income. For New York and California hospitals, we subtracted the hospital's "new" Medicaid DSH payments, i.e. the difference between the DSH payments it received and its contributions to the DSH fund from net income. We calculated total margins (operating and non-operating) for California and Wisconsin hospitals net of "new" DSH. Our information for New York hospitals was limited to operating margins.

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Methods

Simulations

Drawing from the models discussed in Chapter 6, we simulated alternative policies for determining eligibility to receive DSH funds and how the funds would be allocated to eligible acute care facilities. We performed separate simulations for each state so that only intra-state redistributions occur and there are no inter-state redistributions. Total DSH funding in each alternative is the sum of the estimated Medicare payments using current law rules and the federal share of DSH payments to the hospitals in the simulation. Hospitals that are excluded from the Medicare prospective payment system and any Medicaid DSH funds that they received were not included in the simulation. In addition, there were a number of acute care hospitals that we were unable to match with our analysis file, including the New York Health and Hospitals Corporation facilities and several other large safety net hospitals (Table 8.1).

Our baseline comparison is the distribution of current law Medicare funds and "new" Medicaid DSH funds. Since Medicaid "new" DSH and the federal share of DSH are not the same, total DSH funds to the hospitals changed between the current law baseline simulation using "new" DSH and the alternative policy simulations using the federal share of DSH.

Below, we report on the results for four basic simulations designed to show the implications of some of the policy choices regarding the measure used to define eligible hospitals and alternative allocation policies. The simulation parameters are summarized in Table 8.2 and reflect policies that focus increasingly on hospitals with the most financial risk. Simulation A uses the same allocation policy as we used for the HCUP simulation in Chapter 7. It uses the proportion of non-Medicare inpatient days as the low-income patient measure. Simulation B allocates funds based on gross revenues attributable to patients covered by Medicaid and local indigent care programs, self-pay and charity care patients. It uses a lower threshold because Medicare SSI patients are not included in the measure. Simulation C uses financial risk as a percentage of operating costs as the LIP measure and has a 5% threshold. Simulation D also uses financial risk but uses a sliding scale to target additional funds to hospitals with the highest proportion of financial risk.

Bi-variate Analysis

We used simple correlations to compare the relationship between the allocation factors used in the simulations and a hospital's financial risk. We also looked at other issues, such as relationship between financial risk and total margins and the relationship between utilization measures of care for low-income patients and revenue measures.

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Results

California

Based on the information provided by the California Public Hospital Association, the FFY 1998 DSH payments reported by the state to CMS included both state fiscal year (SFY) 1997 supplemental DSH payments and SFY1998 base and supplemental payments (Table 8.3). The DSH payments were supported by $1.3 billion in intergovernmental transfers, so that the net gain to hospitals was $1.1 billion. The state retained $162.8 million of the funds (the difference between the federal share and the "new" DSH amounts).

Among the acute care hospitals in our analysis file, 82 hospitals received $2.2 billion of the Medicaid DSH payments reported for FFY 1998. Eighteen acute care hospitals that are missing from our database received $125 million in DSH payments; specialty hospitals and institutions for mental disease accounted for the remaining DSH payments ($112 million). The multiple payments within the same FFY affected our analysis results. Depending on the hospital's fiscal year end, it may report SFY1997 payments only or both SFY 1997 and 1998 payments. We did not have the resources to examine how significant a problem this might be. In the aggregate, the hospital-level financial data showed slightly lower total Medicaid DSH payments and IGTs than the state's FY1998 data. The "new" DSH estimate is slightly higher. It appears that most hospitals are reporting multiple SFY payments, which has the effect of portraying the Medicaid DSH payments as being higher than they are on average over several years. If we had constructed our baseline using only the SFY 1998 payments, the federal share of Medicaid DSH payments would have been $1,001.8 million, or about 10 percent lower than the amount used in the baseline for our simulations.

In Table 8.5, we report the results of our analysis of the current distribution of DSH payments across classes of California hospitals. Most hospital classes in the table are self-explanatory. The safety net hospitals are those with at least 20 percent of their inpatient days attributable to low-income patients (Medicare SSI, Medicaid, local indigent care programs, self-pay and charity care days based on our HCUP analysis). Hospitals are classified into groupings based on their margins net of Medicare and ''new" Medicaid DSH funds. Financial risk (Medicaid shortfalls, bad debt and uncompensated care) totaled $2.4 billion, or $134 per adjusted day. The dollar-weighted total margin net of DSH payments was -0.4%.

