Preprint on Preprints.org.
ISSN/ISBN: Not available at this time. DOI: 10.20944/preprints202504.1550.v1
Abstract: Background and Objectives: Healthcare payment systems face challenges from fraud and overbilling, often requiring costly and resource‐intensive detection tools. In response, the utility of simple statistical tests was explored in this study as a practical alternative for identifying irregularities in dermatology services payments within the Health Insurance Institute of Slovenia (HIIS). Materials and Methods: Ten‐year‐old anonymized billing data from 30 dermatology providers in Slovenia (population 2 million) were analyzed to evaluate the effectiveness of the methodology while avoiding reputational harm to current providers. The dataset from 2014 included variables such as the “number of services charged”, “total number of points charged” (under Slovenia’s point‐based tariff system at the time), “number of points per examination”, “average examination values (€)”, “number of first examinations”, and “total number of first/follow‐up examinations”. Data credibility was assessed using Benford’s Law (calculating χ2 values and testing null hypothesis rejection at the 95% level), and Grubbs’/Hampel’s/T‐tests were used to identify outliers. Results: Benford’s Law analysis revealed significant deviations for the “number of services charged” (p<0.005), “total number of points charged” (p<0.01), “number of points per examination” (p<0.0005), and “average examination values (€)” (p<0.005), suggesting anomalies. Conversely, data on the numbers of “first” (p<0.7) and “total first/follow‐up examinations” (p<0.3) aligned with Benford’s Law, indicating authenticity. Outlier detection consistently identified two institutions with unusually high values for points per examination and average examination monetary value. Conclusions: Simple statistical tests can effectively identify potential irregularities in healthcare payment data, providing a cost‐effective screening method for further investigation. Identifying outlier providers highlights areas needing detailed scrutiny to understand anomaly causes.
Bibtex:
@misc{,
author = {Andrej F. Plesničar and Nena Bagari Bizjak and Pika Jazbinšek},
title = {Enhancing Healthcare Integrity Using Simple Statistical Methods: Detecting Irregularities in Historical Dermatology Services Payments},
year = {2025},
doi = {10.20944/preprints202504.1550.v1},
url = {https://www.preprints.org/manuscript/202504.1550/v1},
}
Reference Type: Preprint
Subject Area(s): Accounting, Medical Sciences