# Enhancing Healthcare Integrity Using Simple Statistical Methods: Detecting Irregularities in Historical Dermatology Services Payments

## TL;DR

This study shows that simple statistical methods can detect irregularities in dermatology service payments, offering a cost-effective way to flag potential fraud.

## Contribution

The study introduces the use of Benford’s Law and outlier detection tests as a practical and low-cost approach for identifying payment anomalies in healthcare.

## Key findings

- Benford’s Law detected significant deviations in several billing variables, suggesting potential anomalies.
- Outlier detection identified two institutions with unusually high values for points per examination and monetary value.
- Variables like first and total first/follow-up examinations aligned with Benford’s Law, indicating authenticity.

## Abstract

Background and Objectives: Healthcare payment systems face challenges such as fraud and overbilling, which often require 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 service payments within the Health Insurance Institute of Slovenia (HIIS). Materials and Methods: Ten-year-old anonymized billing data from 30 dermatology providers in Slovenia (with a population of 2 million) were analyzed to evaluate the effectiveness of the proposed methodology while aiming to avoid 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 (EUR)”, “number of first examinations”, and “total number of first/follow-up examinations”. Data credibility was assessed using Benford’s Law (for calculating χ2 values and testing null hypothesis rejection at the 95% level), and Grubbs’ test, Hampel’s test, and T-test were used to identify outliers. Results: An analysis using Benford’s Law 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 (EUR)” (p < 0.005), suggesting anomalies. Conversely, data on the numbers of “first” (p < 0.7) and “total first/follow-up examinations” (p < 0.3) were found to align 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.