• Financial risk was concentrated in the large urban hospitals (79%). Rural hospitals bore only 3% of the financial risk.
• County hospitals bore 33% of the financial risk and had a -8.8% total margin net of DSH payments.
• Although the major teaching hospitals (those with 250 or more residents) bore 19% of the financial risk, they also had higher margins net of DSH payments (2.5%). Factors such as the Medicare indirect teaching adjustment and non-operating revenues are likely to be contributing to their relatively higher margins. Hospitals with smaller teaching programs had negative margins.
• On average, hospitals with less than 20 percent of their adjusted patient days or revenues attributable to low-income patients had positive margins while those with a higher proportion of low-income patients had negative margins. However, about a quarter of the safety net hospitals (45 of 198) had margins that were 5% or higher. The two safety net hospitals with margins greater than 25% had negative financial risk: their combined net revenues for Medicaid patients and uncompensated care exceeded the estimated costs of that care by an average of $169 per day.

In total, 269 of the 313 hospitals in the CA analysis file received DSH funds from either Medicare or Medicaid or both programs. The joint new DSH funds (Medicare DSH payments and Medicaid "new" DSH payments) totaled $1.69 billion and resulted in total margins of 6.0%. Overall, the ratio of DSH payments to financial risk was .70.⁽⁵⁾

• The ratio of DSH to financial risk was only .10 for rural hospitals compared to .72 for urban hospitals.
• Facilities owned by county or local government (not including hospital districts) received 39% of the funds. Medicare DSH payments to these facilities had far less impact on their average margin (increasing it from -8.8% to -6.6%) compared to Medicaid DSH payments (which increased margins to 9.0%).
• Facilities owned by hospital districts did not make intergovernmental transfers to fund the Medicaid DSH program. They bore 7% of the financial risk but received only 4% of the new DSH funds, primarily from the Medicare program. Both their ratio of DSH to financial risk (.31) and average margin including DSH payments (3.4%) was considerably lower than other hospitals.
• For the most part, DSH payments appear to be targeted toward safety net hospitals. Safety net hospitals (those with low-income patient ratios of 20% or higher receive 96% of DSH funds. In general, hospitals with a relatively high percentage of low-income adjusted patient days or gross revenues have higher ratios of DSH to financial risk. Than hospitals that have proportionately fewer low-income patients.
• There is some evidence, however, that improvements could be made in how the funds are targeted toward those safety net hospitals that are most financially vulnerable. Three percent of DSH funds are going to hospitals that do not incur financial risk and 16% are received by safety net hospitals with total margins net of DSH that are 5% or higher.

Table 8.6 summarizes the results of selected simulations of alternative DSH allocation policies. More detailed results for these simulations are reported in Appendix D. The allocation formulae used in the simulations are:

• Simulation A: (% Non-Medicare LIP days -15%) x adjusted days x WI
• Simulation B: (% Non-Medicare LIP GR-15%) x adjusted days x WI
• Simulation C: (FR as % operating cost - 5%) x adjusted days x WI
• Simulation D: (FR as % operating cost - 5%) x adjusted days x WI x scaling factor

The simulations use Medicare payments and the federal share of DSH as the baseline. Since the state retained some DSH funds, the DSH baseline for the simulations is higher ($1.88 billion) and, as a result, overall margins are higher in the simulations than in Table 8.4 (6.7% vs. 6.0%). Simulation A concentrates DSH payments on hospitals with 15 percent of their inpatient days attributable to low-income patients (The measure is derived from HCUP data and includes inpatient days attributable to Medicaid, local government indigent care programs, and self-pay patients. It excludes Medicare SSI days and therefore is not the same measure as the one used to establish the hospital classes by proportion of low-income inpatient days). Hospitals with more than 40% low-income patient days would on average receive payments in excess of their financial risk. Six hospitals with no financial risk would receive 2 percent of DSH funds and 35 safety net hospitals with margins greater than 5% would receive 24 percent of DSH funds (Table D.1A in Appendix D).

