Rosella       Machine Intelligence & Data Mining

HomeDataMining & Machine LearningProducts & DownloadsSite MapContact |

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.

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.

(Predictive modeling tips) Large neural networks with a large number of input and hidden nodes lead to overfitting. Large networks, especially with small training datasets, can remember individual records. This is no good predictive model. A better way is to decompose problems into multiple smaller neural networks with a small number of input and hidden layer nodes, each network predicting one type of frauds. Then integerate them with higher level neural networks or use rules to combine them. For more on this deep learning style method, read Rule Engine with Machine Learning, Deep Learning, Neural Network.

Clustering can discover similar fraud cases!

Clustering can be used to find more fraud cases that are similar to known cases. The following figure shows CMSR Data Miner neural clustering. It has 9x9 (=81) cells. Each cell contains providers with "similar" attributes. Round figures are pie charts. Red portions represent fraudulent cases. Green areas are status-unknown portions. Providers within cells with red portions might be hidden cases that may need investigation;

Healthcare fraud clustering