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Tools for Monitoring the Health Care Safety Net

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Assessing Safety Net Provider Financial Health

A Simple Measurement Tool

By Timothy L. Clouse, M.A.; Gilbert Silva III, M.S.; Robert Gilliam, M.B.A., C.M.A.


Contents

Introduction
Measure Selection Process
Measures Selected
Developing a Scoring Method to Quantify the Results
The Tool in Action—Finding and Fixing Providers in Trouble
Application and Modification of the Model
Financial Risk Assessment Worksheet and Required Data
Required Data from Audited Financial Statement Accounts and Their Sources
Glossary of Financial Terms
Appendixes
Acknowledgments

Introduction

The health of a local health care safety net depends on the financial health of its providers. However, determining their financial health is difficult, as there are few standards and the ones that do exist are not valid for outpatient safety net providers. To meet this need, we developed a tool that uses a few common financial measures to identify providers at risk of developing financial problems. The tool, and the methods used to develop it, are easily adapted for use by other outpatient safety net providers. The tool and methods:

  • Use readily available data.
  • Use simple statistics.
  • Include explicit professional judgments.
  • Allow for modifications based on specific situations
  • Are easy to explain.

This tool is designed to measure the financial status of outpatient safety net providers and compares an individual provider's financial situation with that of a representative sample of similar outpatient safety net providers and rates its performance relative to them. Many outpatient safety net providers can use this tool to assess their risks and guide them in finding remedies.

We designed the tool to meet several goals described as follows:

  • Reliability goals.
    • The results should be objective.
    • Outpatient safety net providers in similar situations should have similar risks. For example, it should be possible to compare outpatient safety net providers in small, rural communities that are highly dependent on Medicaid with other similar providers.
    • The tool's estimation of risk should match other risk estimates. For example, if the tool shows that an outpatient safety net provider does not have a financial risk, then an objective reviewer should arrive at the same conclusion.
  • Data access goals.
    • The data needed should be easy to obtain.
    • The number of data elements needed should be small.
  • Acceptability goals.
    • The measures used should be generally accepted as reliable by financial analysts, program monitoring officials, and outpatient safety net provider organization managers.
    • The methods and results should be easy to explain and interpret.

The tool was developed by:

  • Selecting a representative random sample of financial data from outpatient safety net providers.
  • Analyzing the data to find the financial indicators that measured the greatest number of distinct aspects of financial viability.
  • Determining the relative importance of the financial indicators.
  • Setting benchmarks for appropriate performance.

We validated our approach and methods by reviewing them with various financial experts and by having four graduate finance students independently determine the risk level for the outpatient safety net providers in our sample. This chapter describes the development of the model and provides examples of its application. An electronic worksheet for applying the model is available at http://www.ahrq.gov/data/safetynet/tool.asp.

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Measure Selection Process

Initially, we considered 23 financial ratios and other measures for analysis (Appendix 1). We selected them because they are universally recognized by the financial industry as good measures of liquidity, profitability, equity, and business activity. However, several of the measures used similar data in similar ways. Using all of them would have had the effect of overemphasizing some types of financial data at the expense of others.

To determine which variables had the potential to duplicate others, we performed a bivariate correlation on a sample of 153 outpatient safety net providers in the southeast. Several measures were highly correlated (p<0.01), indicating that we could select some measures from the set without sacrificing too much sensitivity.

Variables that had a number of statistically significant correlations with others were the primary candidates for elimination. For example, the cash ratio ((cash + cash equivalents)/current liabilities) is very similar to the current ratio (current assets/current liabilities). While both are measures of a provider's liquidity and the ability to pay current bills, they are highly correlated (a change in one is related to a change in the other), so using both measures in a scoring formula would count the ability to pay current bills twice and would tend to exaggerate the importance of this measure. We retained the current ratio.

After we decided which measures to include, we assigned weights to the measures based on our judgments in consultation with the staff who would be using the model. The weights reflect our professional opinion based on the importance of that measure to financial stability and program requirements. We confirmed the relative importance of these measures in the course of discussions with other financial experts and other providers, academics, and practitioners in the field. We developed the weights iteratively. First, for each measure, we decided whether it was important, of some importance, or of little importance. Once the measures were put into these categories, we repeated the process within each category.

To ensure no bias favoring any particular measure, we deliberately did not compute frequency distributions for these measures within our sample. Thus, we avoided any tendency to prefer measures that would reflect either favorably or unfavorably on the financial condition of our outpatient safety net providers.

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Measures Selected

Using financial ratios as part of an early-warning risk assessment establishes the relationship of one measure to another and expresses those relationships in a single score. This is an excellent management tool because it allows a reviewer to generate ratings on financial strengths or weaknesses quickly, without having to review and analyze each individual number in audited financial statements. However, the value of the data and measures increases considerably when compared against known target values. As a result, we used data from our sample of providers to establish benchmarks for the model.