Simulation B uses information from the financial data on gross patient revenues as the measure of low-income patient utilization and allocates funds to hospitals with gross revenues (inpatient and outpatient) of 15% or higher that are attributable to Medicaid, other government programs for low-income patients, bad debt and uncompensated care. Revenues attributable to Medicare SSI patients are not included in this low-utilization measure. Relative to Simulation A

hospitals that provide a relatively high volume of outpatient services to low-income patients are advantaged. While this is a desirable objective, it is not clear that the result is an improvement over Simulation A. For example, hospitals with no financial risk receive higher DSH payments under Simulation B. The loss of funds by safety net hospitals ($121 million) is a re-distribution between hospitals that have a high proportion of low-income inpatient days and hospitals that have a high percentage of gross revenues attributable to low-income patients and highlights the potential importance of deciding how to define safety net hospitals.

Simulations C and D allocate funds to hospitals with ratios of financial risk to operating expenses above .05. Simulation C increases the margins of hospitals with relatively high financial risk and improves the safety net hospital margins. Hospitals with 0-5% financial risk have the lowest margins. This raises an issue regarding whether a threshold should be used in the allocation policy. Simulation D uses a sliding scale in the allocation formula so that hospitals with higher financial risk receive a relatively greater proportion of funds. The formula that was used in the simulation shifts most funds to hospitals with financial risk ratios above .25. Since on average hospitals in this category already have relatively high margins and the overall ratio of DSH to financial risk is high, this particular sliding scale formula provides greater than 1:1 DSH to financial risk coverage for hospitals with the highest financial risk. This type of coverage may be needed to cover operating losses. Across the classes of safety net hospitals, the allocation improves the margins of financially vulnerable safety net hospitals; however, hospitals with total margins net of DSH in excess of 5% receive 15% of the DSH funds. The results suggest that an allocation policy that takes into account both financial risk and financial viability should be explored, e.g. an allocation based on financial risk capped at an amount that would not increase a hospital's margin above 5%-7%. Hospitals that are otherwise able to cover their financial risk through third-party revenues and other revenues would receive little or no funding.

New York

Based on data provided by the Greater New York Hospital Association, New York hospitals received $1.3 billion in Medicaid DSH payments during 1998 (Table 8.7). This included the indigent pool distributions as well as special payments for financially distressed hospitals and public hospitals. The hospitals in our analysis file account for only 50 percent of this amount, or $676.6 million. We were unable to match 22 acute care hospitals in the New York financial database (including the New York Health and Hospitals Corporation hospitals) that received $663.0 million in total Medicaid DSH funds and $630.1 million in "new" Medicaid DSH funds to the hospitals in our analysis file.⁽⁶⁾ As a result, our analysis file under-represents public hospitals and understates both total DSH and "new" DSH payments. In addition, 14 specialty hospitals received $10 million in total DSH funds and $6 million in "new" Medicaid DSH funds. In calculating the "new" DSH funds, we did not include payer contributions that the hospitals collected and passed through to the indigent care pool.

The 188 New York hospitals in the analysis file incurred financial risk (Medicaid shortfalls, bad debt and uncompensated care) of $1.8 billion, or $80 per day (Table 8.8). The hospitals had operating margins net of "new" DSH payments of -12.4% and operating margins with DSH of -6.8%. For comparison, the operating margins of the acute care hospitals that are missing from the analysis file were -24.4% and -6.8 percent without and with DSH, respectively. A higher proportion of the total DSH payments made to the missing hospitals is "new" DSH funds.⁽⁷⁾ Most hospitals in the analysis file (174 out of 188) received DSH payments from either Medicare or Medicaid or both programs.

• Ninety-five percent of DSH funds went to safety-net hospitals (none of which had operating margins exceeding 5%).
• The NY hospitals with large teaching programs had lower operating margins on average than other hospitals in the state (-8.9% with DSH).
• As was the case with CA hospitals, Medicaid DSH funds are more concentrated on hospitals that serve a high proportion of low-income patients.
• In general, margins decline as the proportion of care furnished to low-income patients increases. However, NY hospitals with fewer than 10 percent low-income patients had slightly below-average margins after DSH payments are taken into account (- 7.2%); in comparison, hospitals with less than 10% of their revenues attributable to low-income patient had operating margins of -3.6 %. Hospitals with more than 50% of their patient population attributable to low-income patients bore 19% of the financial risk and received 29% of DSH payments.
• Given overall negative margins, the question raised by the CA results concerning the appropriateness of making DSH payments to hospitals that are financially viable is less important. Nevertheless, 4% of DSH funds went to hospitals that did not incur financial risk. Across the safety net hospital classes, there is no clear pattern in the ratio of DSH payments to financial risk but there are relatively greater gains by hospitals with the lowest operating margins net of DSH payments.

