Quality Assurance Systems in Healthcare Institutions: Exploring the Impact of AI Audit applications on Internal Controls Effectiveness and Service Quality

Abstract

How much can AI-driven audit software help healthcare firms improve their services and internal controls? The main goal of this study is to answer that question. Our main objective is to design a pipeline that can discover control failures, rate risks based on how significant they are, and offer ways to fix problems that have been found, all while keeping trust in mind. Researchers conducted quasi-experimental investigations both before and after the implementation as integral components of the mixed-methods framework utilized for the evaluation. They also read what was already written about the subject and talked to significant people. When three tertiary care facilities introduced the AI audit layer, the AUPRC dropped from 0.26 to 0.47. The AUROC for finding anomalies rose from 0.78 to 0.91. These two occurrences happened at the same time. Also, it's vital to remember that the average time it took to detect control breaches decreased by 34%, from 7.8 hours to 5.1 hours. It's also crucial to point out that the average response time decreased by 32%, from 14.2 hours to 9.7 hours. This development also resulted in a decline in the number of unfavorable occurrences per 1,000 interactions, from 12.4 to 9.8. Things have become a lot worse. In addition to making the processes easier (from 86 to 54 warnings per 1000; from 5.6 to 3.9 reviewer minutes per case), the probabilistic calibration also became better (from 9.5% to 4.3% ECE; from 0.21 to 0.13 Brier). Patients now perceive that some components of the product's quality have improved since the patient satisfaction score has gone up by 0.7 points. It is in the middle of a scale that goes from 0 to 10. The results show that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A I$</tex> audits could increase the efficacy and dependability of internal controls without necessitating reviewers to put in too much labor. This accords with the paper's objective to enable systems for healthcare quality assurance to integrate ethically conscious approaches.

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