Healthcare Fraud Detection

Fraudulent healthcare claims increase the burden to society. Therefore healthcare fraud detection is now becoming more and more important. Generally, healthcare frauds are not obvious and thus difficult to detect. The followings are typical examples of healthcare fraud techniques used by health care providers and patients;

Providers billing for services not provided.
Providers administering (more) tests and treatments or providing equipments that are not medically necessary.
Providers administering more expensive tests and equipments (up-coding).
Providers multiple-billing for services rendered.
Providers unbundling or billing separately for laboratory tests performed together to get higher reimbursements.
Providers charging more than peers for the same services.
Providers conducting medically unrelated procedures and services.
Policy holders traveling long distance for treatment which may be available nearby. (Possibly scams by bogus providers.)
Policy holders letting others use their healthcare cards.

General Fraud Detection Strategy

If you know who trick your organization and understand how they do that, you know how you can perform effective fraud detection! There are two steps to follow to catch fraudulent claims;

Find out "WHO" and "HOW" they trick your institution;
Healthcare frauds are mostly from healthcare providers. By lodging bogus services, they cheat healthcare insurance companies. These claims and providers tend to have common profiles and patterns. Identify profiles and patterns from known fraudulent cases manually.
Find search rules;
Once you know profiles and patterns of fraudulent providers and claims, develop database search rules and procedures for them. The primary difficulty will be that data that link fraud directly may not exist in claims databases. You may need to identify profiles that link to fraud indirectly.

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 short 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. The subsequent section describes how predictive modeling can be applied.

Data Warehouse/Data Mart for Healthcare Claims Audit

Incorporating the techniques described in previous sections leads to intelligent audit and fraud detection environment. It is noted that healthcare fraud detection requires compilation of potentially very huge data, involving complex computation and sorting operations. Our data mart platform healthcare fraud detection is based on the following architecture. First, claim payment records are transformed and loaded into healthcare fraud data mart. Data is added into data mart, normally monthly or quarterly basis. Summary information is created for providers, doctors and policy holders. Then advanced predictive modeling is applied to score providers. Finally, auditors (and investigators) analyze data using StarProbe fraud analytics.

data mart for healthcare fraud detection.

For Dvelopers

For more information, please write to us (principals only). StarProbe Business Intelligence / SOA provides simple and quick implementation of customized fraud detection systems. For licensees, the following documents are available. Please apply from here;

Foundation for healthcare fraud detection: data mining and data mart.
Data preparation guide for healthcare fraud detection.
StarProbe OLAP configuration guide for healthcare fraud detection.

Data Mining Methods

Data Mining Tools