In New York, total Medicaid distributions to all hospitals and to the hospitals in our analysis file are greater than the federal share of DSH payments. Accordingly, DSH funds for the simulations are about 20 percent lower than the joint Medicare and "new" Medicaid funds under current law ($830 million vs. 1.0 million) and result in lower overall margins (-7.8% vs. -6.8%). The results of the simulations are summarized in Table 8.9. More detailed simulation results are in Appendix D, Tables A8.9A-D.

Simulation A allocates DSH payments to all hospitals with 15% or more Medicaid and self-pay patients and concentrates funding on a smaller set of hospitals (100 versus 174 under current law. The 5 hospitals with 60 percent or more low-income patient days benefit at the expense of the other hospitals. Two of the hospitals have little or no financial risk and account for the high DSH to financial risk ratio and high positive operating margins for the group. Generally, the DSH to financial risk ratio declines as the percentage of low-income utilization or revenue declines. However, hospitals with 50-60% low-income days receive less coverage for their financial risk than hospitals with 40-50% low-income days and, consistent with the California results, 7 hospitals with no financial risk would receive 6 percent of DSH funds.

Simulation B allocates DSH payments using gross revenues attributable to Medicaid, charity care and bad debt. Compared to Simulation A, the DSH to financial risk ratio is somewhat improved across the low-income patient utilization and revenue groups. About $52 million would be re-distributed between hospitals with a relatively high percentage of low-income inpatient days and those with a relatively high percentage of gross revenues attributable to low-income patients (Table A8.9B in Appendix D).

Simulations C and D allocate funds to hospitals with ratios of financial risk to operating expenses above .05. Both simulations improve the operating margins for hospitals with highest financial risk compared to current law policies. There is also a slight improvement for the safety net hospitals with the lowest operating margins net of DSH (less than -25%) compared to current policies after accounting for the overall lower margins in the simulations. Only one hospital in the analysis file benefits from the sliding scale formula used in Simulation D. The results could be quite different if all New York hospitals were included in the analysis. Overall, the ratio of financial risk to operating expenses for the missing acute care hospitals is .15 compared to .08 for the 188 hospitals in the analysis file.

Wisconsin

Wisconsin's small Medicaid DSH program benefited 10 Wisconsin general acute care hospitals in FY1998 with one hospital receiving over 80 percent of the funds going to acute care hospitals. The remainder of the funds went to IMDs, children's and other hospitals and two out-of-state hospitals. Medicaid contributed only 17 percent of the total $39.7 million in the FY1998 DSH funds paid to the acute care hospitals (Table 8.10).

The Medicare program paid DSH to 52 hospitals and accounted for 83% of the funds ($33.1 million) (Table 8.11). Taken together, 54 acute care hospitals in our analysis file received DSH payments under current policies. The financial risk associated with serving low-income patients across Wisconsin hospitals is relatively low ($78 per adjusted day) and is shared across all hospitals so that three hospitals bore financial risk that was more than 10 percent of their operating costs. Only 19 Wisconsin hospitals qualify as safety net hospitals using the definition of 20 percent or more of inpatient days attributable to Medicare SSI, Medicaid, local indigent care programs, self-pay and charity care.

A relatively small number of hospitals would receive DSH funds using alternative allocation policies based on low-income utilization or gross revenues. For example, 13 hospitals would receive DSH if a low-income utilization measure that does not include Medicare SSI beneficiaries were used (Simulation A in Table 8.12). The three hospitals with the highest percentage of low-income patients would receive substantially more DSH funds than their financial risk and their total margins would be substantially above the average for Wisconsin hospitals. In comparison, Simulation C (based on financial risk) would result in DSH payments to 46 hospitals, including 16 of the 19 safety net hospitals.

Having few hospitals with relatively high low-income patient loads and/or financial risk makes it difficult to draw any conclusions about the overall effect the alternative allocation policies might have on hospital classes. However, the results in the hospital classes with only one or two hospitals illustrate that the choice of allocation measure can have substantial impact on individual hospitals. For example, the one hospital with 30-40% low-income patient gross revenues would have a DSH to financial risk ratio of .33 under Simulation A, .89 under simulation B, .52 under Simulation C, and .49 under Simulation D. In dollars, the hospital would receive:

• Current law: $588,260
• Simulation A: $2,316,574
• Simulation B: $6,282,418
• Simulation C: $3,657,925
• Simulation D: $3,454,894

(See Appendix D Table D.3A-D.)