The tool emphasizes liquidity because it is one of the most critical issues facing outpatient safety net providers. Many outpatient safety net providers have small cash reserves and may need 60 days or longer to collect payments. Poor liquidity leads to premature depletion of available cash and other liquid resources—including Federal, State, and local funding resources—faster than the provider can replenish those resources. This results in a cash flow crisis that will eventually threaten solvency and current operations. For example, inadequate liquid resources can damage an outpatient safety net provider's ability to purchase supplies and meet payroll, which can directly affect the quality of care provided.

Once a liquidity crisis develops, extensive resources are needed to correct the financial deficiencies. This has a direct impact on current services, expanding services, and on the provider's ability to meet community health care needs. Liquidity problems may also make it harder to get loans and other sources of capital to replace depreciating assets or to expand services. On the other hand, if liquidity problems are caught early, these resources can be put directly into program operations.

The financial measurement tool can find current liquidity crises and can find developing liquidity problems early enough that corrective action may be possible before the issues threaten operations and require a major commitment of resources to correct the deficiencies.

Although many different types of financial measures exist, we selected the measures listed in Table 1 because we value their ability to describe different aspects of the provider's financial health.

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Developing a Scoring Method to Quantify the Results

We set benchmarks for each measure assuming that good financial performance means that an outpatient safety net provider is doing at least as well as the top 40 percent of all providers. Benchmarks were determined using data from 153 safety net providers in the Southeast.

Point scores for measures are calculated by taking the ratio of the outpatient safety net provider's measure to the benchmark and multiplying that by the weight: for example, if the benchmark was three, the weight was seven, and the outpatient safety net provider's ratio was 1.5, the score would be ((1.5/3)*7), or 3.5 points. Resulting scores ranged from zero to 15 points, with the total for all measures equaling 100 points. If an outpatient safety net provider's score exceeded the benchmark, it received only the maximum number of points for that measure. Benchmarks and scores are shown in Appendix 2.

If data are missing, we recommend assigning the outpatient safety net provider full points. However, the reviewer, who can override this to change the risk level, should be alert for consistent patterns of missing data. We decided to default to full points because doing so allows us to give the provider credit, but still should flag it as having a possible risk. In addition, by using these benchmarks, missing values are replaced with ones fairly close to the median value.

The model is only a tool. It cannot replace a reviewer's professional judgment of an outpatient safety net provider's financial viability. While it does a good job of screening outpatient safety net providers based on the data available, reviewers may have more information that will alter the characterization of the provider's risk level.

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The Tool in Action—Finding and Fixing Providers in Trouble

While using the tool, we have identified some outpatient safety net providers with significant financial discrepancies and others where financial imbalances were just beginning to develop and we could help in time to avoid difficulty.

Example 1. Developing a Recovery Plan

The financial risk tool was very useful for an outpatient safety net provider in the rural South. Few service providers in the area serve people who have no or inadequate health insurance. The provider's main sources of funding are grants, patient fees, Medicaid/Medicare reimbursement, commercial insurance, county funds, charitable contributions, bequests, and other allocations.

In calendar year 2000, the provider had an operating deficit of over $200,000. By the time the audit was completed in early 2001, the deficit was over $300,000. The deficit was caused mainly by increasing personnel costs, decreasing revenues, inadequate physician productivity, increasing delinquent accounts, and increasing use of short-term loans. The gap between revenues and expenditures threatened the provider's viability.

The financial tool identified the provider as having a high risk in 2000, with the overall score from the tool having declined from 61.65 in 1999 to 48.80 in 2000. In retrospect, all of the high-risk clues were present as early as 1998, but early analysis included only a few measures, which alone did not reveal the magnitude or the type of problem. It was only after analyzing the case by using the financial tool described here that the degree of the financial imbalance became apparent, and the areas contributing to the imbalance were identified. The ability of the tool to predict the seriousness of the imbalances contributed to the development of a targeted financial recovery plan for 2001.

The recovery plan resulted in significant improvements in accounts receivables management and in cash flow, and the provider is now credentialed to bill three additional insurance carriers, which has boosted revenues. Monitoring monthly expenditures has allowed the board of directors to adjust budget outlays and effectively manage operations.

Example 2. Early Warning of an Inadequate Fee Structure

In another case, an outpatient safety net provider located in the rural South had an operating loss in its 2002 fiscal year. The financial tool identified the provider as a medium-risk for the year ending 2002. The previous 3 years showed low risk; however, the score leading to the risk level assessment declined an average of 7 to 10 percent in each of the 4 years.

