Fraud Surveillance Model
The growing innovations in artificial intelligence (AI) and machine learning have enhanced the automated methods of fraud surveillance and detection in the healthcare industry (Joudaki et al., 2014). Together with data mining, healthcare organizations can extract useful information from their EHR data for abuse and fraud examination (IT-based auditing model), increasing their fraud surveillance capacity (Joudaki et al., 2014). Besides, based on the Medicare fraud and abuse report of 2021, the Recovery Audit Program allows an organization to detect and collect underpayment and overpayment data from its systems, improving its surveillance capability and ability to lower improper payments (CMS, 2021). Therefore, through the IT-based auditing model and the recovery audit program, healthcare organizations can audit to identify surveillance gaps and recover from fraud and abuse instances, helping them to lower the risks of fraud and abuse.
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