The results for this hospital also illustrates that the indirect measures of serving low-income patients instead of direct measures (e.g., percentage of gross revenues attributable to low-income patients x adjusted inpatient days x wage index instead of gross revenues attributable to low-income patients) have implications for how the funds are distributed. The hospital's relatively low costs help explain the relatively high DSH to financial risk ratio in Simulations C and D (.52 and .49, respectively). If the direct measurement were used instead, the hospital would have received about 50% less in DSH funds and would have had a DSH to financial risk ratio of .25 in Simulation C instead of .52.

Correlation Analysis

We examined the relationship between selected measures of serving low-income patients using 614 hospitals in the three-state analysis file (Table 8.13). We found that the correlation between the ratio of financial risk to operating costs and financial risk per day was .675. It suggests that using an allocation policy using the ratio of financial risk to operating costs could result in a considerably different DSH distribution than a policy that uses financial risk per day or another direct measure of the hospital's financial risk in the allocation policy. The correlation between financial risk as a percent of operating expenses and the percentage of low-income revenues (Medicare SSI, Medicaid, local indigent care programs, uncompensated care and bad debt) was .591. The correlation between the financial risk ratio and other measures of low-income patient services were slightly lower. The percentage of revenues was slightly more correlated with the financial risk ratio than the percentage of inpatient days and the measures that included Medicare SSI patients were also slightly more correlated than the measures that excluded these patients. Between the low-income patient utilization and revenue measures, the correlation was moderately high. For example, the correlation between % low-income days and % low-income revenues was .811 for all patients and .803 for non-Medicare patients only.

Next, we examined the relationship between the percentage of self-pay and no-charge patients and the percentage of operating expenses attributable to uncompensated care and bad debt. The issue is whether a hospital's proportion of self-pay and no-charge patients using claims data is a good proxy for its uncompensated care and bad debt costs. We found that the two measures were poorly correlated (.106). Further investigation is needed to understand why a correlation was not found.

Finally, we examined for safety net hospitals the relationship between DSH allocations, financial risk and the hospital's net income (Table 8.14). We expected to find a negative correlation between the hospital's net income and its financial risk; that is, hospitals with high financial risk have more difficulty generating revenues to cover their expenses. The correlation was moderate (-0.57).

• The correlation between joint DSH funds under current policies and financial risk was .74. Interestingly, the correlation between the joint Medicare and Medicaid current policy DSH funds and the two financial risk measures was higher than either the new Medicaid DSH or Medicare DSH policies separately. The correlation between financial risk and Simulations A and B was similar to the joint distribution.
• The correlation between joint DSH funds and net income is -0.52. The correlation was the same for Medicaid "new" DSH and lower for Medicare DSH (-.41). None of the DSH alternatives are as strongly correlated with net income as joint DSH funds. The highest correlations are with Simulations C and D, which use financial risk as an allocation statistic.
• There is a strong correlation between Simulation C and financial risk (.882). This means that the use of the indirect measure of financial risk (ratio of financial risk to operating expenses X wage index X adjusted patient days) works fairly well for safety net hospitals.
• The correlations between the allocation factors used in Simulations A (utilization) and B (revenues) is very high (.96).

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Conclusions

The California and New York results illustrate the importance of having information on actual IGTs in examining issues related to the current distribution of Medicaid DSH funds. Across-the-board assumptions such as those made Chapter 4 regarding the proportion of DSH funds that are "new" are not substantiated at the hospital-level. CMS should consider expanding the state DSH reports to obtain information on provider contributions to DSH pools as well as the payments from those pools to individual hospitals.

The redistributions that took place between Simulation A and Simulation B highlight the differences between allocations based solely on inpatient care and allocations that take both inpatient and outpatient care into account. While including all care is commonly endorsed as a policy objective, it is not clear from the correlation results that including all care improves the targeting of DSH funds to financially vulnerable safety net hospitals. Also, the inclusion of outpatient care raises issues regarding subsidies to other ambulatory care providers. The role that non-hospital ambulatory care providers play in the safety net for low-income populations is discussed in Appendix E. A policy that concentrates federal support for uncompensated care solely on hospitals may serve to discourage community providers from furnishing substantial amounts of care to indigent populations. It may also have implications for the relative generosity of Medicaid payments for services provided in hospital outpatient departments and clinics and in physician offices.

