An hfm Web Exclusive

Doug Stark, David Mould, and Alec Schweikert

A multihospital system is having difficulty reaching the charity portion of its community benefit goals each year. The system's goal is $2 million, but the past three years it has been averaging only $1.2 million, resulting in unnecessary end-of-year lump sum payoffs to local charities.

Key stakeholders, community benefit leaders, and C-suite executives want to realign targets for the upcoming year that are more realistic, but are hesitant to rely on simply using the $1.2 million average as a goal for the upcoming year. They fear an oversimplified judgment could result in unreasonably low targets, causing increased bad debt write-offs and lower community philanthropy. On the other hand, if the target is too high, they risk putting their 501(c)(3) status in jeopardy and end up at square one, making a last-minute costly contribution to reach goals.

They decide to use advanced forecasting methods to determine the upcoming year's charity write-offs to make their decision.

First they identify key drivers:

  • Internal
    • Financial counselor resources
    •  Staff turnover
    •  Internal charity policy change
    • New consumer-directed healthcare product
    •  Self-pay volume (accounts and dollars)
     
  •  Exogenous
    •  Local population growth
    •  Employment rates
    •  Poverty levels
    •  New regulations concerning charity care
     

Next they acquire the appropriate data using internal, public, and private third-party data sources. Internal sources include patient accounting systems, human resources data, and charity policy information. Public sources include CMS for new regulations and census data for local population growth. Private sources include credit bureau and marketing data for local income and poverty levels.

After using the cause-and-effect model, a forecast is made for the upcoming year of $2.4 million in charity write-offs. Key stakeholders are initially surprised by the forecast, but after considering the new charity policy that lowers federal poverty threshold qualifications and a new consumer-directed product that boosts self-pay balances, they are comfortable with changing the targets.

After setting this initial target of $2.4 million, stakeholders want to also check to see how effective their charity application process is in allocating charity care to the self-pay population. They use third-party data to estimate total charity potential within their self-pay population and test different scenarios of charity discount thresholds. From this analysis, they determine that the goal should be increased to $3.8 million. This indicates that their target will increase due not only to uncontrollable factors such as product and policy change but also to improved performance in the application process.

Publication Date: Tuesday, April 01, 2008

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