| Here's a quick recap to help you get caught up on what we found when we reviewed IRS's oversight of tax-exempt hospitals and the agency's use of artificial intelligence to select returns and issues for examination.
Vague and Outdated Guidance Creates Challenges for Tax-Exempt Hospital Oversight
Why did we do this audit? The Patient Protection and Affordable Care Act requires the IRS to evaluate the community benefit activities of tax-exempt hospitals at least once every three years. In response, the IRS established Community Benefit Activity Reviews to conduct these evaluations and ensure hospitals comply with federal requirements to maintain tax-exempt status. What did we find? There are six factors that may demonstrate a tax-exempt hospital's community benefit. However, the vague definition of community benefit makes it difficult for both hospitals and the IRS to determine if hospitals are providing sufficient community benefits to justify their tax exemption. Additional factors, such as whether a hospital provides financial assistance to those unable to pay, are relevant in determining whether a hospital is providing a benefit to the community. However, the Internal Revenue Code does not specify what eligibility criteria or level of assistance provided is adequate for a financial assistance policy to meet the statutory requirements. Vague or unclear eligibility criteria could potentially cause confusion for patients and inconsistent application of the requirements across hospitals.
Learn more about what we found:
The IRS Could Leverage Examination Results in Artificial Intelligence Examination Case Selection Models and Improve Processes to Evaluate Performance
Why did we do this audit? The IRS's current return selection models have resulted in a high percentage of examinations completed with no change to the tax liability. This wastes resources on unproductive examinations and unnecessarily burdens compliant taxpayers. Artificial intelligence (AI) models can improve the process the IRS uses to select cases for examination. We assessed how effectively the Large Business and International Division and the Small Business/Self-Employed Division use AI models to identify returns and issues for examinations. What did we find? The IRS began using AI several years ago. The IRS revamped how it selects returns and identifies issues for examination by using AI models trained on current return data rather than relying on past audit results. However, historical examination results are informative and should be used by the IRS to monitor and improve AI models when available. For example, the IRS could use examination results to improve return classification and return selection AI models that could potentially identify new areas of noncompliance. The IRS should also consider evaluating ensemble machine-learning for improving the accuracy of identifying noncompliant taxpayers and narrowing the Tax Gap. Additionally, the IRS has not established processes to evaluate whether the performance of AI models is better than prior methods or is achieving the intended objectives. Not evaluating performance results is contrary to federal AI key practices to ensure accountability and responsible AI use.
For more on what we found - and recommended:
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