Healthcare Fraud Detection

Statistical healthcare fraud detection techniques

The net effect of excessive fraudulent claims is excessive billing amounts, higher per-patient costs, excessive per-doctor patients, higher per-patient tests, and so on. This excess can be identified using special analytical tools. Provider statistics include;

• Total amount billed.
• Total number of patients.
• Total number of patient visits.
• Per-patient average billing amounts.
• Per-patient average visit numbers.
• Per-patient average medical tests.
• Per-patient average medical test costs.
• Per-patient average prescription ratios (of specially monitored drugs).
• and many more.

Analytic Healthcare Fraud Detection Methods

Healthcare fraud detection involves account auditing and detective investigation. Careful account auditing can reveal suspicious providers and policy holders. Ideally, it is best to audit all claims one-by-one carefully. However, auditing all claims is not feasible by any practical means. Furthermore, it's very difficult to audit providers without concrete smoking clues. A practical approach is to develop short lists for scrutiny and perform auditing on providers and patients in the short lists. Various analytic techniques can be employed in developing audit short lists. Keep in mind that excessive fraudulent claims lead deviations in aggregate claims statistics. In addition, fraudulent claims often develop into patterns that can be detected using predictive models!

Statistical listings of risky providers

When abusive claims are repeated frequently, the consequent is higher provider statistics. Various provider statistics can be used to identify fraudulent claims. For instance, audit short-lists may include the followings;

• Doctors who treated whopping, say, 50+ patients in a day.
• Providers administering far higher rates of tests than others.
• Providers costing far more, per patient basis, than others.
• Providers with high ratio of distance patients.
• Providers prescribing certain drugs at higher rate than others.
• and so on.

It is noted that statistical analytic techniques can reveal excessive providers who might be outright stupid! But it will be difficult to identify modest level fraud activities.

Predictive Modeling and Deep Learning

Predictive models can predict potential fraudulent claims and providers. Predictive models can be developed using information described above. However it is difficult to obtain training historical data as most fraudulent claims may have not been discovered! To overcome this limitation, predictive scoring models using known cases can be developed. Then these models are used to predictict fraud scores to unknown data to find highest potential fraud cases.