While the provider apparently had a very strong and stable financial program, the tool indicated that problems might lie ahead. They had better-than-average liquidity and good cash flow from operations in almost all years reviewed. Debts and other expenses were paid in a timely manner. Although there were some fluctuations, none of the major financial indicators followed this trend of decline. However, a closer review of the net patient revenue to expense ratio and the gross charges to expenses ratio showed trends similar to the declining score and increased risk.

Managers indicated that they had not increased charges for several years, and that to keep up with expenses, they were going to present a plan to the board of directors requesting changes in overall charges, including an increase in patient co-pay from $5 to $10. Later, managers from the provider indicated that the board accepted the proposal and that the results of the analysis from the financial tool were helpful in supporting the need for increased charges.

In this case, the tool's ability to identify potential imbalances early contributed to the improvements in financial management and collections before the provider was threatened with more serious financial degradation.

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Application and Modification of the Model

The tool is applicable to many types of outpatient care settings. All of the measures in the tool are generally accepted as good indicators of financial performance. In particular, the model should be useful for many types of safety net providers, as long as appropriate adjustments are made.

Direct use of the tool is simple: all of the information required to use it is readily available from audited financial statements or from other financial data systems. To use the tool in this manner, take the provider's financial data and use the model to generate a score. The next section of this paper provides a copy of the financial risk worksheet and instructions for completing it; an electronic version that will perform the calculation automatically is available at http://www.ahrq.gov/data/safetynet/tool.asp. The result will show the provider's financial risk relative to our sample of 153 providers.

The tool can be modified to more closely meet local needs. The extent of the modification depends on the data and resources available and can include:

  • Using locally available data and following the methodology.
  • Changing the weights (importance) of the measures.
  • Changing the level of acceptable performance.
  • Changing the levels of concern so that more (or fewer) providers fall within each risk category.

Using Locally Available Data and Following the Methodology

This is the most elaborate and time-consuming alternative for modifying the tool. However, it may yield the most useful results. This involves using financial data from local providers to create a local, specific measure for the community. Duplicating the method at this stage may require a large amount of data to yield acceptable precision. If the degree of variation in the population is unknown, but the extreme values are (approximately) known, the method shown in Appendix 3 can be used to obtain the desired level of precision. Note that increased levels of precision (smaller confidence intervals) require considerably larger samples.

Changing the Weights (Importance) of the Measures

Weights for each measure reflect our professional judgment about their relative importance. However, importance may vary by the situation and weights may be adjusted to reflect that. To adjust the weights, we suggest using the following procedure (an example is included in Appendix 4):

  • Rank order the measures from most important (failure to meet the benchmark indicates a major problem) to least important (failure to meet the benchmark is unfortunate, but does not mean a financial crisis is imminent).
  • Examine the list and compare each measure with its nearest-ranked neighbors. If it is just as important as the one above it, increase its score so that it is equal to its neighbor. Similarly, if it is not more important than its next lowest neighbor, lower the score. Continue doing this until the relative placement and score of each measure is reasonable. If some measures (or groups of measures) are much more important or less important than that of their neighbors, add or subtract points from them to reflect that and add or subtract the same number of points from more or less important measures to retain the relative ranks.
  • Find the sum of the revised weights, divide each measure's weight by that sum and multiply by 100 ((individual weight/total of weights)*100) to get the points per measure.

Changing the Level of Acceptable Performance

Our benchmarks for acceptable financial performance are arbitrary. The top 40 percent is reasonable, but other benchmarks are reasonable as well, depending on the situation. Percentiles allow for ranking by relative performance within a peer group. Furthermore, percentiles are insensitive to extreme values (unusually high or low values will not distort the results very much) and the results will not be biased toward either end of the range.

Changing the Levels of Concern so that More (or Fewer) Providers Fall Within Each Risk Category

The risk categories are not rigid and can be adjusted to fit the circumstances. We designed our categories to find a reasonable number of at risk outpatient safety net providers, but "reasonable" partly depends on the resources available to do reviews. Our scoring categories put about ten percent of the outpatient safety net providers in the high-risk category, but the definition of "high risk" can be adjusted as needed. For example, it is possible to use the scores without the risk categories and simply devote the most attention to the providers with the lowest (worst) scores and work up the list from there. Using this method is a good way to establish priorities and balance workloads between reviewers.

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Financial Risk Assessment Worksheet and Required Data

Instructions: An outpatient safety net provider can use this worksheet to evaluate its financial risk relative to our sample of outpatient safety net providers. To complete it, acquire all the financial data shown in Table 1 and Table 2. Fill in each box with the information requested and do the calculations. If information is missing, either use an informed judgment as to the actual value or assume that the benchmark is met (i.e., the provider gets full points for that measure). If the resulting calculation yields more points than maximum in that category, only the maximum points are given. For an electronic version of the worksheet that automatically completes the calculations, go to http://www.ahrq.gov/data/safetynet/tool.asp.

