Management of Hospital Foundations: Does Compensation Matter?
Bibliographic record
Abstract
[Abstract] Hospital foundations are run by boards and staff that may work as volunteers, or may be compensated. In addition to spending money on these, foundations often hire external fundraisers to oversee some major event such as a gala party or high-profile athletic event. This paper looks at these types of compensation, plus overall expenses and net assets, to build a regression model to forecast revenue of foundations. We find that expenses, assets, fundraising compensation and board compensation account for 80 percent of the variation in the amount of revenue generated by a foundation. [Keywords] Hospital foundations; board compensation; revenue Introduction All foundations are concerned about raising money to support their missions. But, in order to raise money, foundations usually spend money. Some foundations spend money on high-profile events such as gala parties or community 10-K runs. Some fundraise for a specific cause such as a new wing for their associated hospital. The occurrence of any of these events happens only with the support and hard work of people in the background: the board, the staff, and special events fundraisers. Some foundations rely exclusively on volunteer support and some foundations choose to compensate those involved. In addition to money spent on people, expenses might also include mailings, advertising, event spending, lawyers, accountants, investment managers and web-site specialists, to name a few. Income for a is limited to a few sources: investments and contributions. Foundation support has long been a mainstay of a hospital's search for funds (Morgan and Cohen, 1993). And as costs soar, support may become even more important in the future. Raymond (2005) found that even though health-related philanthropic contributions have doubled the last four decades, health care costs still exceed the rate of increase in giving related to health care. Aggarwal (2008) outlines the enormous projected increases in medical costs in the near-term, from 14.1% of GDP in 2003 to 17.7% by 2012. Given these projected future costs, foundations that support associated hospitals and their communities need to consider how to increase revenue. A study by Pink and Leatt (2006) looks at 80 foundations throughout Canada and found that one of the factors associated with increased revenue was a higher level of expenses. This paper looks at 178 foundations in the U.S. and analyzes areas where money is spent to see what has the greatest impact on revenue. More specifically, this study focuses on two variables common to all foundations, net assets and expenses, and three variables that occur in some, but not all, foundations in various combinations, compensation for board members, for staff, and for fundraising specialists. For these five variables, we investigate which are important for a good regression model to predict revenue, and of those useful for the regression model, which give the most return in revenue when increased. For the purposes of this research, the terms and hospital foundation describe a non-profit organization which devotes its efforts and resources to the support of a single hospital. All foundations researched are non-profit organizations, classified as 501(c)(3) and thus considered taxexempt by the federal government. These organizations are required to file a Form 990 annually to report their finances and revenue generating operations. These 990 filings were the basis for much of the data in this study. Data Set and Model Foundations with revenue less than 30 million were the focus of this study. There were 178 foundations in the sample representing hospitals of varying sizes (as measured by the number of beds) and throughout the United States. Initial listings of hospitals and foundations were located by using internet searches. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".