Allocations based on the proportion of care provided to low-income patients (e.g. revenues) result in very different distributions than an allocation based on financial risk (Medicaid shortfalls, uncompensated care and bad debt). Financial risk, however, is not the same as financial viability (i.e., total margins net of DSH payments). Some of the hospitals with substantial financial risk also have positive margins. The results across all three states highlight the need to clarify the policy goals for DSH funding. The key issue is the extent to which subsidies should be given to hospitals that serve low-income patients but do not incur financial risk or are able to cover their risk with other revenues. A closer examination of the hospitals with substantial gains or losses in moving from an allocation policy based on serving low-income patients to incurring financial risk might help clarify the issues. This examination should consider the role of other federal subsidies such as the Medicare indirect teaching adjustment in explaining why some hospitals with substantial financial risk appear to be in a strong financial position.

Neither the current DSH allocation policies nor the alternative policies examined in the simulations are strongly correlated with a hospital's net income. The factors affecting financial viability may be too complex to be measured with a single statistic. A multi-variate analysis of the factors affecting a hospital's financial risk and it overall financial status using a broader set of hospitals could also help identify additional factors that should be considered in an allocation policy.

Looking in-depth at the relationship between the financial status of hospitals and the distribution of DSH payments was a complex task. The differences in state accounting and reporting practices made it difficult to determine Medicaid shortfalls and to take "new" DSH payments into account. Knowing the DSH payments to individual hospitals is not enough; it is also important to understand how those payments are handled in reporting Medicaid contractual allowances and patient revenues. It is also important to understand how financing occurs for county-owned hospitals in terms of other intergovernmental transfers and deficit funding. An allocation based on financial measures would require uniform reporting by payer. All three states in this analysis require the type of financial information that would be needed.

The "snapshot" approach of looking at one year's data may not be sufficient for an adequate understanding of the financial implications of serving low-income patients. In California, the FY1998 payments included payments from the state's fiscal year 1997 and thus overstated the average DSH payments. The New York indigent care pool was in transition during FY1998 and additional changes were enacted in 2000. Wisconsin's uncompensated care costs have increased 60 percent since 1997. Only the first-year impacts of the Balanced Budget Amendment are reflected in the FY1998 data. These considerations suggest that a multi-year study — perhaps with periodic updating — would be appropriate.

Even more troubling than using one year's data is the lack of a national database that provides uniform information on the quantity of care provided to low-income patients and the financial risk associated with that care. Each state's Medicaid DSH program is idiosyncratic. A close examination of DSH policies in a few states highlights potential issues but a national database is needed to understand the potential re-distributions that might occur both at the market level and across states under a national allocation policy. The BBRA provision requiring the Secretary to collect through the Medicare cost report data on uncompensated costs should help. This provision is effective for cost reporting periods beginning on or after October 1, 2001.

Endnotes

1.  A surcharge is also used to fund graduate medical education.

2.  The Health Care Reform Act of 2000 provided that tobacco settlement funds be used to fully fund the indigent care pool and added supplemental adjustments for rural and high need non-public hospitals ($82 million) and for indigent care at teaching hospitals ($27 million).

3.  The 3% minimum is increased to 11% for qualifying IMDs with an average length of stay that exceeds 60 days for Wisconsin Medicaid recipients.

4.  The formula is:

• If FR GE .05 and LT .10, scaling factor = (FR-.05)*.2
• If FR GE .10 and LT .15, scaling factor = (.01 + (FR-.10)*.3
• IF FR GE .15 and LT .20, scaling factor = (.025 + (FR-.15)*.4
• IF FR GE .20, scaling factor = (.045 + (FR-.20)*.5

5.  We were unable to match 13 hospitals in our analysis file with CA financial data. We eliminated the hospitals so that we would have a matched set of hospitals across the simulations.

6.  The starting point for our analysis file for this project was hospitals that were on HCRIS. The New York HHC facilities file manual cost reports and are not included on HCRIS.

7.  We note that direct comparisons should not be made between the CA and NY hospitals both because of the missing hospitals and the use of operating margins in NY and total margins in CA.

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