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Required Data from Audited Financial Statement Accounts and Their Sources

Table 2 lists the data needed to compute measures in each part of the tool. The measure is shown in the left-hand column, the particular item needed is in the center column, and the item's location is in the right-hand column.

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Glossary of Financial Terms

Accounts Payable: Liabilities for operations that must be paid within one year/reporting cycle of the financial statements. This does not include wages, payroll, or related liabilities.

Allowance for Doubtful Accounts: The amount of gross accounts receivable estimated to be uncollectible.

Bad Debt Expense: Amount of patient's accounts receivable written off as an expense during the reporting period.

Benefits:1 Expenses for employee benefits paid during the reporting period.

Cash: Cash balance not reserved for specific use.

Cash Equivalents: Short-term, liquid investments that are readily convertible to cash or are so near their maturity that they present insignificant risk of changes in value because of changes in interest rates. This will also include cash reserved for specific purposes.

Cash Flow from Financing:1 Change in cash balance caused by cash received for long-term loans or notes. Payments for such debts represent cash outflow from financing activities.

Cash Flow from Investments: Change in cash balance caused by purchase or sale of long-term assets or other capital investments.

Cash Flow from Operations: Change in the cash balance caused by operational cash inflows and outflows. Includes changes in payables and receivables as well as non-cash expenses such as depreciation. Computation usually begins with the net income/change in net assets then adjusts for other operational cash inflows and outflows.

Change in Net Assets: Includes net income/loss and all other adjustments shown to the Net Assets. If a total change is not shown, compute by the difference between net assets at the beginning of the period with net assets at the end of the period.

Charitable Contributions: All income described as charitable income or "donated" supplies and facilities.

Current Assets: Sum of all assets that are expected to be convertible to cash within one year/reporting cycle of the financial statement date.

Current Liabilities: Short-term debts that are to be paid off within 1 year/reporting cycle of the financial statement date.

Depreciation (Accumulated): Total decrease in the book value of long-term assets based on age or use since the asset was first recorded.

Depreciation Expense: Depreciation and amortization of long-term assets expensed in the current reporting period.

Fund Balance (Equity): Net value of the entity as a whole. Equity is equal to Total Assets minus Total Liabilities.

Interest Expense:1 Interest paid for loans and notes shown on the schedule of functional expenses during the reporting period.

Investment Income: Non-operating income from investments. This balance includes interest income and other income from investments.

Long-term Assets: Assets that will provide economic benefit for greater than one year/reporting cycle of the financial statement date. This balance is reported net of any accumulated depreciation for depreciable assets. It typically includes Property, Plant and Equipment. However, for system entry purposes, it also includes any other assets not classified specifically as Current Assets.

Long-term Liabilities: Liabilities or debts that the entity is not likely to pay off within one year/reporting cycle of the financial statement date.

Net Accounts Receivable: This includes only receivables for patient charges. It does not include receivables from grants or other revenue sources. This balance is reported net of any allowances.

Net Patient Revenue: Revenues for services provided that the entity has received or expects to receive. The balance is based on gross charges to patients net of applicable adjustments.

Payroll Taxes:1 Payroll taxes paid during the reporting period.

Restricted Fund Balance: Amount of the total equity/fund balance reserved for specific uses.

Salaries:1 Expenses for salaries paid during the reporting period. This expense line in the CPA report often includes payroll taxes and sometimes it includes benefits.

Sliding Fee Adjustment: Adjustment to gross patient revenues for sliding fees and other deductions other than bad debt expense. Some CPA reports list this as charity care or contractual adjustments in the notes. Charity care adjustments in the footnotes to the financial statements must indicate that the adjustment is a requirement.

Temporary Investments: Non-cash investments with a maturity date within one year/reporting cycle of the financial statement date.

Total Expenses: Total of all expenses, operational and non-operational.

Total Revenues and Support: Sum of operating income, non-operating income, and other support. This includes release of restricted assets.


1 Note that specific items may not be listed on the income statement. However, the audited financial statements will still have a functional classification of expenses in another section.


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Acknowledgments

We wish to acknowledge the assistance of a number of people who helped us in devising this model. In particular, the validity of the model was independently verified by:

  • Patrick LaRose (Georgia State University).
  • Nzula Kieti (Georgia State University).
  • Chantele Bell (Clark Atlanta University).
  • Choka Murugappan (Emory University).
  • Sherman Lee, M.S.B.A. (HRSA San Francisco Regional Division).
  • Rosalba Mangual, B.S. (HRSA Atlanta Regional Division).

Current as of September 2003

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